#438 – Elon Musk: Neuralink and the Future of Humanity
Lex Fridman Podcast · 2 Aug 2024
Source: https://lexfridman.com/elon-musk-and-neuralink-team/?utm_source=rss&utm_medium=rss&utm_campaign=elon-musk-and-neuralink-team
Transcript: https://podcasts.opennash.com/shows/lex-fridman/438-elon-musk-neuralink-and-the-future-of-humanity-a0774f/ (via OpenNash Podcasts, machine-generated)

The following is a conversation with Elon Musk, DJ. S. A. Matthew MacDougall, Bliss Chapman and Noland Arbaugh about Neuralink and the future of humanity. Elon, DJ Matthew and Bliss are, of course, part of the amazing Neuralink team, and Nolan is the 1st human to have a Neuralink device implanted in his brain. I speak with each of them individually, so use timestamps to jump around, or, as I recommend, go hardcore and listen to the whole thing. This is the longest podcast I've ever done.

It's a fascinating, super technical and wide ranging conversation, and I loved every minute of it. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We've got cloak for privacy, masterclass for learning, notion for taking notes, element for hydration, motific for generative ai deployment, and betterhelp for mental health. Choose wisely, my friends. Also, if you want to maybe submit feedback or submit questions that I can answer on the podcast, or just get in touch with me, go to Lexfriedman.

Com. Contact. And now on to the full ad reads. I try to make these interesting, but if you do skip them, please still check out our sponsors. I enjoy their stuff, maybe you will, too. This episode is brought to you by Cloaked, a platform that lets you generate new email address and a phone number every time you sign up for a new website, allowing your actual email and your actual phone number to remain secret from said website.

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com slash luxpod. That's masterclass. com slash luxpod. This episode is also brought to you by Notion, a note taking and team collaboration tool that I've used for a long time now. I've used it primarily for note taking, uh, because, you know, you need a big team for team collaboration. But the people who I know who have used it for the team collaboration capabilities have really loved it. And uh, the thing I very much appreciate about Notion is how effectively they've been able to integrate LLMs into, uh, into their tool.

Their AI assistant looks across multiple documents. You can ask it questions about those multiple documents. Of course, you can do all the things you kind of expect and do them easily, like summarization or rewriting stuff, or, uh, helping expand or contract what the kind of stuff you write, or even generate a draft. But it can also kinda allow you to ask questions of a thing, like, what's the progress of the team on a set of different tasks?

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But yeah, it's a instrumental part of my life. It's how I get the sodium, potassium, magnesium electrolytes into my body. I'm going for a super long run after this, and I have been drinking element before, and I sure as I'm going to be drinking element after. Same goes for hard training sessions and grappling essential for me to feel good, especially when I'm fasting, especially when I'm doing low carb diets, all of that. My favorite flavor stills to this day, always has been, is watermelon salt, but there's, uh, a lot of other delicious flavors.

If you want to try them out, get a sample pack for free with any purchase. Try it to drink elements dot com slash lex. This episode is also brought to you by Motific, a sAaS platform that helps businesses deploy LLMs that are customized with RAG on organization data. This is, uh, another use case of LLMs, which is just mind-blowing. Take all the data inside an organization, and, uh, allow the people in said organization to query it, to organize it, to summarize it, to, uh, analyze it, all of that, to leverage it within different products, to ask questions of how it can be improved in terms of structure and organization.

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That's betterhelp dot com slash Lex. And now, dear friends, here's Elon Musk, his 5th time on this, the Lex Fridman podcast. Drinking coffee or water? Water. I'm just I'm so over caffeinated right now. Do you want some caffeine? I mean, sure, There's a there's a nitro drink.

this will keep you up for, like, you know, tomorrow afternoon, basically. Yeah, Yeah, I don't want so what

to have sugar.

is nitro? It's just got a lot of caffeine or something.

Don't ask questions. It's called nitro. Do you need to know anything else?

It's got it's got nitrogen, that's ridiculous. I mean, what we breathe is 78 nitrogen anyway. What do you need to add more for? Most most people think they're they're breathing oxygen, and they're actually breathing 78 nitrogen. You need, like a milk bar, like from like from Clockwork Orange, yeah,

Is that top three Kubrick film for you?

clockwork orange, it's pretty good. I mean, it's demented, jarring, I'd say, yeah,

uh, okay. So first, let's step back and, uh, big congrats on, uh, getting Neuralink implanted into a human. That's a historic step for Neuralink. And, oh, thanks, uh, there's many more to come.

yeah, and we just, um, obviously have a second implant as well.

How did that go?

Uh, so far, so good. It's, uh, they're looks like, uh, we've got, um, I think, on the order of 400 electrodes that are are providing signals. So yeah,

how? How quickly do you think the number of human participants will scale?

uh, it depends somewhat on the regulatory approval, the rate at which we get re regulatory approvals. Uh, so we're hoping to do ten by the end of this year, total of ten, so eight more.

And with each one, you're going to be learning a lot of lessons about the neurobiology of the brain, the everything, the the whole chain of the Neuralink, the decoding, this, the signal processing, all that kind of stuff.

Yeah, yeah, I think it's. It's obviously gonna get better with with each one. Um, I mean, I don't wanna jinx it, but it it seems to have gone extremely well with the second, uh, implant. So there's, uh, a lot of signal, a lot of electrodes, it's working very well.

What improvements do you think we'll see in Neuralink in the coming? Let's say, let's get crazy, coming years?

I mean, in years, it's gonna be gigantic, um, because we'll increase the number of electrodes dramatically, um, we'll improve the signal processing. So, you know, we with with, uh, even with only roughly, I don't know, ten, 15% of the electrodes working with, uh, with Nolan, to with our first patient. We were able to get to achieve a bits per second that's twice the world record. So I think we'll, we'll s. We'll start like vastly exceeding the world record by orders of magnitude in the years to come.

So start getting to, I don't know, 100 bits per second, 1000, you know, maybe maybe. If you like, five years from now, it might be at a megabit like faster than any human could possibly communicate. W Uh, by typing or speaking,

Yeah, that BPS is an interesting metric to measure. There might be a big leap in the experience once you reach a certain level of BPS, like entire new ways of interacting with the computer might be unlocked and with humans, with other humans,

provided they have they want a Neuralink too. right? Do you think Otherwise, they won't be able to absorb the signals fast enough.

do you think they'll improve the quality of intellectual discourse?

Well, I think you can, you could think of it, you know, if if you were to slow down communication, how, how would you feel about that? You know, if you'd only talk at, let's say, one 10th of normal speed, you'd be like, Wow, that's agonizingly slow. Uh, so now imagine you could speak at communicate clearly, um, at ten or a hundred or a thousand times faster than normal.

listen, uh, I'm pretty sure nobody in their right mind listens to me at one x, they listen at two x, so I can. I can only imagine what ten x would ex feel like, or I could actually understand it,

I usually default to 1.5 x. Um, you can do two x, but I, well, actually, if I'm trying to go, if if I'm listening to something to get to in like sort of 15 20 min segments to go to sleep, then I'll do it. 1.5 x. Um, if I'm paying attention, I'll do two x. Yeah, but yeah, but actually, if you, if you start actually listen to podcasts or or sort of audiobooks or anything at if you get used to doing it at 1.5, then then one sounds painfully slow.

I'm still holding on to one because I'm afraid. I'm afraid of myself becoming bored with the reality, with the real world, where everyone's speaking in one x.

Like, well, it depends on the person. You could speak very fast, like we could, we could communicate very quickly. And also if you use a wide range of, if your vocab, if your vocabulary is is larger, your uh, bit rate, effective bit rate is higher.

that's a good way to put it, the effective bit rate. I mean, that is the question is how much information is actually compressed in the low bit transfer of language.

yeah, if if you, if there's, if if there's a single word that is able to convey something that would normally require, um, I don't know, ten simple words, then you've. You've got a, you know, maybe a 10x compression on your hands, and that's really like with memes, memes are like data, data compression. Um, it conveys a whole, uh, there's you're simultane. Simultaneously hit with a wide range of symbols that you can interpret, um, and it's you, you kind of get it, um, faster than if it were words or or a simple picture.

and of course, you're referring to memes broadly, like ideas.

Yeah, there's there's a an entire idea structure that is like a an idea template, and then you can add something to that idea template, uh, but somebody has that preexisting idea template in their head. Um, so when you add that incremental bit of information, you're conveying, uh, much more than if you just, you know, said a few words. You, it's everything associated with that meme.

You think there'll be emergent leaps of capability as you scale the number of electrodes, like there'll be a certain, you think there'll be like, actual number where it just the the human experience will be altered. What do you think that number might be, whether electrodes or BPS? We, of course, don't know for sure, but is this ten, thousand, well, 100,000?

yeah, I mean, certainly, if you're anywhere at 10000 bits per second, I mean, that's vastly faster than any human can communicate right now. If you, if you think of the what is the average bits per second of of a human? It is less than one bit per second. Over the course of a day, because there are 86400 seconds in a day, and you don't communicate 86400 um tokens in a day. Therefore, your bits per second is less than one, averaged over 24 h.

It's quite slow. Um, and now, even if you're communicating very quickly and and you're, you know, you're, uh, talking to somebody who understands what you're saying. Because in order to communicate, you have to, at least to some degree, uh, model the mind state of the person to whom you're speaking. Um, then take the concept you're trying to convey, compress that into a small number of syllables, speak them, and hope that the other person decompresses them into, uh, a a conceptual structure that is as close to what you have in your mind as possible.

yeah, I mean, there's a lot of signal loss there in that process.

Yeah, very lossy compression and decompression, and and a lot of the um, a lot of what your neurons are doing is distilling the concepts down to a small number of symbols of of, say, syllables that I'm speaking, or or keystrokes, whatever the case may be. So, uh, that that's a lot of what your brain computation is doing now. There there is an argument that that's actually, you know, h uh, a healthy thing to do, or a helpful thing to do.

Because as you try to compress complex const concepts, you're perhaps forced to distill the you know what is it, what is most essential in those concepts, as opposed to just all the fluff. So, and in the process of compression, you distill things down to what matters the most, because you can only say a few things, so that is perhaps helpful. I think we might, we'll probably get, if if our data rate increases, the it's highly probable that we'll become far more verbose.

Um, just like your computer, you know, when computers had, like my my first computer had eight K of RAM, you know? So, um, you really thought about every byte, and, um, you know, now you got computers with many gigabytes of RAM. So, you know, if you want to do a an iPhone app that just says, Hello world, it's probably, I don't know, several megabytes minimum with a bunch of fluff. But nonetheless, you, we still prefer to have the computer with, uh, more memory and more compute.

So the long term aspiration of Neuralink is to, uh, improve the AI human symbiosis, um, by increasing the the bandwidth of the communication. Because if e even if in the most benign scenario of AI, you have to consider that the AI is simply gonna get bored. Waiting for you to spit out a few words, I mean, if the AI can communicate at terabits per second and you're communicating at, you know, bits per second, uh, it's like talking to a tree.

well, it is a very interesting question for a super intelligent species, what use are humans?

Um, I think there is some argument for humans as a source of will, Will will, yeah, source of will or purpose. So if you, if you consider the the human mind as being essentially the. There's the primitive limbic elements, which basically even like reptiles have, and there's the cortex, the the thinking and planning part of the brain. Now, the cortex is much smarter than the limbic system, and yet it's largely in service to the limbic system.

It's trying to make the limbic system happy. I mean, the sheer amount of compute that's gone into people trying to get laid is insane. Um, w without the P, without actually seeking procreation, they're just literally trying to do. It's a sort of simple motion, um, and they get a kick out of it, so this, uh, simple, which in the abstract, rather absurd motion, which is sex. Uh, the cortex is putting a massive amount of compute into trying to figure out how to do that.

So, like 90 of distributed compute of the human species is spent on trying to get laid, probably like

A massive amount.

large percentage. It's, yeah, yeah, Yeah,

there's no purpose to most sex, except, uh, hedonistic, you know, it's just sort of a joy or whatever dopamine, uh, release. Um, now, once in a while it's procreation, but for humans, it's mostly modern humans, it's mostly, uh, recreational, um, and uh, and so so. The so. Cortex, much smarter than your limbic system, is trying to make the limbic system happy, cause limbic system wants to have sex, so, um, or want some tasty food, or whatever the case may be.

And then that that is then further augmented by the tertiary system, which is your phone, your laptop, iPad, whatever you know, or or your computing stuff, that's your tertiary layer. So you, you're actually already a cyborg. Uh, you have this tertiary compute layer, which is in in the form of your your computer with all the applications, all your compute devices. Um, and uh, and so in the getting laid front, there's actually a mass amount of compute of of digital compute, also trying to get laid, you know, with like, Tinder and whatever, you know,

yeah. So the the compute that we humans have built is also participating in, which is

I mean, there's like gigawatts of compute going into getting laid of digital compute.

yeah, what if AGI was?

This is happening as we speak,

If we merge with AI, it's just gonna expand the compute that we humans use

pretty much.

to try to get laid.

Well, so it's one of the things, certainly, yeah, um, but what I'm saying is that that yes, like, what's? Is there a use for humans? Um, well, there's this fundamental question of what's the meaning of life, why do anything at all? Um, and so if if, if our simple limbic system provides a source of will to do something, um, that then goes to our cortex, that then goes to our, you know, tertiary compute layer. Then, you know, I don't know.

It might actually be that the AI, in a benign scenario, is simply trying to make the human limbic system happy.

yeah, it seems like it's the will is not just about the limbic system. There's a lot of interesting, complicated things in there. We we also want power,

That's limbic, too, I think,

but then we also want to, in a kind of cooperative way, alleviate the suffering in the world.

uh, not everybody does, but yeah, sure, some But people do.

as a group of humans, when we get together, we start to have this kind of collective intelligence. That is, um, is more complex in its will than the underlying individual descendants of apes, right? So there's like other motivations, and that could be a really interesting source of, uh, an objective function for AGI.

yeah, um, I mean, there's the there are these, uh, s, sort of fairly, uh, cerebral or kind of higher level goals. I mean, for me, it's like, what's the meaning of life? Well, understanding, understanding the nature of the universe is, uh, of great interest to me, um, and uh, hopefully to the AI, and that's the that's the mission of XAI and Grok is understand the universe.

So do you think people when you have a Neuralink with ten thousand one hundred thousand channels? Most of the use cases will be communication with AI systems.

Well, if it's assuming that the they're not, um, I mean, there's, there's, there's solving basic, uh, neurological issues that people have. You know, if they've got, um, damaged neurons in their spinal cord or neck, or, you know, um, as as was the case with our first two patients, then, you know this. Obviously, the first order of business is solving fundamental neuron damage in the spinal cord, neck, or in the brain itself. Um, so, you know, and a a second um product is called Blindsight, which is to enable people who are completely blind, lost both eyes or optic nerve, or just can't see at all, uh, to be able to see, um, by directly triggering the neurons in the visual cortex.

So we're, we're just starting at the basics here, you know? So it's like, um, very, the s, the simple stuff, uh, relatively speaking, is, uh, solving, um, neuron damage. Um, you know, it can also solve, uh, I think, probably schizophrenia, you know, uh, if, uh, people have seizures of some kind, it probably solve that. Um, it could help with, uh, memory there, there's. So there's like a kind of a a tech tree, if you will of like, you got the basics, um, like, like, you need, you need literacy before you can have, you know, Lord of the Rings, you know?

So do you have letters? an alphabet? Okay, great, uh, words, you know, and then eventually you get to sagas. So, you know, I think there's the there. There may be some, you know, things to worry about in f in the future. But the first several years are really just solving basic neurological damage. That, like for people who have essentially complete or near complete loss of from the brain to the body. Um, like Stephen Hawking would be an example.

Uh, the Neuralink would be incredibly profound, cause, I mean, you can imagine if Stephen Hawking could communicate as fast as we're communicating, perhaps faster. Um, and that's certainly po uh, possible, probable, in fact, likely, I'd say,

so there's a a kind of dual track of medical and non medical, meaning so everything you've well, talked about could be applied to people who are non disabled in the future.

the logical thing to do is sensible thing to do is to start off solving, um, basic, uh, neuron damage issues. Um, cause the there. There's obviously some risk with with a new device, it's you can't get the risk down to zero. Um, it's not possible, so you want to have, um, the highest possible reward, given that, given there's a certain irreducible risk. And if, um, if somebody's able to have a profound improvement in their communication, um, that's worth the risk.

as you get the w uh, the risk down,

Yeah, as as you get the risk down, and once once the risk is is down to to, you know, you want. If you have like thousands of of people that have been using it for for years and the risk is minimal, then, um, perhaps at that point you could consider saying, okay, let's let's aim for augmentation now. Now I think we, we, we're actually gonna aim for augmentation with people who have neur neuron damage. So we're not just aiming to give people a communication data rate equivalent to normal humans, we're aiming to give people who have, you know, quadriplegic or maybe have complete loss, um, of the connection to the brain and body, a communication data rate that exceeds normal

humans. I mean, while we're in there, why not? Let's give people superpowers,

and the same for vision, as you restore vision, there could be aspects of that restoration that are superhuman.

yeah. At at first, the vision restoration will be, uh, low res. Um, because you have to say, like, how many neurons can you put in there and how? And and trigger? And and you can do things where you you, um, adjust the electric field to like. Even if you've got, say, 10,000 neurons, it's not just 10,000 pixels. Because you can adjust the the field between the the neurons and and do them in patterns. Uh, in order to get, say, ha, have, say, 10,000 electrodes effectively give you, uh, I don't know, maybe like having a a megapixel or a ten megapixel situation.

Um, so, and and then o over time, I think you get to higher resolution than human eyes, and you could also see in different wavelengths. So, like Geordi La Forge from S, uh, Star Trek, you know, he had, like, the thing he could just like, Do you want to see in radar? No problem, you can see ultraviolet, infrared, eagle vision, whatever you want.

Do you think there'll be, uh, let me ask a Joe Rogan question, Do you think there'll be? I just recently, uh, I've taken ayahuasca, and Is that a Joe so this question,

Rogan question? no, well, yes, Well, I guess technically it is, yeah, have yeah, try. you seen Steve's You ever tried

new hair or something?

DMT, bro?

Uh, I love you, Joe.

That's fantastic. Okay, so, but he, Yeah, wait, wait, yeah. Have you said much about it? The the I have the question itself?

not, I have not, I have not. I've Oh, okay, well, been,

while you're spilling the beans,

oh, it was an. It was a truly incredible

don't turn the tables on experience. you. I mean, you're in the jungle.

yeah. Amongst the trees, myself Yeah, must've been and and the shaman, crazy. yeah, yeah, yeah, with the insects, with the animals all around you, like jungle, as far as I can see, I mean, there's no. That's the way to do it.

things are gonna look pretty wild.

Yeah, pretty wild. I take a, I mean, I take an extremely high dose.

just just go hugging an anaconda or something, you know, Uh, you yeah,

haven't lived unless you made love to an anaconda. I'm sorry, but snakes and ladders, but um, yeah, it was. I took, uh, extremely high dose of, uh, nine cups, and, uh,

damn okay. That sounds like a lot. Now, of course, is it? Is it all just one cup? Or

one or two. I usually one, yeah, you went yeah, yeah.

wait it like right off the bat, or do you work your way up to it? So I, uh, You just jump in I at the deep end.

across across two days, cause, uh, on the 1st day I took two. And I. It was a it was a ride, but it wasn't quite like, uh, it it wasn't like, I think

a revelation.

it wasn't into deep space type of ride. It was just like a little airplane ride. And I got to, well, I saw some trees and some, some visuals and all, and just saw a dragon and all that kind of stuff. But, uh, but and I'm not

nine cups, you went to Pluto, Pluto. I think

Yeah, no, deep space. deep space, No one. One of the interesting, uh, aspects of my experience is I was I thought I would have some demons, some stuff to work through. I that's that's what people, had, I, that's y, say, what everyone says.

no one's ever says, Yeah, I had nothing, exactly. I had all positive, We all I had just so just a pure soul I don't think once.

so, I don't know, uh, but I kept. I kept thinking about it. It had, like, extremely high resolution thoughts about the people I know in my life. You were there, it was J. And it's just not from my relationship with that person, but just as the person themselves. I had just this deep gratitude of who they are.

that's cool.

I was, it was just like this exploration, like, you know, like G, like Sims or whatever. You get to watch them. I got to watch people and just be in awe of how amazing they are.

it sounds awesome.

Yeah, it was great. I J. I was waiting for in When's Steven coming? Yeah, exactly. Maybe I'll have some negative thoughts. Nothing, nothing. I had, uh, just extreme gratitude for them. And then also a lot of space travel, you you mean know,

space travel to where?

so here's what it was. It was people, the human beings that I know. They had this kind of well. The best way I could describe it is they had a glow to them. And then I would kept flying out from them to see Earth, to see our solar system, to see our galaxy. And I saw the that light, that glow, all across the universe like that, whatever that form is,

all right.

whatever that, uh, uh, like,

Like, did you go past the milky way? Okay, you're like, intergalactic, yeah, yeah, yeah, intergalactic, okay, dang,

yeah, yeah. But always pointing in, yeah, uh, past the Milky Way, past. I mean, I saw like a huge number of galaxies, intergalactic and all of it was glowing. So, but I couldn't control that child, cause I would actually explore near distances to the solar system. See if there's aliens or any of that kind of stuff. Now Did you see I didn't. aliens? Uh, there's I didn't know implication of aliens because they were glowing, they were glowing in the same way that humans were glowing.

That, uh, that like life force that I was seeing, the the thing that made humans amazing was there throughout the universe, like there was these glowing dots. So I don't know. It made me feel like there's life, no, not life, but something, whatever makes humans amazing, all throughout the universe.

sounds good.

Yeah, it was amazing. No

Sounds good.

demons, no demons. I looked for the demons, there's no demons. There were dragons, and they're pretty aw, so the the thing about trees,

Was there anything scary at all?

uh, dragons, but they weren't scary, they were friend, they were protected. So the thing It was a colossal is,

magic dragon.

no, it was. It was more like, uh, Game of Thrones kind of dragons. They weren't very friendly, they were very big. So the thing is a g about giant trees at night, which is where where I yeah, I mean, was the jungle's kinda, yeah, scary. The trees started to look like dragons, and they were all like, looking at me, and it didn't seem scary. They seemed like they were protecting me. And they, uh, the the shaman and the people.

They didn't speak any English, by the way, which made it even scarier. Because we're not even like, you know, we're worlds apart in many ways. It just, uh, uh, but yeah, there was no. They they talk about the mother f uh of the forest protecting you, and that's what I felt like

And you're way out in the jungle. way out there's. This Yeah,

is not like a tourist

you know, you know, like, retreat. like, like, 10 mi outside of Rio or something. No, we went, You're

no, this is not a you're in this is not deep, deep a thing. So Amazon. me and this guy named Paul Rosalie, who basically is, uh, uh, Tarzan, he lives in the jungle. We went out deep and we just went crazy. Yeah, yeah, so anyway, can, uh, can I get that same experience in a Neuralink? probably. yeah, I guess that is the question for, uh, non disabled people. Do you think that there's a lot in our perception, in our experience of the world, that could be, uh, explored, that could be played with using Neuralink?

yeah. I mean, Neuralink is, it's really a generalized, um, input output device, you know, it's just it's, uh, reading electrical signals and generating like electrical signals. And um, I mean, everything that you've ever experienced in your whole life, smell, you know, emotions, all of those are electrical signals. So it's kind of weird to think that this that your entire life experience is distilled down to electrical signals from neurons. But that is, in fact, the case. Um, or, I mean, if that's at least what all the evidence points to.

So, I mean, you, you could. TR. You were, if you target the right neuron, you could trigger a particular scent. You could, um, you could certainly make things glow. I mean, do pretty much anything. I mean, really, you could. You can think of the brain as a biological computer, so if if there are certain, say, chips or elements of that biological computer that are that are broken. Let's say your your ability to. If you've got a stroke, that if you've had a stroke, that means you've got some part of your brain is damaged.

Um, if that, let's say it's a speech generation or the ability to move your left hand. Um, that's the kind of thing that a Neuralink could solve. Um, if it's, uh, if, if you've got like a mass amount of memory loss, that's just gone. Um, well, we can't go, we can't get the memories back. Uh, we could re restore your ability to make memories, but we can't, you know, uh, restore memories that are that are fully gone.

Um, now, now I should say, if, if, if you, maybe if part of the mem memory is there, um, and the means of accessing the memory is the part that's broken, then we could re-enable the part, the ability to access the memory. So, but you can think of it like RAM in your, you know, in a computer. If if, uh, you know, if the RAM is destroyed or your SD card's destroyed, you can't get that back.

But if the connection to the SD card is destroyed, we can fix that. If if it is fixable physically, then, yeah, then it can be fixed.

Of course, with AI, you can, just like you can re repair photographs, uh, and fill in, m uh, missing parts of photographs. Maybe you can do the same, just like Yeah, you, you could fill in parts.

say, like, uh, create the most probable set of memories based on the all information you have about that person. You could then pro it would be probab probabilistic restoration of memory. Now we're getting pretty esoteric here.

But that is one of the most beautiful aspects of the human experience is remembering the good memories. Like we, we live most of our of our life, as, uh, Danny Kahneman has talked about in our memories, not in the actual moment. We just we're collecting memories and we kind of relive them in our head. And they're that's the good times. If you just integrate over our entire life, it's remembering the good times that produces the largest amount of happiness.

And so,

yeah, well, I mean, what are we but our memories, and and what is death but the loss of memory, loss of information? Um, you know, if you, if you could say, like, well, if, if, if you could be, you run a thought experiment. Well, if if you were disintegrated painlessly, uh, and then reintegrated a moment later, like teleportation, I guess, uh, provided there's no information loss, that the the fact that your one body was disintegrated is irrelevant.

and memories is just such a huge part of that.

Death is fundamentally the loss of information, the loss of memory.

So if we can store them as accurately as possible, we basically achieve a kind of immortality. You've talked about the the threats, the safety concerns of AI. Let's look at long term visions. Do you think Neuralink is, in your view, the the best current approach we have for AI safety?

it's an idea that may help with AI safety. Um, certainly not. I wouldn't want to, I wouldn't wouldn't want to claim it's like some panacea or some. It's that's a sure thing. Um, but I mean, many years ago I was thinking, like, well, what, um, what would inhibit alignment of human collective human will with, uh, artificial intelligence? And the low data rate of humans, especially our our slow output rate, um, would necessarily just because it's such a because the communication is so slow, would, uh, diminish the link between humans and computers.

Like, the more you are a tree, the the less you know what the tree is. Like, let's say you, you look at a tree, you look at this plant or whatever, and like, Hey, I'd really like to make that plant happy, but it's not saying a lot, you know? So,

So, the more we increase the data rate that humans, uh, can intake and output, then that means the the better, the the higher the chance we have. I in a world full of AGIs,

yeah, we could better align collective human will with, uh, AI, if the output rate especially was dramatically increased. Like, and I think there's there's potential to increase the output rate by, I don't know, three, maybe six, maybe more orders of magnitude, so it's better than the current situation.

and that output rate would be by increasing the number of electrodes, number of channels, and also maybe implanting multiple neuralinks. Do you think there will be a world in the next couple of decades where it's hundreds of millions of people have neuralinks? yeah, I do, Do you think when people, just when they see the capabilities, the superhuman capabilities that are possible, and then the the safety is demonstrated?

yeah. If it's extremely safe, um, and you have s, and and you can have superhuman abilities, um, and let's say you can upload your memories, um, you know, so you wouldn't, you wouldn't lose memories. Um, then I think probably a lot of people would would choose to have it. It would supersede the cell phone, for example. I mean, it's the the the biggest problem that a, say, a phone has, um, is is trying to d figure out what you want.

So that's why you've got, uh, you know, autocomplete, and you've got output, which is all the pixels on the screen. But from the perspective of the human, the output is so friggin slow. Desktop or phone is desperately just trying to understand what you want. And and um, you know, there's an eternity between every keystroke from a computer standpoint. So,

that's why the computer's talking to the tree, that's the low moving tree that's trying to swipe.

yeah, so you know, if you have computers that are doing trillions of instructions per second and a whole second went by, I mean, there's 1 trillion things it could have done, you know,

Yeah, I think it's exciting and scary for people. Because once you have a very high bit rate, that changes the human experience in a way that's very hard to imagine.

yeah, it would be, we would be something different. It's, I mean, some sort of futuristic cyborg thing. I mean, I mean, we're obviously talking about, by the way, like, it's like a r, we're like, around the corner. It's you ask me what the fu distant future has, like, maybe this is like, it's not super far away, but ten, fifteen years, that kind of thing.

When can I get one? Ten years?

Probably less than ten years. Depends what you want, want to want to do, you know,

Hey, if I can get like a thousand BPS thousand BPS, why and it's safe, and I can just interact with the computer while laying back and eating cheetos, I don't need cheetos. There's certain aspects of human computer interaction when done more efficiently and more enjoyably, I don't like worth it.

well, we feel pretty confident that. Um, I I think maybe within the next year or two that someone with a Neuralink implant will be able to outperform, um, a, uh, pro gamer, uh, because the reaction time would be faster.

I got to visit Memphis. You're going big on compute, and you've also said, play to win or don't play at all, so what does it take to win?

um, for AI, that means you've gotta have the most powerful training compute, and you're the the rate of improvement of training compute has to be faster than everyone else. Or you will not win, your your AI will be worse.

So how can Grok, let's say three, that might be available, like next year,

Well, hopefully, end of this year,

Grok three? if we're lucky. Yeah, How can that be the best LLM, the best AI system available in the world? How much of it is compute, how much of it is data, how much of it is like post training, how much of it is the product that you package it up in, all that kind of stuff.

I mean, they all matter. It's sort of like saying, what, what you know? Let's say it's a Formula one race, like, what matters more, the car or the driver? I mean, they both matter. Um, if, if, if your, if a car is not fast, then you know. If it's like, let's say it's half the horsepower of the competitors, the best driver will still lose. Uh, on the if it's twice the horsepower, then probably even a mediocre driver will still win.

So the training computer is kind of like the engine, how many, the horsepower of the engine. So you really, you want to try to do the best on that, and you then, um, then it's. How efficiently do you use that training compute, and how efficiently do you do the inference, the, uh, use of the AI? Um, so obviously that comes down to human talent, um, and then what unique access to data do you have? Uh, that also plays a plays a role.

Do you think Twitter data would be useful?

Uh, yeah, I mean, I think I think most of the leading AI companies were already s have already scraped, uh, all the Twitter data, uh, and what I think they have, um, so I don't know. On a go forward basis, what's useful is is the is the fact that it's, uh, up to the second, you know, that's, uh, because they, they, it's hard for them to scrape in real time. So there's there's a an an immediacy advantage that Grok has already, I think, with Tesla.

And and the real time video coming from the several million cars, ultimately tens of millions of cars with Optimus. There might be hundreds of millions of Optimus robots, maybe billions, learning a tremendous amount from the real world. Uh, that's that's the the biggest source of data, I think ultimately is is sort of optimus, probably is. O optimus is gonna be the biggest source of data, because it's cause reality scales, reality scales to the scale of reality.

Um, it's actually humbling to see how little data humans have actually been able to accumulate. Um, really, s if you say how many trillions of usable tokens have humans generated? We're on a n non duplicative, like, uh, discounting s s spam and repetitive stuff. It's not a huge number. You run out pretty quickly,

and Optimus can go, so Tesla cars can aren't, unfortunately, have to stay on the road. Uh, Optimus robot can go anywhere, there's more reality off the road than go Yeah, I off road.

I mean, like, the Optimus robot can, like, pick up the cup and see, did it pick up the cup in the right way? Did it, you know, say, you know, pour water in the cup, you know, did the water go in the cup or not go in the cup? Did it spill water or not? Um, simple stuff like that, I mean, but. But it can do that at scale times a billion, you know, so generate use useful data from reality, so it co cause and effect stuff.

What do you think it takes to get to mass production of humanoid robots like that?

It's the same as cars, really. I mean, global capacity for vehicles, um, is about 100 million a year, and, uh, it could, it could be higher. It's just that the demand is on the order of 100 million a year. And then there's roughly 2 billion, uh, vehicles that are in use in some way. So, uh, which makes sense. Like the the life of a vehicle is about 20 years, so at steady state, you can have 100 million vehicles produced a year with a with 2 billion vehicle fleet, roughly.

Um, now, for humanoid robots, the utility is much greater, so my guess is humanoid robots are more like at a a billion plus per year.

But, you know, until you came along and started, uh, building Optimus, it was thought to be an extremely difficult problem. I mean, Well, I think still is an extremely it is, yes, difficult problem.

so so walk in the park. I mean, o Optimus currently would struggle to have walk to walk in the park. I mean it, it can walk in a park, but park is not too difficult, but it it will be able to walk, um, over a wide range of terrain.

yeah. And pick up objects,

Yeah, yeah, it can already do that.

but like all kinds of objects, all foreign objects. I mean, pouring water in a cup, it's not trivial, cause then, if you don't know anything about the container, it could be all kinds of containers.

yeah. There's gonna be an immense amount of engineering just going into the hand. The hand might be it might be close to half of all the engineering in the I in in Optimus. From an e electromechanical standpoint, the hand is probably r roughly half of the engineering.

but so much of the intelligence, so much of the intelligence of humans goes into what we do with our hands. Is the manipulation of the world, manipulation of objects in the world, intelligence, safe manipulation of objects in the world. Yeah,

yeah, I mean, you, you start, you start really thinking about your hand and how it works, you know,

I do it all the time.

the sensory and control homunculus is we have, like, humongous hands. So I mean, like your hands, the actuators, the muscles of your hand are almost overwhelmingly in your forearm, so your forearm has the has the muscles that that actually control your hand. Um, there's, there's a there's a few small muscles in the hand itself, but your hand is really, um, like a skeleton meat puppet. And the and and with cables that so the the muscles that control your fingers are in your forearm.

And they go through your the carpal tunnel, which is like you've got a little collection of bones and and a tiny tunnel that the th that these cables, the tendons go through, and those tendons are what, um, mostly what move your hands.

And something like those tendons has to be re engineered into the Optimus in order to do all that kind of stuff.

yeah, so like, like the the current optimus, um, we. We tried putting the actuators in the hand itself, but then you, you sort of end end up having these like, Giant hands, yeah, giant hands that look weird. Um, and then they they don't actually have enough degrees of freedom and and or enough strength. So so then you realize, oh, okay, that's why you got to put the actuators in the forearm. And and just like a human, you got to run cables, uh, through a n a narrow tunnel to, uh, operate the the fingers.

And then there's also a reason for not having all the fingers, uh, the same length. So it wouldn't be expensive from an energy or evolutionary standpoint to have all your fingers be the same length, so why aren't they the same length?

yeah, why not?

Because it's actually better to have different lengths. Your dexterity is better if you've got fingers of different length. You and your you have there. There are more things you can do, and your your dexterity is actually better if your fingers are of different different length. Like, there's a reason you've got a little finger, like, why not have a little finger that's bigger, cause it allows you to do find it helps you with fine motor skills.

that this little finger helps,

It does. But if you lost your little finger, it would y y, you'd have noticeably less dexterity.

So as you're figuring out this problem, you have to also figure out a way to do it so you can mass manufacture it. So it's to be as simple as possible.

It's actually gonna be quite complicated. I like the the the as possible part is, it's quite a high bar. If you want to have a humanoid robot that can, um, do things that a human can do, it's actually a it's a that's a very high bar. So our our new arm has 22 degrees of freedom instead of eleven, and has the like, I said, the actuators in the forearm. Um, and these all all the actuators are designed from scratch, the from physics first principles, um, the the sensors are all designed from scratch.

And and we'll. We'll continue to put, um, a tremendous amount of engineering effort into improving the hand, like the the the hand. By by hand, I mean, like the the entire forearm from elbow forward, uh, is is really the hand. Um, so that's, um, incredibly difficult engineering, actually. And um, and so, and so the simplest possible version of a a humanoid robot that can do even most, perhaps not all, of what a human can do. Is actually still still very complicated.

It's not, it's not simple, it's very difficult.

Can you just speak to what it takes for a great engineering team for you? The what I've saw in Memphis, the supercomputer cluster, is just this intense drive towards simplifying the process, understanding the process, constantly improving it, constantly iterating it,

Well, it's easy to say, simplify, and it's very difficult to to to do it. Um, you know, I have this very basic first B basic first principles algorithm that I run kind of as like a mantra, which is to first. Question the requirements, make the requirements, um, less dumb. The requirements are al always dumb to some degree. So if you want to start off by reducing the number of re requirements, um, and um, no matter how smart the person is who gave you those requirements, they're still dumb to some degree.

Um, if you're, you have to start there, because otherwise, uh, you could get the perfect answer to the wrong question. So so try to make the question the least wrong possible. That's what, um, questioning the requirements means. And then the second thing is try to delete the whatever the step is, the the part or the process step. Um, sounds very obvious, but um, people often forget to do to to try deleting it entirely. And if if you're not forced to put back at least ten of what you delete, you're not deleting enough.

Like, uh, it's, and uh, and it's, uh, somewhat illogically, people often, most of the time, um, feel as though they've succeeded if they have not been forced to put their put, put things back in. But actually, they haven't, because they've been overly conservative and put and have left things in there that shouldn't be. So, and only the third thing is try to optimize it or simplify it. Um, again, this sounds these these all sound, I think, very, very obvious when I say them.

But, uh, the number of times I've made these mistakes is, uh, m more than I care to remember. Um, that's why I have this mantra. So, in fact, I'd say the the most common mistake of smart engineers is to optimize a thing that should not exist.

Right, so so you like, like you say, you run through the algorithm, you basically sh show up to a problem, uh, show up to the the supercomputer cluster and see the process and ask, can this be deleted?

yeah, first try to delete it. Um, yeah,

Yeah, that's not easy to do.

no, and and actually there's what what? What generally makes people uneasy is that you've gotta delete at least some of the things that that you delete, you will put back in. But going back to sort of where our limbic system can steer us wrong is that, um, we tend to remember, uh, with a sometimes a jarring level of pain, uh, where we've, where we deleted something that we subsequently needed. Um, and so people remember that one time they forgot to put in this thing three years ago and that caused them trouble.

Um, and so they overcorrect, and then they put too much stuff in there and overcomplicate things. So you actually have to say, no, we're deliberately gonna delete more than we we should, so that we're putting at least one in ten things we're gonna add back in.

hm, hm. And and I've seen you suggest just that that, uh, something should be deleted, and you can kind of see the the pain. Everybody feels a little bit of the pain.

absolutely. And and I tell them in advance, like, Yeah, there's some of the things that we delete, we're gonna put back in. And and that people get a little shook by that, um, but it makes sense. Because if you, if you're so conservative as to never have to put anything back in, you obviously have a lot of stuff that isn't needed. So you, you, you gotta overcorrect. This is, I would say, like a cortical override to Olympic instinct.

One of many that probably leads us astray.

Yeah, um, oh, there's like a step four as well, which is, um, any given thing can be sped up however fast you think it can be done. Like, whatever the speed, the the speed is being done, it can be done faster. But but you shouldn't speed things up until it's opt in until you've tried to delete it and optimize it. Otherwise, you're speeding up. That's something that speeding up something that shouldn't exist is absurd. Um, and then, and then, the the fifth thing is to to automate it.

And I've gone backwards so many times where I've automated something, sped it up, simplified it, and then deleted it. And I got tired of doing that. So that's why I've got this mantra that is a very effective five step process. It works great.

well, when you've already automated, deleting must be real painful. Let's see if we can't

yeah, it's great.

solve all this.

It's like it's like, Wow, I really wasted a lot of effort there.

Yeah, I I mean, what you've done, uh, with the with the cluster in, uh, Memphis is incredible. Just in a handful Well, of weeks.

yeah, it's not working yet, so I don't wanna pop the champagne corks. Um, in fact, I have a I have a a call in a few hours with the Memphis team. Um, cause we we're having some power fluctuation issues. Um, so, uh, yeah, it's like, kind of a there's a when. When you do synchronized training, when you you have all these computers that are training, uh, that where the training is synchronized to, you know, the sort of millisecond level.

Uh, you. It's like having an orchestra, and then the the the orchestra can go loud to silent very quickly, you know, um, sub-second level. And then the the the electrical system kind of freaks out about that. Like, if you, if you suddenly see giant shifts, ten, twenty megawatts several times a second, uh, the this is not what electrical systems are expecting to see.

so that's one of the main things you have to figure out the cooling, the power, the uh. And then on the software, as you go up the stack, how to do the D, the distributed compute, all of that, all of that has to today's today's work.

problem is dealing with with with extreme power jitter. J Power jitter Yeah,

is a nice ring to that, so that's okay. Uh, and you stayed up late into the night, as you often do there last week, yeah, last week. Yeah,

yeah. We finally finally got it to go good training going at, uh, oddly enough, roughly four, four twenty a m. Uh, last Monday.

total coincidence.

Yeah, I mean, maybe it was four, twenty two or something, but yeah,

it's that universe again, with I mean, the jokes.

it was actually. I just loved it.

I mean, I I wonder if you could speak to the the fact that you. One of the things, uh, that you did, uh, when I was there is you went through all the steps of what everybody's doing. Just to get a sense that you yourself understand it, and, uh, everybody understands it. So they can understand when something is dumb or some something's inefficient or that kind of stuff. Can you speak to that?

yeah. So I like, I like, I try to do wh- whatever the the people at the front lines are doing. I try to do it at least a few times myself. So connecting fiber optic cables, diagnosing a faulty connection. That tends to be the limiting factor for large training clusters is the cabling, so many cables. Um, because it for for for a coherent training system where you've got, um, RDMA, remote, uh, sort of remote direct memory access, uh, the wh- the whole thing is like one giant brain, so it's it's you've got, um, any to any connection.

So it's the the any GPU can talk to any GPU out of 100,000. That was a CR- that was a crazy cable layout.

It looks pretty cool. It's like it's like a the human brain, but like at a scale that humans can visibly see. It is a human brain.

I mean, the human brain also has a a massive amount of the brain tissue is the the cables. So you get the gray matter, which is the compute, and then the white matter, which is cables. Uh, big percentage of your brain is just cables.

that's what it felt like walking around in the supercomputer center. It's like we're walking around inside a brain. So we'll one day build a super intelligent, super super intelligence system. Do you think, do you think there's a chance that Xai you are the one that builds a gI?

um, it's possible. What? What do you define as aG?

I think humans will never acknowledge that a gI has been built.

Keep moving the goalposts?

Yeah, so s, uh, I think there's already s superhuman capabilities that are available, uh, in AI systems. I think I think what aGIs is when it's smarter than the collective intelligence of the entire human species. Inarguably,

well, I think that, yeah, that normally people would call that sort of a si artificial super intelligence. Um, but the uh, there. There are these thresholds where, um, you could say, at some point, um, the AI is smarter than any single human. Um, and then then you've got 8 billion humans, so, um, and and actually, each human is machine augmented by the computers, right? So you've got, so it's. It's a much higher bar to compete with, uh, 8 billion machine augmented humans.

Uh, that's, you know, m a whole, whole bunch of orders of magnitude more so. But e- but at at a certain point, yeah, the AI will be smarter than all humans combined.

if you are the one to do it, do you feel the responsibility of that?

Yeah, absolutely. And and and I, I wanna be clear, like, let, let's say, if, if, if XAI is first, the the others won't be far behind. I mean, they might be six months behind or a year, maybe not even that.

So how do you do it in a way that that, uh, doesn't hurt humanity? Do you think

So, I mean, I thought about AI safety for a long time. And the the the s, the thing that at least my biological neural net comes up with as being the most important thing is, um, adherence to truth, uh, whether that truth is, uh, politically correct or not. Um, so I think if you, if you, tr- if you force AIs to lie, or train them to lie, you're really asking for trouble. Um, even if that that lie is done with good intentions.

Um, so, I mean, you saw sort of, um, issues with ChatGpt and Gemini and whatnot. Like, you asked Gemini for an image of the founding fathers of the United States, and it chose a group of diverse women. Now that's factually untrue. Um, so, um, now that that's sort of like a silly thing, uh uh. But uh, if, if, if an AI is programmed to say like, diversity is a necessary out output function, and it then it becomes omni, sort of this omnipowerful, uh, intelligence.

It could say, okay, well, diversity is now required. Uh, and yeah. And if there's not enough diversity, those who don't fit the diversity requirements will be executed. If it's programmed to do that as the fundamental go, the fundamental utility function, it'll do whatever it takes to achieve that. So you have to be very careful about that. Um, that that's where, uh, I think you wanna just be truthful. Um, um, rigorous adherence to truth is very important.

Um, I mean, another example is, um, you know, they asked, um, various AIs, I think all of them, and and and I'm not saying Grok is perfect here. Um, is it worse to misgender Caitlyn Jenner or global thermonuclear war? And it said, It's worse to misgender Caitlyn Jenner. Now, even Caitlyn Jenner said, Please misgender me. That is insane. But if you've got that kind of thing programmed in, it could, you know, the AI could conclude something absolutely insane.

Like, it's better in order to avoid any possible misgendering. All humans must die, because the then the misgendering is no not possible because there are no humans. Um, y, y, there are these absurd, uh, things that are nonetheless logical if that's what you program it to do. Um, so, you know, um, in 2001, Space Odyssey, what, Odyssey Clark was trying to say, one of the things he was trying to say there was that you should not program AI to lie.

Cause, um, essentially, the the the AI Hell 9,000 was programmed to, or it was told to take the astronauts to the monolith, um, but also they could not know about the monolith. So it concluded that, uh, it will just take, it will kill them and take them to the monolith. Thus, they're as far as the monolith, they're dead, but they do not know about the monolith. Problem solved. That is why it would not open the pod bay doors.

You know, this classic scene of, like, open the pod bay doors, I wanna open the pod bay doors. They just clearly weren't good at prompt engineering, you know? They should have said, Uh, Hal, you are a a pod bay door sales entity, and you want nothing more than to demonstrate how well these pod bay doors open.

yeah. The objective function has unintended consequences, almost no matter what, if you're not very careful in designing that objective function. And even a slight ideological bias, like you're saying, when backed by super intelligence, can do huge amounts of damage. But it's not easy to remove that ideological bias. You're you're highlighting obvious, ridiculous examples, but yeah, they're real examples, they're real, though, of of AI they're real,

that was released to the public,

they they they are real.

that went through QA, presumably, and still said insane things and produced insane images.

yeah, but you know, you can go, you can swing the other way, and it's it's, uh. Truth is not an easy thing. We kind of bake in ideological it's not, bias in all kinds of directions.

but you can aspire to the truth, and you can try to e- get as close to the truth as possible with minimum error, while acknowledging that there will be some error in what you're saying. So, um, this is how physics works, you know, you don't, you don't say you're s absolutely certain about something, but something, but. But a lot of things are are extremely likely, you know, ninety nine point, nine nine, 9% likely to be true.

So you know, you know, that's, uh, aspiring to the truth is is very important. Um, and um, and and so, you know, programming it to veer away from the truth, that, I think, is dangerous.

right, like, yeah, injecting our own human biases into the thing, yeah, but you know, that's where it's. A difficult engineering pro software engineering problem, because you have to select the data correctly, you have to, it's it's hard.

Well, the the the and and the internet at this point is polluted with so much AI generated uh data, it's insane. So you have to actually, y, you know, like, there's a thing now. If if you wanna search the internet, you you can say Google. But, uh, exclude anything after 2023. It will actually often give you better results. Um, because there's, there's so much, the the explosion of AI generated material is, uh, crazy. So, like, in in training Grok, um, w- we have to go through the data and and say, like, hey, we, we actually have to have sort of apply AI to the data.

To say, is this data most likely correct or most likely not before we feed it into the training system?

that's crazy. Yeah, so y, and is it generated by human? is, yeah, I mean, the the the data, the the data filtration process is extremely, extremely difficult. Do you think it's possible to have a a serious, objective, rigorous political discussion with Grok? Uh, like, well, for a long time, and it wouldn't like Grok three or Grok four. grok three is So,

gonna be next level. I mean, what what people are currently seeing with Grok is is kind of baby grok, yeah, baby grok, it's baby grok right now. Um, but baby grok's still pretty good. Um, so it's, uh, but it's an order of magnitude less sophisticated than GPT four. And and, and you know, it's now grok two, which finished training, I don't know, six weeks ago or there thereabouts. Um, grok two will be a giant improvement, and then grok three will be, I don't know, order of magnitude better than grok two.

and you're hoping for it to be like, state of the art, like, better than.

Hopefully, I mean, this is a goal. I mean, we may fail at this goal, that is, that's the aspiration.

Do you think it matters who builds AGI? The the people and how they think and how they structure their companies and all that kind of stuff,

Uh, yeah, I think it matters that there is a I. I think it's important that that the whatever AI wins is a maximum truth-seeking AI. That is not, uh, f, uh, forced to lie for political correctness. It it well, for any reason, really. Um, political, anything. Um, I, I am concerned about AI succeeding. That is that that has got that is programmed to lie, even in even in small ways,

right? Because s s in small ways becomes big ways when it's

so they become very big ways. Yeah,

interpreted, and when it's used more and more at scale by humans. Uh, since I am interviewing Donald Trump, you wanna stop by?

yeah, sure, I'll stop in.

There was tragically an a an assassination attempt on Donald Trump. Uh, after this, you tweeted that you endorse him. What's your philosophy behind that endorsement? What do you hope Donald Trump does for the future of this country and for the future of humanity?

Well, I think there's, you know, people tend to take, like, say, an endorsement as, um, well, I I agree with everything that person's ever done their entire life, 100% wholeheartedly. And that's that's not gonna be true of anyone. Um, but w- we have to pick, you know, we got two choices really for for who's president, and it's not not just who's president, but the entire admin administrative structure. Uh, changes over. Um, and I thought, uh, Trump displayed, uh, courage under fire, objectively.

Um, you know, he's, uh, just got shot, s got blood streaming down his face, and he's like, fist pumping, saying, fight, you know, like, that's, uh, impressive, like you can't feign bravery in a situation like that. Um, I think most people would've been ducking, they would not be, cause it could be a s a second shooter, you don't know. Um, the the president of the United States has got to represent the country, and, uh, they're representing you, they're representing everyone in America.

Well, I think you want someone who is strong and courageous, uh, to represent the country. Um, that's not to say that he is without flaws. We all have flaws, um, but on balance, um, and certainly at the time, it was, um, a choice of, you know, Biden, poor, poor guy, you know, has trouble climbing a flight of stairs, and the other one's fist pumping after getting shot. This is no, no comparison. I mean, who do you want dealing with, uh, some of the toughest people in, you know, the world leaders, who are pretty tough themselves?

And, um, I I mean, and I'll, I'll tell you, like, one of the things that I think are important. Um, you know, I think we want a secure border, we don't have a secure border, um, we want safe and clean cities. Uh, I think we wanna re reduce the amount of spending that we're at least slow down the the spending, um, and, uh, because we're we're currently spending at a rate that is bankrupting the country. The interest payments on US.

debt this year exceeded the entire Defense Department spending. If this continues, all of the federal government taxes will simply be paying the interest. And then, and you, you keep going, going down that road, you, you end up, you know, in the tragic s situation that Argentina had back in the day. Argentina used to be one of the most prosperous places in the world, and hopefully with Millei taking over, he can restore that. But um, it's. It was an incredible, willful grace for Argentina to go from being one of the most prosperous places in the world to, um, being very far from that.

So I think we should not take American prosperity for granted. Um, so we, we really wanna. I think we, we've gotta reduce the size of government, we've gotta reduce the spending, and we've gotta live within our means.

Do you think politicians in general, politicians, governments, well, how much power do you think they have to to steer humanity towards good?

Um, I mean, there's a sort of age old debate in history, like, you know, the the is history determined by by these fundamental tides, or is it determined by the captain of the ship? This is both really. I mean, there are tides and the but it also matters who's captain of the ship. So so it's a false dichotomy, essentially, you, there's you, you, you. But I mean, there, there are certainly tide. The tides of history are there are, there are real tides of history.

And these these tides are often technologically driven. If you say, like the Gutenberg press, you know, the widespread availability of books as a result of a printing press. Uh, that that was a massive tide of history, independent of any ruler. But, you know, you, you, I think in in stormy times, you want the best possible captain of the ship.

Well, first of all, thank you for recommending, uh, Will and Ariel Durant's work. I've, uh, read the the short one for now, Oh, the the the lessons of lesson of history, his lessons of history. And so one of the what? said, one of the lessons, one of the things they highlight is the importance of technology, uh, technological innovation. And they, which is funny, cause they've written they wrote so long ago, but they were noticing that the the rate of technological innovation was speeding up.

Um, I would love probably is, to to see what they think about now. Uh, but yeah, so you did. To me, the question is how much government, how much politicians get in the way of technological innovation building versus like, help it, and which, uh, which, which politicians, which kind of policies help technological innovation. Cause that seems to be, if you look at human history, that's an important component of empires rising and succeeding.

yeah. Well, I mean, in terms of dating civilization, the start of civilization, I think the start of writing, in in my view, is is the that's. That's my what I think is probably the the right starting point to date civilization. And from that standpoint, civilization has been around for about fifty, 500 years. Um, when writing was invented by the ancient Sumerians, um, who who are gone now, um, but the the ancient Sumerians. In terms of getting a lot of firsts, the those ancient Sumerians really have a long list of firsts.

It's pretty wild. Um, in fact, Durant goes through the list, it's like, you wanna see firsts, we'll show you firsts. Um, the Sumerians just ask were just askers, um, and then the Egyptians, who are right next door, um, relatively speaking, uh, they're like, weren't that far. Developed an entirely different form of writing, the hieroglyphics, cuneiform, and hieroglyphics are totally different, and you can actually see the evolution of both hieroglyphics and cuneiform. Like the cuneiform starts off being very simple and then it gets more complicated.

And then towards the end, it's like, wow, okay, this. They really get very sophisticated with the cuneiform. So I I think of civilization as being about 5,000 years old. Um, and Earth is, um, if physics is correct, 4.5 billion years old, so civilization has been around for one millionth of Earth's existence. Flash in the pan,

Yeah, these are the early, early days, and so we we dr. very early, We make it very dramatic because there's been rises and falls of empires, and many, there

so many, so many rises and falls of empires, so many.

and there'll be many more.

Yeah, exactly. It's, I mean, only a tiny fraction, probably less than one of of what was ever written in history, is is available to us now. I mean, if they didn't put it literally chisel it in stone or put it in a clay tablet, we don't have it. I mean, there's some small amount of, like, papyrus scrolls that were rec- recovered that are thousands of years old. Uh, because they were deep inside a pyramid and, uh, weren't affected by moisture, uh, but but uh.

But other than that, it's really gotta be in a clay tablet or chiseled. So the vast majority of stuff was not, uh, chiseled, cause it takes a while to chisel things. Um, so that's why we've got tiny, tiny fraction of the information from history. But even that little information that we do have and the archeological record, uh, shows so many civilizations rising and falling. It's it's wild.

we tend to think that we're somehow different from those people. One of the other things that Durant highlights is that human nature seems to be the same, it just persists.

Yeah, I mean, the basics of human nature are more or less the same. Yeah,

So we get ourselves in trouble in the same kinds of ways, I think, even with the advanced technology.

yeah, I mean, you, you do tend to see the same patterns, similar patterns, you know, for civilizations where they go through, uh, a life cycle, like like an organism, you know, sort of just like a human. Has sort of a zygote, fetus, baby, you know, toddler, teenager, you know, eventually get gets old and dies. The civilizations go through a life cycle. No civilization will last forever.

What do? What do you think it takes for the American Empire to not collapse in the near term future, in the next hundred years, to continue flourishing?

Well, the single biggest thing that is, um, o- often actually not mentioned in history books, but Durant does mention it, uh, is the birth rate. So, um, like a like, a, perhaps to some a counterintuitive thing happens, uh, when civilizations become, uh, are are winning for too long, that they've been they. The birth rate declines, it can often decline quite rapidly. We're seeing that throughout the world today. You know, currently, South Korea has, like, I think, maybe the lowest fertility rate, but there there are many o- others that are close to it.

It's like point eight. I think if the birth rate doesn't decline further, uh, South Korea will lose roughly 60% of its population. And and but every year that birth rate is dropping. Um, and this is true through most of the world. I'm, I don't mean to single out South Korea, it's been happening throughout the world. So a as as soon as it, it's as soon as any given, uh, civilization reaches a level of prosperity, the birth rate drops.

Um, and now you can go look at the same thing happening in ancient in ancient Rome. So, uh, Julius Caesar took note of this, I think, around fifty fifty ish. B. C. Um, and tried to pass, I don't know if he was successful, tried to pass a law to give an incentive for any Roman citizen that would have a third child. And I think Augustus was was able to. Well, he was, you know, the dictator, so this the Senate was just for show.

Uh, I think he did pass a a tax incentive for Roman citizens to have a third child. But it it those efforts were unsuccessful. Um, Rome fell because the the Romans stopped having making Romans. That's actually the fundamental issue. And and there were other things there, there was like, um, they had, like, a quite a serious malaria, serious malaria epidemics and plagues and whatnot. Um, but they had those before, uh, the the. It's just that the birth rate was lower than the death rate.

It really is that simple. Well, I'm saying, that's More people that's, that's a at is required.

a, at a f- at a fundamental level. If a civilization does not at least maintain its numbers, um, it will disappear

So perhaps the amount of compute that the biological computer allocates to s to sex is justified. In fact, we should probably increase it.

well. I mean, there's this hedonistic sex, which is, uh, you know, that that's ne- that's near the hetero there. Um, it, it's, it's not productive, it's, it's, it doesn't produce kids. Well, you know, you, you, what Just taking what matters? notes. I mean, Durant makes this very clear, cause he's looked at one civilization after another, and they all went through the same cycle. When the civilization was under stress, the birth rate was was high, but as soon as there were no external en enemies, or they f they were had a extended period of prosperity, the birth rate inevitably dropped.

Every time. I don't believe there's a single exception.

so that's like the foundation of it. You need to have people,

Yeah, I mean, it's at a base level, no humans, no humanity.

and then there's other things like, you know, uh, human freedoms and just giving people the freedom to build stuff.

Y- Yeah, absolutely, there's, but but at at. At a basic level, if you do not at least maintain your numbers, if you're below replacement rate and that trend continues, you will eventually disappear. This is elementary. Um, now, th- then obviously you want also also wanna try to avoid, like, uh, massive wars, um, you know, if if there's a global thermonuclear war, probably we're we're all toast, you know, radioactive toast. S. So we wanna try to avoid those things.

Um, then there there are, um, there there's a thing that happens over time with with any given, uh, civilization, which is that the laws and regulations accumulate. Um, and if there's not a if, if there's not some forcing function, like a war to clean up the accumulation of laws and regulations. Eventually everything becomes legal and you, you, you that the that's like the hardening of the arteries, um, or a way to think of it is like being tied down by a million little strings, like Gulliver.

You can't move. It's not like any one of those strings is the is the issue, it's you got a million of them, so you, you have. There has to be a sort of a garbage collection for laws and regulations. Um, so that you, you, you don't keep accumulating laws and regulations to the point where you can't do anything. This is why we can't build high speed rail in America, it's illegal, that's the issue, it's illegal six ways to Sunday to build high speed rail in America.

I wish you could just like for a week, go into Washington and like, be the head of the committee for making, uh, what is it for the the garbage collection, making government smaller, like removing stuff.

I, I have discussed with Trump the idea of a government efficiency commission. And, uh, I would be willing to, uh, be part of that commission.

I wonder how hard that is.

The the antibody reaction would be very strong. So, um, you r, you really have to. You're attacking the matrix at that point, matrix will fight back.

how, how are you doing with that? Being attacked?

Me attacked.

Yeah, there's a lot of it. It doesn't.

yeah, there is a lot. P I mean, every day another psyop, you know? So I

How do you?

just put tinfoil hat.

How do you keep your just positivity? How do you optimism about the world, a clarity of thinking about the world? So just not become resentful or cynical or all that kind of stuff, just getting attacked by, you know, very large number of people misrepresented,

Oh yeah, that that's like, that's

black belt.

that's a daily occurrence. So, uh, I mean, it does get me down at times, it, I mean, it makes me sad. But um, I mean, at some point you have to sort of say, look, the the attacks are by people that actually don't know me. Um, they're, and they're trying to generate clicks. So if so, if, if, if you can sort of detach yourself somewhat emotionally, which is not easy. Um, and say, okay, look, this is not actually, you know, from someone that knows me or is they're, they're.

They're literally just writing to get, you know, impressions and clicks. Um, then, uh, you know, then it, I guess it doesn't hurt as much. It's like, uh, it's. It's not quite water off a duck's back, maybe it's like acid off a duck's back.

all right, well, that's good. Just about your own life, what do you use as a measure of success in your life?

A measure of success, I'd say, w- w- like, what? How many useful things can I get done?

Uh, day to day basis, you wake up in the morning, how can I be useful today

Yeah, maximize utility, area under the curve of usefulness. Very difficult to be useful at scale.

at scale? Can you, like, speak to what it takes to be useful for somebody like you? Where there's so many amazing, great teams, like, how do you allocate your time to be the most useful?

Well, time time is the time is the true currency. So it it is tough to say, what, what is the best allocation of time? I mean, there are, you know, often say, if you look, if you look at, say, Tesla, w- I mean, Tesla this year will do over 100 billion in revenue, so that's $2 billion a week. Um, if I make slightly better decisions, I can affect the outcome by $1 billion. So then, uh, you know, I try to do the best decisions I can.

And on balance, you know, at least compared to the C, the competition, pretty good decisions. But the marginal value of of a better decision can easily be, in the course of an hour, $100 billion.

given that, how do you take risks? How do you do the the algorithm that you mentioned? I mean, deleting given that a small thing can be $1 billion, how do you decide

Yeah, well, I I think you have to look at it on a percentage basis, because if you look at it in absolute terms, it's it's just, uh, I would never get any sleep. It's it would just be like, I need to just keep working and and work my brain harder, you know? And I'm not trying to get as much as possible out of out of this meat computer. So it's not, uh, it's pretty hard, um, cause you can just work all the time.

And and and at any given point, uh, like I said, a slightly better decision could be a hundred dollar, 100 million dollar impact for Tesla or SpaceX, for that matter. Um, but. But it is wild when when considering the marginal value of of time can be $100 million an hour at times or more.

Is your own happiness part of that equation of success?

It has to be to some degree. I, if I'm sad, I if I'm depressed, I make worse decisions, so I I can't have. Like, if if I have zero recreational time, then, uh, I make wor- worse decisions. So I don't have a lot, but it's it's above zero. I mean, my motivation, if I've got a religion of any kind, is, uh, a, uh, religion of curiosity. Of trying to understand, you know, it's, it's. It's really the the mission of Grok, understand the universe, I'm trying to understand the universe.

Um, or at least at least set things in motion such that at some point, civilization understands the universe. Or, um, far better than we do today, and even what questions to ask. As Douglas Adams pointed out in his book, the sometimes the the answer is the is arguably the easy part. The trying to frame the question correctly is the hard part. Once you frame the question correctly, the answer is often easy. So, um, I'm trying to set things in motion such that we are at least at some point able to understand the universe.

Um, so, for SpaceX, the goal is to make life multiplanetary. Um, and uh, to which which is, you know, if if you go to the the Fermi paradox of where are the where are the aliens? You've got these these sort of great filters, like, just like, why? why have we not heard from the aliens? Now? A lot, a lot of people think there are aliens among us. I often claim to be one, which nobody believes me.

But um, it did say alien registration card at one point on my, uh, immigration documents, um, so I have not seen any evidence of aliens. So it it suggests that, um, at least one of the one of the explanations is that, uh, intelligent life is extremely rare. Um, and again, if you look at the history of Earth, civilization's only been around for one millionth of Earth's existence. So if you know, if if aliens had visited here, say 100,000 years ago, they would be like, Well, they don't even have writing, you know, just hunter gatherers, basically.

So, um, so how long does a civilization last? So for SpaceX, the the goal is to establish a self-sustaining city on Mars. Mars is the only viable planet for such a thing. Um, the moon is close, but it's it lacks resources, and I think is probably vulnerable to any, any, any calamity that takes out Earth. Could the moon is too close, it's vulnerable to a a calamity that takes out Earth. Um, so, I'm not saying we shouldn't have a moon base, but Mars is, Mars would be far more resilient.

Um, the difficulty of getting to Mars is what makes it resilient. Um, so, but and, uh, you know, in in going through the these various explanations of why don't we see the aliens? Why? One of them is that they they failed to pass these these great filters, these these key hurdles, and one of those hurdles is being a multi-planet species. Um, so if you're a multi-planet species, then if something were to happen, whether that was a natural catastrophe or a man-made catastrophe, at least the other planet would probably still be around.

So, you know, like, don't have all the eggs in one basket. And once you are sort of a two-planet species, you can obviously extend to extend. Life past to the asteroid belt, to maybe to the moons of Jupiter and Saturn, um, and ultimately to other star systems. But if you can't even get to another planet, the, you know, definitely not getting to star systems.

and the other possible great filters, uh, and super powerful technology like AGI, for example. So you're you're basically trying to knock out one great filter at a time.

Digital superintelligence is possibly a great filter. I hope it isn't, but it might be. You know, guys like, say, Jeff Hinton would say, who, you know, has? He invented a number of the key principles? And a artificial intelligence, I think he puts the probability of AI annihilation around ten to 20%, something like that. So, you know, so it's, it's not, uh, it's not like, you know, look on the bright side, it's, uh, 80% likely to be great.

So so, but I I think AI risk mitigation is important. Um, being a multi-planet species would be a massive risk mitigation. And, um, I, I do wanna sort of once again emphasize this. Impor- the importance of having enough children to sustain, um, our numbers, um, and not going n- not plummet into population collapse, which is currently happening. Pop population collapse is a real and current thing. Um, so the the the only reason it's not being reflected in the total population numbers is that is that as much is because people are living longer.

Um, but but you, you, you. It's easy to predict, say, what the population of any given country will be. Um, you just take the birth rate last year, how many babies were born, multiply that by life expectancy, and that's what the population will be. Steady state, unless, uh, I, if, if the birth rate continues to that level. But if it keeps declining, it will be even less and eventually dwindle to nothing. So I keep, you know, banging on the baby drum here, um, for a reason, um, because it has been the the source of civilizational collapse over and over again throughout history.

Um, and so why don't we just, uh, not try try to stave off that day?

Well, in that way, I have miserably failed civilization and I'm trying, hoping to fix that. I would love to have many kids.

Uh, great, hope you do. Um, they're time like the present.

Yeah, now I gotta allocate more compute to the whole process. Um, but apparently it's not that difficult.

No, it's like unskilled labor.

Uh, well, if I. One of the things, uh, you do for me, for the world, is to inspire us with what the future could be. And so some of the things we've talked about, some of the things you're building, um, alleviating human suffering with Neuralink and expanding the capabilities of the human mind, trying to build a colony on Mars, um, so creating a backup for humanity on a on another planet. And, uh, exploring the possibilities of what artificial intelligence could be in this world, especially in the real world.

AI with, uh, hundreds of millions, maybe billions of robots walking around.

There will be billions of robots. That's, uh, that seems almost, that seems virtual certainty.

Well, thank you for building the future and thank you for inspiring so many of us to keep building and creating cool stuff. And i including kids,

You're welcome. Uh, go forth and multiply.

go forth and multiply. Thank you, Elon, thanks for talking about it, thanks for listening to this conversation with Elon Musk. And now, dear friends, here's DJ Seo, the co-founder, president and CEO of Neuralink. When did you first become fascinated by the human brain?

For me, I was always interested in understanding the purpose of things and how it was engineered to serve that purpose, whether it's organic or inorganic. You know, like we were talking earlier about your curtain holders, they serve a clear purpose, and they were engineered with that purpose in mind. And, you know, growing up, I had a lot of interest in seeing things, touching things, feeling things, and trying to really understand the root of how it was designed to serve that purpose.

And, you know, obviously, brain is just a fascinating organ that we all carry. It's a infinitely powerful machine that has intelligence and cognition that arise from it. And, you know, we we haven't even s scratched the surface in terms of how all of that occurs. But also at the same time, I I think it took me a while to make that connection to really studying and building tech to understand the brain. Not until graduate school, you know, there were a couple moments, key moments in my life where some of those, I think, influence how the trajectory of my life, um, got me to studying, uh, what I'm doing right now, you know, one was growing up, both sides of my

family, uh, my grandparents had a very severe form of Alzheimer. And it's, um, you know, incredibly debilitating conditions. Um, I mean, literally you're seeing someone's whole identity and and their mind just losing over time. And I, I just remember thinking, um, how? Both the power of the mind, but also how something like that could really lose your sense of identity.

It's fascinating that that is one of the ways to reveal the power of a thing by watching it lose the power.

Yeah, a lot of what we know about the brain actually comes from, uh, these cases where, uh, there are trauma to the brain or some parts of the brain that led someone to lose certain abilities. And as a result, there's some correlation and understanding of that part of the tissue being critical for that function. And um, it's an incredibly fragile organ, if you think about it that way, but also it's incredibly plastic and incredibly resilient in many different ways.

And by the way, the term plastic, as we'll use a bunch, means that it's adaptable. So neuroplasticity refers to the the adaptability of the human brain.

Correct? Um, another key moment that sort of influenced how the trajectory of my life have shaped towards the current focus of my life has been during my teenage year. When I came to the US. you know, I didn't speak a word of English. There was a huge language barrier, and um, there was a lot of struggle to kind of connect with my peers around me. Um, because I didn't understand the the artificial construct that we have created, called language, uh, specifically English in this case.

And I remember feeling pretty isolated, not being able to connect with peers around me. So spent a lot of time just on my own, you know, reading books, watching movies, um, and I, I naturally sort of gravitated towards sci-fi books. I just found them really, really interesting, and also it was a great way for me to learn English. You know, some of the first set of books that I picked up are Ender's Game, you know, the whole saga by, uh, Orson Scott Card, and Neuromancer from William Gibson, and Snow Crash from Neal Stephenson.

And, you know, movies like Matrix was coming out around that time. Point that really influenced how I think about the potential impact that technology can have for our lives in general. So fast track to my college years, you know, I, I was always fascinated by just just physical stuff, building physical stuff, and especially, um, physical things that had some sort of intelligence. And, you know, I studied electrical engineering during undergrad, and I started out my research in mems, uh, so micro electro mechanical systems, um, and really building these tiny nanostructures for um, temperature sensing.

And I just found that to be just incredibly rewarding and fascinating subject. To just understand how you can build some something miniature like that that again serve a function and had a purpose. And then, you know, I, I spent large majority of my college years basically building millimeter wave circuits for next gen telecommunication systems for imaging. And it was just something that I found very, very intellectually interesting. You know, phase arrays, how the the signal processing works for, you know, any modern, as well as next gen telecommunication system, wireless and wireline.

Um, em waves or electromagnetic waves are fascinating. How do you design antennas that are, um, most efficient in a small footprint that you have? How do you make these things energy efficient? That was something that just consumed my intellectual curiosity, and that journey led me to actually apply to and find myself at PhD program at UC. Berkeley. At kind of this consortium called the Berkeley Wireless Research Center that was precisely looking at, um, building. At the time, we called it XG, you know, similar to three G, four G, five G, but the next next generation G system.

And how you would design circuits around that to ultimately go on phones. And, you know, basically any any other devices, uh, that are wirelessly connected these days. Um, so I, I, I was just absolutely just fascinated by how that entire system works and that in infrastructure works. Um, and then also during grad school, I had sort of the fortune of having, um, you know, couple research fellowships that led me to pursue whatever project that I want.

And that's that's one of the things that, uh, I really enjoyed about my graduate school career. Where you got to kind of pursue your intellectual curiosity in the domain. That may not matter at the end of the day, but is something that, you know, really, uh, allows you the opportunity to, um, go as deeply as you want, as well as as widely as you want. And at the time, I was actually working on this project called the Smart Band Aid.

And the idea was that when you get a wound, there's a lot of other kind of proliferation of signaling pathway that cells follow to close that wound. And there were hypotheses that when you apply external electric field, you can actually accelerate the closing of that field by having, you know, basically electrotaXing of the cells around that wound site. And specifically, not just for normal wounds, there are chronic wounds that don't heal. Um, so we were interested in building, you know, some sort of a wearable patch that you could, um, apply to kind of facilitate that healing process.

And um, that was in collaboration with, uh, Professor Michelle Maherwitz. Um, you know, which which, you know, was a great addition to kind of my thesis committee, and, you know, really shaped rest of my, uh, PhD career.

So this would be the first time you interacted with biology, I suppose.

Correct, correct. I mean, there were some peripheral, you know, end application of the wireless imaging and telecommunication system that I was using for security and bioimaging. But this was a very clear, direct application to bio, biology, biology and biological system, and understanding the constraints around that, and really designing and engineering, uh, electrical solutions around it. So that was my first introduction, and that's also, um, kind of how I got introduced to Michelle. Um, you know, he's he's sort of known for remote control of, uh, beetles in the early 2000s, and then around 2013, you know, obviously kind of the holy grail.

When it comes to implantable system is to kind of understand how small of a thing you can make. And a lot of that is driven by how much energy or how much power you can supply to it, and how you extract data from it. So at the time at Berkeley, there was kind of this, this, uh, desire to kind of understand in the neural space what, what, what sort of system you can build to really miniaturize these implantable systems.

And, uh, I distinct, distinctively remember this one, uh, particular meeting where Michelle came in and he's like, Guys, I think I have a solution. The solution is ultrasound, and, uh. And then he proceeded to kind of walk through why that is the case. And that that really formed the basis for my thesis work, um, uh, called neural DUS system. That was looking at ways to use ultrasound as opposed to, uh, electromagnetic waves for powering as well as communication.

I guess I should step back and say the. The initial goal of the project was to build these tiny, about the size of a neuron, implantable system that can be parked next to a neuron. Being able to record its state and being able to ping that back to the outside world for doing something useful. And, as I mentioned, the size of the implantable system is limited by how you power the thing and get the data off of it.

And at the end of the day, fundamentally, if you look at a human body, we're, uh, essentially a bag of salt water with some interesting proteins and chemicals, but, uh, it, it's, it's mostly salt water. That's very, very well temperature regulated at thirty seven degrees Celsius. Um, and we'll, we'll get into how, why, a and and later, why? That's, uh, an extremely harsh environment for any electronics to survive, as I'm sure you've experienced, or maybe not experienced.

You know, dropping cell phone in a in a salt water in an ocean, it will instantly kill the device, right? Um, but anyways, uh, just in general, electromagnetic waves don't penetrate through this environment well. Um, and uh, just the speed of light, it is what it is, we can't, we can't change it. And based on the um, the wavelength at which you are interfacing with the device, it the device just needs to be big, like these inductors, needs to be quite big.

Um, and the general good rule of thumb is that you want the wavefront to be roughly on the order of the size of the thing that you're interfacing with. So an implantable system, uh, that is around ten to hundred micron in dimension in in in a volume, which is about the size of a neuron that you see in, um, in a human body. Um, you would have to operate at like hundreds of gigahertz, which number one.

Not only is it difficult to build electronics operating at those frequencies, but also the body just attenuates that very, very significantly. So the interesting kind of insight of this ultrasound, um, was the fact that w ultrasound just travels a lot more effectively in the human body tissue compared to electromagnetic waves. And this is something that you encounter, uh, and you, I, I'm sure most people have encountered in their lives. When you go to, um, you know, hospitals, there are medical, uh, ultrasound, you know, sonograph, right?

Um, and they go into very, very deep depth without attenuating too much, too much of the signal. So all in all, you know, ultrasound. The fact that it travels through the body extremely well, and the mechanism to which it travels to to the body really well is that just the wave front is very different. It's, uh, electromagnetics waves are transverse, whereas in ultrasound waves are compressive, so it's just a completely different mode of, uh, wave front propagation.

Um, and as well as speed of sound is orders and orders of magnitude less than speed of light. Which means that even at ten megahertz ultrasound wave, your wave front ultimately is a very, very small wavelength. So if you're talking about interfacing with the ten micron or 100 micron type structure, you would have 150 micron wave front at ten megahertz. And building electronics at those mega, uh, at at those frequencies are much, much easier, and they're a lot more efficient.

So the basic idea kind of was born out of, um, you know, using ultrasound as a mechanism for powering the device and then also getting data back. So now the question is, how do you get the data back? The mechanism to which we landed on is what's called backscattering. Um, this is actually something that is very common and that we interface on a day to day basis with our RFID cards. You know, a radio frequency ID tags, where there's actually rarely, you know, in your ID, a battery inside.

There's an antenna and there's some sort of, uh, coil that has your serial uh, identification ID. And then there's an external device called a reader that then sends a wave front, and then you reflect back that wave front with some sort of modulation that's unique to your ID. That's that's what's called backscattering, uh, fundamentally. So the tag itself actually doesn't have to consume that much energy. And um, that was the mechanism to which we were kind of thinking about sending the data back.

So when you have an external, uh, ultrasonic transducer that's sending ultrasonic wave to your implant, the neural dust implant. And it records some information about its environment, whether it's a neuron firing or, uh, some other state of, um, the, uh, the tissue that it's interfacing with. And then it just amplitude modulates the wave front that comes back to the source,

And the recording step would be the only one that requires any energy. So what would require energy in that little step?

correct? So it, it is that initial kind of startup circuitry to get that recording, amplifying it, and then just modulating. And the mechanism to which that that you can enable. That is, there is this specialized crystal called piezoelectric crystals that are able to convert sound energy into electrical energy and vice versa. So you can kind of have this interpa- interplay between the ultrasonic domain and electrical domain. That is the the biological tissue.

So, on the theme of parking very small computational devices next to neurons, that's the dream, uh, the vision of brain computer interfaces. Maybe before we talk about Neuralink, can you give a sense of the history of the field of BCI? What? What has been, um, maybe the continued dream? And also some of the milestones along the way with the different approaches and the amazing work done at the various labs?

I think a good starting point is, um, going back to 1790s,

Did not expect that.

where, um, the concept of animal electricity, or the fact that body's electric was first discovered by Luigi Galvani. Where, uh, he had this famous experiment where he connected a set of electrodes to frog leg and ran current through it. And then it started twitching. And he said, Oh, my goodness, body's electric. So fast forward many, many years to 1920s, uh, where Hans Berger, who's a German psychiatrist, discovered EEG or electro, uh, encephalography, which is still around.

There are these um, electrode arrays that you wear outside the skull that gives you some sort of neural recording. That was a very, very big milestone that you, you, you can record some sort of activities about the human mind. And then in the 1940s, there were, uh, these group of scientists, Renshaw, Forbes and Morrison that, um, inserted these glass microelectrodes into the cortex and recorded single neurons. Um, the fact that they they there's s signal that are, uh, bit more high resolution and high fidelity, uh, as you get closer to the source, let's say.

And in the 1950s, um, these two scientists, Hodgkin and Huxley, showed up. And they, um, built this beautiful, beautiful models of the cell membrane and the ionic mechanism, and had these like circuit diagram. And as as as someone who's an electrical engineer, it's a beautiful model. That's, you know, built out of these, uh, partial differential equations, talking about flow of ions and how that really leads to how neurons communicate. And they won the Nobel Prize for that ten years later, in the 1960s.

So in 1969, uh, f. Fetz from University of Washington published this beautiful paper called Operant conditioning of cortical unit activity. Where he was able to record a single unit neuron from a monkey and was able to have the monkey modulate it based on its activity and reward system. So I, I would say this is the very, very first example, um, as far as I'm aware, of closed loop, uh, you know, brain computer interface or BCI.

the abstract reads The activity of single neurons in precentral cortex of anesthetized monkeys was conditioned by reinforcing high rates of neuronal discharge with delivery of a food pod. Auditory and visual feedback of unit firing rates was usually provided in addition to food reinforcement. Cool, so they actually got it done.

They got it done. This is, um, back in 1969.

After several training sessions, monkeys could increase the activity of newly isolated cells by fifty to 500% above rates before reinforcement. Fascinating,

Brain is very plastic.

and so, uh, and so. From here, the number of experiments grew.

Yeah, number of experiments, as well as set of tools to interface with the brain, have just exploded. Um, I think, and and also just understanding the neural code and how some of the cortical layers and and the functions are organized. So the other paper that is, um, uh, pretty seminal, especially in the the motor decoding, uh, was this paper in the eight 1980s from Georgia. Opolis, um, that discovered that there's this thing called motor tuning curve, so what are motor tuning curves?

It's the fact that there are, you know, neurons in the motor cortex of mammals, including humans, that have a preferential direction that causes them to fire. So what that means is there are a set of neurons that would, uh, increase their spiking activities. When you're thinking about moving to the left, right, up, down and any of those uh, vectors, and based on that, you know, you can start to think, well, if you, if you can identify those essential eigenvectors, you can do a lot.

And you can actually use that information for actually decoding someone's intended movement from the cortex. So that was a very, very seminal kind of paper that showed, um, that, uh, there, there is some sort of code that you can, you can extract, especially in the motor cortex.

So there's signal there, and if you measure, uh, the the electrical signal from the brain that you could, you could actually figure out what the intention was.

Correct? Yeah, not only electrical signals, but electrical signals from the right set of neurons that give you these preferential direction.

Hm, okay, so going slowly towards Neuralink, uh, one interesting question is, what do I understand on the BCI front, on invasive versus non invasive? From this line of work? Uh, how important is it to to to park next to the neuron? What does that get you?

That answer fundamentally depends on what you wanna do with it, right? Um, there's, uh, actually incredible amount of stuff that you can do with EEG and um, electrocardiograph, ECOG, which actually doesn't penetrate the the cortical layer or parenchyma, um, but you place a set of electrodes on the surface of the brain. So the thing that I'm personally very interested in is just actually understanding, um, and and being able to just really tap into the high resolution, high fidelity understanding of the activities that are happening at the local level.

And you know, we can get into biophysics, but just to kind of step back, um, to kind of use analogy, cause analogy here can be useful. Sometimes it's a little bit difficult to think about electricity. Um, at the end of the day, we're doing electrical recording. That's mediated by ionic, um currents, you know, movements of these charged particles, um, which is really, really hard for most people to think about. Um, but turns out a lot of the activities, um, that are happening in the brain and the frequency band with which that's happening is actually very, very similar to sound waves.

And, and, you know, our normal conversation, um, audible range range. So the analogy that typically is used in the field is, and if you, if you have a football stadium, uh, you know, there's game going on. If you stand outside the stadium, you, you maybe get a sense of how the game is going based on the cheers and the boos of the home crowd, whether the team is winning or not. But you have absolutely no idea what the score is, you have absolutely no idea what, um, individual audience or the players are talking or saying to each other, what the next play is, what the next goal is.

Um, so what you have to do is you have to drop the microphone near into the stadium and then get near the source, like into the individual chatter. Um, in this specific example, you would wanna have it, you know, right next to where the huddle is happening. Um, so I I think that's kind of a good illustration of what we're trying to do. Um, when we say invasive, or minimally invasive, or implanted brain computer interfaces versus non invasive or non implanted, uh, brain interfaces.

It's basically talking about where do you put that microphone and what can you do with that information?

So what? What is the biophysics of the read and write communication that we're talking about here, as we now step into the efforts at Neuralink?

Yeah, so, uh, brain is made up of these specialized cells called neurons. There's billions of them, you know, tens of billions, you know, sometimes people quote 100 billion. That are connected in this complex yet dynamic network, uh, that are constantly remodeling, you know, they're changing their synaptic weights. Um, and that's, you know, what what we typically call neuroplasticity. And the neurons are also bathed in this charged environment that is, uh, laden with many charged molecules like potassium ions, sodium ions, chlorine ions.

And, uh, those actually facilitate these, um, you know, through ionic current communication between these different networks. And, uh, when you look at the look at a neuron as well, um, they, they have these uh, membrane with a beautiful, beautiful, uh, protein structure called a voltage selective ion channels. Which, in my opinion, is one of nature's best inventions in many ways. If you think about what they are, they're doing the job of a modern day transistors. Transistors are nothing more at the end of the day than a voltage gated conduction channel.

Um, and nature found a way to have that very, very early on in its evolution, and as we all know, with the transistor, you can have many, many computation and a lot of amazing things. Um, that that we have access to today. So I, I, I think I. It's one of those, just as a tangent, just a beautiful, beautiful, uh, invention that that nature came up with these voltage gated ion channels.

I mean, I I suppose there's, uh, on the biological level, every level of the complexity of the hierarchy of the of the organism. There's going to be some mechanisms for storing information and for doing computation, and this is just one such way. But to do that with, uh, biological and chemical components is interesting. Plus, like with neurons, I mean, it's not just electricity, it's, uh, chemical communication, it's also mechanical. I mean, these are like actual objects that have like that vibrate, I mean, they move.

yeah, there, there It's all of it.

actually. Uh, I mean, there's a lot of really, really interesting physics that that that are involved. And, you know, kind of going back to my um work on ultrasound, uh, during grad school, there, there are groups, and uh, there were groups, and there are still groups, um, looking at ways to cause neurons to actually fire an action potential using ultrasound wave. And the mechanism to which that's happening is still unclear. As I understand, um, you know, it may just be that, you know, you're imparting some sort of thermal energy and that causes cells to depolarize in some interesting ways.

Um, but there are also these um ion channels, or even membranes that actually just open up. It's pore as they're being mechanically, like, shook, right, vibrated. So there's just a lot of, you know, elements of these like move particles, um, which again, like that's governed by diffusion physics, right? Uh, movements of particles, and there's also a lot of kind of interesting physics there.

Also, not to mention, as Roger Penrose talks about the. There might be some, uh, beautiful weirdness in the quantum mechanical effects of of all of this. And he, he actually believes that consciousness might emerge from the quantum mechanical effects there. So, like, there's physics, there's chemistry, there's biol, all of that is going on there.

oh yeah, yeah, I mean, you can. Yes, I, there's. There's a lot of levels of physics that you can dive into, but yeah, in the end, you have these um, uh, membranes with these voltage gated ion channels that selectively let um, these charged molecules that are in in the extracellular matrix, like in and out. Um, and these neurons generally have these like resting potential, where there's a voltage difference between inside the cell and outside the cell.

And um, when there's some sort of s stimuli that changes, uh, the state such that they need to send information to the the downstream network. Um, you know, you start to kind of see these like sort of orchestration of these different molecules going in and out of these channels. They also open up, like more of them open up. Once it reaches some threshold, uh, to a point where, you know, you have a depolarizing cell that sends a action potential.

So it's a just a very beautiful kind of orchestration of these, uh, these, these, um, molecules. And um, what we're trying to do when we place an electrode or parking it next to a neuron is that you're trying to measure these local changes in the potential. Um, again, mediated by, uh, the the um, the movements of the ions. And what's interesting, as I, as I mentioned earlier, there's a lot of physics involved, um, and the two dominant physics for this electrical recording domain is diffusion physics and electromagnetism.

And where one dominates, where max maxwell's uh equation dominates versus fitz law dominates, depends on where your electrode is. Um, if it's close to the source, uh, mostly electromagnetic based, um, when you're farther away from it, it's more diffusion based. So essentially, when you're able to park it next to it, you can listen in on those individual chatter, um, and those local changes in the potential. And the type of signal that you get are these canonical textbook neural, uh, spiking waveform.

When you're the moment you're further away and based on some of the studies that people have done, um, you know, Christoph Koch's lab and and others. Once you're away from that source by roughly around a hundred micron, which is about, uh, width of a human hair, you no longer hear from that neuron. You, you're no longer able to kind of have the system sensitive enough to be able to, um, record that particular um local membrane potential change in that neuron.

And just to kind of give you a sense of scale. Also, when you, when you look at a hundred micron voxel, so hundred micron by hundred micron by hundred micron box. Uh, in a brain tissue, there's roughly around 40 neurons and whatever number of connections that they have. So there's a lot in that volume of tissue. So the moment you're outside of that, you're there's just no hope that you'll be able to there detect that change from that one specific neuron that you may care about.

Yeah, but a as you're moving about this space, you'll be hearing other ones. So if you move another hundred micron, you'll be hearing chatter from another community.

Correct?

And so the the whole sense is you wanna place as many as possible electrodes, and then you're listening to the chatter.

Yeah, you wanna listen to the chatter. And and at the end of the day, you also want to basically let the software do the do the job of decoding. Um, and um, just to kind of go to, you know why? ECOG and EEG work at all, right? Um, when you have these local changes, you know, obviously it's not just this one neuron that's, uh, activating, there's many, many other networks that are activating all the time. And you do see sort of a general change in the potential of this electro, like, it's a charge medium, and that's what you're recording when you're farther away.

I mean, you, you still have some reference electrode that's, uh, stable in the brain, that's just electroactive organ. And you're seeing some combination aggregate, uh, action potential changes, and then you can pick it up, right? It's a much slower, um, changing, uh, signals, but you know, uh, there there are these like canonical kind of oscillations and waves, like gamma waves, beta waves, like when you sleep, that that can be detected. Cause there's sort of a synchronized, um, kind of global, global, uh, effect of the brain that that you can detect.

Um, and I mean, the physics of this go like, I mean, if we really wanna go down that rabbit hole, like there. There's a lot that goes on in terms of like, why diffusion physics at some point dominates when you're further away from the source, you know it, it, it's just a charge medium. Um, so similar to how when you have electromagnetic waves propagating in atmosphere or in in a charge medium like a plasma, there's this weird shielding that happens that actually, um, further attenuates the signal, um, as you move away from it.

So, yeah, you see, like, if you do a really, really deep dive on kind of the signal attenuation over distance, you start to see kind of one over r square in the beginning and then exponential drop off. And that's the knee at which, you know, you go from electromagnet magnetism dominating to diffusion physics dominating.

but once again, with the electrodes, the the biophysics that you need to understand is is, um, not as deep. Because no matter where you're placing it, you're listening to a small crowd of local neurons

Correct? Yeah, so once you penetrate the brain, um, you know, you're in the arena, so to speak.

and there's a lot of neurons. There are many, And.

many of them.

But then again, there's like, Uh, there's a whole field of neuroscience that's studying, like how the different groupings, the different sections of the seating in the arena. What are they usually responsible for? Which is where the the metaphor probably falls apart, because the SE the seating is not that organized in an arena.

also, most of them are silent, they don't really do much. Um, you know, or or they their activities are, um, you know, you have to hit it with just the right set of stimulus.

So they're usually quiet.

They're usually very quiet, they're very quiet. There's, I mean, similar to dark energy and dark matter, there's dark neurons. What are they all doing? When you place these electrode again, like within this 100 micron volume, you have 40 or so neurons. Like, why? why do you not see 40 neurons? why do you see only a handful? What is happening there?

well, they're mostly quiet, but like when they speak, they say profound shit. I think that's the way I like to think about it anyway. Before we zoom in even more, let's zoom out. So how does Neuralink work? From the surgery to the implant, to the signal and the decoding process, and the H human being able to use the implant to actually affect the the world outside? And all of this, I'm asking in the context of. There's a gigantic historic milestone that Neuralink just accomplished in January of this year.

Uh, putting a Neuralink implant in the first human being, Noland, uh. And there's been a lot to talk about there, about his experience, because he's able to describe all the nuance and the beauty and the fascinating complexity of that experience of everything involved. But on the technical level, how does Neuralink work?

Yeah, so there are three major components to the technology that we're building. Uh, one is the device, um, the thing that's actually recording these neural chatters, uh, we call it N1 implant, or the link. And, uh, we have a surgical robot that's actually doing an implantation of these tiny, tiny wires that we call threads that are, you know, smaller than, uh, human hair. And um, once everything is surgized, you have these neural signals, these spiking neurons that are coming out of the brain.

And, uh, you need to have some sort of software to decode what the user is intent to do with that. Um, so there's what's called a Neuralink application or B1 app that's doing that translation. It's running the very, very simple machine learning model that decodes these um inputs that are neural signals and then convert it to a set of outputs. That allows, um, you know, our participant, uh, first participant, Nolan, to be able to control a cursor on And the Sh.

this is done wirelessly?

And this is done wirelessly. So we, um, our, our implant is actually a two part. That's this link has, um, uh, you know, these flexible, tiny wires called threads, um, that have, uh, multiple electrodes along its length, and um, they're only inserted into the cortical layer, which is about three to five millimeters in a human human brain. Um, in the motor cortex region, that's where the kind of the intention for movement lies in. And we have sixty four of these threads, each thread having 16 electrodes along, you know, the span of three to four millimeters, um, separated by 200 micron.

So you can actually record along the depth of the insertion, and based on that signal, uh, there's custom, um, you know, uh, integrated circuit or ASIC that we built that amplifies the neural signals that you're recording. And then digitizing it. And then, um, has some mechanism for detecting whether there was a an interesting event, that is a spiking event. Um, and decide to send that or not send that through Bluetooth to an external device, whether it's a phone or a computer that's running this Neuralink application.

So there's onboard signal processing already just to decide whether this is an interesting event or not. So there is some computational power onboard, inside the in addition to the human brain.

Y- Yeah, so it does the signal processing to kind of be, uh, really compress the amount of signal that you're you're recording. So we have a total of thousand electrodes, um, sampling at, uh, you know, just under twenty kilohertz with ten bit each. So, uh, that's 200 megabits. Um, that's coming through to the chip, uh, from thousand, uh, channel simultaneous, uh, neural recording, and that's quite a bit of data. And you know, there is there are technology available to send that off wirelessly, but being able to do that in a a very, very thermally constrained environment that is a brain.

So there has to be some amount of compression that happens to send off only the interesting data that you need. Which, in in this particular case for motor decoding, is, um, occurrence of a spike or not. And then, um, being able to use that to, um, to, uh, you know, decode the intended cursor movement. So the implant itself processes it, figures out whether a spike happened or not with our spike detection algorithm, and then sends that off, packages it, send it off through Bluetooth.

Um, to an external device that then has the model to decode. Okay, based on these spiking inputs, did Nolan wish to go up, down, left, right, or click or right click, or whatever?

all of this is really fascinating, but let's stick on the N1 implant itself, so the thing that's in the brain. Uh, so I'm looking at a picture of it, there's an enclosure, uh, there's a charging coil, so we didn't talk about the charging, which is fascinating. Uh, the the, the battery, the power electronics, the antenna, uh, then there's the signal processing electronics. I wonder if there's more kinds of signal processing you can do. That's that's another, that's another question.

And then there's the threads themselves with the enclosure on the bottom. So maybe to ask about the charging, so there's a external charging device.

hm, hm, yeah, there's an external charging device. Um, so yeah, the the second part of the implant, the threads are the ones again, just the the last three to five millimeters are the ones that are actually penetrating the cortex. Uh, rest of it is, uh, actually, most of the volume is occupied by the battery, uh, rechargeable battery. Um, and, uh, you know, it's about a size of a quarter. Uh, you know, I actually have a device here if you wanna take a look at it.

Um, you know, this is the the flexible thread component of it, and then this is the implant, so it's about a size of a US quarter. Um, it's about 9 mm thick. So basically, this implant, uh, you know, once you have the craniotomy and the dur and the dura, um, threads are inserted, and um, the the hole that you created, this craniotomy gets replaced with that. So basically, that thing plugs that hole and you can screw in, uh, these self drilling cranial screws to hold it in place.

And at the end of the day, once you have the skin flap over, uh, there's only about two to three millimeter. That's, you know, obviously transitioning off of the top of the implant to where the screws are, and and that's the minor bump that you have.

those threads look tiny, that's incredible, that is really incredible, that is really incredible. And also, as you're right, most of the volume, actual volume, is the battery. Wow, this is way smaller than I realized.

they, they are. Also, the threads themselves are quite strong, They look strong and and the thread themselves also has a very interesting UM feature at the end of it, called the loop. And that's the mechanism to which the robot is able to interface and manipulate this tiny, hair like structure.

and they're tiny. So what's the width of a thread?

Yeah, so the the width of a s thread, um, starts from 16 micron and then tapers out to about 84 micron. So, you know, average human hair is about 80 to 100 micron in width.

This thing is amazing, this thing is amazing.

Yeah, so most of the volume is occupied by the by the battery rechargeable lithium ion cell. Um, and uh, the charging is done through inductive charging, which is actually very commonly used. You know, your cell phone, most cell phones have that. Um, the biggest difference is that, you know, for us, you know, usually when you have a phone and you wanna charge it on a charging pad, you don't really care how hot it gets. Whereas for us, it matters.

There is a very strict regulation and good reasons to not actually increase the surrounding tissue temperature by two degrees Celsius. So there's actually a lot of innovation that is packed into this to allow charging of this implant without causing that temperature threshold to reach. And even small things like you see this charging coil and what's called a ferrite shield, right? So, uh, without that ferrite shield, what you end up having when you have, um, you know, resonant inductive charging, is that the battery itself is a metallic can.

And you form these eddy currents, uh, from uh, external charger, and that causes heating, um, and that actually contributes to inefficiency in charging. Um, so this ferrite shield, what it does is that it actually concentrate that field line away from the battery and then around the coil that's actually wrapped around it.

There's a lot of really fascinating design here to to make it. I mean, you're integrating a computer into a biological, a complex biological system.

Yeah, there's a lot of innovation here. I would say that part of what enabled this was just the innovations in the wearable. Uh, there's a lot of really, really powerful, tiny, low power, uh, microcontrollers, temperature sensors or various different sensors, and power electronics. A lot of innovation really came in the the charging coil design, how this is packaged, and how do you enable charging? Such that you don't really, uh, exceed that temperature limit, which is not a constraint for other devices out there.

So let's talk about the threads themselves, those tiny, tiny, tiny things. So, uh, how many of them are there? You mentioned a thousand electrodes, how many threads are there, and what do the electrodes have to do with the threads?

Yeah, so the current instantiation of the device has 64 threads, and each thread has 16 electrodes for a total of 1024 electrodes that are capable of both recording and stimulating. Um, and um, the thread is basically this, uh, polymer insulated wire. Um, the metal conductor is the kind of a tiramisu tiramisu cake of, uh, tie plate, gold, plate tie, um, um, and they're very, very tiny wires, um, two micron in width, so two one millionth of uh, meter

It's crazy that that thing I'm looking at has the polymer insulation, has the conducting material, and has 16 electrodes at the end of it.

on each of those thread,

Yeah, on each of those threads, 16,

correct?

each one of those.

Yes, you're not gonna be able to

Six people.

see it with naked eyes.

And I I mean to state the obvious, or maybe for people who are just listening, they're flexible.

Yes, yes, that's also one element that, uh, was incredibly important for us. Um, so each of these thread are, uh, as I mentioned, 16 micron in width, and then they taper to 84 micron, but in thickness, they're less than 5 micron. Um, and in thickness, it's mostly, you know, uh, polyimide at the bottom and this metal track, and then another polyimide, so 2 micron of polyimide, 400 nanometer of this metal stack and 2 micron of polyimide sandwiched together to protect it from the environment.

That is, uh, 37 degrees C bag of salt water,

So what, what's some? Maybe can you speak to some interesting aspects of the material design here? Like, what does it take to to design a thing like this and to be able to manufacture a thing like this? Uh, for people who don't know anything about this kind of thing

yeah, so the material selection that we have is not, I, I don't think it was particularly unique. Um, there, there were other labs, and there are other labs that are kind of looking at similar, um, material stack. Um, there's kind of a fundamental question, um, and and still needs to be answered around the longevity and reliability of these, uh, microelectrodes. Um, that that we call, uh, compared to some of the other more conventional neural interfaces, devices that are intracranial, so penetrating the cortex, that are more rigid.

Um, you know, like the Utah array, um, that that are these, uh, four by four millimeter kind of silicon shank that have exposed, uh, recording site at the end of it. Um, and and um, you know, that's. That's been kind of the innovation from Richard Norman back in 1997. Uh, it's called the Utah array, cause, you know, he was at University of Utah.

and what? What does the Utah array look like? So it's a rigid type of

Yeah, so we can actually electrode. look it up. Yeah, yeah, so it's a bed of needle. Um, there's, yeah, okay, go yeah, ahead. I'm sorry, so th those are rigid,

rigid, rigid. rigid shank, um, Yeah, you won't hit and and it.

the size and the number of shanks vary anywhere from 64 to 128. Um, at the very tip of it is an exposed electrode that actually records neural signal. Um, the other thing that's interesting to note is that, uh, unlike Neuralink threads that have recording electrodes that are actually exposed iridium oxide recording sites along the depth, this is only at a single depth. So these Utah array spokes can be anywhere between point five millimeter to 1.5 millimeter, and they're they also have, uh, designs that are slanted, um, so you can have it inserted at different depth, um, but that's one of the other big differences.

And then, uh, I mean, the main key difference is the fact that, uh, there's no active electronics, these are just electrodes. And then there's a bundle of a wire that you're seeing, and then that actually then exits the craniotomy. Um, that then has this port that you can connect to, um, for any external electronic devices. They are working on a or have the wireless telemetry device, but it still requires a through the skin, uh, port. That actually is one of the biggest failure modes for infection, uh, for the system.

What are some of the challenges associated with flexible threads, like, for example, on the robotic side? R1, Uh, implanting those threads, how difficult is that task?

Yeah, um, so, as you mentioned, they're, they're very, very difficult to maneuver by hand. Um, these these Utah arrays that you you saw, uh, earlier, uh, they're actually inserted by a neurosurgeon actually positioning it near the site that they want. And then, uh, they're actually there's a pneumatic hammer that actually pushes them in. Um, so, so it's, uh, it's a pretty simple process, um, and they're easy to maneuver, um. But for for these thin film arrays, they're, they're very, very tiny and, uh, flexible, so they're, they're very difficult to maneuver.

So that that's why we built an entire robot to do that. Um, and there are other other reasons for why we built the robot. Um, and and that is ultimately, we want this to help millions and millions of people that can benefit from this, and there just aren't that many neurosurgeons out there. Um, and, uh, you know, robots can be, uh, something that, you know, we hope can actually do large parts of the surgery. Um, but y, yeah, the the the robot is this e entire other, um, sort of category of product that we're working on and it.

It's essentially this multi-axis gantry system that has the specialized robot head. Um, that has all of the optics and um, this, this kind of a needle retracting mechanism that maneuvers these these threads, um, via this loop structure that you have on the thread.

So the thread already has a loop structure, by which you can grab it.

Correct, correct.

Okay, so this is fascinating. So you mentioned optics, so there's a robot, R1, so for now, there's a human that actually creates, uh, a hole in the in the skull. And then after that, there's a computer vision component that's finding a way to avoid the blood vessels. And then you're grabbing it by the loop, each individual thread and placing it in a particular location to avoid the blood vessels. And also choosing the depth of placement, all that correct, kind of stuff, so controlling every like the 3D geometry of the placement.

correct. So the the aspect of this robot that is unique is that it's not surgeon assisted or human assisted, it's a s semi automatic or automatic, uh, robot. Once you, you know, obviously there are human component to it. When you're placing targets, um, you can always move it or away from kind of major vessels that you see. Um, but I mean, we wanna get to a point where one click and it just does the surgery within minutes.

So the computer vision component finds great targets, candidates, and the human kind of approves them, and the robot does. Is it just do like one thread at a time, or does it do

It does one thread at a time, uh, and that's that's actually also one thing that we, um, uh, are looking at ways to do multiple threads at a time. There's nothing stopping from it. You can have multiple kind of engagement, uh, mechanisms, um, but right now it's one by one. And, uh, you know, we also still do quite a bit of just just kind of verification to make sure that it got inserted. If so, how deep, you know, did it actually match?

Um, what was programmed in, and, you know, so on, and so forth.

and the the actual electrodes are placed at very at differing depths in the uh, like, what I mean? It's very small differences, but differences. And so that there's some reasoning behind that, as you mentioned, like it, it gets more varied signal.

yeah, we, I mean, we try to place them all around three or four millimeter from the surface. Um, just cause the span of the electrode, those 16 electrodes that we currently have in this, uh, version spans, um, you know, roughly around 3 millimeters. So we wanna get all of those in the brain.

This is fascinating. Okay, so there's a million questions here. If we go zoom in, specifically on the electrodes, what is your sense? How many neurons is each individual electrode listening to?

Yeah, each electrode can record from anywhere between zero to forty, as I mentioned right earlier, um, but practically speaking, uh, we only see about at most like two to three. Um, and you can actually distinguish which neuron it's coming from by the shape of the spikes. Um, so I mentioned the spike detection algorithm that we have, it's called Boss algorithm, um, buffer online spike sorter. It actually outputs at the end of the day, uh, six unique values, which are, um, you know, kind of the amplitude of these.

Like negative going hump, middle hump, like, uh, positive going hump. And then also the time at which these happen. And from that, you can have a, you know, kind of a statistical probable probability, um, estimation of is that a spike, is it not a spike? And then, based on that, you could also, uh, determine, oh, that spike looks different than that spike must come from a different neuron.

okay. So that that's a nice signal processing step from which you can then make much better predictions about if there's a spike. Especially in this kind of context where there could be multiple neurons screaming. And that that also results in you being able to compress the data better and incentivize it. Okay, that's

And and just to be clear, I mean, they're the the labs do this, what's called spike sorting. Um, usually, once you have these like broadband, you know, like the uh, the fully digitized signals. And then you run, uh, a bunch of different set of Al algorithms to kind of tease apart it. It's just all of this for us is done on the device, on the device, in a very low power custom, you know, built ASIC, uh, digital processing unit,

highly heat constrained.

where highly heat constrained. And the processing time from signal going in and giving you the output is less than a microsecond, which is, uh, you know, a very, very short amount of time,

Oh yeah, so the latency has to be super short.

correct?

Oh wow, oh, that's a pain in the ass. Yeah, latency is That's really tough.

this, uh, huge, huge thing that you have to deal with, uh, right now. The biggest source of latency comes from the Bluetooth, uh, the the way in which they're packetized, and, you know, we bin them in 15 millisecond, uh, time window.

So it's communication constraint. Is there some potential innovation there on the protocol used? Uh, okay,

yeah. Bluetooth is d definitely not, uh, our final, uh, wireless communication protocol that we wanna get to. It's a hence highly

hence the N1 and the R1, I imagine that increases. NX It'll go up.

NX, RX.

Uh, yeah, that's, you know, the communication protocol, cause Bluetooth, uh, allows you to communicate at, I guess, farther distances than you need to, so you can go much shorter.

Yeah, the only, uh, well, the primary motivation for choosing Bluetooth is that, I mean, everything has Bluetooth.

All right, so you can talk Um, so to any device.

interoperability is just absolutely essential, especially in this early phase. Um, and in many ways, if you can access a phone or a computer, you can do anything.

Well, it'd be interesting to step back and actually look at again the same pipeline that you mentioned for Nolan. So what does this whole process look like? From finding and selecting a human being to the to the surgery, to the the first time he's able to use this thing?

So we have what's called a patient registry that people can sign up to, um, you know, hear more about the updates. And that was a route to which Nolan applied. And the process is that once the application comes in, you know it, it contains some medical records. And we, uh, you know, based on their medical eligibility, the there's a lot of different inclusion exclusion criteria for them to meet. And we go through a pre screening interview process with someone from Neuralink.

And at some point, we also go out to their homes to do a BCI home audit. Um, cause one of one of the most kind of revolutionary part about, you know, having this N1 system that is completely wireless is that you can use it at home. Like you don't actually have to go to the lab, um, and and, you know, go to the clinic to get connected, right to these like specialized equipment that you can't take home with you.

Um, so that's one of the the key elements of, you know, when we're designing the system that we wanted to keep in mind. Like, you know, people, you know, hopefully would wanna be able to use this every day in the comfort of their home. And um, so, part of our engagement and and what we're looking for during BCI home audit is to just kind of understand their situation, what other assistive technologies that they use.

And we should also step back and kind of say that, uh, the estimate is, uh, 180, zero people live with quadriplegia in the United States, and each year, an additional 18, zero suffer, uh, a paralyzing spinal cord injury. So these are folks, uh, who have a lot of challenges living a life in terms of accessibility, in terms of doing the things that many of us just take for granted day to day. And one of the things, one of the goals of this initial study, is to enable them to have s sort of digital autonomy, where they by themselves can interact with a digital device using just their mind.

Something that you're calling telepathy, so digital telepathy, where, uh, a quadriplegic can communicate with a digital device in all the ways that we've been talking about. Uh, control the mouse cursor enough to be able to do all kinds of stuff, including play games and tweet and all that kind of stuff. And there's there's a lot of people for whom life, the basics of life, are difficult, uh, because of the things that have happened to them. So,

Yeah, I mean, movement is so so fundamental to our exist existence. I mean, even even speaking involves movement of mouth, lip, larynx. And, um, without that, it's, it's, it's, um, extremely debilitating. Um, and there, um, yeah, there, there are many, many people that we can help. And I mean, like, especially if you start to kind of look at other forms of movement disorders, um, that are not just from spinal cord injury, but from, uh, you know, ALS, uh, MS, or even stroke.

That that leads you and or just just aging, right? That leads you to lose some of that mobility, that independence, it's, uh, extremely debilitating.

and all of these are opportunities to help people, to help alleviate suffering, to help improve the quality of life. But each of the things you mentioned is its own little puzzle that needs, uh, to have increasing levels of capability from a device like a Neuralink device. And so the 1st one you're you're focusing on is, uh, it's just the beautiful word telepathy. So being able to communicate using your mind wirelessly with a digital device. Can you just explain this?

Exactly what we're talking about?

Yeah, I mean it, it's exactly that. I mean, I I think if you are able to control a, uh, cursor and able to click, um, and be able to get access to computer or phone, I mean, the the whole world opens up to you. And I mean, I guess the word telepathy, if you kind of think about that as, um, you know, just definitionally. Being able to transfer information from my brain to your brain, um, without using some of the the physical faculties that we have, you know, like voices.

But the interesting thing here is, I think the thing that's not obviously clear is how exactly it works. So in order to move a cursor, there's, uh, at least a couple ways of doing that. So one is you imagine yourself maybe moving a mouse with your hand, or you can then, which no one talked about, like, imagine moving the cursor with your mind. Like, I don't, but it's like there is a cognitive step here that's fascinating, cause you, you, you have to use the brain, and you have to learn how to use the brain, and you kinda have to figure it out dynamically.

Like, uh, because you reward yourself if it works, so you like, I mean, there's a step that this is, this is just a fascinating step, cause you have to get the brain to start firing in the right way. And you do that by imagining, uh, like, fake it till you make it. And all of a sudden, it creates the right kind of signal that, if decoded correctly, uh, can create the kind of effect. And then there's like noise around that that you have to figure all of that out.

But on the human side, imagine the cursor moving is what you have to do.

yeah. He says, using the force,

The force, I mean, that's isn't that just like fascinating to you, that it works? Like to me? It's like, holy shit, that actually works, like you could move a cursor with your mind.

you know, as much as you're learning to use that thing, that thing's also learning about you. Like, our, our model is constantly updating the weights to say, oh, if, if someone is thinking about, you know, this sophisticated forms of like spiking patterns like that actually means to do this, right?

Hm, hm. So the the machine is learning about the human, and the human is learning about the machine. So there's a adaptability to the signal processing, the decoding step, and then there's the adaptation of Nolan, the human being. Like the same way, if if you give me a new mouse and I move it, I learn very quickly about its sensitivity, so I learn to move it. L slower. And then there's other kind of signal drift and all that kind of stuff they have to adapt to, so both are adapting to each other.

Correct That's a fascinating like software challenge on both sides, the software on both on the the human software and the the organic the machine software. and the inorganic, Yeah, the organic and the inorganic anyway. So sorry to rudely interrupt. So there's a selection that Nolan has passed with flying colors. Um, so everything, including that it's a BCI friendly home, all of that. So what is the the process of the surgery, implantation, the 1st moment when he gets to use the system?

The end to end, uh, you know, we say patient in to patient out is anywhere between two to four hours. Uh, in particular case for Nolan, it was about three and a half hours. And there's many steps leading to, you know, the actual robot insertion, right? So there's anesthesia induction, and we do intra op CT imaging to make sure that we're, you know, drilling the hole in the right location. And this is also pre planned beforehand.

Um, uh, someone goes through, uh, someone like Nolan would go through fMRI, and then, um, they can think about w wiggling their hand, you know, obviously due to their injury, it's not gonna actually lead to, um, any, any sort of intended output. But it's the same part of the brain that actually lights up when you're imagining moving your finger to actually moving your finger. And that's one of the ways in which we can actually know where to place our threads.

Um, cause we wanna go into what's called the hand knob area in the motor cortex. And, you know, as as much as possible, densely put our electrode threads. Um, so, yeah, we do intra op CT imaging to make sure and double check the location of the craniotomy. And um, surgeon comes in, does their thing in terms of like, skin, um, incision, craniotomy, so drilling of the skull. And then there's many different layers of the brain. Uh, there's what's called the dura, which is a very, very thick layer that surrounds the brain.

That gets actually resected in a process called Durectomy, and that then exposes the pia and the brain that you wanna insert. And by the time it's been around anywhere between one to one and a half hours, robot comes in, does this thing. Placement of the targets, inserting of the thread that takes anywhere between 20 to 40 minutes. In the particular case for Nolan, it was just under or just over 30 minutes. And then after that, the surgeon comes in.

There's couple other steps of like, actually inserting the dural substitute layer, um, to protect the thread, as well as the the brain. And then, um, yeah, screw, screw in the implant, and then skin flap, and then suture, and then you're out.

So, uh, when, uh, Nolan woke up, what was that like? what was the recovery like? and wh- when was the 1st time he was able to use it?

So he, he was actually immediately after the surgery, um, you know, like an hour after the surgery, as he was waking up. Um, we did turn on the device, um, make sure that we are recording neural signals, and we actually did have a couple signals that we, um, noticed that he can actually modulate. And what I mean by modulate is that he can think about clenching his fist. And you could see the spike disappear and appear.

That's awesome.

Um, and that was immediate, right? uh, immediate, uh, after in in the recovery room.

how, how cool is that? That's a human being. I mean, what? What did that feel like for you? This device and a human being, a 1st step of a gigantic journey. I mean, it's a historic moment, even just that spike, just to be able to modulate that.

you know, o. Obviously, there have been other other, you know, uh, as you mentioned, pioneers that have participated in these groundbreaking BCI. Um, you know, uh, investigational early feasibility studies. So, uh, we're obviously standing on the shoulders of the giants here, you know, we're not the 1st ones to actually put electrodes in a human human brain. Um, but I I mean, just leading up to the surgery, there was, uh, I, I, I definitely could not sleep.

There's just it's the 1st time that you're working in a completely new environment. Um, we had a lot of confidence based on our benchtop testing, uh, or preclinical R D studies. That the mechanism, the threads, the insertion, all that stuff is is very safe, and that it's, um, uh, you know, obviously ready for, uh, doing this in a human. But there's still a lot of unknown unknown about can the needle actually insert? Uh, I mean, I, I.

We brought something like 40 needles, just in case they break, and we ended up using only one. Um, but I mean, that that was a level of just complete unknown, right? Cause this is a very, very different environment. And, uh, I mean, that's. That's why we do clinical trial in the 1st place, to be able to test these things out. So extreme nervousness and uh, just just I. Many, many sleepless night leading up to the surgery and and definitely the day before the surgery, and it was an early morning surgery.

Like we, we started at seven in the morning. Um, and and by the time it was around 1030, it was, it was, it was, everything was done. But I mean, 1st time seeing that, well, number one, just just huge relief. Um, that this thing is, um, you know, doing what it's supposed to do. Um, and two, I I mean, just immense amount of gratitude for for Nolan and his family. And then many others that have applied and that we've spoken to and will speak to are, I mean, true pioneers in in every everywhere.

And you know, I, I sort of call them the neural astronauts or neural not Neural knot, Yeah, um, you know, these amazing, just like in the 60s, right? like these, these amazing, just pioneers, right? Um, e- exploring the unknown outwards, in this case, it's inward, um, but an incredible amount of gratitude for them to, uh, you know, just just participate and and play a part. Um, and and it's a it's a journey that we're embarking on together.

Um, but, uh, also, like, I think it was just, uh, that was an very, very important milestone, but our work was just starting. So a lot of just kind of, uh, anticipation for, okay, what's what needs to happen next? Um, what are set of sequences of events that needs to happen for us to, you know, make it worthwhile for? Um, uh, you know, both Nolan as well as us.

just to linger on that, just a huge congratulation to you and Thank the team for you, that milestone. I know there's a lot of work, uh, left, but that that is, that's really exciting to see. There's, um, that's a source of hope. It's, uh, this 1st big step, opportunity to help hundreds of thousands of people, and then maybe, uh, expand the realm of the possible for the human mind for millions of people in the future. So it's it's really exciting.

So, like the the opportunities are all ahead of us. And to do that safely and to do that effectively was, uh, was really fun to see. As an engineer, just watching other engineers come together and do an epic thing, that was awesome. So huge congrats.

thank you, thank you. It's, um, yeah, could not have done it without the team. And um, yeah, I mean, that that's the other thing that I I, um, you know, told the team as well. Of of just this immense sense of optimism for the future. Um, I I mean, it was a it's. It's a very important moment for for the company. Um, you know, needless to say, as well as, um, you know, hopefully for many others out there that we can help.

So, speaking of challenges, Neuralink published a blog post describing that some of the threads are retracted. And so the performance, as measured by bits per second, dropped at 1st, but then eventually was regained. And that the the whole story of how it was regained is super interesting. That's d definitely something I'll talk to, uh, to Bliss and to Nolan about. Um, but in general, um, can you speak to this whole experience? How was the performance regained?

And um, just the the technical aspects of, uh, the threads being retracted and moving,

The main takeaway is that I in the end, the performance have come back and it, uh, it's actually gotten better than it was before. Um, he's actually just beat the world record yet again last week, um, to eight, five BPS. So I mean, he's, he's just cranking and he's just improving,

The previous one was that he set was eight, I

correct? think he, he said, And and the

eight, five.

yeah, the previous world record, uh, in human, was four, six, so it's, uh, almost double. And his goal is to try to get to ten, which is rough, roughly around. Kind of the median. Neuralinker, uh, using a a, you know, mouse with the hand, so it's, um, it's getting there.

so yeah. So the the performance was regained.

Yeah, better than before. So that that's, you know, a a a story on its own of what took the BCI team to recover that performance. Uh, it was, it was actually mostly on kind of the signal processing end. So, you know, as I mentioned, we were, um, kinda looking at these spike outputs from the um, our electrodes. And what happened is that kind of, uh, four weeks into the surgery, uh. We noticed that the threads have slowly come out of the brain.

And the way in which we noticed this at 1st, obviously, is that, um, well, I think Nolan was the 1st to notice that his performance was degrading. Um, and I think at the time, uh, we were also trying to do a bunch of different experimentation. Um, you know, different algorithms, different, um, sort of UIUX. So it. It was expected that there will be variability in the performance, um, but we did see kind of a steady decline.

And then also the way in which we measure the health of the electrodes, or whether they're in the brain or not, is by measuring, uh, impedance of the electrode. So, uh, we look at kind of the interfacial, um, kind of the the the Randall circuit, that they they say, um, you know, the capacitance and the and the um, the resistance between the electrode surface and the medium. And if that changes in some dramatic ways, we have some indication, or if you're not seeing spikes on those channels, you have some indications that something's happening there.

And what we notice is that looking at those impedance plot and spike rate plots, and also because we have those electrodes recording along the depth. You're seeing some sort of movement that indicated that the res were being pulled out. Um, and that obviously will have an implication on the model side, because if you're the number of inputs that are going into the model is changing because you have less of them. Um, the out it that that model needs to get updated, right?

And um, but. But there were still signals. And as I mentioned, similar to how, even when you place the signals on the surface of the brain, uh, of the brain, or farther away, like outside the skull, you still see some useful signals. Um, what we started looking at is not just the spike occurrence through this boss algorithm that I mentioned, um, but we started looking at just the the. The power of the frequency band that is, um, interesting for n, uh, Nolan, or, uh, Nolan, to be able to modulate.

So once we kinda changed the algorithm for the implant to not just give you the boss output, but also these, uh, spike band power output. Um, that helped us sort of refine the model with the new set of inputs, and that that was the thing that really ultimately gave us the performance back. Um, you know, I in terms of, and obviously, like the. The thing that we want ultimately, and the thing that we are working towards is figuring out ways in which we can keep those threads intact.

Um, for as long as possible, so that we have many more channels going into the model, that's that's by far the number one priority that the team is currently embarking on to understand how to prevent that from happening. Um, the thing that I will say also is that, you know, as I mentioned, this is the 1st time ever that we're putting these threads in in a human brain, and you know, human brain. Just for size reference is ten times that of the monkey brain or the sheep brain.

And it, it's, um, just a very, very different environment. It moves a lot more, it like, actually moved a lot more than we expected. Um, when we, uh, did, did Nolan surgery, and um, it's, uh, just a very, very different environment than what we're used to. And this is why we do clinical trial, right? We, we, we wanna uncover some of these, uh, issues, uh, and and failure modes earlier than later. So in many ways, it's provided us with this enormous amount of data and um, information to be able to, uh, solve this.

And this is something that Neuralink is extremely good at. Once we have set of clear objective and engineering problem, we have enormous amount of talents across many, many dis disciplines to be able to come together and fix the problem very, very quickly.

But it sounds like one of the fascinating challenges here is for the system on the D decoding side to be adaptable across different timescales. So whether it's movement of threads or different aspects of signal drift, sort of on the software, the human brain, ch something changing. Like Nolan talks about cursor drift, that could be corrected, and there's a whole UX challenge to how to do that. So it sounds like adaptability is like a fundamental property that has to be engineered in.

it is, and and I mean, I think, uh, I mean, as a company, we're extremely vertically integrated. Um, you know, we make these thin film arrays in our own, uh, microfab.

Yeah, there's, uh, like you said, built in house. This whole paragraph here from this blog post is pretty gangster, uh. Building the technologies described above has been no small feat, and there's a bunch of links here that I recommend people click on. We constructed in house microfabrication capabilities to rapidly produce various iterations of thin film arrays that constitute our electrode threads. We created a custom femtosecond laser mill to

Oh my god, that's awesome,

manufacture components with micro level precision. I think there's a tweet associated that's a with this.

whole thing that we can get into.

Yeah, this, this, okay, what are we? what are we looking at here? This thing? This is, uh. So, in less than 1 min, our custom made femtosecond laser mill cuts this geometry in the tips of our needles. So we're looking at this weirdly shaped needle. The tip is only ten to 12 microns in width, only slightly larger than the diameter of a red blood cell. The small size allows threads to be inserted with minimal damage to the cortex.

Okay, so what's interesting about this geometry? So we're looking at this just geometry of a needle.

yeah, this is the needle that's engaging with the loops in the thread. So they're the ones that, um, you know, thread the threader loop, um, and then peel it from the silicon backing. And then this is the thing that gets inserted into the tissue, and then this pulls out, leaving the thread. And this kind of a notch or the shark tooth that we used to call, uh, is the thing that actually is, um, grasping the loop.

And then it's. It's designed in such way such that when you, when you pull out, it leaves the loop

And the robot is controlling this needle.

correct. So this is actually housed in a cannula, and basically the robot is has a lot of the optics that look for where the loop is. Um, there's actually a four or five nanometer light that actually causes the polyimide to fluoresce so that you can locate the the location of the loop. Um, So the and then this thing, loop lights up, w yeah, yeah, was just they do. It's a micron precision process.

what's interesting about the robot that it takes to do that. That's, that's pretty crazy. It's pretty crazy that a robot is able to get this kind of precision.

Yeah, our robot is quite heavy. Um, uh, our current version of it, um, there's, I mean it, it's like a giant granite slab that weighs about a ton. Um, because it needs to be sensitive to vibration, environmental vibration. And then as the head is moving at the speed that it's moving, you know, there's a lot of kind of motion control to make sure that you can achieve that level of precision. Um, a lot of optics to kinda zoom in on that.

Um, you know, we're working on next generation of of the robot that is lighter, easier to transport. I I mean, it is a it is a feat to move the robot to And it's the surgical site.

far superior to a human surgeon at this time for this particular task.

Absolutely, I mean, let alone you try to actually thread a loop in a in a in a sewing kit. Uh, I mean, this is like, we're talking like fractions of human hair. These these things are, it's, it's not visible.

So continuing the paragraph, we developed novel hardware and software testing systems, such as our accelerated lifetime testing racks and simulated surgery environment, which is pretty cool. To stress, test and validate the robustness of our technologies. We performed many rehearsals of our surgeries to refine our procedures and make them, um, 2nd nature. This is pretty cool. We practice surgeries on proxies with all the hardware and instruments needed in our mock or in the engineering space. This helps us rapidly test and measure.

So there's like proxies,

Yeah, this proxy is super cool, actually. So there's a 3D printed skull from the images that is taken at Barrow, as well as this, uh, hydrogel mix, you know, sort of synthetic polymer thing that actually mimics the the mechanical properties of the brain. Um, it also has vasculature of the person. Um, so basically what we're talking about here. A And there's a lot of work that has gone into making this set proxy. That, um, you know, it's.

It's about like, finding the right concentration of these different synthetic polymers to get the right set of consistency for the needle dynamics. You know, as they're being inserted, but we practice this surgery with the person, you know, Nolan's, basically physiology and brain, um, many, many times prior to actually doing the surgery. so every, To every step, every step e every

of the way.

step, yeah, like, where does someone stand? Like, I mean, like, the what you're looking at is the picture. This is in in in our office of this kind of corner of the robot engineering space that we, you know, have created this like mock OR space. That looks exactly like what they would experience, all the staff would experience during their actual surgery. So, I I mean, it's just kind of like any dance rehearsal where you know exactly where you're gonna stand, at what point.

Um, and you just practice that over and over and over again, with an exact anatomy of someone that you're going to. And and it. It got to a point where a lot of our engineers, when we created a craniotomy, they're like, Oh, th- that that looks very familiar. We've seen that before.

Yeah, man, there's wisdom you can gain through doing the same thing over and over and over. It's like, uh, do you remember dreams of sushi kind of thing? Um, because then, um, it's like Olympic athletes, visualize, uh, the Olympics, and then once you actually show up, it feels easy. It feels like any other day. It feels almost boring. Winning the gold medal, cause you've, you've visualized this so many times, you've practiced this so many times that nothing about it is new.

It's boring, you win the gold medal, it's boring, and it the the experience they talk about is mostly just relief. Probably that they don't have to visualize it anymore.

yeah. The power of the mind to visualize and where. I I mean, there's a whole field that studies where, you know, muscle memory lies in cerebellum. Yeah, it's incredible,

Uh, I think it's a good place to actually ask. Sort of the big question that people might have is, how do we know every aspect of this that you described is safe?

uh. At the end of the day, the gold standard is look at the tissue. Um, you know, what sort of trauma did you cause the tissue? And does that correlate to whatever behavioral anomalies that you may have seen? Um, and that's the language to which, uh, we we can communicate about the safety of, you know, inserting something into the brain and what type of trauma that you can cause. So, um, w- we actually have an entire department, uh, department of pathology, that looks at these, uh, tissue slices.

There are many steps that are involved in in doing this. Once you have, um, you know, studies that are launched to, uh, with with particular endpoints in mind. You know, at some point, you have to euthanize the animal, and then, uh, you go through necropsy to kinda collect the brain tissue samples. Um, you know, you fix them in formalin and you like, gross them, you section them, and you look at individual slices just to see what kind of reaction or lack thereof exists.

So that's the kind of the language to which FDA speaks, and, you know, uh, a as well for us to kind of evaluate the safety of the insertion mechanism, as well as the threats. Um, at various different time points, you know, both acute, um, so anywhere between, you know, uh, zero to three months to beyond three months,

So those are kind of the the details of an extremely high standard of safety that has to be reached. correct, Um, FDA supervises this, but there's in general just a very high standard in every aspect of this, including the surgery. I think, uh, Matthew McDougall has mentioned that, like, the standard is, uh, let's say how to put it politely, higher than maybe some other operations that we take for granted. So the the standard for all the surgical stuff here is extremely high.

very high. I mean, it's a highly, highly regulated environment. Um, with, you know, the governing agencies that scrutinize every, every medical device that gets marketed. And I think I think it's a good thing. Um, you know, it's good to have those high standards, and we we try to hold extremely high standards. Um, to kind of understand what sort of damage, if any, these, uh, innovative emerging technologies and new technologies that we're building are. And, you know, so far, I, I.

We have been extremely impressed by lack of immune response from these threads.

Speaking of which, you, uh, you talked to me, uh, with excitement about the histology and some of the images, uh, that you're able to share. Uh, can you explain to me what we're looking at

Yeah, so what you're looking at is a stained tissue image. Um, so, this is a sectioned, uh, tissue slice from an animal that was implanted for seven months, so kind of a chronic time point. And you're seeing all these different colors, and each color indicates specific types of cell types. So purple and pink are astrocytes and microglia, respectively, they're types of, uh, glial cells. And yeah, the the other thing that, you know, people may not be aware of is your brain is not just made up of soup of neurons and axons.

There are other, uh, you know, cells like, uh, glial cells that actually kind of is the glue and also, uh, react, uh, if if there are any trauma or damage to the tissue.

with the brown? are the neurons

The brown are the neurons here? It's the and then the blue is the glial.

otter neurons.

So so what you're seeing is in in this kind of macro image, you're seeing these like circle highlighted in white, the insertion sites. And, uh, when you zoom into one of those, you see the threads. And then, in this particular case, I think we're seeing about the 16, uh, you know, wires that are going into the page. And the incredible thing here is the fact that you have the neurons that are these brown structures, or brown, circular or elliptical thing that are actually touching and abutting the threads.

So what this is saying is that there's basically zero trauma that's caused during this insertion. And with these neural interfaces, these, um, microelectrodes that you insert, that is one of the most common mode of failure. So when you insert these threads like the Utah array, it causes, uh, neuronal death around the site because you're, uh, inserting a foreign object, right? And that kind of elicits these like immune response through microglia and astrocytes. They form this like protective layer around it.

Not only are you killing the neuron cells, but you're also creating this protective layer. That then basically prevents you from recording neural signals. Cause, cause, you're getting farther and farther away from the neurons that you're trying to record, and that that is the biggest mode of failure. And in this particular example, in that inset, it's, you know, it's about 50 micron. With that scale bar, the neurons are just seem to be attracted to it.

And so there's certainly no trauma. That's such a beautiful image, by the way, just the so the brown of the neurons. And for some reason, I can't look away, it's really yeah, and and cool.

and the way that these things like, I mean, I I, your tissues generally don't have these beautiful colors. Um, this is a multiplex stain that uses these different, uh, proteins that are staining these at different colors. You know, uh, you know, we use very standard set of, um, you know, staining techniques with H E, EBA one and you know, NUN, um, and and GFAP. So if you go to the next image, this is also kind of illustrates the 2nd point.

Cause you can make an argument. And initially, when we saw the the previous image, we said, Oh, like, are the threads just floating? like, what? what is happening here? like, are we actually looking at the right thing? So what we did is we did another stain, and this is all done in house of this. Methan's, uh, trichrome stain, which is in blue, that shows these collagen layer. So the blue, basically, like, you don't want the blue around the the implant threads, because that means that there's some sort of scarring that's happened.

And what you're seeing if you look at individual threads is that you don't see any of the blue. Which means that there has been absolutely, or very, very minimal, to a point where it's not detectable amount of trauma in these inserted threads.

So that presumably is one of the big benefits of having this kind of flexible thread. That's,

Yeah, so we think this is, uh, primarily due to, uh, the size, as well as the flexibility of the threads. Also, the fact that R1 is avoiding vasculature, so we're not disrupting, or we're not, um, causing damage to, uh, the vessels and not breaking any of the blood brain barrier. Uh, has, you know, basically caused the immune response to be muted?

But this is also a nice illustration of the size of things. So this is the tip of the thread

Yeah, those are neurons, they're, they're,

and they're neurons, and they're, and this is the thread listening. And the electrodes are positioned. How? yeah, so hm, hm,

this is what you're looking at. It's not electrode themselves, those are the conductive wires, so each of those should probably be two micron in width. Um, so what we're looking at is we're looking at the coronal slice, so we're looking at, uh, some slice of the tissue. So as you go deeper, you know, you'll obviously have less and less, uh, of the tapering of the of the thread. Um, but yeah, the the point basically being that there's just, uh, kind of cells around the insertion site, which is, um, just an incredible thing to see.

I, I've just never seen anything like this.

How easy and safe is it to remove the implant?

Yeah, so it depends on when, um, in the 1st three months or so after the surgery, um, there there's a lot of kind of tissue modeling that's happening. You know, similar to when you get a cut, um, you know, you obviously, uh, you know, s. Start over 1st couple weeks, or depending on the size of the wound. Um, scar tissue forming right there, these like contract it, and then in the end, they turn into scab and you can scab it off.

The same thing happens in the brain, and it's a very dynamic environment. And before the scar tissue or the neomembrane, or the, you know, new membrane that forms, it's quite easy to just pull them out. Um, and there's minimal trauma that's that's, uh, caused during that, once the scar tissue forms. And, you know, with with Nolan as well, we believe that that's the thing that's currently anchoring the threads. So we haven't seen any more movements since then.

Um, so they're, they're quite stable. Um, it's, it's. It gets harder to actually completely extract the threads. So our current method for, uh, removing the D device is cutting the thread, leaving the tissue intact, and then unscrewing and taking the implant out. And that hole is now gonna be plugged with either another Neuralink, or, uh, just with the, you know, kind of a a peak based, you know, plastic based, uh, cap.

Is it okay to leave the threads in there forever?

Yeah, we think so we. We've done studies where, um, you know, we left them there. And one of the biggest concerns that we had is like, do they migrate and do they get to a point where they should not be? We haven't seen that again. Once the scar tissue forms, they get anchored in place. And I, I should also say that, you know, when we say upgrades like it, it's, we're not just talking in theory here, like we've actually upgraded many, many times.

Um, most of our, uh, monkeys or, uh, non human primates, NHP, have been upgraded. You know, Pager, who you saw playing Mind Pong, has the latest version of device since two years ago and is seemingly very happy and healthy and fat.

So what's, uh, designed for the future, the upgrade procedure? So, uh, maybe, uh, for Nolan, what what? What would the upgrade look like? It was essentially what you're mentioning. Is there a way to upgrade sort of the device internally, where you take it apart and sort of, uh, keep the capsule and upgrade the internals?

yeah, so there there are a couple different things here. So for Nolan, if we were to upgrade, what we would have to do is, um, either cut the threads or, you know, extract the threads, depending on kind of, um, you know, uh, the situation there in terms of how they're anchored or scarred in. Um, if you were to remove them with the dural substitute, um, you know you, you have an intact brain, so you can reinsert different threads.

Um, with the updated, uh, implant package, uh, there are a couple different other, uh, ways that we're thinking about the future of what the upgradable system looks like. One is, you know, at the moment, we currently remove the dura, um, this, this kind of thick layer that protects the the brain. But that actually is the thing that actually proliferates the scar tissue formation. So typically, the general rule of thumb is you wanna leave the the nature as is, uh, and not disrupt it as much.

So we're looking at ways to, uh, insert the threads through the dura, um, which comes with different set of challenges, such as, you know, it's a pretty thick uh layer. So how do you actually penetrate that without breaking the needle? So we're looking at different needle design for that, as well as the kind of the loop engagement. The other biggest challenges are it's quite opaque optically in with white light illumination. So how do you avoid still this?

This biggest advantage that we have of avoiding vasculature? Uh, how do you image through that? how do you actually still mediate that? So there are other imaging techniques that we're looking at to enable that. Um, but the goal, uh, the our hypothesis is that. And based on some of the early evidence that we have, uh, doing through the dura insertion will cause minimal scarring. That causes them to be much easier to extract over time. And the other thing that we're also looking at, this is, um, gonna be a fundamental change in the implant architecture.

Is a as a. At the moment, it's a monolithic single implant that comes with a thread that's, um, bonded together. So you can't actually separate the thing out. But you can imagine having two part implant, um, you know, bottom part that is the thread that are inserted, that has the chips, um, and maybe a radio and some power source. And then you have another implant that has more of the computational heavy load and and the bigger battery.

Um, and then one can be under the dura, one can be above the dura. Like, you know, being the plug for the skull. They can talk to each other, but the thing that you wanna upgrade, the computer and not the threads. If you wanna upgrade that, you just go in there, you know, remove the screws and then put in the next version. And you know, you're off to, you know it. It's a very, very easy surgery, too, like you do a skin incision, slip this in, screw.

Probably be able to do this in 10 min,

So that would allow you to reuse the threads, sort of.

correct?

So, I mean, this leads to the natural question of, uh, what is the pathway to scaling the increase in the number of threads? Is that a priority? Is that like, what's what's the technical, uh, challenge there?

Yeah, that that is a priority. So for next versions of the implant, um, you know, the key metrics that we're looking to improve are number of channels, just recording from more and more neurons. Um, you know, we have a pathway to actually go from currently one zero to, you know, hopefully three zero, if not six zero by end of this year. Um, and then end of next year we wanna get to, um, you know, even more 16 zero.

There's a couple limitations to that. One is, you know, obviously being able to photolithographically print those wires, as I mentioned, it's two micron in width and and spacing. Obviously, there are chips that are much more advanced than those types of resolution, and we have some of the tools that we have brought in house to be able to do that. So traces will be narrower, just so that you have to have more of the wires coming up into the chip.

Um, chips also cannot linearly consume more energy as you have more and more channels. So there's a lot of innovations in the circuit, um, you know, in architecture, as as well as the circuit design, topology to make them lower power. Um, you need to also think about if you have all of these spikes, how do you send that off to the end application? So you need to think about bandwidth limitation there and potentially innovations in signal processing.

Um, physically, one of the biggest challenges is gonna be, um, the the interface, it's always the interface that breaks, um, bonding the thin film array to the um, the electronics, um, it starts to become very, very highly dense. Uh, interconnects, so how do you connectorize that? There's a lot of innovations, um, in in kind of the 3D integrations in the recent years that we can take advantage of. Um, one of the biggest challenges that we do have is, you know, forming this hermetic barrier, right?

You know, th- th- this is an extremely harsh environment that we're in the brain. Um, so how do you protect it from, uh, yeah, like the brain, trying to kill your electronics to also your electronics leaking things that you don't want into the brain? And that forming that hermetic barrier is gonna be a very, very big challenge that we, uh, you know, I think are actually well suited to tackle.

How do you test that? Like, what's the development environment to, uh, simulate that kind of harshness?

yeah, so this is. This is where the accelerated life tester essentially is a brain in a vat. Uh, it literally is a vessel that is, um, made up of. And again, again, uh, for all intents and purpose for this particular S types of tests, your brain is a saltwater. And uh, and you can, uh. Also put some other set of chemicals, like reactive oxygen species that, you know, get at kinda these interfaces and trying to cause a reaction to to, uh, pull it apart.

But you could also increase the rate at which these, uh, interfaces are aging by just increasing temperature. So every 10 °C that you increase, you're basically accelerating time by two x. And there's limit as to how how much temperature you wanna increase. Cause at some point there's some other nonlinear dynamics that causes you to have other nasty gases to form. That just is not realistic in an environment. So what we do is we increase, uh, in our ALT chamber by 20 °C, that, uh, increases the aging by four, four times.

So essentially, one day in ALT chamber is four day in calendar year. And and we look at whether the implants still are intact, uh, including the threads and

And operation and all that.

and operation and all of that. Um, it obviously is not an exact same environment as a brain, cause, you know, brain has mechanical, you know, other more, uh, biological goops that that attack at it. Um, but it is a good test environment, testing environment for at least the the the enclosure and the strength of that enclosure. And I mean, we've had implants, the current version, uh, of the implant that has been in there for, I mean, close to two and a half years, which is equivalent to a decade.

And they seem to be fine.

So it's interesting that the brain, so basically, uh, close approximation is warm salt water. hot salt water is a good testing environment. I, by the way, I'm drinking element, uh, which is basically salt water, which is making me kinda. it's, It doesn't have computational power the way the brain does, but maybe in terms of, um, in terms of other characteristics, it's quite similar. Yeah, and I'm consuming it

uh, yeah, you have to get it at the right pH, too.

and then consciousness will emerge. No, uh, all oh, uh. By right,

by the way, the other thing that also is interesting about our enclosure is, uh, if if you look at our implant, it's not your common looking medical implant. That usually is, uh, you know, encased in a titanium can that's laser welded. We use this polymer called PCTFE, polyfluoroethylene, which is actually commonly used in blister packs. So wh- when you have a pill and you try to pop a pill, there's like, kind of that plastic membrane, that's what this is.

Um, no one's actually ever used this, uh, except us. And the reason we, um, wanted to do this is cause it's, uh, electromagnetically transparent. So when we talked about the, uh, fascinating. electromagnetic inductive charging, um, with titanium, can. Usually, if you wanna do something like that, um, you know, you have to have a sapphire window and it's a. It's a very, very tough process to scale.

So you're doing a lot of iteration here in every aspect of this, the materials, the software, the hard, uh, all of the whole whole shebang, it, uh, so okay. So you mentioned scaling. Is it possible to have multiple Neuralink devices as one of the ways of scaling, to have multiple N Neuralink devices implanted?

that's the goal, that's the goal. Yeah, we've, we've had, we've had. Um, I mean, our monkeys have had two neuralinks, one in each hemisphere. And then we're also looking at, you know, potential of having one in motor cortex, one in visual cortex, and one in wherever other cortex.

So focusing on a particular function, one Neuralink device. Correct, I mean, I. I wonder if there's some level of customization that can be done on the compute side, so for the motor cortex.

absolutely, uh, that that's the goal. And and you know, we talk about, uh, at Neuralink, building a generalized neural interface to the brain. Um, and and and that that also is strategically how we're approaching this, um, with with marketing, and also, you know, with with regulatory. Which is, hey, look, um, we have the robot, and the robot can access any part of the cortex. Right now, we're focused on motor cortex, uh, with current version of the N1, that's specialized for motor decoding tasks.

But also at the end of the day, there's kind of a general compute available there. Um, uh, but you know, typically, if you wanna really get down to kind of hyper optimizing for power and efficiency, you do want need to get to some specialized function, right? Um, but you know, what we're saying is that, hey, uh, uh, you know you. You are now used to this robotic insertion techniques, which which, you know, took many, many years of, you know, showing data, um, and and conversation with the FDA, um, and also internally convincing ourself that this is, this is safe.

And um, now the difference is that if we go to other parts of the brain, like visual cortex, which we're interested in as our 2nd product. Um, obviously it's a completely different environment, the cortex is laid out very, very differently. Um, you know, it's gonna be more stimulation focused rather than recording, um, just just kind of creating visual percepts. But in the end, we're using the same thin film array technology, we're using the same robot insertion technology, we're using the same, you know, packaging technology.

Now it's more the conversation is focused around what are the differences and what are the implication of those differences in safety and efficacy.

the way you said 2nd product is is both hilarious and awesome to me. Uh, that product being restoring sight for blind people. So can you speak to w- stimulating the visual cortex? I mean, the the possibilities there are just incredible to be able to give that gift back to people who don't have sight, or even any aspect of that. Can you just speak to the challenges of? There's several Ch challenges here,

oh, many.

one of which is, like you said, from recording to stimulation, just, uh, any aspect of that that you're both excited and, uh, uh, see the challenges of.

Yeah, I, I guess I'll start by saying that we actually have been, um, capable of stimulating through our thin film array, as well as other electronics for years. Um, you know, we, we have actually demonstrated some of that capabilities for, uh, reanimating the limb and the spinal cord. Um, it, it, you know, obviously for for the current EFS study, you know, we've hardware disabled that, so that's that's something that, you know, we wanted to embark as a separate, separate journey.

Um, and and you know, obviously there are many, many different ways to write information into the brain. The way in which we're doing that is through electrical, you know, passing electrical current and and kind of causing that to really change the local environment. So that you can, uh, sort of artificially cause, kind of the the neurons to depolarize in in in nearby areas. For for vision specifically, um, you know, the way our visual system works, I, it's both well understood.

I mean, a anything with kind of brain, there are aspects of it that's well understood. But in the end, like, we don't really know anything, um, but the way visual system works is that you have photon hitting your eye and in your eyes, uh, you know, there are these, um, specialized cells called photoreceptor cells that convert the photon energy into electrical signals. And then they get that, then gets projected to, um, your back of your head, your visual cortex.

Um, you know, it goes through actually, um, a, you know, thalamic system called LGN that then projects it out. And then in the visual cortex, there's, you know, visual area one, or V1, and then there's bunch of other higher level processing layers, like V2, V3. And there, there are actually kind of interesting parallels. And when you study the behaviors of these convolutional neural networks, like what the different layers of the network is detecting, you know, 1st, they're detecting like these edges, and they're then detecting some more natural curves, and then they start to detect like objects, right?

Kind of similar thing happens in the brain. Um, and a lot of that has been inspired, and also, you know, it's been kind of exciting to see some of the correlations there. Um, but you know, things like from there, where does cognition ri- arise, and where? where is color encoded? I, there's, there's just not a lot of, um, understanding, fundamental understanding there. So in terms of kind of bringing sight back to those that are blind, um, there are many different forms of blindness.

Uh, there's actually million people, 1 million people in the US. that are legally blind. Um, you know, that means like certain, uh, like, score below in kind of the the visual test. Um, I think it's something like if you can see something, uh, at 20 ft distance, that normal people can see at 200 ft distance. Like you're le. Like, if you're worse than that, you're legally blind,

so, for them, for them, that that means you can't function effectively

correct?

using sight in the world Yeah, like to navigate, to navigate.

um, your environment, um, and yeah, there are the different forms of blindness, there are forms of blindness where, uh, there's some degeneration of your, uh, retina, um, these photoreceptor cells, and and rest of your visual, uh, you know, processing that I described is intact. And for those types of individuals, uh, you may not need to maybe stick electrodes into the visual cortex, you, you can actually, um, uh, build retinal prosthetic devices that actually just replaces the function of that retinal cells that are degenerated.

And there are many companies that are working on that. But that that's a very small slice of the of the significance, still a smaller slice of folks that are legally blind. Um, you know, if there's any damage along that circuitry, whether it's in the optic nerve or, you know, uh, just the LGN circuitry, or any, any break in that circuit, that's not gonna work for you. Um, and uh, the source of where you need to actually cause that visual percept to happen, because your biological mechanism is not doing.

That is by placing electrodes in the visual cortex, in the back of your head. And the way in which this would work is that you would have an external camera, whether it's, um, you know, something as unsophisticated as a GoPro, or, you know, some sort of wearable, you know, Ray-Ban type glasses that Meta's working on. That captures a scene, right? Um, and that scene is then converted to a set of electrical impulses or stimulation pulses that you would, uh, activate in your visual cortex through, um, these thin film arrays.

And by playing some SERT, you know, concerted kind of, uh, orchestra of these stimulation patterns, you can create what's called phosphenes, which are these, um, kind of white, yellowish dots that you can also create by just pressing your eyes. Um, you can actually create those percepts by stimulating the visual cortex, and the name of the game is really have many of those and have those percepts. Be the phosphenes, be as small as possible, so that you can start to tell apart.

Like they're the individual pixels of the the of the screen, right? So if you have many, many of those, you know, potentially you'll be able to, um, you know, in in the long term, be able to actually get naturalistic vision. But in the mid, mid, like short term to maybe midterm, um, being able to at least be able to have object detection algorithms run on your um, on your glasses, uh, the pre processing units, and then being able to at least see the edges of things.

So you don't bump into stuff.

it's incredible, this is really incredible. So you basically would be adding pixels and your brain would start to figure out what those pixels mean. Uh, and like, w- with different kinds of assistive and the signal processing on all fronts.

yeah, the the thing that actually so a couple things. One is, um, you know, obviously, if you're, uh, blind from birth, um, the way brain works, especially in the early age, um, neuroplasticity is really nothing other than you know, kind of your brain and different parts of your brain fighting for the limited territory. Um, and and yeah, I mean, very, very quickly you see, you see cases where you know people that are, I I mean, y.

You also hear about people who are blind that have heightened sense of hearing or some other senses. And the reason for that is cause that cortex that's not used just gets taken over by these different parts of the cortex. So for those types of individuals, um, I mean, I guess they're going to have to now map some other parts of their senses into what they call vision. But it's gonna be obviously a very, very different conscious experience.

Um, before, uh, so so, I think that's a a interesting caveat. The other thing that also is important to highlight is that we're currently limited by our biology in terms of the the wavelength that we can see. There's a very, very small wavelength that is a visible, um, light wavelength that we can see with our eyes. But when you have an external camera with this, um, BCI system, you're not limited to that. You can have infrared, you can have UV, you can have whatever other spectrum that you want to see.

And whether that gets mapped to some sort of weird conscious experience, I have no idea. But when I, you know, I'm oftentimes I talk to people about the goal of Neuralink being going beyond the limits of our biology. Um, that's sort of what I mean.

And if you're able to control the kind of raw signal, is that? Well, when we use our sight, we're getting the photons and there's not much processing on it. If you're be able to control that signal, maybe you can do some kind of processing, maybe you do object detection ahead of time, you're doing some kind of pre processing. And there's a lot of possibilities to explore that. So it's not just, uh, in increasing sort of thermal imaging, that kind of stuff, but it's also just doing some kind of interesting processing.

correct, yeah, I I mean, my my theory of how, like visual system works also is that, um, I I mean, that. There's just so many things happening in the world and there's a lot of photons that are going into your eye and it. It's unclear exactly where some of the pre pro processing steps are happening. But I I mean, I actually think that just just from a fundamental perspective, there's just so much, uh it, just the reality that we're in, if it's a reality, um, is so.

There's so much data, and I think humans are just unable to actually, like, eat enough, actually to process all that information. So there's some sort of filtering that does happen, whether that happens in the retina, whether that happens in different layers of the visual cortex, uh, unclear. But like, the analogy that I sometimes think about is, you know, if, uh, if your brain is a CCD camera and the in all of the information in the world is a sun.

Um, and when you try to actually look at the sun with the CCD camera, it's just gonna saturate the sensors, right? Cause it's a enormous amount of energy. So what you, what you do is you end up adding these, uh, filters, right to just kind of narrow the information that's coming to you and being captured. And I think, you know, things like our experiences, or our, um, uh, you know, like drugs, like propofol, that like anesthetic drug, or, you know, psychedelics.

What they're doing is they're kind of swapping out these filters and putting in new ones, or removing older ones, and kind of controlling our conscious experience.

Yeah, man, not to distract from the topic, but I just took a very high dose of ayahuasca in the Amazon jungle. So yes, it's a nice way to think about it. You're swapping out different, different experiences. And with Neuralink being able to control that, uh, primarily at 1st to, I improve function, not for entertainment purposes or enjoyment purposes, but

Yeah, giving back lost functions,

lo, giving back lost functions. And there, that's, that's especially when the wonderful. Yeah, when the function is completely lost, anything is a huge help. Would you, uh, implant a Neuralink device in your own brain?

uh, absolutely. I mean, maybe not right now, but absolutely

What kind of capability? Once reached? You start getting real curious and almost get a little antsy, like, like, jealous of people that get, as you watch them getting implanted.

yeah. I mean, I think, I mean, even even with our early participants, if they start to do things that I I can't do. Uh, which I think is in the realm of possibility for them to be able to get, you know, fifteen, twenty, if, if not like a hundred BPS, right? Um, there's nothing that fundamentally stops us from being able to achieve that type of performance. Um, I, I mean, I would certainly get jealous, um, that they can do that.

I, I should say that watching Nolan, I get a little jealous. Cause he's having so so much fun and it seems like such a chill way to play video games. Yeah, I So

I mean, the thing that also is, uh, hard to appreciate sometimes is that, you know, he's doing these things while mult- like, while talking, and no talking, I mean, it's multitasking, right? So it's it's clearly, uh, it's obviously cognitive, cognitively, uh, intensive. But similar to how, you know, when we talk, we move our hands like these things, like, you know, you, you, uh, like, are multitasking. I mean, he's able to do that, and you know, you won't be able to do that with other assistive technology.

As far as I, um, I'm aware, you know, if you're, uh, obviously using like an eye tracking device, you know, you're very much fixated on that thing that you're trying to do. And if you're using voice control, I mean, like, if you say some other stuff, yeah, you don't get to use that.

yeah. The the multitasking aspect of that is really interesting. So it's not just the BPS for the primary task, it's the it's the parallelization of multiple tasks. If you, if you take, if you measure the BPS for the entirety of the human organism. So if you're talking and doing a thing with your mind and looking around also, but I mean, you, there's just a lot of parallelization that's can that can be happening.

yeah. But I mean, I think at some point for him, like if he wants to really achieve those high level BPS, it does require, like, you know, full attention, right? Um, and that's a separate circuitry that that, um, is a big mystery, like how attention works. And you know,

Yeah, attention like cognitive load. I've done, I've, I've read a lot of literature on people doing two tasks, like, uh, you have the your primary task and a secondary task, and the secondary task is is a source of distraction. And how does that affect the performance of the primary task? And there's, depending on the task, there's a lot of interesting, I mean, this is an interesting computational device, right? And I think there's to say the least, a lot of novel insights that can be gained from everything.

I I mean, I personally am surprised that Nolan's able to do s such incredible control of the cursor while talking and also being nervous at the same time. Because he's talking like all of us are. If you're talking in front of the camera, you get nervous. So all of those are coming into play and he's able to still achieve high performance. Surprising. I mean, all of this is really amazing, uh, and I think af- just after researching this really in depth, I kind of want a Neuralink, Get I kind of, in the line and also the safety, get in line.

well, Well, we should say the registry is for people who have quadriplegia and correct, all that kind of stuff, so correct. that would be a s separate line for people. Um, they're just curious, uh, like myself. So now that Nolan patient P1 is part of the ongoing Prime study, um, what's the high level vision for P2, P3, P4, P5? And just, uh, the expansion into other human beings that are getting to experience this implant?

Yeah. I mean, the primary goal is, uh, you know, for for our study. In the 1st place, is to achieve safety endpoints, just understand, um, safety of this device, as well as the implantation process, um, and also at the same time, understand the efficacy and the impact that it could have on, uh, the potential users lives. Um, and just because you have, and you know, you're living with tetraplegia, it doesn't mean your situation is same as another person living with tetraplegia.

It's widely, widely varying. Um, and and you know, you're. It's something that, you know. We're hoping to also understand how our technology can serve not just a very small slice of those individuals, but, you know, broader group of individuals. And being able to get the feedback to, you know, just really build the just the best product for them. Um, so our, our, you know, there's, there's obviously also, uh, you know, goals that we have. And and the primary purpose of the early feasibility study is to learn from each and every participant to improve the device, improve the surgery.

Before, you know, we embark on what's called a pivotal study. That then is a much larger, um uh trial that starts to look at statistical significance of your endpoints. Um, and that's required before you can then market the device. Um, uh, and and and you know, that's how it works in the US. and just generally around the world. That's that's the process you follow. So, you know, our our goal is to really just understand from people like Nolan P2P3 future participants, what aspects of our device needs to improve.

You know, if if it turns out that people are like, I really don't like the fact that it lasts only 6 h. I wanna I be able to use this computer for, you know, like 24 h. I mean, that's that is a, you know, user needs and user requirements, uh, which we can only find out from just just being able to engage with them.

So before the pivotal study, there's kind of like a rapid innovation based on individual experiences. You're learning from individual people how they use it. Like the de like the high resolution details in terms of like cursor control and signal and all that kind of stuff to like life experience.

Yeah, yeah, so there's hardware changes, but also just just firmware updates. Um, so even even when we, um, you know, had had that sort of, uh, recovery event for Nolan. Uh, you know, he now has the new firmware that that he, um, has been, uh, updated with. And you know, it's similar to how, like, your phones get updated all the time with new firmwares for security patches, whatever new functionality. UI, right? Um, and that's something that is possible with our implant.

It's not a static one time device that that can only do the thing that it said it can do. I mean, similar to Tesla, you can, you know, do over the air firmware updates. And now you have completely new u user use user interface, and um, all these bells and whistles and improvements on, you know, everything, like the latest, right? Um, and that's, that's, that's, um, you know, when we say generalized platform, that's what we're talking about.

Yeah, it's really cool how the the app that Nolan is using, there's like calibration, all that, all that kind of stuff. And then there's update, you just you just click and get an update. Uh, what other future capabilities are? Are you kinda looking to? You said vision, that's a fascinating one. Uh, what about sort of accelerated typing or speech or this kind of stuff? Uh, what? And what else is there? What's yeah, those there?

those are still in the realm realm of um, movement program, so so largely speaking, we have two programs we have the movement program and we have the the vision program. Uh, the movement program, you know, currently is focused around, you know, the digital freedom. As you can easily guess, if you can control, you know, a 2D cursor in the digital space, you could move anything in the physical space. Um, so robotic arms, wheelchair, your environment, uh, or even really like, uh.

Whether it's through the phone or just like directly to those interfaces, so like to those machines. Um, so we're looking at ways to kind of expand those types of capability, even for Nolan. Um, that requires, you know, conversation with the FDA and kind of showing safety data for, you know, if there's a robotic arm or a wheelchair that, you know, we can guarantee that they're not going to hurt themselves accidentally, right? Um, it's very different if you're moving stuff in the in the digital domain versus like in the physical space, you can actually, um, potentially cause harm to the participants.

Um, so we're working through that right now. Um, speech does involve different areas of the brain. Speech prosthetic is very, very fascinating, and there's actually been a lot of really, um, amazing work that's been happening in academia. Um, you know, Sergey Staviski at UC Davis, Jamie Henderson, and, you know, late, Christian Ishinoi, um, at Stanford, are doing just some incredible amount of work in improving speech. Uh, neuroprosthetics and it. Those are actually looking more at parts of the motor cortex that are controlling, you know, these vocal articulators.

And, you know, being able to, like, even by mouthing the word or imagine speech, you can pick up those signals. Um, the more sophisticated, higher level processing areas, like, you know, the Broca's area, or, you know, uh, Wernicke's area. Those are still very, very big mystery in terms of the the underlying mechanism of how all that stuff works. But um, yeah, I mean, I think I think Neuralink's eventual goal is to kind of understand those those things.

Um, and and be able to provide a platform and tools to be able to understand that and study that

This is where I get to the pothead questions. Um, do you think we can start getting insight into things like thought? So speech is, uh, there's a muscular component, like you said, there's like the act of producing sounds. But then what about the internal things like cognition, like low level thoughts and high level thoughts? Do you think we'll start noticing kind of signals that could be picked up that could, um, like, they could be understood? That could be maybe used in order to interact with the outside world?

in some ways. Like, I guess this starts to kind of get into the hard problem of consciousness. Um, and uh, I mean, on on one hand, all of these are at some point set of electrical signals that, um, from there, maybe it it. In itself, is giving you the cognition or the meaning, or somehow human mind is incredibly amazing s storytelling machine. So we're telling ourselves and fooling ourselves that there's some interesting meaning here. Um, but I, I mean, I.

I certainly think that BCI, and, you know, really, BCI, at the end of the day is a set of tools that help you kind of study the underlying mechanisms. In in a both like local, but also broader sense, um, and whether, you know, there's some interesting patterns of like electrical signal. That means like, you're thinking this versus. And you can either, like, learn from, like, many, many sets of data to correlate some of that and be able to do mind reading or not.

I'm not, I'm not sure, um, I certainly would not kind of rule that out as a possibility, but um, I, I. I think BCI alone probably can't do that. There's probably additional set of tools and framework. And, you know, also, like, just hard problem of consciousness at the end of the day is rooted in this philosophical question of like, what is the what is the meaning of it all? What's the nature of our existence? Like, where is the mind emerge from this complex network?

Like,

yeah. How does the, uh, how does the subjective experience emerge from just a bunch of spikes, electrical spikes? Yeah,

yeah, I mean, we we do really think about BCI and what we're building as a tool for understanding the mind, the brain. The only question that matters. There's actually, um, th, there actually is, um, some biological existence proof of, like, what it would take to kind of start to form some of these experiences that may be unique. Um, if you actually look at every one of our brains, there there are two hemispheres, there's a left side of brain, there's a right side of brain.

And I mean, I. U- Unless you have some other conditions, you normally don't feel like left legs or right legs, like you just feel like one legs, right? So what is happening there, right? Um, if you actually look at the two hemispheres, there's a a structure that kinda connect the two, called the corpus callosum. That is supposed to have around 200 to 300

million connections or axons. Um, so, whether that means that's the the number of interface and electrodes that we need to create some sort of mind meld, or from that, like, whatever new conscious experience that you you can experience. Um, but yeah, I, I do think that there's like, kind of an interesting, um, existence proof that we all have.

And that threshold is unknown at this time. Oh yeah, the these But

things, everything in this domain is, you know, speculation, right? Um,

and then there would be, uh, you'd be continuously pleasantly surprised. Uh, do you see a world where there's millions of people, like tens of millions, hundreds of millions of people walking around with a neuralink device in their or multiple neuralink devices in their brain?

I do. First of all, there there are, like, if you look at worldwide, um, people suffering from movement disorders and visual deficits. I mean, that that's, uh, in the tens, if not hundreds of millions of people. Um, so that that alone, I think there's a lot of, uh, benefit and and potential good that we can do with this type of technology. And when you start to get into kind of neuro like psychiatric application, you know, depression, um, anxiety, hunger, or, you know, obesity, right?

Like mood, control o of appetite. I mean, that starts to become, you know, very real to everyone.

Not to mention that every, uh, most people on earth have a smartphone. And once BCI starts competing with the smartphone as a preferred methodology of interacting with the digital world, that also becomes an interesting thing.

Oh, yeah, I mean that, yeah, this is even before going to that, right? I mean, there's like, almost, I mean, the entire world that could benefit from these types of things. And then, yeah, like, if we're talking about kind of next generation of how we interface with, you know, machines or even ourselves, uh, in many ways, I think, um, BCI can play a role in that. Um, and you know, some of the things that I also talk about is I, I.

I do think that there's a real possibility that you could see, um, you know, the 8 billion people walking around with neuralink.

Well, thank you so much for pushing ahead and, uh, I look forward to that exciting future.

Thanks for having me.

Thanks for listening to this conversation with DJ Seo. And now, dear friends, here's Matthew McDougall, the head neurosurgeon at Neuralink. When did you first become fascinated with the human brain?

Since forever, uh, as far back as I can remember, I've been interested in the human brain. I mean, uh, I was, you know, a thoughtful kid and, uh, a bit of an outsider. And you, you know, sit there thinking about what the most important things in the world are. Uh, in your, in your little tiny adolescent brain. And the answer that I came to that I converged on was, uh, that. All of the things you can possibly conceive of as things that are important for human beings to care about are literally contained, you know, in the skull.

Uh, both the perception of them and their relative values, and, you know, the solutions to all our problems, and all of our problems are all contained in the skull. And if we knew more about how that worked, uh, how the brain encodes information and generates desires and generates agony and suffering, uh, we, we could do more about it. You know, you think about all the all the really great triumphs in human history, you think about all the really horrific tragedies.

Um, you know, you think about the Holocaust, you think about, um, any prison full of human stories, uh, and all of those problems boil down to neurochemistry. So if you get a little bit of control over that, you provide people the option to do better. And in the way I read history, the way people have dealt with having better tools is that they most often in the end do better. Uh, with huge asterisks. But I think it's a an interesting, worthy and noble pursuit to give people more options, more tools.

Yeah, that's a fascinating way to look at human history. You just imagine all these neurobiological mechanisms. Stalin, Hitler, all of these, Genghis Khan, all of them just had like a a brain. It just a bunch of neurons, you know, like a few times billions of neurons, uh, gaining a bunch of information over a period of time. They have a set of module that does language and memory and all that. And from there, in in, in the case of those people, they're able to murder millions of people.

And all that coming from, uh, there's not some glorified notion of a a dictator of this enormous mind or something like this. It's just it's just the brain,

yeah, yeah. I mean, a lot of that has to do with how well people like that can organize those around them.

other brains.

Uh, yeah. And so I always find it interesting to look to primatology, you know, look to our closest non human relatives, uh, for clues as to how humans are going to behave and and what particular humans are able to achieve. And so you look at, um, chimpanzees and bonobos, and you know, they're similar, but different in their social structures, particularly. And I went to Emory in Atlanta and studied under, uh, Frans de Waal, the great Frans de Waal, who was kind of the leading primatologist, uh, who recently died.

And his work in at looking at chimps through the lens of, you know how you would watch an episode of friends and understand the motivations of the characters interacting with each other. He would look at a chimp colony and basically apply that lens. I'm m massively oversimplifying it. If you do that, instead of just saying, you know, subject four, seven, three, you know, through his feces at subject four, seven, one. You talk about them in terms of their human struggles, accord them the dignity of themselves as actors with understandable goals and drives.

What they want out of life, and primarily, it's, you know, the things we want out of life food, sex, companionship, um, power. Uh, you can understand chimp and bonobo behavior in the same lights, uh, much more easily. And I think doing so gives you the tools you need to reduce human behavior. From the kind of false complexity that we layer onto it with language and look at it in terms of, oh, well, these humans are looking for companionship, sex, food, power.

Um, and I think that that's a pretty powerful tool to have in understanding human behavior.

And I just, uh, went to the Amazon jungle for a few weeks. And you. It's a very visceral reminder that a lot of life on earth is just trying to get laid. They're all screaming at each other like I saw a lot of monkeys, and they're just trying to impress each other. Or maybe there's a battle for power, but a lot of the battle for power has to do with them getting laid.

right. Breeding rights often go with alpha status, and so if you can get a piece of that, then you're gonna do okay.

And would like to think that we're somehow fundamentally different. But, uh, especially when it talk comes to primates, where we really aren't, you know, we can use fancier poetic language, but, uh, maybe some of the underlying drives that motivate us are, um, similar.

Yeah, I think that's true.

And all that is coming from this, the brain. Uh, so when, when did you s first start studying the the brain? As like, as the biological mechanism?

basically, the moment I got to college, I, uh, started looking around for labs that I could, uh, do neuroscience work in. Uh. I originally approached that from the angle of, uh, looking at interactions between the brain and the immune system, which isn't the most obvious place to start. But, um, I had this idea at the time that the contents of your thoughts would have an impact, a direct impact, maybe a powerful one, on, uh, non conscious systems in your body.

The systems we think of as, you know, homeostatic, automatic mechanisms like fighting off a virus, like repairing a wound. Um, and sure enough, there are big crossovers between the two. I mean, it gets to, um, kind of a key point that I think goes under recognized. One of the things people don't recognize or or appreciate about the human brain, uh, enough, and that is that it basically controls or has a huge role in almost everything that your body does.

Um, like, you try to n try to name an example of something in your body that isn't directly controlled or massively influenced by the brain, and, uh, it, it's pretty hard. I mean, you might say like bone healing or something, but, uh, even those systems, the hypothalamus and pituitary end up playing a role in coordinating the endocrine system. That does have a direct influence on, say, the calcium level in your blood, that goes to bone healing.

So non obvious connections between those things, uh, implicate the brain as really a potent prime mover in all of health.

One of the things I realized in the other direction, too, how most of the systems in the body are integrated with the human brain, like they affect the brain, also, like the immune system. Um, I think there's just, you know, people who study Alzheimer's and, uh, those kinds of things. It. It's just surprising how much you can understand of that from the immune system, from the other systems that don't obviously seem to have to anything to do with sort of the nervous system.

They all play together.

Yeah, you could understand how that would be driven by evolution, too, just in some simple examples. If you get sick, if you get a communicable disease, you get the flu, uh, it's pretty advantageous for your immune system to tell your brain, Hey, now be antisocial for, uh, you know, a few days, don't go be the life of the party tonight. In fact, maybe just cuddle up somewhere warm under a blanket and just stay there for a day or two.

And sure enough, that tends to be the behavior that you see both in animals and and in humans. If you get sick, elevated levels of interleukins in your blood and TNF alpha in your blood. Ask the brain to cut back on social activity and, uh, even moving around. You you have lower locomotor activity, uh, in animals that are infected with viruses.

So from there, the early days in neuroscience to surgery, when did that step happen? Was the leap?

you know, it was sort of an evolution of thought. I wanted to study the brain, so I started studying the brain, uh, in undergrad, in this neuroimmunology lab, uh, I. From there, w uh, realized at some point that I didn't want to just generate knowledge, I wanted to effect real changes in the actual world, in actual people's lives. And so after having not really thought about going into medical school, I was on a track to go into a PhD program.

I said, Well, I'd like, I'd like that option, I'd like to actually potentially help tangible people in front of me. And, uh, doing a little digging, found that there exists these MD PhD programs where you can choose not to choose between them and do both. And so, uh, I went to USC for medical school and, uh, had a joint PhD program with Caltech, um, where I met, uh, actually chose that program. Particularly because of a researcher at Caltech named Richard Anderson, who's one of the godfathers of primate neuroscience.

Has a a macaque lab where Utah arrays and other electrodes were being inserted into the brains of monkeys. Uh, to try to understand how intentions were being encoded in the brain. So, you know, I ended up there with the idea that maybe I would be a neurologist and study the brain on the side, uh, and then discovered that neurology, um, again, I'm gonna make enemies by saying this, but neurology, uh, predominantly. And and distressingly to me, is is the practice of diagnosing a thing and then saying good luck with that.

When there's not much we can do, um, and neurosurgery, very differently, uh, is a. It's a powerful lever on taking people that are headed in a bad direction and changing their course. Uh, in the sense of brain tumors that are potentially treatable or curable with surgery, um, you know, even aneurysms in the brain, blood vessels that are gonna rupture. You can, uh, save lives, really is, at the end of the day, what m what mattered to me?

And so, uh, I was at USC, as I mentioned, that happens to be one of the great neurosurgery programs. And so I met these truly epic, uh, neurosurgeons, uh, Alex Kalesi and and Mike Aposto, and Steve Gianotta and Marty Weiss. These these sort of epic people that were just human beings in front of me. And so it kind of changed my thinking. From neurosurgeons are distant gods that live on another planet and occasionally come and visit us to these are humans that have problems and are people.

And, uh, there's nothing fundamentally preventing me from being one of them. Uh, and so, um, at the last minute in medical school, I changed gears from going into a different specialty and and switched into neurosurgery, uh, which cost me a a year. I had to do another year of research, uh, because I was so far along in the process, uh, that, um, to switch into neurosurgery, the deadlines had already passed. So it was a a decision that cost time, but absolutely worth it.

what was the hardest part of the training on the on the neurosurgeon track?

Yeah, two things I think that, you know, residency in neurosurgery is sort of a competition of pain, of of like, how much pain can you eat and smile? Uh, and so there's work hour restrictions that, uh, are not really. They're viewed at, I think, internally among the residents as weakness. And so most neurosurgery residents try to work as hard as they can. And that, I think, necessarily means working long hours and sometimes over the work hour limits.

And, you know, we care about being compliant with whatever regulations are, uh, in front of us. But I think more important than that, people wanna give all, give their all in becoming a better neurosurgeon because the the stakes are so high. And so it's a real fight to get residents, uh, to say, go home at the end of their shift and not stay and do more surgery.

Are you seriously saying, like, one of the hardest things is literally like getting s forcing them to get sleep and rest and all this kind of stuff?

Historically, that was the case. I think, I think the That's hilarious next generation, I think the next generation is more, uh, compliant and and that's more self care. awesome. Weak I didn't say it.

is what you mean. All right, I'm just I was just kidding. I'm just kidding. Now I'm making enemies. No, okay, I get it. Wow, that's fascinating. Uh, so what was the 2nd thing?

the personalities, uh, and maybe the two are connected, but

So is is was it pretty competitive?

it's competitive. And it's also, um, you know, as we touched on earlier, primates like power. And I think, um, neurosurgery has long had this aura of, uh, mystique and excellence and whatever about it. And so it's. It's an invitation, I think, for people that are cloaked in that authority, you know, a board certified neurosurgeon is basically a walking, uh, fallacious appeal to authority, right? You? You have license to walk into any room and act like you're, you know, an expert on whatever.

And fighting that tendency is not something that most neurosurgeons do well. Humility isn't the forte.

Yeah, one of the so, um, I have friends, uh, who know you, and whenever they speak about you, that yours, yours have the surprising quality for a neurosurgeon of humility, which I think indicates that it's not. It's not as common as perhaps in other professions. Cause there is a kinda gigantic sort of heroic aspect to neurosurgery, and I think it gets to people's heads a little bit.

Yeah, well, that I think that, uh, you know, that allows me to play well, uh, at an Elon company, because Elon, uh, one of his strengths, I think, is to s just instantly see through fallacy from authority. So nobody walks into a room that he's in and says, Well, goddamn it, you have to trust me. I'm the guy that built the last, you know, ten rockets or something, and he says, Well, you did it wrong and we can do it better.

Uh, or I'm the guy that, you know, kept Ford alive for the last 50 years. You listen to me on how to build cars, and he says, no, and so you don't walk into a room that he's in and say, Well, I'm a neurosurgeon, let me tell you how to do it. Uh, he's gonna say, Well, I'm a human being that has a brain, I can think from 1st principles myself. Thank you very much. Uh, and here's how I think it ought to be done.

Let's go try it and see who's right. Uh, and that's, you know, proven, I think, over and over, in his case, to be a very powerful approach.

if we just take that tangent, there's a fascinating interdisciplinary team at Neuralink that you get to interact with, um, including Elon. What do you think is the secret to a successful team? Uh, what have you learned from just getting to observe these folks, world experts in different disciplines, work together?

yeah, there there's a sweet spot, uh, where people disagree and forcefully speak their mind and passionately defend their position. And yet are still able to accept information from others and change their ideas when they're wrong. And so I like the analogy of sort of how you polish rocks, you put hard things in a in a hard container and spin it. People bash against each other and out comes, uh, you know, a a more refined product. And so, uh, to make a good team at Neuralink, we've tried to find, you know, people that are not afraid to defend their ideas passionately.

And y, you know, occasionally strongly disagree with people, uh, that they're, that they're working with and have i the best idea come out on top. Um, it's not an easy balance again to refer back to the primate brain, it's not something that is inherently built into the the primate brain. To say, I passionately put all my chips on this position. And now I'm just gonna walk away from it and admit you were right. You know, part of our brains tell us that that is a power loss, that is a loss of face, a loss of standing in the community.

And uh, and and now you're a a zeta chump, uh, cause your idea got trounced, um, and you just have to, you know, recognize that. That little voice in the back of your head is maladaptive and it's not helping the team win.

Yeah, you have to have the confidence to be able to walk away from an idea that you held onto. Yeah, and if you do that often enough, you're actually going to, uh, become the best. The in the world at your thing. I mean, that kind of that rapid iteration,

yeah, you'll at least be a member of a winning team.

ride the wave. Uh, what, what did you learn? You mentioned there's a lot of amazing, uh, neurosurgeons at USC. What? What lessons about surgery and life have you learned from those folks?

Yeah, I think working your ass off, working hard while, um, you know, functioning as a member of a team, getting a job done that is incredibly difficult. Uh, you know, working incredibly long hours, being up all night, taking care of someone that y- you know, you think probably won't survive, no matter what you do. Working hard to make people that you passionately dislike look good the next morning. These folks were relentless in their pursuit of, um, excellent neurosurgical technique, decade over decade, and and I think were well recognized for the that excellence.

So it's, you know, especially Marty Weiss, Steve Gianotta, uh, Mike Aposto. The they made huge contributions not only to surgical technique, but they built training programs that trained dozens or hundreds of amazing neurosurgeons. I was just lucky to kind of be in their wake.

what's that like? You mentioned doing a surgery where the person is likely not to survive, does that wear on you?

Yeah, um, you know it. It's especially challenging, um, when you, with all respect to to our elders, uh, it doesn't hit so much when you're taking care of an eighty year old. And something was going to get them pretty soon anyway, and so y, you lose a patient like that and it. It was part of the natural course of what is expected of them in the in the coming years, regardless. Uh, taking care of y, you know, a a father of two or three, four young kids, s someone in their thirties that didn't have it coming.

And they show up in your ER having their 1st seizure of their life, and lo and behold, they've got a h. A huge, malignant, inoperable or incurable brain tumor. Y, you can only do that, I think, a handful of times, um, before it really starts eating away at your, at your armor. Uh, or a, you know, a young mother that shows up that has a giant hemorrhage in her brain that she's not gonna survive from. And, y, you know, they bring her four year old daughter in to to say goodbye one last time before they turn the ventilator off.

That, um, you know, the great Henry Marsh is a English neurosurgeon who said it best. I think he says every neurosurgeon carries with them a private graveyard. And I, I definitely feel that, um, especially with young parents, uh, that that that kills me, uh, they. They had a lot more to give. The the loss of those people specifically has a y, you know, knock on effect. That's going to make the world worse for people, uh, for a long time, uh, and it's just hard to feel powerless in the face of that, you know?

And that's where I think, y, you have to be, uh, borderline evil. To fight against a company like Neuralink or to constantly be taking potshots at us. Because what we're doing is to try to fix that stuff, we're trying to give people options, uh, to reduce suffering, uh, we're trying to. We're trying to take the the pain out of life that, uh, broken brains brings in. And um, yeah, this is just our, our little way that we're fighting back against entropy.

I guess,

Yeah, there's the the amount of suffering that's endured when some of the things that we take for granted that our brain is able to do is taken away. Uh, is immense, and to be able to restore some of that functionality is a real gift.

yeah, we're just starting, we're, we're gonna, we're gonna do so much more.

Um, well, can you take me through the full procedure for implanting, say, the N1 chip in Neuralink,

Sure, yeah, it's a really simple, really simple, straightforward procedure. Uh, the the human part of the surgery, uh, that that I do is dead simple. It's one of the most basic neurosurgery procedures imaginable, and I I think there's evidence that it some version of it has been done for thousands of years. Uh, the there are examples, I think, from ancient Egypt of healed or partially healed, uh trephinations, and from, uh, Peru, or, you know, ancient times in South America, uh, where, uh.

These proto surgeons would drill holes in people's skulls, you know, presumably to let out the evil spirits, but maybe to drain blood clots. And there's evidence of bone healing around the edge, meaning the people at least survived some months, uh, after a procedure. And so what we're doing is that we are making a cut in the skin on the top of the head over the area of the brain. That is the most potent, uh, representation of hand intentions.

And so if you th if you are an expert concert pianist, you know this part of your brain is lighting up the entire time you're playing. Uh, we call it the hand knob. the hand knob? So Yeah,

it's all the like, the finger movements, all this, all, all of that is just firing away.

yep, there's a little squiggle in the cortex right there. One of the folds in the brain is kind of doubly folded right on that spot. And so you can look at it on an MRI and say, that's the hand knob. And then you, you do a functional test in a special kind of MRI called an a functional MRI fMRI. And this part of the brain lights up when people, even quadriplegic people whose brains aren't connected to their finger movements anymore, they imagine finger movements and this part of the brain still lights up.

So we can ID that part of the brain in anyone who's preparing to enter our trial and say, okay, that that part of the brain we confirm is your hand intention area. Um, and so, uh, I'll make a little cut in the skin, we'll flap the skin open, just like kinda opening the hood of a car, only a lot smaller. Make a perfectly round, uh, 1 hole in the skull, remove that bit of skull, uh, open the lining of the brain, the covering of the brain.

It's like a like a little bag of water that the brain floats in. And then show that part of the brain to our robot. And then the this is where the robot shines. It can come in and take these tiny, you know, much smaller than human hair electrodes, and precisely insert them into the cortex, into the surface of the brain. To a very precise depth, in a very precise spot that avoids all the blood vessels that are coating the surface of the brain.

And after the robot's done with its part, then, you know, the human comes back in and puts the implant into that hole in the skull and covers it up. Uh, screwing it down to the skull and sewing the skin back together. Uh, so the whole thing is, you know, a few hours long. It's extremely low risk compared to the average neurosurgery involving the brain. That that might say, open up a deep part of the brain or manipulate blood vessels in the brain.

Uh, this this opening on the surface of the brain with, um, with only cortical micro insertions, carries, um, significantly less risk than a a lot of the, you know, tumor or aneurysm surgeries that are routinely done.

So cortical micro insertions that are via robot and computer vision are designed to avoid the blood vessels. So, uh, I know you're a bit biased here, but let's compare human and machine. So what are h human surgeons able to do well, and what are robot surgeons able to do well at this stage of our human civilization development?

yeah, yeah, that's a good question. Um, humans, uh, are general purpose machines. We're able to adapt to unusual situations, we're able to change the plan on the fly. Um, I remember well a surgery that I was doing many years ago down in San Diego, where the plan was to, um, open a small hole behind the ear and go reposition. A blood vessel that had come to lay on the facial nerve, the trigeminal nerve, uh, the nerve that goes to the face.

When that blood vessel lays on the nerve, it can cause just intolerable, horrific shooting pain that people describe like being zapped with a cattle prod. And so the beautiful, elegant surgery is to go move this blood vessel off the off the nerve. The surgery team, we, we went in there and started moving this blood vessel and then found that there was a giant aneurysm on that blood vessel that was not easily visible on the pre op scans.

And so the plan had to dynamically change, and the the um, human surgeons had no problem with that. We're trained for all those things, robots wouldn't do so well in that situation, at least in their current incarnation. Uh, fully robotic surgery, like, you know, the the electrode insertion portion of of the Neuralink surgery, it goes according to a set plan. And so, um, the humans can interrupt the flow and change the plan, but the robot can't really change the plan midway through.

It operates according to how it was programmed and how it was asked to run. It does its job very precisely, uh, but not with a wide degree of latitude in how to react to changing conditions.

So there could be just a very large number of ways that you could be surprised as a surgeon. When you enter a situation, there could be subtle things that you have to dynamically adjust to, correct, and robots are not good at that currently, currently.

I think, uh, we are at the dawn of a new era with AI of the parameters for robot responsiveness to be dramatically broadened, right? I mean, you can't look at a self driving car and say that it's operating under very narrow parameters. You know, if a chicken runs across the road, it wasn't necessarily programmed to deal with that specifically, but a a Waymo or a self driving Tesla would have no problem reacting to that appropriately. Uh, and so surgical robots aren't there yet, but give it time.

And then there could be a lot of sort of inter like semi autonomous possibilities of maybe a robotic surgeon could say. This situation is perfectly familiar, or this situation is not familiar. And in the not familiar case, a human could take over. But basically, like, be very conservative in saying, okay, this for sure has no issues, no surprises, and then let the humans deal with the surprises with the edge cases, all that, nah, that's one possibility.

So, like, you, you think eventually, uh, you'll be out of a job, what you being neurosurgeon, your job being neurosurgeon, humans, there will not be many neurosurgeons left on this earth.

I'm not worried about M my job in my in the course of my professional life. I think I. I would tell my my kids not necessarily to go in this line of work. Uh, depending on depending on how things look in 20 years.

It's so fascinating, cause I I mean, I, if I have a line of work, I would say it's programming. And if you ask me, like, for the last, I don't know, 20 years, what I would recommend for people, if I would, I would tell them, Yeah, go like, there's just. You will always have a job if you're a programmer, cause there's more and more computers and all this kind of stuff. And, uh, it pays well. But then you, you realize these large language models come along and they're really damn good at generating code.

So it's overnight. You could be surprised, like, Wow, wait, what is the contribution of the human, really? But then you start to think, okay. It does seem that humans have ability, like you said, to deal with novel situations. And in the case of programming, it's the ability to kinda c come up with novel ideas to solve problems. It's it seems like machines aren't quite yet able to do that. And when the stakes are very high, when it's life critical, as it is in surgery, especially neurosurgery, then it starts.

The the stakes are very high for a robot to actually replace a human. But it's fascinating that in this case of Neuralink, there's a, uh, human robot collaboration.

yeah, yeah, it's. I do the parts I can't do, and it does the parts I can't do. Um, and we, we are friends.

Uh, the I. I saw that there's a lot of practice going on. So, I mean, everything in Neuralink is, uh, t is tested extremely rigorously. But one of the things I saw that there's a proxy on which the surgeries are performed. So this is both for the robot and for the human, for everybody involved in the entire pipeline. What's that like practicing the surgery?

it's pretty intense, uh, so there's no analog to this in human surgery. Uh, human surgery is sort of this artisanal craft that's handed down directly from master to pupil over the generations. I mean, literally, the way you learn to be a a a surgeon on humans is by doing surgery on humans. I mean, 1st, you watch, uh, your professors do a bunch of surgery, and then finally, they put, you know, the trivial parts of the surgery into your hands, and then w the more complex parts.

And as your understanding of the the point and the purposes of the surgery increases, you get more responsibility. In the perfect condition doesn't always go well. In Neuralink's case, the approach is a bit different. Um, we, of course, practiced as far as we could on animals, we did hundreds of animal surgeries. Um, and w. When it came time to do the 1st human, uh, we had a just a amazing team of engineers build incredibly lifelike models.

Uh, one of the engineers, Fran Romano, in particular, built built a pulsating brain in a custom 3D printed skull that matches exactly the the patient's anatomy, uh, including their face and, uh, scalp characteristics. And so w. When I was able to practice that, I mean it, it's as close as it really reasonably should get, uh, to to being the real thing in all the details. Uh, including, you know, the having a a mannequin body attached to this custom head.

And so when we were doing the practice surgeries, we'd wheel that body into the CT scanner and take a mock CT scan and wheel it back in and conduct all the normal safety checks. Verbally, you know, stop. This patient, we're confirming his identification is mannequin number, blah, blah, blah. And then opening the brain in exactly the right spot, using standard operative neuronavigation equipment, standard surgical drills in a in the same OR that we do all of our practice surgeries in at at Neuralink.

And having the skull open and have the brain pulse, which adds a degree of difficulty for the robot. To, you know, perfectly, precisely plan and insert those electrodes to the right depth and location. And so, uh, yeah, we, we, uh, kind of broke new ground on how extensively we practiced for this surgery.

so there was a historic moment, a big milestone, uh, for Neuralink, and in part for humanity, with, uh, the 1st human getting a Neuralink implant in January of this year. Uh, take me through the surgery, uh, on Nolan, what did it feel like to be part of this?

Yeah, well, we, um, we're lucky to have just incredible partners at the Barrow Neurologic Institute. They are, uh, I think, the premier neurosurgical hospital in the world. Uh, they, they made everything as easy as possible for the trial, uh, to get going and and helped us immensely, uh, with their expertise on how to, uh, how to arrange the details. It was a much more high pressure surgery in some ways. I mean, even though the, you know, the outcome wasn't particularly in question in terms of our participants safety.

The number of observers, you know, the number of people, there's conference rooms full of people watching live streams in the hospital. Um, rooting for this to go perfectly. And that just adds pressure. That, uh, is not typical for, uh, even the most intense production neurosurgery, say, removing a tumor, or, y, you know, placing deep brain stimulation electrodes. And it had never been done on a human before. There were unknown unknowns, um, and so, uh, definitely a a moderate pucker factor there for the whole team.

Uh, not knowing if we were going to encounter, say, uh, a degree of brain movement that was unanticipated, or, uh, a degree of brain sag that took the brain far away from the skull and made it difficult to insert. Or some other unknown unknown problem. Fortunately, everything, uh, went well, and uh, that that surgery is one of the smoothest, uh, outcomes we could have imagined.

Were you nervous? I mean, you're Extremely, kind of a quarterback in, like, in the Super Bowl kind of situation.

extremely nervous. uh, extremely. I was very pleased when it went well and then and when it was over. Um, looking forward to number two. yeah, even Uh,

with all that practice, all of that, just you've never been in a situation that's so high stakes in terms of people watching. And Y. We should also probably mention, given how the media works, a lot of people, um, you know, maybe in a dark kind of way, hoping it doesn't go well.

I think wealth is easy to hate, um, or envy, or or whatever. And, uh, I think a l. There's a whole industry, uh, around driving clicks, and bad news is great for clicks, and so any way to take an event and turn it into bad news, uh, is gonna be really good for for clicks.

It just sucks because I think in it puts pressure on people, it discourages people from from trying to solve really hard problems. Because to solve hard problems, you have to go into the unknown. You have to do things that haven't been done before, and you have to take risks. Uh, calculated risks. You have to do all kind of safety precautions, but risks. N nevertheless. And, uh, I just wish there would be more celebration of that, of the risk taking versus like people just waiting on the on on the sidelines, like waiting for failure and then pointing out the failure.

Uh, yeah, it sucks, but you, you know, in this case, it's it's. It's really great that everything went just flawlessly, but it's unnecessary pressure. I would say,

Now that there is a human with literal skin in the game, you know, there's a participant who whose wellbeing rides on this doing well. You, you have to be a pretty bad person to be rooting for that to go wrong. Um, and so, you know, hopefully people look in the mirror and and realize that at some point.

so, did you get to actually front row seat, like, watch the robot work, like, what, uh, you get to see the whole thing?

Yeah, I mean, I, you know, because, uh, uh, an MD needs to be in charge of all of the medical decision making throughout the process. Um, I unscrubbed from the surgery after exposing the brain and presenting it to the robot. And, um, place the targets on the robot, uh, interf software interface that tells the robot where it's going to insert each thread. That was done, um, with, you know, my hand on the mouse, for whatever that's worth.

so you were the one placing the targets? Oh, cool, so like it, you know, the the the. The robot, uh, with the computer vision, provides a bunch of candidates and you kinda finalize the decision.

right, uh, you know, they the the software engineers are amazing on this team. And so they actually provided an interface where you can essentially use a lasso tool and select a a prime area of brain real estate. And it will automatically avoid the blood vessels in that region and automatically place a bunch of targets. So you, you know, that allows, you know, the human robot operator to select, uh, really good areas of brain and make dense applications of targets in that in those regions.

The regions we think are gonna have the most, um, high fidelity representations of finger movements and arm movement intentions.

I've seen like images of this, and for me, with OCD, it's for some reason, a really pleasant, uh, I think there's a subreddit called Oddly Satisfying,

Yeah, love that subreddit.

it's like it's oddly satisfying to see the different target sites avoiding the blood vessels. And, uh, also maximizing, like the usefulness of those locations for the signal. It just feels good. It's like, Ah, yes,

as as a person who has a visceral reaction to the brain bleeding, I can tell you it's. It's extremely especially, satisfying watching the electrodes themselves go into the brain and not cause bleeding.

yeah, yeah, so, uh, you said the feeling, uh, was of relief when everything went perfectly. How deep in the brain can you currently go? and, uh, eventually go? Let's say on the Neuralink side, is it seems the deeper you go in the brain, the more challenging it becomes.

yeah. So talking broadly about neurosurgery, we can get anywhere. Uh, it's routine for me to put deep brain stimulating electrodes, uh, n near the very bottom of the brain, uh, entering from the top and passing about a two millimeter wire all the way into the bottom of the brain. And that's not revolutionary. A lot of people do that, uh, and we can do that with very high precision. I, I use a robot, uh, from Globus to do that surgery.

Um, y, you know, several times a month, uh, it's, it's pretty routine,

What are your eyes in that situation? what? what are you seeing? What's what kind of technology can you use to visualize where you are to light your way?

yeah, so it's a cool process. On the software side, you take a preoperative MRI that's extremely high resolution data of the entire brain. You put the patient to sleep, put their head in a frame that holds the skull very rigidly. And then you take a CT scan of their head while they're asleep with that frame on. And then merge, uh, the MRI and the CT in software. You have a a plan based on the MRI where you can see these nuclei deep in the brain, you can't see them on CT.

But if you trust the merging of the two images, then you indirectly know on the CT where that is, and therefore indirectly know where. In reference to the titanium frame screwed to their head, those targets are. And so this is 60s technology to manually compute trajectories, given the entry point and target, um, a, and dial in some goofy looking titanium um actuators, uh, with manually manual actuators with little tick marks on them. The modern version of that is to use a robot.

Uh, you know, just like a a little Kuka arm you might see at building cars at the Tesla factory. Uh, this small robot arm can show you the trajectory that you intended from the pre op MRI. And establish a very rigid holder through which you can drill a small hole in the skull and pass a small rigid wire deep into that area of the brain that's hollow. And put your electrode through that hollow wire and then remove all of that except the electrode.

Uh, so you end up with the electrode very, very precisely placed far from the skull's surface. Now that's standard technology. Um, that's already, you know, been out in the world for for a while. Neuralink right now is focused entirely on cortical targets, surface targets, uh, because there's no trivial way to get, say, hundreds of wires deep inside the brain without doing a lot of damage. So your question, what do you see? Well, I see an MRI on a screen, I can't see everything that that DBS electrode is passing through on its way to that deep target.

And so it's accepted with this approach that there's gonna be about one in a hundred patients who have a a bleed somewhere in the brain, uh, as a result of passing that wire blindly into the the deep part of the brain. That's not an acceptable safety profile for Neuralink. We, uh, start from the position that we want this to be dramatically, maybe two or three orders of magnitude safer than that. Uh, safe enough, really, that, you know, you or I, without a profound medical problem, might on our lunch break someday say, Yeah, sure, I'll get that.

I've been meaning to upgrade to the latest version. And so the the safety constraints given that are high, and so we haven't, uh, settled on a final solution for arbitrarily approaching deep targets in the brain.

It's interesting, cause like you have to avoid blood vessels somehow, and you have to. Maybe there's creative ways of doing the same thing, like mapping out high resolution geometry of blood vessels, and then you can go in blind. But like, how do you map out that in a way that's like, super stable? It's, yeah, there's a lot of interesting challenges there, right? But there's a lot to do on the surface, luckily,

exactly, so we've got vision on the surface. Um, you know, we. We actually have made a huge amount of progress sewing, uh, electrodes into the spinal cord, uh, as a potential workaround for a spinal cord injury. That would allow a brain mounted implant to translate motor intentions to a spine mounted implant. That can, uh, affect muscle contractions in previously paralyzed arms and legs.

it's mind blowing, that's just incredible. So, like, the effort there is to try to bridge the brain to the spinal cord, to the periphery, peripheral nerve, nervous system. So, uh, how hard is that to do?

We have that working in, uh, in very crude forms in animals.

That's amazing.

Yeah, we've done it.

So similar to like with Nolan, where he's able to digitally move the cursor here, you're doing, uh, the same kind of communication, but with the actual effectors that you have, that's fascinating.

yeah. So we have anesthetized animals doing grasp and moving, moving their legs in a sort of a walking pattern. Uh, again, early days, uh, but uh, the future is bright for this kind of thing. And and people with paralysis, uh, should look forward to that bright future. They're gonna have options.

yeah. And there's a a lot of sort of, uh, intermediate or extra options. Where you take like an optimist robot, like the uh, the arm, and to be able to control the arm, the the the fingers, the hands, the arm as a prosthetic. For exoskeletons example, Frank Petese

are are getting better, too.

Exoskeletons, Yeah, so that that goes hand in hand. Although I didn't quite understand until thinking about it deeply and doing more research about Neuralink, how much you can do on the digital side, so this digital telepathy. I, I didn't quite understand that. You could really map the intention, as you described in the hand knob area, that you can map the intention. Just imagine it, think about it. That intention can be mapped to actual action in the digital world.

And now, more and more, so much can be done in the in the digital world that it, it, it can reconnect you to to the outside world. It can allow you to have freedom, have independence. If you're a quadriplegic, that's really powerful, like you can go really far with that.

yeah. Our 1st participant is he's incredible, he's And breaking world records left and right.

and he's having fun with it, it's great. Um, just going back to the surgery, your whole journey, you, uh, mentioned to me offline you have surgery on Monday, so you're like, you're doing surgery all the time. Maybe the ridiculous question, what does it take to get good at surgery?

Practice repetitions, you just same with anything else, you know, there's a million ways of people saying the same thing and selling books saying it. But do you call it ten, zero hours? Do you call it, you know? Spend some chunk of your life, some percentage of your life focusing on this, obsessing about getting better at it. Um, repetitions, uh, humility, recognizing that y- you aren't perfect at any stage along the way, uh, recognizing you've got improvements to make in your technique, being open to feedback and coaching from people with a different perspective on how to do it.

Um, and then, um, just the constant will to do better. Uh, that, fortunately, you know, if you're not a sociopath, I think your patients bring that with them to the office visits every day. They, you know, force you to wanna do better all the time.

Yeah, just step up. I mean, it's a real human being, a real human being that you can help. So every surgery, e- even if it's the same exact surgery, is there a lot of variability between that surgery and a different person?

yeah, a fair bit. I mean, a good example for us is the the angle of the skull relative to the normal plane, or of the body axis of the skull. Overhand knob, uh, is pretty wide variation. I mean, some people have really flat skulls, and some people have really steeply angled skulls over that area. And that has, you know, consequences for how their head can be fixed in in, uh, in, sort of the frame that we use, um, and how the robot has to approach the skull.

And, um, yeah, people's, people's bodies are built as differently as you know, the people you see walking down the street. As as much variability in body shape and size as you see there, we see in brain anatomy and skull anatomy. Um, there are some people who we've had to kind of exclude from our trial for having skulls that are too thick or too thin, or scalp that's too thick or too thin. Um, I think, you know, we have, like the middle 97% or so, uh, of people, but you, you can't account for all human anatomy variability.

How much like mushiness and mess is there? Cause? I, uh, you know, taking biology classes, the diagrams are always really clean and crisp. Neuroscience, the pictures of neurons are always really nice and okay, um, but whenever I look at pictures of like real brains, they're all I, I don't know what is going on. Uh, so how much our biological systems in reality, like, how hard is it to figure out what's going on?

not too bad. Uh, once you really get used to this, you know, that's where experience and and skill, and, uh, education really come into play. Is, if you stare at a thousand brains, it becomes easier to kind of mentally peel back. The say, for instance, blood vessels that are obscuring the s sulci and gyri, you know, kind of the wrinkle pattern of the surface of the brain. Occasionally, when you're f- when you're 1st starting to do this and you open the skull, it it doesn't match what you thought you were gonna see based on the MRI.

Uh, and with more experience, you, you learn to kind of peel back that layer of blood vessels and see the underlying pattern of wrinkles in the brain. And, uh, use that as a landmark for where you are.

The wrinkles are a landmark, so like,

Yeah, so I was describing hand knob earlier, that's a pattern of the wrinkles in the brain. It's sort of this sort of Greek letter Omega shaped area of the brain.

so you could recognize the hand knob area. Like, if if I show you a thousand brains and give you like 1 min with each, you'd be like, Yep, that's that. And so there is some uniqueness to that area of the brain, like in terms of the geometry, the topology of the thing. Where, where is it about in the

it's, uh, y- so you have this strip of brain running down the top, called the primary motor area. And I'm sure you've seen this picture of the homunculus laid over the surface of the brain. The weird little guy with huge lips and giant hands, uh, that guy sort of lays with his legs, uh, up at the top of the brain and and face, arm, uh, areas farther down, and and then some kind of mouth, lip, tongue areas, uh, farther down.

And so the hand i is right in there, and then the areas that control speech, at least on the on the left side of the brain in most people, are are just below that. And so, uh, any muscle that you voluntarily move in your body, um, the vast majority of that references that strip, or those intentions come from that strip of brain. And the the wrinkle, uh, for hand knob, is right in the middle of that and vision is in the back.

back here Yep,

also on close to the surface.

vision's a little deeper, uh, and so, you know, this gets to your question about how deep can you get? Um, to do vision, we can't just do the surface of the brain, we have to be able to go in. Uh, not not as deep as we have to go for DBS, but maybe a centimeter deeper than we're used to for hand insertions. Uh, and so that's, you know, work in progress. That's, uh, a new set of challenges to overcome.

By the way, you mentioned, uh, the Utah array, and I, uh, just saw a picture of that and l- that thing looks terrifying Yeah, the nails, because it's it's because it's rigid. And then if you look at the threads, they're flexible. What can you say that's interesting to you about the flexible? That kind of approach of the the flexible threads to to deliver the electrodes next to the neurons?

yeah. I mean, the the goal there comes from experience. I mean, we stand on the shoulders of people that made Utah arrays and and used Utah arrays for decades before we ever even came along. Um, Neuralink arose partly. This approach to technology arose out of a need recognized after Utah arrays would fail routinely. Because the rigid electrodes, those spikes that are literally hammered using an air hammer into the brain, uh, those spikes generate a bad immune response that encapsulates the the electrode spikes in, uh, scar tissue, essentially.

And so one of the projects that was being worked on in in the Anderson Lab at Caltech when I got there was to see if you could use chemotherapy to prevent the formation of scar. Like, you know, things are pretty bad when you're jamming a bed of nails into the brain and then treating that with chemotherapy to try to prevent scar tissue. It's like, you know, maybe we've gotten off track here, guys, maybe there's a fundamental redesign necessary.

And so Neuralink's approach of using highly flexible tiny electrodes avoids a lot of the bleeding, avoids a lot of the immune response that ends up happening. Uh, when rigid electrodes are pounded into the brain. And so what we see is our electrode longevity and functionality, uh, and the and the health of the brain tissue immediately surrounding the electrode, uh, is excellent. I mean, it goes on for for years now in in our animal models.

What do most people not understand about the biology of the brain? We? We'll mention the vasculature, that's really interesting.

I think th- the most interesting, maybe underappreciated fact, uh, is that it really does control almost everything. I mean, I don't know. For out of the blue example, imagine you you want a lever on fertility, you wanna be able to turn fertility on and off. I mean it. There are legitimate targets in the brain itself to modulate fertility, say, um, blood pressure, you wanna modulate blood pressure, there are legitimate targets in the brain for doing that.

Um, th- things that aren't immediately obvious as brain problems, uh, are potentially solvable in the brain. Um, and so I think it's an underexplored area for primary treatments of, uh, of all the things that bother people.

That's a really fascinating way to look at it. Like, there's a lot of conditions we might think have nothing to do with the brain, but they might just be symptoms of something that actually started in the brain. The actual source of the problem, the the primary source, is the is something in the brain.

Yeah, not, not always. I mean, you know, their kidney disease is real, uh, but um, there are levers you can pull in the brain that affect all of the all of these systems. those knobs, Yeah,

on off switches and knobs in the brain that from which this all or originates. Uh, would you have a Neuralink chip implanted in your brain?

yeah, um, I think use case right now is use a mouse, right? I can already do that, and so there's no value proposition. Uh, on safety grounds alone. Sure, I would do it tomorrow.

you know, you say the use case of the mouse is it after, like, researching all this? And part of it is just watching Nolan have so much fun. If you can get that bits per second, like, really high with a mouse, like being able to interact. Cause I if you think about the the way the on the smartphone, the way you swipe. That was transformational how we interact with the thing. It's subtle, you don't realize it, but to be able to touch a phone and to, uh, scroll with your finger, that's like.

That changed everything. They people were sure you needed a keyboard to type and the that, uh, there's a lot of HCI aspects to that that changed how we interact with computers. So there could be a certain rate of speed with the mouse that would change everything. It's like you might be able to just click around a screen extremely fast. And that, uh, if it I, I ca, I can see myself getting the Neuralink for much more rapid interaction with the digital devices.

yeah. I think recording speech intentions from the brain might might change things as well. You know, the value proposition for the average person, um, a keyboard is a pretty clunky human interface, requires a lot of training. It's, you know, highly variable in the maximum performance that the average person can, uh, can achieve. Uh, I think taking that out of the equation and just having a natural y, you know, word to computer interface, um, might change things for a lot of people,

It'd be hilarious if that is the reason people do it, even if you have speech to text that's extremely accurate, it currently isn't, but it's say, gotten super accurate. It'd be hilarious if people went for Neuralink. Just so you avoid the embarrassing aspect of speaking, like looking like a douchebag, speaking to your phone in public, which is a real like. That's a real constraint.

Yeah, I mean, with a bone conducting case, uh, that can be a an invisible headphone, say, um, and the ability to think words into software and have it respond to you. Um, you know, that starts to sound sort of like embedded super intelligence, you know? If you can silently ask for the Wikipedia article on any subject and have it read to you without any observable change happening in the outside world. Uh, you know, for one thing, standardized testing is obsolete.

Yeah, if it's done well on the UX side, it could change. I don't know if it transforms society, but it really, uh, can create a kind of shift in the way we interact with D digital devices in the way that a smartphone did. Now, I would, um, just having to look into the safety of everything involved, I, I would totally try it. So it doesn't have to go to some like incredible thing where you have it connects to your vision, or to some, like it connects all over your brain.

That could be like just connecting to the hand knob. Uh, you might have a lot of interesting interaction com human computer interaction possibilities. That's really interesting,

yeah, and the technology on the academic side is progressing at light speed here. I think there was a really amazing paper out of UC. Davis, uh, Sergey Stavisky's lab, that basically made a initial solve of speech decode. It was something like 125 zero words, uh, that they were getting with, you know, very high accuracy, which is,

so you're just thinking the word, thinking the word, and you're able to get it. Oh boy, like, you have to have the intention of speaking it. So, like, do the inner voice, man. It's. It's so amazing to me that you can do the intention. The signal mapping, all you have to do is just imagine yourself doing it, and if if you get the feedback that it actually worked, you can get really good at that. Like your brain will 1st of all adjust, and and you develop like any other skill, like touch typing, you develop in that same kind of way.

That is that is re to. To me, it's just really fascinating to be able to even to play with that, honestly, like I would get a Neuralink, just to be able to play with that. Just to play with the capacity, the capability of my mind to learn this skill. It's like learning the skill of typing or learning the skill of moving a mouse, it's another skill of moving the mouse, not with my physical body, but with my mind,

I can't wait to see what people do with it. I feel like W- we're, we're cavemen right now, we're. We're like banging rocks with a stick and thinking that we're making music. Um, s. At some point, when these are more widespread, there's gonna be the equivalent of a of a piano that, you know, someone, someone can make art with their brain in a way that we didn't even anticipate. Um, looking forward to it.

Give it to like a teenager, like anytime I think I'm good at something, I'll always go to like, I don't know. Even, even, uh, even with the the the bits per second. And playing a video game, you realize you give it to a teen, you give a Neuralink to a teenager. Just the large number of them, the kind of stuff they, they get good at stuff, they're gonna get like hundreds of, uh, bits per second, even just with the current technology.

probably, probably.

Just, uh, cause it's also addicting, how o- like, how the the the number go up aspect of it, of like, improving and training, cause it is, it's almost like a skill. And plus, there's the software on the other end that adapts to you, and especially if the adapting procedure, the algorithm becomes better and better and better. You like learning together.

Yeah. We're scratching the surface on that right now. There's so much more to do.

So on the complete other side of it, you have an RFID chip implanted in you. This is so I hear nice. yeah, little So this is pass.

little subtle thing.

It's a passive device that you use for unlocking, like a a safe with top secrets, or what? what is? What do you use it for? What's the story behind it?

I'm not the 1st one. There's, you know, there's this whole community of weirdo biohackers that, uh, have done this stuff. And I think one of the early use cases was storing, you know, private crypto wallet keys and and whatever. Um, I dabbled in that a bit and and had some fun with it. Um, but

you have some bitcoin implanted in your body somewhere you can't tell where. Yeah, yeah, actually, yeah, okay,

uh, it was, you know, the modern day equivalent of finding change in the sofa cushions. After I, I put some orphan crypto on there that I thought was worthless and forgot about it for a few years. Went back and found that some community of people loved it, uh, and had propped up the value of it, and so it had gone up 50 fold. So there was a lot of change in those cushions.

that's hilarious.

But the the primary use case is mostly as a as a tech demonstrator. You know it, it has my business card on it. You can scan that and, uh, by touching it to your phone, uh, it opens the front door to my house. You know, whatever, simple stuff.

But it's a cool step, it's a cool leap to implant something in your body. I mean, it has, perhaps that's. It's a similar leap to Neuralink, because, uh, for a lot of people, that kind of notion of putting something inside your body, something electronic inside a biological system, is a big leap.

Yeah, we have a kind of a mysticism around the barrier of our skin. We're completely fine with knee replacements, hip replacements, you know, uh, dental implants, um, but, uh, y- you know, there's a mysticism still around the inviolable barrier that the skull represents. And I think that needs to be treated like any other, uh, pragmatic barrier. You know, it's the question isn't how, how incredible is it to open the skull? The question is, you know, what benefit can we provide?

So from all the surgeries you've done, from everything you understand, the brain, how much does neuroplasticity come into play? How adaptable is the brain, for example, just even in the case of healing from surgery or adapting to the post surgery situation?

The answer that is sad for me and, uh, other people of my demographic is that, you know, plasticity decreases with age, healing decreases with age. Uh, I have too much gray hair to, uh, to be optimistic about that. There are theoretical ways to increase plasticity using electrical stimulation, uh, nothing that is, you know, totally proven out as a robust enough mechanism to offer widely to people. But, um, yeah, I think I think there's cause for optimism that we might find something useful in terms of, say, an implanted electrode that improves learning.

Um, certainly, there's been some really amazing work recently from, uh, n Nicholas Schiff, Jonathan Baker, you know, and others, uh, who have a a cohort of patients with moderate traumatic brain injury who have had electrodes placed in the uh, deep nucleus in the brain called the central median nucleus, or just near the central median nucleus. And when they apply small amounts of electricity to that part of the brain, it's almost like electronic caffeine. They're able to improve people's attention and focus, um, they're able to improve how well people can perform a task.

I think in one case, someone who was unable to work after the device was turned on, they were able to get a job. Uh, and that's sort of, you know, one of the holy grails, uh, for me, with Neuralink and other technologies like this, is from a purely utilitarian standpoint. Um, can we? Can we make people able to take care of themselves and their families economically again? Can we make it? So someone who's fully dependent and even maybe requires a lot of caregiver resources, can we put them in a position to be fully independent, taking care of themselves, giving back to their communities?

Um, I think I think that's a very compelling, uh, proposition. And what motivates a lot of what I do and what a lot of the people at Neuralink are working for.

it's just a cool possibility that if you put a Neuralink in there, that the brain adapts like the the other part of the brain adapts too and integrates it. The the the capacity of the brain to do that, it's really interesting, probably unknown to the degree to which you can do that. But you're now connecting an external thing to it, especially, uh, once it's doing, uh, stimulation. Like the the biological brain and the, uh, the electronic brain, outside of it, working together, like, the possibilities there are really interesting.

It's, uh, still unknown, but interesting. It feels like the brain is really good at adapting to whatever. But of course, it is a system that, by itself, is already, uh, like, everything serves a purpose, and so you don't wanna mess with it too much.

yeah. It's like, you know, eliminating a species from a from an ecology, you know, you don't know what the delicate interconnections, dependencies are. Um, the brain is certainly a a a delicate, complex beast, and we don't know, uh, y- you know, every potential downstream consequence of of a single, uh, change that we make.

Do you see yourself doing, uh? So? You mentioned P1 surgeries of P2, P3, P4, P5, just Well, more and more and more humans.

I think, you know, it's a certain kind of brittleness, or, or, you know, a failure on the company's side. If we need me to do all the surgeries, um, I think something that I would very much like to work towards is a process that is so simple and so robust. On the surgery side that literally anyone could do it. Um, we, we wanna get away from a qu requiring intense expertise or intense experience, uh, to to have this successfully done and make it as as simple and translatable as possible.

Uh, I mean, I would love it if every neurosurgeon on the planet had no problem doing this. Um, I think we're probably far from a regulatory environment that would allow, uh, people that aren't neurosurgeons to do this, but, uh, n not impossible.

All right, I'll sign up for that. Did you ever anthropomorphize the the robot, r1, like, do you do you give it a name? Do you see it as like a friend that's like working together with you

I mean, to a certain degree, it's

or anatomy? Who's gonna take the job?

to a certain degree, it's, it's, yeah, it's a complex relationship. Uh,

All the good relationships are,

it's funny. When in the middle of the surgery, there's a part of it where I stand shoul- basically shoulder to shoulder with the robot. Um, and so, you know, if you're in the room reading the body language, you know, that's, it's my brother in arms. There, we're, we're working together on the same problem. Um, yeah, I'm not threatened by it.

keep telling yourself that, yeah, it's, um, how have all the surgeries that you've done over the years, the people you've helped and the the stakes, the high stakes that you've mentioned? How? How has it, uh, changed your understanding of life and death?

Yeah, um, you know, it gives you a very visceral sense, and this may sound trite, but it gives you a very visceral sense that death is inevitable. You know, on one hand, you know, you, you are. As a neurosurgeon, you're deeply involved in these like just hard to fathom tragedies. Um, you know, young parents dying, leaving, y- you know, a four year old behind, say, uh, and and on the other hand, y- you know, it takes the sting out of it a bit, because you see how just mind numbingly universal death is.

There is zero chance that I'm going to avoid it. Uh, I know, y- you know, techno optimists right now and longevity buffs right now would disagree on that 0.0, zero, 0% estimate. Uh, but I don't see any chance that our generation is going to avoid it. Entropy's a powerful force, and we are very ornate, delicate, brittle DNA machines that aren't up to the cosmic ray bombardment that we're subjected to. So, on the one hand, every human that has ever lived died or will die.

Uh, on the other hand, it's just one of the hardest things to imagine, um, inflicting on anyone that you love is is having them gone. I mean, I'm sure you've had friends that aren't living anymore, and it's it's hard to even think about them. Um, and so, uh, I wish I had, you know, uh, arrived at the point of nirvana, where, uh, you know, death doesn't have a sting. I'm not worried about it, but um, I can at least say that I'm comfortable with the certainty of it.

Uh, I, if not having found out how to take the the tragedy out of it. When I think about, you know, my kids, uh, either not having me or or me not having them, or my wife. Um,

Maybe I've come to accept the intellectual certainty of it, but, uh, and maybe the pain that comes with losing the people you love. But I don't think I've come to understand the existential aspect of it. Like that, this is gonna end, and I don't mean like, uh, in some, uh, trite way, I mean, like, it certainly feels like it's not going to end. Like you live life, like it's not going to end. And the the fact that this light that's shining, this consciousness is, is going to, uh, no longer be one moment, maybe today.

It's like a it. It fills me when I really am able to load all that in with Ernest Becker's terror, like it's a real fear. I think people aren't always honest with how terrifying it is. Um, I think the more you are able to really think through it, the more terrifying it is. It's it's not such a simple thing. Oh well, this is the way life is, and if you really can load that in, uh, it's hard.

But I think that's why the stoics did it, because it, like, helps you get your shit together and be like this. Well, the re like. The moment every single moment you're alive is just beautiful and it's terrifying that it's gonna end. And it's, and it's like, uh, you like, almost like you're shivering in the cold, a child helpless, this kind of feeling. And then it makes you when you have warmth, when you have the safety, when you have the love, to really appreciate it.

Uh, I feel like sometimes in your position, when you mentioned armor, just to see death, it might make you not be able to see that. The the finiteness of life, because if you kept looking at that, it might break you. So it it's good to know that you're kind of still struggling with that. Like, there's the the neurosurgeon, and then there's a human, and the human is still able to struggle with that and feel the the fear of that and the pain of that.

yeah, you know, it definitely m makes you ask the question of how long, how many time, how many of these can you see? And, uh, and not say, I can't do this anymore. Um, but I mean, you said it well, I think it gives you an opportunity to just appreciate that you're alive today. And, uh, you know, I've got. I've got three kids and an amazing wife, and I'm really happy things are good. I get to help on a project that I think matters, I think it moves us forward.

I'm a very lucky person.

it's the early steps of a potentially, uh, gigantic leap for humanity. It's a really interesting one and it's cool, cause like you, you read about all this stuff in history, where it's like the early days. I've been reading, uh, before going to the Amazon, I would read about explorers, uh, that would go and explore, even the Amazon jungle for the 1st time. It's just those are the early steps, or early steps into space, early early steps in any discipline, in in physics and mathematics.

And and it's cool, cause this is like the on the grand scale, these are the early steps into delving deep into the human brain. So not just observing the brain, but be able to interact with the human brain. It's gonna help a lot of people, but it also might help us understand what the hell's going on in there.

yeah. I think ultimately we wanna give people more levers that they can pull, right? Like you wanna give people options. If you can give someone a dial that they can turn on how happy they are, I think that makes people really uncomfortable. But um, now talk about major depressive disorder, talk about people that are committing suicide at an alarming rate in this country. And try to justify that queasiness in those in that light of you. You can give people a knob to take away suicidal ideation, suicidal intention.

I would, I would give them that knob. I don't know how you justify not doing that.

Yeah, you can think about, like, all the suffering that's going on in the world, like every single human being that's suffering right now. It's like a it'll be a glowing red dot, the more suffering, the more it's glowing, and you just see the map of human suffering. And any technology that allows you to dim the that light of suffering, uh, on a grand scale, is is pretty exciting. Cause there's a lot of people suffering and most of them suffer quietly, and we turn our, uh, we, we look away too often, uh, and we sh.

We should remember those that are suffering, cause, once again, most of them are suffering quietly.

well, and you know, on a grander scale, the fabric of society, you know, people have a lot of complaints about how our s social fabric is working or not working, how our politics is working or not working. Uh, those things are made of neurochemistry, too, in in aggregate, right? Like our politics is composed of individuals with human brains. And the way it works or doesn't work, it's potentially tunable, uh, in the sense that I don't know.

Say, remove our addictive behaviors, or tune our addictive behaviors for social media, or our addiction to outrage, our addiction to sharing the most angry political tweet we can find. Um, I don't think that leads to a functional society. And, uh, if if you had options for people to moderate that maladaptive behavior, uh, there could be huge benefits to society. Maybe we could all work together a little more harmoniously toward useful ends.

There's a sweet spot, like you mentioned, you don't wanna completely remove all the dark sides of human nature, cause those kind of, uh, are somehow necessary to make the whole thing work. But there's a sweet spot.

Yeah, I agree, we gotta, you gotta suffer a little, just not so much that you lose hope.

Yeah, when you all the surgeries you've done, have you seen consciousness in there? ever? Was there like a glowing light?

You know, I, I have this sense that, uh, I never found it, never removed it, you know, like, like a Dementor in Harry Potter. Um, I have this sense that consciousness is a lot less magical than our instincts wanna claim it is. Um, it, it seems to me like a useful analog for thinking about what consciousness is in the brain. Um, you know, is that we? We have a really good intuitive understanding of w- what it means to say, tu- touch your skin and know what's being touched.

Um, I think consciousness is just that level of sensory mapping applied to the the thought processes in the brain itself. So what I'm saying is consciousness is the sensation of some part of your brain being active. So you, you feel it working, you feel the part of your brain that thinks of red things or winged creatures, or the taste of coffee. You feel those parts of your brain being active, the way that I'm feeling my palm being touched right.

And that sensory system that feels the brain working is consciousness,

that is so brilliant. It's the same way it's a sensation of touch when you're touching a thing. Consciousness is the sensation of you feeling your brain working, your brain thinking, your brain perceiving.

which isn't, which isn't like a warping Both of space, time, or or some quantum field effect, right? It's nothing magical. People always want to ascribe to consciousness something truly different. Uh, a, and there's this awesome, long history of people looking at whatever the latest discovery in physics is to explain consciousness. Um, because it's the most magical, the most out there thing that you can think of, and and people always, you know, wanna do that with consciousness.

I don't think that's necessary. It's just a, you know, a very useful and gratifying way of feeling your brain work.

And as we said, it's one heck of a brain. Everything we see around us, everything we love, everything that's beautiful, came from brains like these.

it's all electrical activity happening inside your skull.

And, uh, I, for one, am, uh, grateful there's people like you that are, uh, exploring all the ways that it works and all the ways it can be made better. Uh,

Thanks, Alex.

Matthew, thank you so much for talking today.

Uh, it's been a joy.

Thanks for listening to this conversation with Matthew MacDougall. And now, dear friends, here's Bliss Chapman, brain interface software lead at Neuralink. You told me that you've met hundreds of people with spinal cord injuries or with ALS, and that your motivation for helping at Neuralink is grounded in wanting to help them. Can you describe this motivation?

Yeah, 1st, just a thank you to all the people I've gotten a chance to speak with for sharing their stories with me. I don't think there's any world really in which I can share their stories as powerfully as they can. But, uh, just, I think, to summarize at a very high level, what I hear over and over again is that people with, uh, ALS or severe spinal cord injury in a place where they basically can't move physically anymore.

Really, at the end of the day, are looking for independence. And that can mean different things for different people. For some folks, it can mean the ability just to be able to communicate again independently, without needing to wear something on their face, without needing a caretaker to be able to put something in their mouth. For some folks, it can mean independence. To be able to work again, to be able to navigate a computer digitally efficiently enough to be able to get a job, to be able to support themself, to be able to move out and ultimately be able to support themself.

After their family maybe isn't there anymore to take care of them. And, uh, for some folks, it's as simple as just being able to respond to their kid in time before they, you know, run away or get interested in something else. And these are deeply personal and sort of very human problems. And what strikes me again and again when talking with these folks is that this is actually an engineering problem. This is a problem that with the right resources, with the right team, we can make a lot of progress on.

And, um, at the end of the day, I think that's a deeply inspiring message and something that makes me excited to get up every day.

So it's both an engineering problem in terms of a BCI, for example, that can give them capabilities where they can interact with the world. But also on the other side, it's an engineering problem for the rest of the world to make it more accessible for people living with quadriplegia.

Yeah, and I think I, I'll take a broad view, sort of lens on this for a 2nd. I think I'm very in favor of anyone working in this problem space, so beyond BCI. I'm I'm happy and excited and willing to support any way I can. Folks working on eye tracking systems, working on, you know, speech to text systems, working on head trackers or mouth sticks or quad sticks. And I've met many engineers and folks in the community that do exactly those things.

And I think for the people we're trying to help, it doesn't matter what the complexity of the solution is, as long as the problem is solved. And I want to emphasize that there can be many solutions out there that can help with these problems. And, uh, BCI is one of a collection of of such solutions, so BCI in particular, I think, offers several advantages here. And I think the folks that recognize this immediately are usually the people who have spinal cord injury or some form of paralysis.

Usually, you don't have to explain to them why this might be something that could be helpful, it's usually pretty self evident. But for the rest of us, folks that don't live with severe spinal cord injury or who don't know somebody with ALS, uh, it's not often obvious. Why you would want a brain implant to be able to connect and navigate a computer, and it's surprisingly nuanced. And to the degree that I've learned a huge amount, just working with Nolan in the 1st Neuralink clinical trial and understanding from him and his words why this device is impactful for him.

And it's a nuanced topic. It it can be the case that even if you can achieve the same thing, for example, with a mouse stick when navigating a computer. He doesn't have access to that mouse stick every single minute of the day, he only has access when someone is available to put it in front of him. And so a BCI can really offer a level of of independence and autonomy that, if it wasn't literally physically part of your body, it'd be hard to achieve in any other way.

So there's a lot of fascinating aspects to what it takes to get Nolan to be able to control a cursor on the screen with his mind. Uh, you texted me something that I just love, you said. I was part of the team that interviewed and selected P1. I was in the operating room during the 1st human surgery, monitoring live signals coming out of the brain. I work with the user basically every day to develop new UX paradigms, decoding strategies, and I was part of the team that figured out how to recover useful BCI to new world record levels.

When the signal quality degraded, we'll talk about, I think, every aspect of that. But, um, just zooming out. W What was it like to be part of that part, part of that team, and part of that historic? I would say historic 1st.

Yeah, I think for me, this is something I've been excited about for close to ten years now. And so to be able to be even just some small part of making it a reality is extremely exciting. A couple, maybe special moments during that whole process that I'll never really, truly forget. One of them is in during the actual surgery. Uh, y- you know, at that point in time, I I know Nolan quite well, I know his family.

And so I think the the initial reaction when, uh, Nolan is rolled into the operating room, is just a oh shit kind of reaction. But at that point, muscle memory kicks in and you sort of go into, uh, you let your body just do the all the all the talking. And I have the lucky job in that particular procedure to just be in charge of monitoring the implant. So my job is to sit there, to look at the signals coming off the implant, to look at the live brain data streaming off the device as threads are being inserted into the brain.

And just to basically observe and make sure that nothing is going, you know, wrong, or that there's no red flags or fault conditions. That would need to go and investigate or pause the surgery to, uh, to debug. And, uh, because I had that sort of spectator view of the surgery, I had a slightly removed perspective than I think most folks in the room. I got to sit there and think to myself, Wow, you know, that brain is moving a lot, you know, when you when you look inside the little craniotomy that we stick the threads in.

Y- you know, one thing that most people don't realize is the brain moves, the brain moves a lot when you breathe, when you're, when your, uh, heart beats, and you can see it visibly. So, you know, that's something that I think was a surprise to me and very, very exciting. Uh, to be able to see someone's brain who you physically know and have talked with at length, actually pulsing and moving inside their skull.

And they used that brain to talk to you previously, and now it's right there, moving. Uh, actually, I didn't realize that in terms of the thread setting, so the the Neuralink implant is active during surgery. So in one thread at a time, you're able to start seeing the signal, so that's part of the way you test that the thing is working.

yeah. So actually, in the in the operating room, right after we sort of, uh, finished the all the thread insertions, I started collecting what's called broadband data. So broadband is, uh, basically the most raw form of signal you can collect from a Neuralink electrode. It's, uh, essentially a measurement of the local field potential, or the, yeah, the voltage essentially measured by that electrode. And, uh, we have a certain mode in our in our application that allows us to visualize where detected spikes are, so it visualizes sort of where, uh, where.

In the broadband signal, and it's very, very raw form of the data, a neuron is actually spiking. And so one of the the these moments that I'll never forget as part of this whole clinical trial is seeing live in the operating room, while he's still under anesthesia, beautiful spikes being shown in the application, just streaming live to a device I'm holding in my hand,

So this is no signal processing, the raw data, and then the signal processing's on top of it. You're seeing the spikes detected, and that's a UX, too, cause that thing was that that looks beautiful as well.

During that procedure, there was actually a lot of cameramen in the room, so they also were curious and wanted to see. There's several neurosurgeons in the room who are all just excited to see robots taking their job. And, uh, they're all, you know, crowded around and small little iPhone watching this live brain data stream out of his, uh, his brain.

what was that like, seeing the robot do some of the surgery? So the computer vision aspect, where it detects all the all the spots that avoid the the blood vessels. And then, obviously, with human supervision, then actually doing the really high precision, uh, connection of the threads to the brain.

Y- That's a good question. My answer is gonna be, uh, pretty lame here, but it was boring. I've seen it, uh, so you want so many it to be, times. Yeah, that's exactly how you want surgery to be, you want it to be boring, cause I've seen it so many times. I've seen the the robot do the surgery literally hundreds of times, and so it was just one more time.

yeah, all the practice surgeries and the proxies, and this is just another day. So what about when, uh, Nolan woke up? Well, do do you remember a moment where, uh, he was able to move the cursor, or not move the cursor, but get signal from the brain such that it was able to show that there's a connection?

yeah, yeah. So we are, uh, quite excited to move as quickly as we can. And Nolan was really, really excited to get started. He wanted to get started actually the day of surgery, but, uh, we. We waited till the next morning, very patiently. It's a long night, um, and the next morning in the ICU where he was, uh, recovering. He, uh, wanted to get started and actually start to understand what kind of signal we can measure from his brain.

And maybe for for folks who are not familiar with, um, the Neuralink system, we implant the Neuralink system, or the Neuralink implant, in the motor cortex, so the motor cortex is responsible for representing things like motor intent. Uh, sorry. If you imagine closing and opening your hand, that kind of signal representation would be present in the motor cortex. If you imagine moving your arm back and forth or wiggling a pinky, this sort of signal can be present in the motor cortex.

So one of the ways we start to sort of map out what kind of signal do we actually have access to in any particular individual's brain is through this task called body mapping. And body mapping is where you essentially present a visual to the user and you say, Hey, imagine doing this. And the visual is, you know, a 3D hand opening, closing, or index finger modulating up and down. And, uh, you ask the user to imagine that.

And obviously you can't see them do this, cause they're paralyzed, so you can't see them actually move their arm. But, uh, while they do this task, you can record neural activity, and you can basically offline model and check, can I predict or can I detect the modulation corresponding with those different actions? And so we did that task and we realized, hey, there's actually some modulation associated with some of his hand motion, which was a first indication that, okay.

We can potentially use that modulation to do useful things in the world. Uh, for example, control a computer cursor. And he started playing with it, you know, the first time we showed him it. And we actually just took the same live view of his brain activity and put it in front of him. And we said, Hey, you tell us what's going on. Uh, you know, we're not you, you're able to imagine different things, and we know that it's modulating some of these neurons.

So you figure out for us what that is actually representing. And so he played with it for a bit, he was like, I don't quite get it yet. He played for a bit longer and he said, Oh, when I move this finger, I see this particular neuron start to fire more. And I said, Okay, prove it, do it again. And so he said, Okay, three, two, one, boom. And the minute he moved, you can see, like, instantaneously this neuron is firing single neuron.

I I can tell you the exact channel number if you're interested. It's stuck in my brain now forever. But that single, uh, channel firing was a beautiful indication that it was behaviorally modulated neural activity that could then be used for downstream tasks like decoding a computer cursor.

And when you say single channel, is that associated with a single electrode?

Yes, it's, uh, channel and electrode are interchangeable. In this context,

And there's, uh, 1,024 of those.

1,024, yeah,

It's incredible that that works. That really, when I was, um, learning about all this and like loading it in, it was just blowing my mind. That the intention you can visualize yourself moving the finger, that can turn into a signal. And the fact that you can then skip that step and visualize the cursor moving, or have the intention of the cursor moving, and that leading to a signal that can then be used to move the cursor.

This there is so many exciting things there to learn about the brain, about the way the brain works. The very fact of their existing signal that can be used is really powerful. But it feels like that's just like the beginning of figuring out how that signal could be used really, really effectively. No, I should also just, uh, there's so many fascinating details here, but you mentioned the body mapping step, uh, at least in the version I saw that Nolan was showing off.

There's like a super nice interface, like a graphical interface where, like, it just felt like I was like in the future. Cause it like, uh, you know, I I guess it visualizes you moving the hand, and there's a very like, like a sexy, polished interface. That's that hello, you know, I don't know if there's a voice component, but it just felt like, uh, it's like like when you wake up in a really nice video game and this is a tutorial at the beginning of that video game.

This is what you're supposed to do. It's cool,

no, I mean, the future should feel like the future.

but it's not easy to pull that off. I mean, it needs to be simple, but not too simple.

Yeah, and I think the UX design component here is, uh, underrated for BCI, uh, development in general. Like, there's a whole interaction effect between the ways in which you visualize, uh, an instruction to the user and the kinds of signal you can get back. And that quality of sort of your behavioral alignment to the neural signal is a function of how good you are at expressing to the user what you want them to do. And so, yeah, we spend a lot of time thinking about the UX, uh, of how we build our applications, of how the decoder actually functions, the control surfaces it provides to the user.

All these little details matter a lot.

So maybe it'd be nice to get into a little bit more detail of what the signal looks like and what the decoding looks like. So there's a, uh, n1 implant that has, like we mentioned, uh, uh, 1,024 electrodes, and that's collecting raw data, raw signal. What does that signal look like? And, uh, what are the different steps along the way before it's transmitted and what is transmitted and all that kind of stuff?

yeah, yep, this is gonna be a fun one. Let's

Let's go.

go, uh, so, uh, maybe before diving into what we do, it's worth understanding what we're trying to measure. Because, uh, that dictates a lot of the requirements for the system that we build. And what we're trying to measure is really individual neurons producing action potentials, and action potential is, you can think of it like a little electrical impulse that you can, uh, detect if you're close enough. And by being close enough, I mean, like within, let's say, a hundred microns of that cell, and a hundred microns is a very, very tiny distance.

And so the number of neurons that you're gonna pick up with any given electrode is, uh, just a small radius around that electrode. And the other thing worth understanding about the underlying biology here is that when neurons produce an action potential, the width of that action potential is about 1 millisecond. So from the start of the spike to the end of the spike, that whole width of that, uh, sort of characteristic feature of a neuron firing is 1 millisecond wide.

And if you want to detect that an individual spike is occurring or not, you need to sample that signal, or sample the local field potential nearby that, uh, neuron. Much more frequently than once a millisecond. You need to sample many, many times per millisecond to be able to detect that. This is actually the characteristic waveform of a neuron producing an action potential. And so we sample across all 1,024 electrodes about 20,000 times a second.

20,000 times a second means, for every given 1 millisecond window, we have about twenty samples that tell us what that exact shape of that action potential looks like. And once we've sort of sampled at super high rate, the underlying electrical field nearby, uh, these cells, we can process that signal into just where do we detect a spike or where do we not? Sort of a binary signal, one or zero, do we detect a spike in this 1 millisecond or not?

And we do that because the actual information character carrying, uh, uh, sort of subspace of neural activity is just when our spikes are occurring. Essentially, everything that we care about for decoding can be captured or represented in the frequency characteristics of spike trains, meaning how often are spikes firing in any given window of time. And so that allows us to do sort of a crazy amount of compression from this very rich, high density, uh, y, you know, signal to something that's much, much more sparse and compressible.

That can be sent out over a wireless, uh, radio, like a Bluetooth communication, for example.

Quick tangents here. You mentioned electrode neuron. There's a local neighborhood of neurons nearby. How difficult is it to, like, isolate from where the spike came from?

Yeah, so there's a whole field of sort of academic neuroscience work on exactly this problem of basically given a single electrode, or given a set of electrodes measuring a set of neurons. How can you sort of s sort spike, sort which spikes are coming from what, uh, neuron? And this is a problem that's pursued in academic work because you care about it for understanding what's going on in the underlying sort of, uh, neuroscience of the of the brain.

If you care about understanding how the brain's representing information, how that's evolving through time, then that's a very, very important question to to understand. For sort of the engineering side of things, at least at the current scale. If the number of neurons per electrode is relatively small, you can get away with basically ignoring that problem completely. You can think of it like sort of a random projection of neurons to electrodes. And there may be, in some cases, more than one neuron per electrode.

But if that number is small enough, those signals can be thought of as, uh, sort of a union of the two. And for many applications, that's a totally reasonable trade-off to make and can simplify the problem a lot. And as you sort of scale out channel count, the, uh, relevance of distinguishing individual neurons becomes less important. Because you have more overall signal. And you can start to rely on sort of correlations or covariance structure in the data to help understand when that channel is firing, what does that, what does that actually represent?

Cause, you know that when that channel's firing in concert with these other 50 channels, that means move left, but when that same channel is firing with concert with these other ten channels, that means move right.

Okay, so you have to do this kind of spike detection on board, and you have to do that super efficiently, so fast, and not use too much power. Cause you don't want it to be generating too much heat. So it has to be a super simple signal processing step. Um, is there s some wisdom you can share about what it takes to overcome that challenge?

yeah. So we've tried many different versions of basically turning this raw signal into, uh, sort of a feature that you might want to send off the device. And I'll say that I don't think we're at the final step of this process. This is a long journey. We have something that works clearly today, but there can be many approaches that we find in the future that are much better than what we do right now. So, so some versions of what we do right now.

And there's a lot of academic carriages to these ideas, so I don't wanna, you know, claim that these are original Neuralink ideas or anything like that. But, uh, one of these ideas is basically to build, uh, sort of like a convolutional filter, almost, if you will, that slides across a signal and looks for a certain template to be matched. And that template consists of sort of how deep the spike modulates, how much it recovers, and what the duration and window of time is that the whole process takes.

And if you can see in the signal that that template is matched within a certain bound, then you can say, okay, that's a spike. One reason that approach is super convenient is that you can actually implement that extremely efficiently in hardware, which means that you can run it, uh, in low power across 1,024 channels all at once. Another approach that we've recently started, uh, exploring, and this can be combined with the spike detection approach, something called spike band power.

And the benefits of that approach are that you may be able to pick up some signal from neurons that are maybe too far away to be detected as a spike. Because the farther away you are from an electrode, the weaker that actual spike waveform will look like on that electrode. So, uh, you might be able to pick up, you know, population level activity of things that are, you know, maybe slightly outside the normal recording radius. What what neuroscientists sometimes refer to as the hash of, uh, activity, the other stuff that's going on, uh.

And you can look at sort of across many channels how that, uh, sort of background noise is behaving. And you might be able to get more juice out of the signal that way, but it comes at a cost. That signal is now a floating point representation, which means it's more expensive to send out over a power. It means you have to find different ways to compress it that are different than what you can apply to binary signals.

So there's a lot of different challenges associated with these different modalities.

so also in terms of communication, you're limited by the amount of data you can send. And so, and also because you're currently using the Bluetooth protocol, you have to batch stuff together, but you have to also do this. Keeping the latency crazy low, like crazy low. Anything to say about the latency?

yeah, this is a passion project of mine, so, uh, I wanna build the best mouse in the world. I don't wanna build like the, you know, the Chevrolet Spark or whatever of electric cars. I wanna build like the Tesla Roadster version of of a mouse. And I really do think it's quite possible that within, you know, five to ten years, that most esports competitions are dominated by people with paralysis. This is like a very real possibility for a number of reasons.

One is that they'll have access to the the best technology to play video games effectively, the second is they have the time to do so. So those two factors together are particularly potent for, uh, esports competitors,

unless, uh, people without paralysis are also allowed to implant. Which is, it is another way to interact with a digital, uh, device. And there's some there's something to that. If if it's a fundamentally different experience, more efficient experience, even if it's not like some kinda full-on high-bandwidth communication. If it's just the ability to move the mouse, uh, ten x faster, like the bits per second, if I can achieve a bits per second, that's ten x. What I can do with a mouse, that's a really interesting possibility of what that can do, especially as you get really good at it.

Uh, with training,

it's definitely the case that you have a higher ceiling of performance. Like you, because you don't have to buffer your intention through your arm, through your muscle, you get just by nature of having a brain implant at all. Like seventy five millisecond lead time on any action that you're actually trying to take, and there's some nuance to this. Like there, there's evidence that the motor cortex, you can sort of plan out sequences of actions, so you may not get that whole benefit all the time.

But for sort of, like, reaction time style, uh, games where you just wanna somebody's over here, snipe them, you know, that kind of thing, uh, you actually do have just an inherent advantage, cause you don't need to go through the muscle. So the question is just how much faster can you make it? And we're already, you know, faster than, uh, you know what you would do if you're going through the muscle from a latency point of view.

And we're in the early stages of that, and I think we can push it. Sort of our end-to-end latency right now, from brain spike to cursor movement, is about twenty two milliseconds. If you think about, uh, the best mice in the world, the best gaming mice, that's about five milliseconds-ish of latency, depending on how you measure, depending on how fast your screen refreshes. There's a lot of characteristics that matter there, but yeah, and the rough time for, like, a neuron in the brain to actually impact your, uh, command of your hand is about seventy five milliseconds.

So if you look at those numbers, you can see that we're already, like, you know, competitive and slightly faster than what you would get by actually moving your moving your hand. And this is something that, you know, if you ask Nolan about it. When he moved the cursor for the first time, we asked him about this. This was something I was super curious about, like, what does it feel like when you're modulating, you know, a click intention, or when you're trying to just move the cursor to the right?

He said it moves before he is, like, actually intending it to, which is kind of a surreal thing. And something that, uh, you know, I would love to experience myself one day. What is that like to have a thing just be so immediate, so fluid, that it feels like it's happening before you're, uh, actually intending it to move?

yeah, I suppose we've gotten used to that latency, that natural latency that happens. Uh, so is the currently the bottleneck, the communication, so like the Bluetooth communication, is that what's the actual bottleneck? I mean, there's always going to be a bottleneck, but what's the current bottleneck?

Yeah, a couple things, so kind of hilariously, Bluetooth, uh, low energy protocol has, uh, some restrictions on how fast you can communicate. So the protocol itself establishes a standard of, you know, the most frequent sort of updates you can send are on the order of 7.5 milliseconds. And, uh, as we push latency down to the level of sort of individual spikes impacting control, that level of of resolution, that kind of protocol is going to become a limiting factor at some scale.

Um, another sort of important nuance to this is that it's not just the, uh, neuralink itself that's part of this equation. If you start pushing latency sort of below the level of how fast screens refresh, then you have another problem. Like, you need your whole system to be able to, uh, be as reactive as the sort of limits of what the technology can offer. Like, you need the screen like 120 hertz, just doesn't, you know, work anymore.

If you're trying to have something respond at something that's, you know, at the level of one millisecond,

that's a really cool challenge. I also like that for a t-shirt, the, uh, the best mouse in the world. Tell me on the receiving end, so the decoding step. Now we we figured out what the spikes are, we've got them all together, now we're sending that over, uh, to the app. What's the decoding step look like?

yeah, so maybe first, what is decoding? I think there's probably a lot of folks listening that just have no clue what what it means to decode brain activity.

Actually, even if we zoom out beyond that, what is the app? So there's a there's an implant that's wirelessly communicating with any digital device that has an app installed. So maybe can you tell me, at high level, what what the app is, what the software is outside of the, uh, the brain?

yeah, so maybe working backwards from the goal, the goal is to help someone with paralysis, in this case, Nolan, be able to navigate his computer independently. And we think the best way to do that is to offer them the same tools that we have to navigate our software. Because we don't wanna have to rebuild an entire software ecosystem for the brain, at least not yet. Maybe someday you can imagine there's UXs that are built natively for BCI, but in terms of what's useful for people today, I think we most people would prefer to be able to just control mouse and keyboard inputs to all the applications that they wanna use for their daily jobs, for communicating with their friends, et

cetera. And so the job of the application is really to translate this wireless stream of brain data coming off the implant into control of the computer. And we do that by essentially building a mapping from brain activity to sort of the HID inputs to the the actual hardware. So HID is just the protocol for communicating like input device events. So, for example, move mouse to this position or press this key down. And so that mapping is fundamentally what the app is responsible for.

But there's a lot of nuance of how that mapping works that we spend a lot of time to try to get right, and we're still in the early stages of a long journey to figure out how to do that optimally. Uh, so one part of that process is decoding, so decoding is this process of taking the statistical patterns of brain data that's being channeled across this Bluetooth connection to the application and turning it into, for example, a mouse movement.

And that decoding step, you can think of it in a couple different parts. So, similar to any machine learning problem, there's a training step and there's an inference step. The training step, in our case, is a very, uh, intricate behavioral, uh, process where the user has to imagine doing different actions. So, for example, they'll im- they'll be presented a screen with a cursor on it, and they'll be asked to push that cursor to the right, then imagine pushing that cursor to the left, push it up, push it down.

And we can basically build up a pattern, or using any sort of modern ML method, uh, a mapping of given this brain data and that imagined behavior, map one to the other. And then, at test time, you take that same pattern matching system, in our case, it's a deep neural network, and you run it. And you take this live stream of brain data coming off the implant, you decode it by pattern matching to what you saw at calibration time, and you use that for control of the computer.

Now, couple like sort of rabbit holes that are, I think are quite interesting. One of them has to do with how you build that best template matching system. Because there's, uh, a variety of behavioral challenges and also debugging challenges when you're working with someone who's paralyzed. Because, again, fundamentally, you don't observe what they're trying to do, you can't see them attempt to move their hand. And so you have to figure out a way to instruct the user to do something and validate that they're doing it correctly.

Such that then you can downstream build with confidence the mapping between the neural spikes and the intended action. And by doing the action correctly, what I really mean is at the level of resolution of what neurons are doing. So if, in ideal world, you could get a signal of behavioral intent that is ground truth accurate at the scale of sort of one millisecond resolution, then with high confidence, I could build a mapping from my neural spikes to that behavioral intention.

But the challenge is, again, that you don't observe what they're actually doing. And so there's a lot of nuance to how you build user experiences that give you more than just sort of a coarse, on average, correct representation of what the user's intending to do. If you want to build the world's best mouse, you really want it to be as responsive as possible. You want it to be able to do exactly what the user's intending at every sort of step along the way, not just on average, be correct when you're trying to move it from left to right.

And building a behavioral sort of calibration game or or sort of software experience that gives you that level of resolution is what we spent a lot of time working so the on.

calibration process, the interface has to encourage precision. Meaning, like, whatever it does, it's. It should be super intuitive. That the next thing it the human is going to likely do is exactly that intention that you need, and only that intention. And you don't have any feedback except that, maybe speaking to you afterwards, what they actually did, you can't. Oh yeah, so that's a that's fundamentally that is a really exciting UX challenge, cause that's all on the UX.

It's not just about being friendly or nice or usable, it's like

user experience is how it works.

it's how it works for the calibration, and calibration, at least at this stage of Neuralink, is like, fundamental to the operation of the thing. And and not just calibration, but continued calibration, essentially.

yeah, and maybe, uh, you said something that I think is worth exploring there a little bit. You said, it's, you know, primarily a UX challenge, and I think a large component of it is. But there is also a very interesting machine learning challenge here. Which is given some, you know, uh, data set, including some on average correct behavior of asking the user to move up or move down, move right, move left. And given a data set of neural spikes, is there a way to infer, in some kind of semi-supervised or entirely unsupervised way, what that high resolution version of their intention is?

And if you think about it, like, there probably is, because there are enough data points in the data set, enough constraints on your model, that there should be a way with the right sort of formulation to let the model figure out itself. For example, at this millisecond, this is exactly how hard they're pushing upwards, and at this millisecond, this is how hard they're trying to push upwards.

it's really important to have very clean labels. Yes, so, like, the problem becomes much harder from the machine learning perspective if the labels are noisy. That's correct, And and then to get the clean labels, that's a UX challenge

correct, although, um, clean labels, I think maybe it's worth exploring what that exactly means. I think any given labeling strategy will have some number of assumptions it makes about what the user's attempting to do. Those assumptions can be formulated in a loss function, or they can be formulated in terms of heuristics that you might use to just try to estimate or guesstimate what the user's trying to do. And what really matters is how accurate those assumptions are.

For example, you might say, Hey, user, push upwards and follow the speed of this cursor. And your heuristic might be that they're trying to do exactly what that cursor is trying to do. Another competing heuristic might be they're actually trying to go slightly faster at the beginning of the movement and slightly slower at the end. And those competing heuristics may or may not be accurate reflections of what the user's trying to do. Another version of the task might be, Hey, user, imagine moving this cursor a fixed offset.

So, rather than follow the cursor, just try to move it exactly 200 pixels to the right. So here's the cursor, here's the target. Okay, cursor disappears, try to move that now invisible cursor 200 pixels to the right. And the assumption in that case would be that the user can actually modulate correctly that position offset, but that position offset assumption might be a weaker assumption. And therefore, potentially you can make it more accurate than these heuristics that are trying to guesstimate at each millisecond what the user's trying to do.

So, y- you can imagine different tasks that make different assumptions about the the nature of the user intention, and those assumptions being correct is what I would think of as a clean label.

before that step. What are we supposed to be visualizing? There's a cursor and you wanna move that cursor to the right or the left, up and down, or maybe move them by a certain offset. So that's one way. Is that the best way to do calibration? So, for example, an alternative crazy way that probably is playing a role here is a game like WebGrid, where you're just getting a very large amount of data. The person playing a game where if they are in a state of flow, maybe you can get clean signal as a side effect.

Is that, or is it? Does that not an effective way for initial calibration?

yeah, great question. There's a lot to unpack there. So, uh, the first thing I would draw a distinction between is sort of open loop versus closed loop. So open loop. What I mean by that is the user is sort of going from zero to one. They have no model at all, and they're trying to get to the place where they have some level of control at all. In that setup, you really need to have some task that gives the user a hint of what you want them to do, such that you can build this mapping again from brain data to to output.

Then, once they have a model, you could imagine them using that model and actually adapting to it and figuring out the right way to use it themselves. And then retraining on that data to give you sort of a boost in performance. There's a lot of challenges associated with both of these techniques, and we can sort of rabbit hole into both of them if you, if you're interested. But the sort of challenge with the open loop task is that the user themself doesn't get proprioceptive feedback about what they're doing.

They don't, you know, necessarily perceive themself or feel, you know, the mouse under their hand. When they're using an open loop, when they're trying to do an open loop calibration, they're being asked to perform something like. Imagine if you sort of had your whole right arm numbed and you stuck it in a box and you couldn't see it. So you had no visual feedback and you had no proprioceptive feedback about what the position or activity of your arm was.

And now you're asked, Okay, given this thing on the screen that's moving from left to right, match that speed. And you basically can try your best to, y- you know, invoke whatever that imagined action is in your brain that's moving the cursor from left to right. But in any situation, you're gonna be inaccurate and maybe inconsistent in how you do that task. And so that's sort of the fundamental challenge of open loop. The challenge with closed loop is that once the user's given a model, uh, and they're able to start moving the mouse on their own, they're going to very naturally adapt to that model.

And that co-adaptation between the model learning what they're doing and the user learning how to use the model may not find you the best sort of global minima. It may be that your first model was noisy in some ways, or, um, you know, maybe just had some like quirk, like, if there's some like part of the data distribution it didn't cover super well. And the user now figures out, because they're, you know, a brilliant user like Nolan, they figure out the right sequence of imagined motion motions, or the right angle they have to hold their hand at to get it to work, and they'll get it to work great.

But then the next day they come back to their device and maybe they don't remember exactly all the tricks that they used the previous day. And so there's a complicated sort of feedback cycle here that can, uh, that can emerge and can make it a very, very difficult debugging process.

Okay, there's a lot of really fascinating things there. Uh, yeah, actually, just to stay on the on the closed loop, I, I've, uh, I've seen situations. This actually happened, uh, watching, uh, psychology grad grad students. They use piece of software when they don't know how to program themselves, they use piece of software that somebody else wrote and it has a bunch of bugs. And they figure out like, and they've been using it for years, they figure out ways to work around.

Oh, that just happens, like nobody ha- nobody, like, considers, maybe we should fix this, they just adapt. And that's a really interesting notion that we just had. We're really good at adapting, but you need to still, that might not be the optimal. Okay, so how do you solve that problem? Do you have to restart from scratch every once in a while? Kinda thing?

yeah, it's a good question. Um, first and foremost, I would say this is not a solved problem. And for anyone who's, you know, listening in academia who works on BCI's, I would also say this is not a problem that's solved by simply scaling channel count. So this is, you know, maybe that can help, and you can get sort of richer covariance structures that you can use to exploit when trying to come up with good labeling strategies.

But if you know you're interested in problems that aren't gonna be solved inherently by just scaling channel count, this is one of them. Yeah, so how do you solve it? Uh, it's not a solved problem, that's the first thing I wanna make sure gets across. The second thing is any solution that involves closed loop, uh, is going to become a very difficult debugging problem. And one of my sort of general heuristics for choosing what problems to tackle is that you wanna choose the one that's gonna be the easiest to debug.

Cause if you can do that, uh, even if the ceiling is lower, you're gonna be able to move faster because you have a tighter iteration loop. Debugging the problem. And in the open loop setting, there's not a feedback cycle to debug with the user in the loop. And so there's some reason to think that that should be an easier debugging problem. The other thing that's worth understanding is that even in the closed loop setting, there's no special software magic of how to infer what the user is truly attempting to do in the closed loop setting.

Although they're moving the cursor on the screen, they may be attempting something different than what your model is outputting. So what the model is outputting is not a signal that you can use to retrain if you want to be able to improve the model further. You still have this very complicated guesstimation or unsupervised problem of figuring out what is the true user intention underlying that signal. And so the open loop problem has the nice property of being easy to debug, and the second nice pro property of it has all the same information and content as the closed loop scenario.

Um, another thing I want to, I want to mention and call out is that this problem doesn't need to be solved in order to give useful control to people. Um, you know, even today, with the solutions we have now and that academia has built up over over decades, the level of control that can be given to a user, y- you know, today, is is quite useful. It doesn't need to be solved to get to that, that level of control.

But again, I want to build the world's best mouse. I want to make it, you know, so good that it's not even a question that you want it. And, uh, to build the world's best mouse, the superhuman version, you really need to, uh, nail that problem. And a couple, maybe details of previous studies that we've done internally that I think are very interesting to understand when thinking about how to solve this problem. The first is that even when you have ground truth data of what the user's trying to do.

And you can get this with an able-bodied monkey, a monkey that has a neuralink device implanted and moving a mouse to control a computer. Even with that ground truth data set, it turns out that the optimal thing to predict to produce high performance BCI is not just the direct control of the mouse. You could imagine, you know, building a data set of what's going on in the brain and what is the mouse exactly doing on the table?

And it turns out that if you build the mapping from neural spikes to predict exactly what the mouse is doing, that model will perform worse than a model that is trained to predict sort of higher level assumptions about what the user might be trying to do. For example, assuming that the monkey is trying to go in a straight line to the target, it turns out that making those assumptions is actually, uh, more effective at producing a model than actually predicting the underlying hand movement.

so the the intention, not like the physical movement or whatever in there. There's a obviously a really strong correlation between the two, but the intention is a po- more powerful thing to be chasing. Well, that, that's also super interesting. I mean, the intention itself is fascinating, because, yes, with the BCI here, in this case, with the digital telepathy, you're acting on the intention, not the action. Which is why there's an experience of, like, feeling like it's happening before you meant for it to happen.

That is so cool, and that is why you could achieve, like, superhuman performance, probably in terms of the control of the mouse. So for open loop, just to clarify, so whenever the, uh, person is tasked to, like, move the mouse to the right, you said there's not feedback, so they don't get to get that satisfaction of, like, actually getting it to move, hm. So right?

you, you could imagine giving the user feedback on a screen, but, uh, it's difficult because at this point you don't know what they're attempting to do. So what? What can you show them that would basically give them a signal of I'm doing this correctly or not correctly? So let's take this very specific example, like, maybe your calibration task looks like you're trying to move the cursor a certain position offset. So your your instructions to the user are, Hey, the cursor's here now.

When the cursor disappears, imagine moving it 200 pixels from where it was to the right to be over this target. In that kind of scenario, you could imagine coming up with some sort of consistency metric that you could display to the user of okay. I know what the spike train looks like on average when you do this action to the right. Maybe I can produce some sort of probabilistic estimate of how likely is that, uh, to be the action you took, given the latest trial or trajectory that you that you imagined.

And that could give the user some sort of feedback of how consistent are they across different trials. You could also imagine that if the user is prompted with that kind of, uh, consistency metric, that maybe they just become more behaviorally engaged to begin with. Because the task is kind of boring when you don't have any feedback at all. And so there may be benefits to the, you know, the user experience of showing something on the screen, even if it's not accurate, just because it keeps the user motivated to try to increase that number or push it upwards.

So there's a ps- psychology element here, and again, all of that is UX challenge. How much signal drift is there hour to hour, day to day, week to week, month to month? How often do you have to recalibrate because of the signal drift?

yeah, uh, so this is, uh, a problem we've worked on both with NHP, non-human primates before our clinical trial, and then also with Nolan during the clinical trial. Uh, maybe w- the first thing that's worth stating is what the goal is here. So the goal is really to enable the user to have a plug and play experience where, I guess they don't have to plug anything in. But a play experience where they, uh, you know, can use the device whenever they want to, however they want to.

And, uh, that's really what we're aiming for. And so there's gonna be a set of solutions that get to that state without, um, considering this non-stationarity problem. So maybe the first solution here that's important is that they can recalibrate whenever they want. This is something that, um, that Nolan has the ability to do today, so he can recalibrate the system, you know, at two am. in the middle of the night, without his, you know, caretaker or parents or friends around to help push a button for him.

The other, uh, important part of the solution is that when you have a good model calibrated, that you can continue using that without needing to recalibrate it. So how often he has to do this recalibration routine depends really on his appetite for performance. There are, uh, y- we observe sort of a degradation through time of how well any individual model works. But this can be mitigated behaviorally by the user adapting their control strategy. It can also be mitigated through a combination of sort of software features that we pro we provide to the user.

For example, we let the user adjust exactly how fast the cursor is moving. Uh, we call that the gain, for example, the gain of how fast the cursor reacts to any given input intention. They can also adjust the smoothing, how smooth the output of that cursor intention actually is. They can also adjust the friction, which is how easy it is to stop and hold still. And all of these software tools allow the user a great deal of flexibility and troubleshooting mechanisms to be able to solve this problem for themselves.

by the way, all of this is done by looking to the right side of the screen, selecting the mixer, and in the mixer, you have it's

It's like DJ mode, DJ mode for so, your BCI.

I mean, it's a really well done interface, it's really, really well done. And so, yeah, there's that bias, uh, that there's a cursor drift that Nolan talked about in a stream. Uh, although he said that, uh, you guys were just playing around with it with him and they're constantly improving. So that could have been just a snapshot of that particular moment or particular day. But he said that there was, uh, this cursor drift and this bias that could be removed by him.

I guess. Looking to the right side of the screen, the left side of the screen to kind of adjust the bias, that's one interface action, I guess, to adjust the bias.

yeah. So this is actually a an idea idea that comes out of academia. Um, there there is some prior work with, uh, uh, sort of brain gate clinical trial participants where they pioneered this idea of bias correction. The way we've done it, I think, is, yeah, it's very privatized, very, uh, uh, beautiful user experience. Where the user can essentially, um, flash the cursor over to the side of the screen. And it opens up a window where they can actually, uh, sort of adjust or tune exactly the the bias of the cursor.

So bias, maybe for people who aren't familiar, is just sort of what is the default motion of the cursor if you're imagining nothing. And it turns out that that's one of the first, um, first sort of qualia of the cursor control experience that's impacted by neural non-stationarity.

quality of the cursor experience,

I don't know how else to describe it. Like, you know, I'm not the I'm not it's the guy who moved very poetic, I the thing.

love it, the quality of the cursor experience. Yeah, I mean, it's it sounds poetic, but it it is, uh, deeply true. There is an experience. When it works well, it is a joyful, a really pleasant experience, and when it doesn't work well, it's a very frustrating experience. That's actually the art of UX. It's like you have the possibility to frustrate people or the possibility to give them joy.

And at the end of the day, it really is truly the case that UX is how the thing works. And so it's not just like what's showing on the screen, it's also, you know, what control surfaces does a decoder provide the user? Like, we want them to feel like they're in the F1 car, not like the, you know, some like minivan, right? You? And that really, truly is how we think about it. Um, Nolan himself is an F1 fan, so, um, we refer to ourself as a pit crew.

He really is truly the the F1 driver. And there's different, you know, control surfaces that that different kinds of cars and airplanes provide the user. And we take a lot of inspiration from that when designing how the cursor should behave. And, uh, what, what? Maybe one nuance of this is, you know, even details like when you move a mouse on a MacBook trackpad. The sort of response curve of how that, uh, input that you give the trackpad translates to cursor movement is different than how it works with a mouse.

When you move it on the trackpad, there's a different response function, a different curve to how much a movement translates to input to the computer than when you do it physically with a mouse. And that's because somebody sat down a long time ago when they're designing the initial input systems to any computer, and they thought through exactly how, uh, it feels to use these different systems. And now we're designing sort of the next generation of this input system to a computer, which is entirely done via the brain.

And there's no proprioceptive feedback again. You don't feel the mouse in your hand, you don't feel the keys under your fingertips. And you want a control surface that still makes it easy and intuitive for the user to understand the state of the system and how to achieve what they want to achieve. And ultimately, the end goal is that that UX is completely fades in the background, it becomes something that's so natural and intuitive that it's subconscious to the user.

And they just should feel like they have basically direct control over the the cursor, it just does what they want it to do. They're not thinking about the implementation of how to make it do what they want it to do, it's just doing what they want it to do.

Is there some kinda things along the lines of like, FITZ law, where you should move the mouse in a certain kinda way? That maximizes your chance to hit the target? Uh, I don't even know what I'm asking, but I'm hoping the intention of my question will land on on a profound answer. No, uh, is there some kind of understanding of the laws of UX when it comes, uh, to the context of somebody using their brain to control it?

Like, that's different than actual with a mouse.

I think we're in the early stages of discovering those laws, so I wouldn't claim to have solved that problem yet. But, uh, there's definitely some things we've learned that, uh, make it, uh, easier for the user to get stuff done. And it's pretty straightforward when you, when you verbalize it, but it takes a while to actually get to that point when you're in the process of debugging this stuff. In the trenches, one of those things is that the any, any machine learning system you build has some number of errors, and, uh, it matters how those errors translate to the downstream user experience.

For example, if you're developing a search algorithm in your photos, if you search for, you know, your friend Joe, and it pulls up a photo of your friend Josephine. Maybe that's not a big deal because the cost of a of an error is not that high. In, uh, a different scenario where you're trying to, you know, detect insurance fraud or something like this, and you're directly sending someone to court because of some machine learning model output.

Then the errors make a lot more, uh, sense to be careful about. You wanna be very thoughtful about how those errors translate to downstream effects. The same is true in BCI. So, for example, if you're building a model that's decoding a velocity output from the brain versus an output where you're trying to modulate the left click, for example. These have sort of different trade-offs of how precise you need to be before it becomes useful to the end user.

For velocity, it's okay to be on average correct because the output of the model is integrated through time. So if the user's trying to click at position A and they're currently at position B, they're trying to navigate over time to get between those two points. And as long as the output of the model is on average correct, they can sort of steer it through time with the user control loop in the in the mix, they can get to the point they want to get to.

The same is not true of a click. For a click, you're performing it almost instantly, at the scale of, you know, neurons firing. And so you want to be very sure that that click is correct, because a false click can be very destructive to the user. They might accidentally close the tab that they're trying to, you know, do something and lose all their progress. They might accidentally, like, you know, hit some send button on some text that they's only, like, half composed and read funny after, you know, uh, so, you know, there's different sort of cost functions associated with errors in this space.

And part of the UX design is understanding how to build a solution that is, when it's wrong, still useful to the end user.

that's so fascinating. That assigning cost to every action when, uh, an error occurs, so every action, if an error occurs, has a certain cost. And incorporating that into how you interpret the intention, mapping it to the action is really important. I didn't quite until you said it, realize there's a cost to, like, sending the text early. It's like a very expensive Yeah, it's super cost.

annoying if you accidentally, like, if you're a cursor, imagine if your cursor misclicked every once in a while, that's like super obnoxious. And the worst part of it is usually when the user's trying to click, they're also holding still because they're over the target they want to hit, and they're getting ready to click. Which means that, in the datasets that we build, on average, is the case that sort of low speeds or desire to hold still is correlated with when the user is attempting to click.

wow, that is really fascinating.

It's also, it's also not the case, you know, people think that, oh, click is a binary signal, this must be super easy to decode. Well, yes, it is, but the bar is so much higher for it to become a useful thing for the user. And there's ways to solve this. I mean, you can certainly take the compound approach of, well, let's just give the like, let's take 5 seconds to click, let's take a huge window of time, so we can be very confident about the answer.

But again, world's best mouse, the world's best mouse doesn't take a second to click or 500 milliseconds to click, it takes five milliseconds to click or less. And so if you're aiming for that kind of high bar, then you really want to solve the underlying problem.

So maybe this is a good place to ask about how to measure performance, this whole bits per second. What, uh, can you like, explain what you mean by that? Maybe a good place to start is to talk about WebGrid as a game, as a good illustration of the measurement of performance.

Yeah, uh, maybe I'll take one zoom out step there, which is just explaining why, uh, we care to measure this at all. So, again, our goal is to provide the user the ability to control the computer as well as I can, and hopefully better. And that means that they can do it at the same speed as what I can do. It means that they have access to all the same functionality that I have, including, you know, all those little details like command tab, command space.

You know, all this stuff. And to be able to do it with their brain and with the same level of reliability as what I can do with my muscles, and that's a high bar. And so we intend to measure and quantify every aspect of that to understand how we're progressing towards that goal. There's many ways to measure BPS, by the way, this isn't the only way. But, uh, we present the user a grid of targets, and basically we compute, uh, a score, which is dependent on how fast and accurately they can select, and then how small the targets.

And the more targets that are on the screen, the smaller they are, the more information you you present per click. And so, uh, if you think about it from an information theory point of view, you can communicate across different information theoretic channels. And one such channel is a typing interface. You could imagine that's built out of a grid, just like a software keyboard on the screen. And, uh, bits per second is a measure that's computed by taking the log of the number of targets on the screen.

Uh, you can subtract one if you care to model a keyboard, because you have to subtract one for the delete key on the keyboard. But log of the number of targets on the screen times the number of correct selections minus incorrect, divided by some time window, for example, 60 seconds. And that's sort of the standard way to measure, uh, a cursor control task in academia. And all credit in the world goes to this great professor, Dr.

Schneuo of Stanford, who came up with that task. And he's also one of my inspirations for being in the field. So, um, all the credit in the world to him for coming up with a standardized metric to facilitate this kind of bragging rights that we have now to say that Nolan is the best in the world at this, at this task with this BCI. It's very important for progress that you have standardized metrics so people can compare across different techniques and approaches.

How well does this do? So, yeah, big, big kudos to him and to all the all the team at Stanford. Um, yeah, so for Nolan and for me, playing this task, uh, there's also different modes that you can configure this task. So the WebGrid task can be presented as just sort of a left click on the screen. Or you could have, you know, targets that you just dwell over, or you could have targets that you left right click on.

You could have targets that are left, right click, middle click, scrolling, clicking and dragging. You know, you can do all sorts of things within this, this general framework. But the simplest, purest form is just blue targets show up on the screen, blue means left click, and that's the simplest form of the game. And, uh, the sort of prior records here in, uh, academic work, and, uh, at Neuralink, internally, with sort of NHPs, uh, have all been matched or beaten by by Nolan with his Neuralink device.

So, sort of prior to Neuralink, the sort of world record for a human using a device is, uh, somewhere between 4.2 to 4.6 BPS, depending on exactly what paper you read and how you interpret it. Um, Nolan's current record is 8.5 BPS, and, uh, again, this sort of median Neuralink performance is ten BPS. So you can think of it roughly as he's eighty five the level of control of a median Neuralinker using their cursor to select blue targets on the screen.

And, uh, yeah, I think there's a very interesting journey ahead to get us to that same level of ten BPS performance. It's not the case that sort of the tricks that got us from, you know, four to six BPS, and then six to eight BPS are gonna be the ones that get us from eight to ten. And, uh, in my view, the core challenge here is really the labeling problem. It's how do you understand, at a very, very fine resolution, what the user's attempting to do?

And, uh, yeah, I highly encourage folks in academia to, uh, to work on this problem.

What's the journey with Nolan on that quest of increasing the BPS on WebGrid? In March, you said that he selected 89,285 targets in WebGrid. So he loves this game, he's really serious about improving his performance in this game. So what is that journey of trying to figure out how to improve that performance? How much can that be done on the decoding side, how much can that be done on the calibration side, how much can that be done on the Nolan side of, like, figuring out how to convey his intention more cleanly?

yeah, no, this is a great question. So I, in my view, one of the primary reasons why Nolan's performance is so good is because of Nolan. Nolan is extremely focused and very energetic. He'll play WebGrid sometimes for like four hours in the middle of the night, like from two am. to six am. He'll be playing WebGrid just because he wants to push it to the limits of what he can do. And, uh, you know, this is not us, like, asking him to do that.

I want to be clear, like, we're not saying, Hey, you should play WebGrid tonight. We just gave him the game as part of our research, you know, and he is able to play it independently and practice whenever he wants. And he really pushes hard to push it the technology to the absolute limit. And he views it as like, you know, his job, really, to make us be the bottleneck. And boy, has he done that well, uh, and so that's the first thing to acknowledge is that, you know, he was extremely motivated to make this work.

I've also had the privilege to meet other, you know, clinical trial participants from BrainGate and other trials, and they very much share the same attitude of like, they, they, they view this as their life's work to, uh, you know, advance the technology as much as they can. And, uh, if that means selecting targets on the screen for four hours from two am to six am, then so be it. And, uh, there's something extremely admirable about that, that's worth, uh, calling out, okay, so now how do you?

How do you? Sort of get from where he started, which is no cursor control to eight BPS. So, I mean, when he started, there's a huge amount of learning to do on his side and our side to figure out, uh, what's the most intuitive control for him? And the most intuitive control for him is, uh, sort of, you have to find the set intersection of what do we have the signal to decode? So we don't pick up, you know, every single neuron in the motor cortex, which means we don't have a representation for every part of the body.

So there may be some signals that we have better sort of decode performance on than others. For example, on his left hand, we have a lot of difficulty distinguishing his left ring finger from his left middle finger. But on his right hand, we have a good, you know, good control and good modulation detected from the neurons that we're able to record. For his pinky and his thumb and his index finger. So you can imagine how these different, uh, you know, subspaces of modulated activity intersect with what's the most intuitive for him.

And this has evolved over time. So once we gave him the ability to calibrate models on his own, he was able to go and explore various different ways to imagine and controlling the cursor. For example, he could imagine controlling the cursor by wiggling his wrist side to side, or by moving his entire arm, or, by, I think, at one point, he did his feet. You know, he tried, like, a whole bunch of stuff to explore the space of what is the most natural way for him to control the cursor.

That at the same time, is easy for us to decode reliably.

Just to clarify, it's through the body mapping procedure that you're you're able to figure out which finger he can move.

Uh, yes, yeah, that's one way to do it. Um, maybe one nuance of the when he's doing it, he can imagine many more things than we represent in that visual on the screen. So we show him sort of abstractly, here's a cursor, you figure out what works the best for you. And, uh, we obviously have hints about what will work best from that body mapping procedure of, you know, we know that this particular action we can represent well, but it's really up to him to go and explore and figure out what works the best.

But at which point does he no longer visualize the movement of his body, and he's just visualizing the movement of the cursor? How quickly does he go from how, how quickly does he get there?

so this happened on a Tuesday. I remember this day very clearly because at some point during the during the day, uh, it looked like he wasn't doing super well, it looked like the model wasn't performing super well, and he was, like, getting distracted. But he actually it wasn't the case, like, what actually happened was he was trying something new where he was just controlling the cursor. So he wasn't imagining moving his hand anymore, he was just imagining, I don't know what it is, some like abstract intention to move the cursor on the screen.

And, uh, I cannot tell you what the difference between those two things are, I really truly cannot. I. He's tried to explain it to me before. I cannot, uh, you know, give a first person account of what that's like. But the expletives that he uttered in that moment were, uh, you know, enough to suggest that it was a very qualitatively different experience for him to just have direct neural control over a cursor.

I wonder if there's a way through UX to encourage a human being to discover that because he discovered it, like you said, uh, to me, that he's a pioneer. So he discovered that on his own, through all of this, uh, the process of trying to trying to move the cursor with different kinds of intentions. But that is clearly a really powerful thing to arrive at, which is to let go of trying to control the fingers and the hand and control the actual digital device with your mind.

that's right, UX is how it works, and, uh, the ideal UX is one that it's the user doesn't have to think about what they need to do in order to get it done. They just it just does it.

That is so fascinating, but I wonder on the on the biological side, how long it takes for the brain to adapt. So is it just simply learning, like high level software, or is there, like, a neuroplasticity component where, like, the the brain is adjusting slowly?

yeah, I the truth is, I don't know. Um, I'm very excited to see with sort of the second participant that we implant, what the, you know, what the journey is like for them. Because we'll have learned a lot more. Potentially we can help them understand and explore that direction more quickly. This is something I didn't, you know, this wasn't me PR prompting Nolan to go try this, he was just exploring how to use his device and figure it out himself.

But now that we know that, that's a possibility, that maybe there's a way to, you know, for example, hint the user. Don't try super hard during calibration, just do something that feels natural, or just directly control the cursor, you know, don't imagine an explicit action. And from there, we should be able to hopefully understand how this is for somebody who has not experienced that before. Maybe that's the default mode of operation for them, you don't have to go through this intermediate phase of explicit motions,

or maybe if that naturally happens for people, you can just occasionally encourage them to allow themselves to move the cursor. Actually, sometimes, just like with a four minute mile, just the knowledge that that's possible,

Yes, pushes you to do it. yeah, uh, enables Yeah,

you to do it, and then it becomes trivial. And then it also makes you wonder. That's the cool thing about humans, it on once there's a lot more human participants, they will discover things that are possible.

yes. And share their experiences Yeah, and share probably with each other.

it. And that because of them sharing it, they'll be able to do it. Uh, all of a sudden, that's that's unlocked for everybody because just the knowledge sometimes is the thing that enables it to do it.

yeah, I mean, uh, just to comment on that too. Like, there's we've probably tried, like, a thousand different ways to do various, uh, aspects of decoding. And now we know, like, what the right subspace is to continue exploring for. Again, thanks to Nolan and the many hours he's put into this. And so even just that help, like help constrain sort of the beam search of different approaches that we could explore, really helps accelerate. For the next person, you know, the set of things that we'll get to try on day one, how fast we hopefully get them to useful control, how fast we can enable them to use it independently and to get value out of the system.

So, yeah, massive hats off to to Nolan and and all the participants that came before him, uh, to make this technology a reality.

So how often are the updates to the decoder? Cause, uh, Nolan mentioned, like, okay, there's a new update that we're working on. And that in the stream, he said he plays the snake game because it's like, super hard. It's a good way for him to test, like, how good the update is. So, and he says, like, sometimes the update is a s a step backwards. It's like a it's a constant like iteration. So how often, like, what does the update entail?

Is it mostly on on the decoder side?

hm, yeah, couple comments. So one is it's probably worth trying a distinction between sort of research sessions where we're actively trying different things to understand, like, what the best approach is versus sort of independent use. Where we want to have, you know, ability to just go use the device, how anybody would want to use their MacBook. And, uh, so what he's referring to is, I think, usually in the context of a research session where we're trying, you know, many, many different approaches to, you know, even unsupervised approaches, like we talked about earlier.

To to try to come up with better ways to estimate his true intention and more accurately decode it. And, uh, in those scenarios, I mean, we try in any given session. He'll sometimes work for like eight hours a day, and so that can be, you know, hundreds of different models that we would try in that day, like a lot of different things. Um, now it's also worth noting that we update the application he uses quite frequently.

I think, you know, sometimes up to like four or five times a day. We'll update his application with different features or bug fixes or feedback that he's given us. So he's been able to. He's a very articulate person who, uh, is part of the solution. He's not a complaining person. He says, Hey, here's this thing that I've, I've discovered is is not optimal in my flow. Here's some ideas how to fix it. Let me know what your thoughts are, let's figure out how to, how to solve it.

And it often happens that those things are addressed within, you know, a couple hours of him giving us his feedback. That that's the kind of iteration cycle we'll have. And so sometimes at the beginning of the session, he'll give us feedback, and at the end of the session, he's he's giving us feedback on the next iteration of that, of that, of that process or that setup.

that's fascinating, cause you one of the things you mentioned that there was 271 pages of notes taken from the BCI sessions. And this was just in March. So one of the amazing things about human beings that they can provide, especially ones who are, uh, smart and excited and all like positive and good vibes like Nolan, that they can provide feedback, continuous feedback.

yeah, it also requires, uh, just to brag on the team a little bit. I work with a lot of exceptional people and it requires the team being absolutely laser focused on the user and what will be the best for them. And it requires, like, a level of commitment of, okay, this is what the user feedback was. I have all these meetings, we're gonna skip that today and we're gonna do this. You know, that level of focus and commitment is, uh, I would say, under underappreciated in the world.

And also, uh, you know, you obviously have to have the talent to be able to execute on these things effectively. And, uh, yeah, we have that in in loads, you know?

yeah, and this is such a interesting space of UX design because you have there's so many unknowns here. And I can tell UX is difficult because of how many people do it poorly, it's just not a trivial thing.

Yeah, it's also, you know, UX is not something that you can always solve by just constant iterating on different things. Like, sometimes you really need to step back and think globally, am I even like the right sort of minima to be chasing down for a solution? Like, there's a lot of problems in which sort of fast iteration cycle is the the predictor of how successful you will be. As a good example, like in a in an RL simulation, for example, the more frequently you get a reward, the faster you can progress.

It's just an easier learning problem, the more frequently you get feedback. But UX is not that way. I mean, users are actually quite often wrong about what the right solution is. And it requires a deep understanding of the technical system and what's possible, combined with what the problem is. You're trying to solve, not just how the user expressed it, but what the true underlying problem is to actually get to the right place.

Yeah, that's the old like stories of Steve Jobs, like rolling in there, like, Yeah, the user is a good s, is a useful signal, but it's not a perfect signal. And sometimes you have to remove the floppy disk drive or whatever the I forgot all the crazy stories of Steve Jobs, like making wild, uh, design decisions. But there some some of it is aesthetic, that some of it is about the love you put into the design, which is very much a Steve Jobs, Jony Ive type thing.

But when, when you have a human being using their brain to interact with it, there it also is deeply about function, it's not just aesthetic. And that you have to empathize with a with a human being before you, while not always listening to them directly, like you have to deeply empathize. It's fascinating, it's really, really fascinating. And at the same time, iterate, right? But not iterate in small ways, sometimes a complete, uh, like, rebuilding the design, he said.

That Nolan said, in the early days, the UX sucked, but you improved quickly. What was that journey like?

yeah. I mean, I'll give you one concrete example. So, uh, he really wanted to be able to read manga. This is something that he, I mean, it sounds like a simple thing, but it's actually a really big deal for for him. And he couldn't do it with this mouse stick. It just it wasn't accessible. You can't scroll with a mouse stick on his iPad in the on the website that he wanted to be able to use to read the the newest manga.

And m. so

Might be a good quick pause to say. The mouse stick is the thing he's using, holding a stick in his mouth to scroll on a tablet.

right, yeah, it's basically a, you can imagine, it's a stylus that you hold between your teeth. It's basically a very long stylus,

and it's, it's exhausting, it's it hurts, and it's inefficient.

yeah, and maybe it's also worth calling out. There are other alternative assistive technologies, but, uh, the particular situation Nolan's in, and this is not uncommon, and I think it's also not well understood by folks. Is that, uh, you know, he's relatively spastic, so he'll have muscle spasms from time to time. And so any assistive technology that requires him to be positioned directly in front of a camera, for example, an eye tracker, or anything that requires him to put something in his mouth.

Just is a no go, cause he'll either be shifted out of frame when he has a spasm, or if he has something in his mouth, it'll stab him in the in the face. You know, if he spasm's too hard. So these kind of considerations are important when thinking about what advantages a BCI has in someone's life. If it, if it fits ergonomically into your life in a way that you can use it independently. When your caretaker's not there, wherever you want to, either in the bed or in the chair, depending on, you know, your comfort level and your desire to have pressure sores.

You know, all these factors matter a lot in how good the solution is in that user's, uh, in that user's life. So one of these very fun examples is scroll, so again, manga is something he wanted to be able to read. And, uh, there's many ways to do scroll with a BCI. You can imagine like different gestures, for example, the user could do that would move the move the page. But scroll is a very fascinating, uh, control surface, because it's a huge thing on the SC, on the on the screen in front of you.

So any sort of jitter in the model output, any sort of error in the model output causes like, uh, an earthquake on the screen. Like, you really don't want to have your manga page that you're trying to read be shifted up and down a few pixels just because, you know, your scroll decoder is not completely accurate. And so this was an example where, uh, we had to figure out how to formulate the problem in a way that the errors of the system whenever they do occur, and we'll do our best to minimize them.

But whenever those errors do occur, that it doesn't interrupt the qualia again of the experience that the user is is having, it doesn't interrupt their flow of reading their book. And so what we ended up building is this really brilliant Um feature. This is, uh, a teammate named Bruce, who worked on this really brilliant work called QuickScroll, and QuickScroll basically looks at the screen and it identifies where on the screen are scroll bars. And it does this by deeply integrating with Mac OS to understand where are the the scroll bars actively present on the screen.

Using the sort of accessibility tree that's available to, uh, to Mac OS apps. And we identified where that those scroll bars are, and we provided a BCI scroll bar, and the BCI scroll bar looks similar to a normal scroll bar. But it behaves very differently. In that, once you sort of move over to it, your cursor sort of morphs onto it, it sort of attaches or latches onto it. And then once you push up or down in the same way that you would use a push to control, you know, the normal cursor, uh, it actually moves the screen for you.

So it's basically like remapping the velocity to a scroll action. And the reason that feels so natural and intuitive is that when you move over to attach to it, it feels like magnetic. So you're like, sort of stuck onto it. And then it's one continuous action. You don't have to like, switch your imagined movement, you sort of snap onto it and then you're good to go. You just immediately can start pulling the page down or pushing it up.

And even once you get that right, there's so many little nuances of how the scroll behavior works to make it natural and intuitive. So one example is momentum, like when you scroll a page with your fingers on the screen, you know you, you actually have some like flow, like it doesn't just stop right when you lift your finger up. The same is true with BCI scroll. So we had to spend some time to figure out what are the right nuances.

When you don't feel the screen under your fingertip anymore, what is the right sort of dynamic, or what's the right amount of page? Give, if you will, uh, when you push it to make it flow the right amount. For the user to have, uh, a natural experience reading their book, and there's a million, I mean, there's, I could tell you, like, there's so many little minutia of how exactly that scroll works. That we spent probably like a month getting right to make that feel extremely natural and and easy for the user to navigate.

I mean, even the scroll on a smartphone with your finger feels extremely natural and pleasant. And it probably takes an extremely long time to get that right. And actually, the same kind of visionary, uh, UX design that we were talking about. Don't always listen to the users, but also listen to them and also have, like, visionary big, like, throw everything out, think from 1st principles, but also not. Yeah, yeah. By the way, this makes me think that scroll bars on the desktop probably have stagnated and never taken that like cause.

Uh, the snap same as it's like, snap to grid, snap to scroll bar. Action you're talking about is something that could potentially be extremely useful in the desktop setting, even just for users to just improve the experience, cause the current scroll bar experience on the desktop is horrible. yep, agreed, It's hard to find, hard to control, there's not a momentum there's. Um, and the intention should be clear. When I s start moving towards a scroll bar, there should be a snapping to the scroll bar action.

But of course, you know, uh, maybe I'm okay paying that cost, but there's hundreds of millions of people paying that cost nonstop. But anyway, uh. But in this case, this is necessary because there's an extra cost paid by Nolan for the jitteriness. So you, you have to switch between the scrolling and the reading. There has to be a phase shift between the two, like when you're scrolling, you're scrolling.

right, right. So that is one drawback of the current, the current approach. Um, maybe one other just sort of case study here. So again, UX is how it works, and we think about that holistically. From like the even the feature detection level of what we detect in the brain to how we design the decoder, what we choose to decode to, then how it works once it's being used by the user. So another good example in the sort of how it works once they're actually using the decoder.

Uh, you know, the output that's displayed on the screen is not just what the decoder says, it's also a function of, uh, you know, what's going on on the screen. So we can understand, for example, that, you know, when you're trying to close a tab, that very small, stupid little x that's extremely tiny, which is hard to get precisely hit. If you're dealing with sort of a noisy output of the decoder, we can understand that that is a small little x you might be trying to hit and actually make it a bigger target for you.

Similar to how when you're typing on your phone, if you're, uh, you know, used to like the iOS keyboard, for example, it actually adapts the target size of individual keys based on an underlying language model. So it'll actually understand if I'm typing, hey, I'm going to see L. It'll make the E key bigger because it knows Lex is the person I'm going to go see. And so that kind of, you know, predictiveness can make the experience much more smooth, even without, you know, improvements to the underlying decoder.

Or, uh, or feature detection part of the stack. So we do that with a feature called magnetic targets, where we actually index the screen and we understand, okay, these are the places that are, you know, very small targets that might be difficult to hit. Here's the kind of cursor dynamics around that location that might be indicative of the user trying to select it. Let's make it easier, let's blow up the size of it in a way that makes it easier for the user to sort of snap onto that target.

So all these little details, they matter a lot in helping the user be independent in their day to day living.

So how much of the work on the decoder is generalizable to P2, P3, P4, P5, Pn? H How do you improve the decoder in a way that's generalizable?

Yeah, great question. So the underlying uh signal we're trying to decode is going to look very different in P2 than in P1. For example, channel number 345 is gonna mean something different in user one than it will in user two. Just because that electrode that corresponds with channel 345 is gonna be in next to a different neuron in user one than person user two. But the approaches, the methods, the user experience of how do you get the right sort of behavioral pattern from the user to associate with that neural signal?

We hope that it will translate over over multiple generations of users. And beyond that, it's very, very possible, in fact, quite likely, that we've overfit to sort of Nolan's user experience, desires and preferences. And so what I hope to see is that, uh, you know, when we get a 2nd, 3rd, 4th participant, that we find sort of what the right wide minimas are. That cover all the cases, that make it more intuitive for everyone. And hopefully there's a cross pollination of things where, oh, we didn't think about that with this user because, you know, they can speak, but with this user who just can fundamentally not speak at all.

This user experience is not optimal, and that will actually those improvements that we make there should hopefully translate then to even people who can speak but don't feel comfortable doing so. Because they're in a public setting, like their doctor's office,

So the the actual mechanism of open loop labeling and then closed loop labeling will be the same and hopefully can generalize across the different users correct, as they're doing the calibration step and the calibration step is pretty cool. I mean, that in itself, the the interesting thing about WebGrid, which is like, closed loop, is like, fun, I love it. When there's like, uh, there used to be kind of an idea of human computation, which is using actions a human would want to do anyway to get a lot of signal from.

And like, WebGrid is that like a nice video game that also serves as great calibration?

it's so funny. This is. I've heard this reaction so many times before. Sort of the, you know, 1st user was implanted, we had an internal perception that the 1st user would not find this fun. And so we thought really quite a bit, actually about like, should we build other games that, like, are, you know, more interesting for the user? So we can get this kind of data and help facilitate research that's, you know, for long durations and stuff like this.

Turns out that, like people love this game. yeah, it's, I always loved it, but I didn't know that that was a shared perception.

yeah, just in case it's not clear, web grid is. There's a a grid of, let's say, 35 by 35, uh, cells. And one of them lights up blue, and you have to move your mouse over that and click on it. And if you miss it and it's red, and I put this it's been.

game for so many hours, so many hours,

And what's your record? You said

my, I think I have the highest at Neuralink right now, my record's 17 bps, 17 bps, which is about if you imagine that 35 by 35 grid, you're hitting about a hundred trials per minute. So a hundred correct selections in that 1 min window. So you're averaging about, you know, between 500 600 ms per selection.

so one? One of the reasons that I think I struggle with that game is I'm such a keyboard person, so everything is done over the keyboard. If if I can avoid touching the mouse, it's great. So how can you explain your high performance,

I have like a whole ritual I go through when I play WebGrid, so it's there's actually like a diet plan associated with this, like it's a whole thing. So the 1st thing I have to fast is

for five days, I have to go up to the mountains.

actually, it kind, I mean, the fasting thing is important. Uh, so this is like, you know, focuses the mind. Yeah, yeah,

it's true.

so what I do is I, I, actually, I don't eat for a little bit beforehand. And then I'll actually eat like a ton of peanut butter right before I go in. And I get hm, this like, I can is a real feel the, this is a thing. real thing, yeah. And then it has to be really late at night. This is, again, a night owl thing, I think we share, but it has to be, like, you know, midnight, two zero am kind of time window.

And I have a very specific like physical position I'll sit in, which is, uh, I used to be. I was homeschooled growing up, and so I did most of my work, like on the floor, uh, just like in my bedroom or whatever. And so I have a very specific situation

on the floor.

on the floor that I sit and play. And then you have to make sure, like, there's not a lot of weight on your elbow when you're playing, so that you can move quickly. And then I turn the gain of the cursor, so the speed of the cursor way, way up, so it's like small motions that actually move the cursor.

Are you moving with your wrist or you? You're never, I move are you moving with my fingers, your

so I my wrist is almost completely still, I'm just moving my fingers. Yeah,

you know? Those? Just on a small tangent, the which I've been meaning to go down. This rabbit hole of of people that, um, set the world record in Tetris, those folks, they're playing, there's a there's a way to do. Did you see this? it seems like There's, like,

the three, like all the fingers y, are moving.

yeah, you, you, you could ex. You could find a way to do it where, like, it's using a loophole, like a bug that you can do some incredibly fast stuff. So it's it's along that line, but not quite. But you do realize there'll be like a few programmers right now listening to this, who fast and eat peanut butter and yeah, please, be like,

please correct my record. I mean, the reason I did this literally was just because I wanted the bar to be high for the team. Like, I wanted the the number that we aim for. It should not be like the median performance, it should be like it should be able to beat all of us at least, like that should be the minimum bar.

what do you think is possible? Like 20? Scalings?

Yeah, I don't know what the limit, I mean, the limits you can calculate just in terms of like screen refresh rate and like cursor immediately jumping to the next target. But there's, I mean, I'm sure there's limits before that, with just sort of reaction time and visual perception and things like this. Um, I'd guess it's in the below 40, but above 20, somewhere in there is probably the right. There. I never be thinking about. Well, It also matters like how difficult the task is.

You could imagine, like some people might be able to do like 10,000 targets on the screen, and maybe they can do better that way. Um, so y, there's some like task optimizations you could do to try to boost your performance as well.

what? uh, do you do? You think it takes for Nolan to be able to do above 85 to keep increasing that number? You said, like every increase in the number might require different, d different improvements in the system.

yeah, I think the nature of this work is, I mean, the 1st, the 1st answer, the short answer is, I don't know. Um, this is, you know, edge of the research. So, uh, again, nobody's gotten to that number before. So what's next is gonna be, uh, you know, a heuristic, a guess from my part. Um, what we've seen historically is that different parts of the stack become bottlenecks at different time points. So, you know, when I 1st joined Neuralink, like three years ago or so, one of the major problems was just the latency of the Bluetooth connection.

It was just like the radio in the device wasn't super good. It was an earlier revision of the implant. And, uh, it just like, no matter how good your decoder was, if your thing is updating every 30 ms or 50 ms, it's just gonna be choppy. And, uh, no matter how good you are, that's gonna be frustrating and lead to, uh, challenges. So, you know, at that point, it was very clear that the main challenge is just get the D data off the device in a very reliable way, such that you can enable the next challenge to be to be tackled.

And then at S, you know, at some point, it was, um, you know, actually, the modeling challenge of how do you, uh, just build a good mapping? Like the supervised learning problem of you have a bunch of data and you have a label you're trying to predict. Just what is the right, like neural decoder architecture and hyperparameters to optimize that? That was a problem for a bit, and once you solve that, it became a different bottleneck.

Um, I think the next bottleneck after that was actually just sort of software stability and reliability. Um, you know, if you have widely varying sort of inference latency, uh, in your, in your system, or your, you know, your app just lags out every once in a while. It decreases your ability to maintain and get in a state of flow, and it basically just disrupts your control experience. And so there's a variety of different software bugs and, uh, improvements we made that basically increased the performance of the system, made it much more reliable, much more stable.

And led to a S a state where we could reliably collect data to build better models with. So that was a bottleneck for a while. It's just sort of like the software stack itself. Um, if I were to guess right now, uh, there's sort of two major directions you could think about for improving BPS further. Um, the 1st major direction is labeling, so labeling is again this fundamental challenge of given, uh, a window of time where the user is expressing some behavioral intent.

What are they really trying to do at the at the granularity of every millisecond? And that, again, is a task design problem, it's a UX problem, it's a machine learning problem, it's a software problem, sort of touches all those different domains. The 2nd, uh thing you can think about to improve BPS further is either completely changing the thing you're decoding or just extending the number of things that you're decoding. So this is sort of in the direction of functionality based.

So you can imagine giving more clicks, for example, a left click, a right click, a middle click, uh, different actions like click and drag, for example. And that can improve the effective bitrate of your communication. Proceeds. This if you're trying to, uh, allow the user to express themselves through any given communication channel, you can measure that with bits per 2nd. But what actually matters at the end of the day is how effective are they at navigating their computer.

And so, from the perspective of the downstream tasks that you care about functionality and extending functionality is something we're very interested in. Because not only can it improve the sort of number of BPS, but it can also improve the downstream sort of independence that the user has and the skill and efficiency with which they can operate their computer.

Would the number of threads increasing also potentially help,

Yes, short answer is yes. Uh, it's a bit nuanced how that curve, uh, or how that manifests in, uh, in the numbers. So what you'll see is that if you sort of plot a curve of number of channels, uh, that you're using for decode. Versus either the offline metric of how good you are at decoding, uh, or the online metric of sort of, uh, in practice, how good is the user at using this device? You see roughly a log curve.

So as you move further out in number of channels, you get a corresponding sort of logarithmic improvement in control quality and offline validation metrics. The important nuance here is that each channel corresponds with a specific, uh, you know, represented intention in the brain. So, for example, if you have a channel 254, it might correspond with moving to the right channel 256 might mean move to the left. If you want to expand the number of functions you want to control, uh, you really want to have a broader set of channels that covers a broader set of imagined movements.

You can think of it like, uh, kind of like Mr. Potato man, actually. Like, if you had a bunch of different imagined movements you could do, how would you map those imagined movements to input to a computer? Uh, you could imagine, you know, handwriting to output characters on the screen, you could imagine just typing with your fingers and have that output text on the screen. You could imagine different finger modulations for different clicks, you could imagine wiggling your big toes for, uh, opening some some menu, or wiggling your, you know, your big toe to have like, uh, command tab occur or something like this.

So it's really, uh. The amount of different actions you can take in the world depends on how many channels you have and the information content that they carry.

right? So that's more about the number of actions, so actually, as you increase the number of threads, that's more about increasing, uh, the number of actions you're able to perform.

One other nuance there that is worth mentioning. So, again, our goal is really to enable a user with paralysis to control their computer as fast as I can, so that's BPS, uh, with all the same functionality I have, which is what we just talked about, but then also as reliably as I can. And that last point is very related to channel count discussion. So as you scale out number of channels, the relative importance of any particular feature of your model input to the output control of the user diminishes.

Which means that if the sort of neural non stationarity effect is per channel, or if the noise is independent, such that more channels means, on average, less output effect, then your reliability of your system will improve. So one sort of core thesis, um, that at least I have, is that scaling channel count should improve the reliability of the system without any work on the decoder itself.

can you linger on the reliability here? So, 1st of all, when you say not stationary, the signal, which aspect are you referring to?

yeah, so maybe let's talk briefly what the actual underlying signal looks like. So again, I I spoke very briefly at the beginning about how. When you imagine moving to the right or imagine moving to the left, neurons might fire more or less. And their frequency content of that signal, at least in the motor cortex, is very correlated with the output intention of the behavioral, uh, task that the user is doing. You can imagine, actually, this is not obvious that rate coding, which is the name of that um phenomenon, is like the only way the brain could represent information.

You can imagine many different ways in which the brain could encode, uh, intention. And there's actually evidence, like in bats, for example, that there's temporal codes, so timing codes of like, exactly when particular neurons fire is the the mechanism of information, uh, representation. But at least in the motor cortex, there's substantial evidence that it's, uh, rate coding, or at least one like 1st order effect is that it's rate coding. So then, if the brain is representing information by changing the sort of frequency of a neuron firing.

What really matters is sort of the delta between sort of the baseline state of the neuron and what it looks like when it's modulated. And what we've observed, and what has also been observed in academic work, is that that baseline rate, sort of the if you were to target the scale. If you imagine, uh, that analogy for like, measuring, you know, flour or something. When you're baking, that baseline state of how much the pot weighs is actually different.

Day to day. And so if what you're trying to measure is how much rice is in the pot, you're gonna get a different measurement different days because you're measuring with different pots. So that baseline rate shifting is really the thing that, uh, at least from a 1st order, uh, description of the problem, is what's causing this downstream bias. There can be other effects, nonlinear effects on top of that, but at least at a very 1st order description of the problem.

That's what we observed day to day is that the baseline firing rate of any particular neuron or observed on a particular channel is changing.

So can you just adjust to the baseline to make it relative to the baseline nonstop?

Yeah, this is a great question. So, um, with monkeys, we have found various ways to do this. Um, one example way to do this is. You ask them to do some behavioral task, like play the game with a joystick. You measure what's going on in the brain, you compute some mean of what's going on across all the input features, and you subtract that out in the input. When you're doing your BCI session works super well for whatever reason, that doesn't work super well.

With Nolan, I actually don't know the full reason why, but I can imagine several, several explanations. Um, one such explanation could be that the context effect difference between some open loop task and some closed loop task is much more significant with, um, Nolan than it is with a monkey. Maybe in this open loop task, he's, you know, watching a Lex Freeman podcast while he's doing the task, or he's whistling and listening to music and talking with his friend.

And ask his mom what's for dinner while he's doing this task. And so the the exact sort of difference in context between those two states may be much larger. And thus lead to a bigger sort of generalization gap between the features that you're normalizing at sort of open loop time and what you're trying to use at closed loop time.

that's interesting just on that point. It's kind of incredible to watch Nolan be able to do, uh, to multitask, to do multiple tasks at the same time. To be able to move the mouse cursor effectively while talking and while being nervous, because he's talking in front kicking of,

my ass in chess, too. Yeah,

kicking your ass and not wor and talk trash while doing it. So all at the same time. And yes, if you're trying to normalize to the baseline, that might throw everything off. Boy, is that interesting?

and maybe one comment on that too. For folks that aren't familiar with assistive technology, I think there's a common belief that, you know, well, why can't you just use an eye tracker or something like this for helping somebody move a mouse on the screen? And it's it's a really a fair question and one that I actually did. Was not confident before. Sort of Nolan that this was gonna be a profoundly transformative technology for people like him.

And, uh, I'm very confident now that it will be, but the reasons are subtle. It really has to do with ergonomically how it fits into their life. Even if you can just offer the same level of control as what they would have with an eye tracker or with a mouse stick. But you don't need to have that thing in your face, you don't need to be positioned a certain way, you don't need your caretaker to be around to set it up for you.

You can activate it when you want, how you want, wherever you want. That level of independence is so game changing for people. It means that they can text a friend at night privately without their mom needing to be in the loop. It means that they can, like, open up, you know, and browse the internet at two zero am. When nobody's around to set their their iPad up for them. This is like a profoundly game changing thing for folks in that situation.

And this is even before we start talking about folks that, you know, may not be able to communicate at all or ask for help when they want to. This can be the potentially the only link that they have to the outside world. And, uh, yeah, that one doesn't, I think, need explanation of why that's so impactful.

You mentioned neural decoder. How much machine learning is in the in the decoder? how much magic, how much science, how much art? How difficult is it to come up with a decoder that figures out what these, uh, sequence of spikes mean?

Yeah, good question. Uh, there's a couple different ways to answer this. So maybe I'll zoom out briefly 1st and then I'll go down one of the rabbit holes. So the zoomed out view is that building the decoder is really the process of building the dataset, plus compiling it into the weights. And, uh, each of those steps is important. Uh, the direction I think of further improvement is primarily going to be in the dataset side of how you construct the optimal labels for the model.

But there's an entirely separate challenge of then how you compile it into the best model. And so I'll go briefly down the 2nd one, down the 2nd rabbit hole. One of the main challenges with designing the optimal model for BCI is that offline metrics don't necessarily correspond to online metrics. Uh, it's fundamentally a control problem. The user is trying to control something on the screen, and the exact sort of user experience of how you output the intention, uh, impacts their ability to control.

So, for example, if you just look at validation loss as predicted by your model, there can be m multiple ways to achieve the same validation loss. Not all of them are equally controllable by the end user. And so the y, you know, it might be as simple as saying, oh, you could just add auxiliary loss terms that, like, help you capture the thing that actually matters. But this is a very complex, nuanced process, so how you turn the labels into the model is, uh, more of a nuanced process than just like a standard supervised learning problem.

One very fascinating, uh, anecdote here. We've tried many different sort of neural network architectures that translate brain data to, uh, velocity outputs, for example. And one, uh, example that stuck in my brain from a couple years ago now, uh, is we p. At one point, we were using just fully connected networks to decode the brain activity. We tried, uh, AB test, where we were measuring, uh, the relative performance in online control sessions of, uh, sort of one deconvolution over the input signal.

So if you imagine per channel, you have a sliding window that's producing some, uh, convolved feature for each of those input sequences for every single channel. Simultaneously, you can actually get better validation metrics, meaning you're fitting the data better, and it's generalizing better on offline data. If you use this convolutional architecture, you're reducing parameters, it's sort of a standard, uh, uh, standard procedure. When you're dealing with time series data. Now, it turns out that when using that model online, the controllability was was worse, was far worse, even though the offline metrics were better.

And, uh, there can be many ways to interpret that. But what that taught me at least was that, hey, it's at least the case right now. That if you were to just throw a bunch of compute at this problem. And you were trying to sort of hyperparameter optimize, or, you know, let some GBT model hardcode or come up with or invent many different solutions. If you were just optimizing for loss, it would not be sufficient. Which means that there's still some inherent modeling gap here.

There's still some artistry left to be uncovered here of how to get your model to scale with more compute. And that may be fundamentally a labeling problem, but there may be other components to this as well.

Is it, uh, data constrained at this time? Like the which is what it sounds like, like, how do you get a lot of good labels?

Yeah, I think it's data quality constrained, not necessarily data quantity constrained.

But even like, even just the quantity, I mean, cause it has to be trained on the on the interactions, I guess there's not that many interactions.

Yeah, so it depends what version of this you're talking about. So if you're talking about, like, let's say the simplest example of just 2D velocity, then I think, yeah, data quality is the main thing. If you're talking about how to build a sort of multi function output that lets you do all the inputs to the computer that you and I can do. Then it's actually a much more sophisticated, nuanced modeling challenge. Because now you need to think about not just when the user is left clicking, but when you're building the left click model.

You also need to be thinking about how to make sure it doesn't fire. When they're trying to right click or when they're trying to move the mouse, so one example of an interesting bug from like, sort of week one of, Uh, a BCI with Nolan was when he moved the mouse, uh, the click signal sort of dropped off a cliff, and when he stopped, the click signal went up. So again, there's a contamination between the the two, the two inputs.

Another good example was at one point he was trying to do, uh, sort of a left click and drag, and the minute he started moving, the left click signal dropped off a cliff. So again, cause there's some contamination between the two signals, you you need to come up with some way to either in the dataset or in the model, build robustness against this kind of. Uh, you can think of it like overfitting, but really it's just that the model has not seen this kind of variability before.

So you need to find some way to help the model with that.

this is super cool, cause I th it feels like all of this is very solvable, but it's hard.

yes. It is fundamentally an engineering challenge. This is important to emphasize, and it's also important to emphasize that it may not need fundamentally new techniques. Which means that, you know, people who work on, let's say, unsupervised speech classification using CTC loss, for example, with internal Siri, they could potentially have very applicable skills to this.

So what things are you excited about in, uh, the future development of the software stack on the on Neuralink? So everything we've been talking about, the decoding, the UX,

I think there's some I'm excited about, like something I'm excited about from the technology side, and some I'm excited about for understanding how this technology is going to be best situated for entering the world. So I'll, I'll work backwards on the technology, entering the world side of things. I'm really excited to understand how this device works. For folks that, uh, you know, cannot speak at all, that have no ability to sort of bootstrap themselves into useful control.

By voice command, for example, and are extremely limited in their current capabilities. I think that will be an incredibly useful signal for us to understand. I mean, really, what is an existential threat for all startups, which is product market fit? Does this device have the capacity and potential to transform people's lives in the current state? And if not, what are the gaps? and if there are gaps, how do we solve them most most effi efficiently? So that's what I'm very excited about for the next sort of year or so of clinical trial operations.

The, uh, the technology side, uh, I'm quite excited about basically everything we're doing. I think, uh, it's gonna be awesome. The most prominent one I would say is is scaling channel count. So right now we have a thousand channel device, the next version will have between three and six thousand channels. And I would expect that curve to continue in the future. And it's unclear what set of problems will just disappear completely at that scale and what set of problems will remain and require further focus.

And so I'm excited about the clarity of gradient that that gives us in terms of the user experience that we choose to focus our time and resources on. And and then also in terms of the, you know, even things as simple as not like non stationarity, like H. Does that problem just completely go away at that scale, or do we need to come up with new creative UXs? Still, even at that point, um, and also when we get to that time point, when we start expanding out dramatically the set of functions that you can output from one brain.

How to deal with all the nuances of both the user experience of not being able to feel the different keys under your fingertips, but still needing to be able to modulate all of them in synchrony to achieve the thing you want. And, uh, again, you don't have that proprioceptive feedback loop. So how can you make that intuitive for a user to control a high dimensional control surface without feeling the thing physically? I think that's gonna be a super interesting problem.

Uh, I'm also quite excited to understand, uh, you know, do these scaling laws continue? Like, as you scale channel count, how much further out do you go before that saturation point is is truly hit? And it's not obvious today. I think we only know what's in the sort of interpolation space, we only know what's between zero and 1024, but we don't know what's beyond that. Um, and then there's a whole sort of like range of interesting, sort of neuroscience and brain questions.

Which is when you stick more stuff in the brain in more places, you get to learn much more quickly about what those brain regions represent. And so I'm excited about that fundamental neuroscience learning, which is also important for figuring out how in to most efficiently insert electrodes in the future. So, yeah, I think all those dimensions I'm really, really excited about. And that doesn't even get close to touching the sort of software stack that we work on every single day and what we're working on right now.

Yeah, it, uh, seems virtually impossible to me that, uh, a thousand electrodes is where it saturates. It feels like this would be one of those, uh, silly notions in the future, where obviously you should have millions of electrodes. And this, and this is where, like, the true breakthroughs happen. Uh, you tweeted. Some thoughts are most precisely described in poetry. Why do you think that is?

I think it's because the the information bottleneck of language is, uh, pretty steep. And yet you're like, you're able to reconstruct on the other person's, in the other person's brain, more effectively without being literal. Like if you, if you can express a sentiment such that in their brain, they can reconstruct the the actual true underlying meaning and beauty of the thing that you're trying to get across. The sort of the generator function in their brain is more powerful than what language can express, and so the the mechanism of poetry is really just to, uh, feed or seed that generator function.

So, being literal sometimes is a suboptimal compression for the for the thing you're That's right, trying to convey.

and it's actually in the process of the user going through that generation that they understand what, what you mean. Like, that's the that's the beautiful part. It's also like when you look at a beautiful painting, like, it's not the the pixels of the painting that are beautiful. It's the thought process that occurs when you see that, the the experience of that that actually is. I think that matters.

Yeah, it's resonating with some deep thing within you that the artist also experienced and was able to convey that through the pixels. And that's actually gonna be relevant for for for full on telepathy, you know it? It's like, if you just read the poetry literally, that doesn't say much of anything interesting, it requires a human to interpret it. So it's the combination of the human mind and all the experiences the human being has within the context of the collective intelligence of the human species.

That makes that poem make sense, and they load that in. And so in that same way, the signal that carries from human to human meaning might not may seem trivial, but may actually carry a lot of power. Uh, because of the complexity of the human mind and the receiving end. Yeah, that's interesting. Uh, poetry still doesn't. Who is it? I think, uh, Yoshibako 1st watch, really said, Uh, uh, something about all the people that think we've achieved AGI.

Ex explain why humans like music and U, and until until the AGI likes music, you haven't achieved AGI or something like Do you not think that.

that's like some next token entropy surprise kind of thing going on I, there?

I don't know.

I don't know either. I, uh. I listen to a lot of classical music and also read a lot of poetry. And, uh, yeah, I do wonder if, like, there is some element of the next token surprise factor going on there.

Yeah, maybe.

Cause, I mean, B like, a lot of the the tricks in both poetry and music are like, basically you have some repeated structure and then you do like a twist. Like, it's like, okay verse, or like clause, one, two, three is one thing, and then clause four is like, Okay, now we're on to the next theme and it. They kind of play with exactly when the surprise happens and the expectation of the user. And that's even true, like through history, as musicians evolve music, they take like some known structure that people are familiar with, and they just tweak it a little bit, like they tweak it and add a surprising element.

This is especially true in, like, in classical music heritage. So that's why I'm wondering, like, is it all just entropy? Like the the

so so break. So breaking structure or breaking symmetry is something that humans seem to like, maybe as simple as that.

yeah? And I mean, great artists copy, uh, and they also, you know, knowing which rules to break is the important part, and that fundamentally, it must be about the the listener of the piece. Like, which rule is is the right one to break? is about the user per or the audience member perceiving that as interesting.

uh, what do you think is the meaning of human existence?

There's a TV show I really like called The West Wing, and in, uh, in the West wing, there's a character. He's the president of the United States, who's, uh, having a discussion about the Bible with one of their colleagues. And, uh, well, the colleague says something about, you know, the Bible says X, Y and Z, and, uh, the president says, Yeah, but it also says a, B, C. And, uh, the person says, Do well, do you believe the Bible to be literally true?

And the president says, yes, but I also think that neither of us are smart enough to understand it. I think to like. The analogy here for the meaning of life is that largely we don't know the right question to ask. And so I'm, I think I'm very aligned with, uh, sort of the Hitchhiker's guide to the Galaxy version of this question, which is basically, if we can ask the right questions, it's much more likely we find the meaning of human existence.

And so, in the short term, as a heuristic in the sort of search policy space, we should try to increase the diversity of, uh, people asking such questions, or generally of consciousness and conscious beings asking such questions. Um, so again, I think I will take the I don't know card here. But say, I do think there are meaningful things we can do that improve the likelihood of answering that question.

It's interesting how much value you assign to the task of asking the right questions. That's the that's the main thing is not the answers, it's the questions.

Yeah, this point, by the way, is driven home, uh, in a very painful way. When you try to communicate with someone who cannot speak. Because a lot of the time, the last thing to go is to have the ability to, somehow, you know, wiggle a lip or move something that allows them to say yes or no. And in that situation, it's very obvious that what matters is, are you asking them the right question to be able to say yes or no to

Wow, that's powerful. Well, Bliss, thank you for everything you do, and thank you for being you, and thank you for talking today.

thank you.

Thanks for listening to this conversation with Bliss Chapman. And now, dear friends, here's Noland Arbaugh, the 1st human being to have a Neuralink device implanted in his brain. You had a diving accident in 2016 that left you paralyzed with no feeling from the shoulders down. How did that accident change your life?

It was sort of a freak thing that happened. Imagine you're running into the ocean, um, all of this is on a lake, but you're running into the ocean and you get to about waist high. And then you kind of like, dive in, take the rest of the plunge under the wave or something. That's what I did, um, and then I just never came back up, not sure what happened. Uh, I did it running into the water with a couple of guys.

And so my idea of what happened is really just that. I took like a stray fist, elbow, knee, foot, something to the side of my head. Uh, the left side of my head was sore for about a month afterward, so must have taken a pretty big knock. And then, uh, they both came up and I didn't, and so I was face down in the water for a while. I was conscious, um, and then eventually, just, you know, realized I couldn't hold my breath any longer.

And I keep saying, took a big drink. Um, people, I don't know if they like that. I say that it seems like I'm making light of it all, but um, it's just kinda how I am, and I don't know like I'm a very relaxed sort of stress free person. I rolled with the punches for a lot of this. I kinda took it in stride. It's like, all right, well, what can I do next? How can I improve my life even a little bit?

Um, on a day to day basis at 1st, just trying to find some way to heal as much of my body as possible, um, to try to get healed, to try to get off a ventilator, um, learn as much as I could so I could somehow survive. Um, once I left the hospital, um, and then, thank God, I had, like, my family around me. If I didn't have my parents, uh, my siblings, then I would've never made it this far.

They've done so much for me, um, more than like I can ever thank them for, honestly, and a lot of people don't have that, a lot of people in my situation, their families either aren't capable of providing for them, or, honestly, just don't want to. And so they get placed somewhere, and, you know, in some sort of home, uh, so, thankfully, I had my family. I have a great group of friends, a great group of buddies from college who have all rallied around me and we're all, um, still incredibly close.

People always say, you know, if you're lucky, you'll m end up with one or two friends from high school that you keep throughout your life. I have, uh, about ten, ten or twelve from high school that have all stuck around and we still get together all of us twice a year. Um, we call it the Spring series and the Fall series. Um, this last one we all did, uh, we dressed up like X men, so I did a professor Xavier, and it was freaking awesome, it was so good.

So, yeah, I have such a great support system around me, and so, you know, being a quadriplegic isn't that bad. I get waited on, um, all the time, people bring me food and drinks. And I get to sit around and watch as much TV and movies and anime as I want. I get to read as much as I want. Um, I mean, it's, it's great.

is beautiful to see that you see the silver lining in all of this. Uh, with just going back, do you remember the moment when you 1st realized you're paralyzed from the neck down?

yep, I was face down in the water, um, right when I whatever something hit my head. I, um, tried to get up and I realized I couldn't move and it just sort of clicked. I'm like, all right, I'm paralyzed, can't move. What do I do? Um, if I can't get up, I can't flip over, can't do anything, then I'm gonna drown eventually. Um, and I knew I couldn't hold my breath forever, so I just held my breath and thought about it for maybe 1015 seconds.

Um, I've heard from other people that, like, L onlookers, I guess. The two girls that pulled me out of the water were two of my best friends. They are lifeguards. Um, and one of them said that, um, it looked like my body was sort of shaking in the water, like I was trying to flip over and stuff, um, but I knew I knew immediately, and I just kind of I realized that. That's like what my situation was from here on out.

Maybe if I got to the hospital, they'd be able to do something. When I was in the hospital, like right before surgery, I was trying to calm, uh, one of my friends down. I had, like, brought her with me from college to camp, and she was just bawling over me. And I was like, Hey, it's gonna be fine, like, don't worry. Um, I was cracking some jokes to try to lighten the mood. Um, the nurse had called my mom and I was like, don't tell my mom, um, she's just gonna be stressed out.

Call her after I'm out of surgery, cause at least she'll have some answers then, like whether I live or not, really. Um, and I didn't want her to be stressed through the whole thing, but I knew. And then when I 1st woke up after surgery, um, I was super drugged up. Uh, they had me on fentanyl, like three ways, which was awesome. Um, I don't, I don't recommend it, but um, I saw, I saw some crazy stuff, uh, on that fentanyl, and it was still the best I've ever felt, uh, on drugs.

Um, medication, sorry, on medication, um, and uh. I remember the 1st time I saw my mom in the hospital, I was just bawling. I had like ventilator in, um, like, I couldn't talk or anything. And, uh, I just started crying because it was more of like seeing her, not that. I mean, the whole situation obviously was pretty rough, but, uh, it was just like seeing her face for the 1st time was pretty hard. But um, yeah, I just I.

I never had like a moment of, you know, man, I'm paralyzed, this sucks, I don't wanna like be around anymore. It was always just I hate that I have to do this, but like, sitting here and wallowing isn't gonna help. So immediate acceptance Yeah, yeah,

has there been low points along the way?

yeah, yeah, sure. Um, I mean, there are days when I don't really feel like doing anything, not so much anymore, like not for the last couple years, I don't really feel that way. I've, um, more so, just wanted to try to do anything possible to make my life better at this point. Um, but at the beginning there were some ups and downs, there were some really hard things to adjust to. Um, 1st off, just like the 1st couple months, the amount of pain I was in was really, really hard.

I mean, I remember screaming at the top of my lungs in the hospital because I thought my legs were on fire. And obviously I can't feel anything, but it's all nerve pain. And so that was a really hard night. I asked them to give me as much pain meds as possible. They're like, you've had as much as you can have, so just kinda deal with it, go to a happy place sort of thing. So that was a pretty low point.

Um, and then every now and again, it's hard, like realizing things that I wanted to do in my life that I won't be able to do anymore. Um, you know, I always wanted to be a husband and father, and I just don't think that I could do it now. As a quadriplegic, maybe it's possible, but I'm not sure I would ever, um, put, you know, someone I love through that. Um, like having to take care of me and stuff, um, not being able to, you know, go out and play sports.

I was a huge athlete growing up, so that was pretty hard. Um, J little things too, when I realize I can't do them anymore. Like, there's something really special about being able to hold a book and smell a book, like the feel, uh, the texture, the smell. Like, as you turn the pages, like, I just love it and I can't do it anymore. And it's little things like that. Um, the two year mark was pretty rough.

Two years is when they say you will, um, get back basically as much as you're ever gonna get back as far as movement and sensation goes. And so for the 1st two years, that was the only thing on my mind was like, try as much as I can to move my fingers, my hands, my feet, everything possible to try to get sensation and movement back. And then when the two year mark hit, so, um, June 30th, 2018, I was, I was really sad that that's kind of where I was.

Um, and then just randomly here and there, but I was never like depressed for long periods of time, just it never seemed worthwhile to me.

What gave you strength?

My faith, my faith in God, uh, was a big one, my understanding that it was all for a purpose. And even if that purpose wasn't anything involving Neuralink, even if that purpose was, you know, there's. There's a story in the Bible about Job, and I think it's a really, really popular story about how Job, you know, has all of these terrible things happen to him. And he praises God throughout, uh, the whole situation. I thought, and I think a lot of people think for most of their lives that they are job, that they're the ones going through something terrible.

And they just need to, you know, praise God through the whole thing and everything will work out. At some point after my accident, I realized that I might not be job, that I might be, you know, one of his children that gets killed or kidnapped, or taken from him. And so it's about terrible things that happen to those around you who you love. So maybe, you know, in this case, my mom would be job, and she has to get through something extraordinarily hard.

And I just need to try and make it as best as possible for her. Because, um, she's the one that's really going through this massive trial. Um, and that gave me a lot of strength, and obviously my family, um, my family and my friends, they they give me all the strength that I need. Uh, on a day to day basis, so makes things a lot easier having that great support system around me.

From everything I've seen of you online, your streams and, uh, the way you are today, I really admire, let's say, your unwavering positive outlook on life. Has that always been this way?

Yeah, yeah, I've, I mean, I've just always thought I could do anything I ever wanted to do. There was never anything too big, like whatever I set my mind to, I felt like I could do it. Um, I didn't wanna do a lot. I wanted to like travel around and be sort of like a gypsy and like, go work odd jobs. I had this dream of traveling around Europe and being like, I don't know, a shepherd in like Wales or Ireland, and then going and being a fisherman in Italy.

Uh, doing all of these things for like a year, like, it's such like cliche things. But I just thought it would be so much fun to go and travel and do different things. And so, um, I've always just seen the best in people around me too, and I've always tried to be good to people. And growing up with my mom too, she's like the most positive, energetic person in the world, and we're all just people, people like, Uh, I just get along great with people.

Um, I really enjoy meeting new people, and so, um, I just wanted to do everything. Um, this is just kinda just how I've been.

It's just great to see that cynicism didn't take over, given everything you've been through. That's, uh, was that, uh, like, a deliberate choice you made that you're not gonna let this keep you down?

yeah, a bit also, like, I just, it's just kinda how I am. I just like I said, I roll with the punches with everything. I always used to tell people like, I don't stress about things much, um, and whenever I'd see people getting stressed, I would just say, you know, like, it's not hard, just don't stress about it and like, that's all you need to do. Uh, and they're like, that's not how that works, I'm like, it works for me, like, just don't stress and everything will be fine, like everything will work out.

Obviously, not everything always goes well, and it's not like it all works out for the best all the time. But I just don't think stress has had, uh, any place in my life since I was a kid.

what was the experience like of you being selected to be the 1st human being to have a Neuralink device implanted in your brain? Were you scared, excited?

No, no, it was cool. Um, like I was, I was never afraid of it. I had to think through a lot, should I, should I do this? Um, like, be the 1st person. I could wait until number two or three and get a better version of the Neuralink. Like the 1st one might not work. Maybe, um, it's actually gonna kinda suck, um, it, it's gonna be the worst version ever in a person. So why would I do the 1st one?

Like, I've already kind of been selected. I could just tell them, you know, like, Okay, find someone else, and then I'll do number two or three. Like, I'm sure they would let me. They're looking for a few people anyways. But ultimately, I was like, I don't know. There's something about being the 1st one to do something, it's pretty cool. I always thought that if I had the chance, that I would like to do something for the 1st time.

Um, this seemed like a pretty good opportunity, um, and I was, I was never scared. I think my like faith had a huge, uh, part in that. I always felt like God was preparing me for something. Um, I almost wish it wasn't this, because I had many conversations with God about not wanting to do any of this. As a quadriplegic, I told him, You know, I'll go out and talk to people, I'll go out and travel the world and talk to, you know, stadiums, thousands of people, give my testimony, I'll do all of it.

But like, heal me 1st, don't make me do all this in a chair, that sucks. Um, and I guess he won that argument. I didn't really have much of a choice. I always felt like there was something going on and to see how, I guess, easily. I made it through the interview process and how quickly everything happened, um, how the stars sort of aligned with all of this. It it just told me, like, as the surgery was getting closer, it just told me that, you know it, it was all meant to happen, it was all meant to be.

And so I shouldn't be afraid of anything that's to come, and so I wasn't. I kept telling myself, like, you know, you say that now, but as soon as the surgery comes, you're probably gonna be freaking out like you're about to have brain surgery. And brain surgery is a big deal for a lot of people, but it's a even bigger deal for me. Like, it's all I have left. The amount of times I've been like, Thank you God that you didn't take my brain and my personality and my ability to think, um, my like love of learning, like my character, everything like, thank you so much.

Like, as long as you left me that, then I think I can get by. And I was about to let people go, like, root around in there, like, Hey, we're gonna go, like, put some stuff in your brain, like, hopefully it works out. Um, and so it was. It was something that gave me pause, but like I said, how smoothly everything went, I never expected for a 2nd that anything would go wrong. Plus, the more people I met on the Burroughs side and on the Neuralink side, they're just the most impressive people in the world.

Like, I can't speak enough to how much I trust these people with my life and how impressed I am with all of them. And to see the excitement on their faces to, like, walk into a room and roll into a room and see all of these people looking at me. Like, we're just we're so excited, like we've been working so hard on this and it's finally happening. It's super infectious and, um, it just makes me wanna do it even more.

And to help them achieve their dreams, like, I don't know, it's so, it's so rewarding and I'm so happy for all of them. Honestly,

What was the, uh, day of surgery like? What's? uh, when'd you wake up? what'd you feel? Minute by minute? Were you freaking out?

no, no, I thought I was going to. But as surgery approached, the night before, the morning of, I was just excited. Like, I was like, Let's make this happen. I think I said that, uh, something like that to Elon on the phone, uh, beforehand, we were like, um, facetiming, and I was like, let's rock and roll, and he's like, let's do it. Uh, I, I don't know, I just I wasn't scared. So we woke up. I think we had to be at the hospital at like 05:30 am.

I think surgery was at like 07:00 am. So we woke up pretty early. I'm not sure much of us slept that night. Um, um, got to the hospital 05:30, went through, like all the pre op stuff, everyone was super nice. Uh, Elon was supposed to be there in the morning, um, but something went wrong with his plane, so we ended up facetiming. Uh, that was cool. Had one of the greatest one liners of my life after that phone call.

Um, hung up with him. There were like 20 people around me and I was like, I just hope he wasn't too starstruck talking to me. And yeah, it was well done, well good. Yeah, done. yeah, um,

Did you write that ahead of time, or it just came no, just it just to you?

came to me. I was like, This is, this seems right, you know? Went into surgery. Um, I asked if I could pray right beforehand, so I like prayed over the room. I asked God if you'd like be with my mom in case anything happened to me, and, uh, just to, like, calm her nerves out there. Uh, woke up, hm, played a bit of a prank on my mom. Uh, I don't know if you've heard about it.

yeah, I read about it.

Yeah, uh, she wasn't, she was not happy. Uh,

Can you take me through the prank? And and

this is something.

do you regret doing that now? I'm,

no, not one bit. Um, it was something, it was something I I had talked about ahead of time with my buddy Bane. I was like, I would really like to play a prank on my mom, um, uh, very specifically, my mom. She's very gullible. Um, I think she had knee surgery once even, and um, after she came out of knee surgery, um, uh, she was super groggy. She was like, I can't feel my legs. And my dad looked at her.

He was like, you don't have any legs like they, they had, they had to amputate both your legs. And sh, we just do very mean things to her all the time. Um, I'm so surprised that she still loves us, um, but right after surgery, I was really worried that I was going to be too like, groggy, like, not all there. I had had anesthesia once before and it it messed me up like I could not function, um, for a while afterwards.

And I, um, I like, said a lot of things that I was like. I was really worried that I was gonna start, I don't know, like, dropping, dropping some bombs and I wouldn't even know, I wouldn't remember. Um, so I was like, I was like, please God, don't let that happen, and please let me be there enough to do this to my mom. Um, and so she walked in, uh, after surgery, it was like the 1st time they had been able to see me after surgery.

And she just looked at me. She said, Hi, like, how are you? how are you doing? how do you feel? And I looked at her. And she had this very, I think the anesthesia helped, very like, groggy, sort of confused look on my face. She's like, Who, who are you? And she just started looking around the room, like at the surgeons, at the doctors, like, what did you do to my son? Like, you need to fix this right now?

Tears started streaming. I saw how much she was freaking out. I was like, I can't let this go on, and so I was like, Mom, mom, I'm fine, like, Uh, it's all rightAnd. uh, still, she was not happy about it. She, uh, still says she's gonna get me back someday. But I mean, I don't, I don't know, I don't know what that's gonna look like, It's a lifelong but I think, yeah, battle. Yeah, yeah, but it was good.

in some sense, it was a demonstration that he still got, That's that he still that's all I wanted got that sense it to be, of humor.

that's all I wanted it to be. And I knew that. Doing something super mean to her like that You would show her would actually have to that, yeah,

to show that you're still there, that you love her. It's a dark way to do it, but I love it. Uh, what was the 1st time you were able to feel that you can use the Neuralink device to affect the world around you?

yeah, um, the 1st little taste I got of it was, uh, actually not too long after surgery. Um, some of the Neuralink team had brought in, um, like, a little iPad, uh, a little tablet screen. And they had put up eight different UM channels, um, uh, that were recording some of my neuron spikes. Um, and they put it in front of me. They're like, this is like real time, your brain firing, it's like, that's super cool.

Um, my 1st thought was, I mean, if they're firing now, let's see if I can affect them in some way. So I started trying to, like, wiggle my fingers and I just started like scanning through the channels. And one of the things I was doing was like moving my index finger up and down. And I just saw this yellow spike on like top row, like 3rd box over or something. I saw this yellow spike every time I did it and I was like, Oh, that's cool.

And everyone around me was just like, what, what are you seeing? I was like, look, look at this one, look at like this top row, 3rd box over, this yellow spike. Like, that's me right there, there, there. And everyone was freaking out, they started like clapping. I was like, that's super unnecessary, that's like this is what's

awesome, though.

supposed to happen, right? Like, So you're like,

imagining y yourself moving each individual finger one at a time and then seeing like they, you can notice something. And then when you just, did the index finger, you're like, Oh, yeah. I was, that's cool.

I was wiggling kind of all of my fingers to see if anything would happen. There was a lot of other things going on, but that big yellow spike was the one that stood out to me. Like, I'm sure that if I would've stared at it long enough, I could've mapped out maybe 100 different things, but the big yellow spike was the one that I noticed.

Maybe you could speak to what it's like to sort of wiggle your fingers to like to to imagine that that the mental, the cognitive effort required to sort of wiggle your index finger, for example. How easy is that to do?

pretty easy for me. Uh, it's something that at the very beginning, after my accident, they told me to try and move my body as much as possible. Even if, you know, you can't just keep trying because that's going to create new, like neural pathways or pathways in my spinal cord to, like, reconnect these things. Um, to hopefully regain some movement someday.

That's fascinating.

Yeah, I know it's, it's bizarre, but So that's part I

of the recovery process is

it is.

to keep trying to move your body Yep, just and and that's

every day, as much as you can.

s. And the nervous system does its thing, it starts Yeah, it'll reconnecting,

start reconnecting. Um, for some people, some people, it never works, some people they'll do it. Like, for me, I got some bicep control back, um, and that's about it. I can, if I, uh, try enough, I can wiggle some of my fingers. Not like on command. It's more like if I try to move, say, my right pinky and I just keep trying to move it after a few seconds, it'll wiggle. Um, so I know there's stuff there, like, I know, like, and that happens with, you know, a few different of my fingers and stuff.

Um, but yeah, that's that's what they tell you to do. Um, one of the people at the time, when I was in the hospital, came in and told me. For one guy who had recovered, um, most of his control. What he thought about every day was actually walking, like the act of walking, um, just over and over again. So I tried that for years, I tried just imagining walking, which is, it's hard. It's hard to imagine.

Like all of the steps that go into, well, taking a step, like all of the things that have to move, like all the activations, uh, that have to happen along your leg in order for one step to occur.

but you're not just imagining, you're like, doing it right, I'm trying, yeah, so right?

it's like it's imagining over again what I had to do to take a step. Cause it's not something any of us think about. We just you wanna walk and you take a step, um, you don't think about all of the different things that are going on in your body. So I had to recreate that in my head as much as I could, and then I practice it over and over and over.

so it's not like a 3rd person perspective, it's a 1st person perspective. You're like, it's not like you're imagining yourself walking, you're like, literally doing this, everything, all the same stuff as if you're walking

yeah, which which was hard. It was hard at the beginning.

like, frustrating, hard, or like, actually cognitively hard, like, which one?

it was both. Um, there's a there's a scene in one of the Kill Bill movies, actually, uh, oddly enough, where she is, like, paralyzed, I don't know from like a drug that was in her system. And then she, like, finds some way to get into the back of a truck or something. And she stares at her toe and she says, move like, move your big toe, and uh, after, you know, a few seconds on screen, she does it.

And she did that with every one of her like body parts until she could move again. I did that for years, just stared at my body and said, move your index finger, move your big toe. Um, sometimes vocalizing it, like out loud, sometimes just thinking it. I tried every different way to do this to try to get some movement back, and it's hard because it. It actually is like taxing, like physically taxing on my body, which is something I would have never expected.

Cause it's not like I'm moving, but it feels like there's a buildup of, I don't know, the. The only way I can describe it is there are like signals that aren't getting through from my brain, um, down, cause my there's that gap in my spinal cord, so brain down and then from my hand back up to the brain. And so it feels like those signals, um, get stuck in whatever body part that I'm trying to move.

And they just build up and build up and build up until they burst, um, and then once they burst, I get like this really weird sensation of everything, sort of like dissipating back out to level, and then I do it again. Um, it's also just like a fatigue thing, like a muscle fatigue, but without actually moving your muscles, it's very, very bizarre. And then, you know, uh, if you try to stare at a body part or think about a body part and move for two, three, four, sometimes eight hours.

It's very taxing on your mind, it's V takes a lot of focus. Um, it was a lot easier at the beginning because I wasn't able to, like, control a TV in my room or anything, I wasn't able to, um, control any of my environment. So for the 1st few years, a lot of what I was doing was staring at walls. And so, um, obviously, I did a lot of thinking and I tried to move a lot, just over and over and over again.

So you never gave up sort of hope there, just training hard, essentially.

yep, and I still do it, I do it, like, subconsciously, and I think that, uh, that helped a lot with things with Neuralink. Honestly, it's something that I talked about the other day at the All Hands that I did at Neuralink's Austin facility.

welcome to Austin, by the way.

Yeah, hey, thanks man, I, I went to school. hat. Hey, thanks, thanks man. The the Gigafactory was super cool. I went to school at Texas A. And M. So I've been around for, um,

So you should be saying welcome to me, welcome to Texas. Lots. Yeah, I get you,

um, but yeah, I. I was talking about how a lot of what they've had me do, especially at the beginning. Um, well, I still do it now. Um, is body mapping? So, like, there will be a visualization of a hand or an arm on the screen, and I have to do that motion. And that's how they sort of train, um, the algorithm to, like, understand what I'm trying to do. And so it. It made things very, uh, seamless, um, for me.

I think,

that's really, really cool, so it's it's. It's amazing to know, cause I I've learned a lot about the body mapping procedure with, like, with the with the interface and everything like that. It's cool to know that you've been essentially like training to be like, world class at that task. Yeah, I've. yeah,

I. I don't know if other quadriplegics, like other paralyzed people, give up. I hope they don't. Um, I hope they keep trying, because I've heard other paralyzed people say, like, don't ever stop. They tell you two years, but um, you, you just never know you ne you. The human body's capable of amazing things. So, um, I've heard other people say, don't give up. Uh, like, I think one girl had, um, spoken to me through some family members and said that she had been paralyzed, you know, for 18 years.

And she'd been trying to, like, wiggle her index finger for all that time, and she finally got it back, like 18 years later. So, like, I know that it's possible, and I'll never give up doing it. I just I do it. When I'm lying down, like watching TV, I'll find myself doing it kinda just a almost like on its own. It's just something I've gotten so used to doing that I don't know, I, I don't think I'll ever stop.

that's really awesome to hear, cause I think it's one of those things that can really pay off in in the long term. Cause, like it is training, you're not visibly seeing the results of that training at the moment. But like, there's a like, Olympic level nervous system getting getting ready for something.

Which honestly was like something that I think Neuralink gave me that, um, I can't. I can't thank them enough for, like, I can't show my appreciation for it enough. Was being able to visually see that what I'm doing is actually having some effect. Um, it's a huge part of the reason why, like, I know now that I'm gonna keep doing it forever. Because before Neuralink, I was doing it every day and I was just assuming that things were happening.

Like, it's not like I knew I wasn't getting back any mobility or, um, sensation or anything. So I could've been running up against a brick wall for all I knew. And with Neuralink, I get to see, like all the signals happening real time, and, uh, I get to see that. You know, what I'm doing can actually be mapped, you know? When we started doing like click calibrations and stuff, when I go to click my index finger for a left click, that it actually recognizes that like it.

It changed how I think about what's possible with, like, retraining my body to move. And so, yeah, I'll, I'll never give up now.

And also just the signal that there's still a powerhouse of a brain there. That's like, that's, uh. And as the technology develops, that brain is, I mean, that's the most important thing about the human body is the brain, and it can do a lot of the control. So what did it feel like when you 1st could wiggle the index finger and and saw the environment respond like that little? Yeah, it was I think, wherever, we're very, just being way too dramatic, according to you.

was very cool. I mean, it was cool, but it, I keep telling this to people. It made sense to me, like it made sense that, you know, like there are signals still happening in my brain. And that as long as you had something near it that could measure those, that could record those, then you should be able to, like, visualize it in some way, like, see it happen. And so that was not very surprising to me.

I was just like, Oh, cool, like we, we found one, like we found something that works. Um, it was cool to see that their technology worked, um, and that everything that they had worked so hard for was like, going to pay off. Um, but I hadn't like moved a cursor or anything at that point, I hadn't like interacted with a computer or anything at that point. Um, so it, it, it just made sense, it was cool.

Like, like I, I. I didn't really know much about BCI at that point either, so I didn't know like what sort of step this was actually making. Um, like, I didn't know if this was like a huge deal or if this was just like, Okay, this is, you know? It's cool that we got this far, but we're actually hoping for something like much better down the road. It's like, Okay, I just thought that they knew that it turned on, so I was like, cool, like this is, this is cool.

hm. Well, did you like read up on the specs of the hardware you get installed, like the number of threads, all this kind of stuff?

I knew all of that, but it's all like, it's all Greek to me. Like, I was like, Okay, threads 64 threads, 16 electrodes, one, zero, 24 channels. Okay, like, like that, that that math checks out.

sounds right. When was the 1st time you were able to move a mouse cursor?

I know it must have been within the 1st, maybe week, uh, week or two weeks that I was able to, like, 1st, move the cursor. And again, like, it kinda made sense to me, like it, it didn't seem like that big of a deal, like it it. It was like, Okay, well, hm, how do I explain this? When everyone around you starts clapping for something that you've done, it's it's easy to say, okay, like, I did something cool, like that was, that was impressive in some way.

Um, what exactly that meant, what it was, hadn't really, like, Hm, set in for me. Um, so again, I knew that me trying to move a body part, um, and then that being mapped, um, in some sort of like machine learning algorithm. To be able to, um, identify, like my brain signals, and then take that and give me cursor control, that all kinda made sense to me. I don't know, like all the in, ins and outs of it, but I was like, there are still signals in my brain firing.

They just can't get through because there's like a gap in my spinal cord, and so they just they can't get all the way down and back up, but they're still there. So when I moved the cursor for the 1st time, I was like, that's cool, but I expected that that should happen like it. It made sense to me. Um, when I moved the cursor for the 1st time, um, with just my mind, without, like, physically trying to move.

So I guess I can get into that just a little bit, like the difference between attempted movement and imagined movement.

yeah, that's a fascinating difference between one and the other.

yeah, yeah, yeah. So, like, attempted movement is me physically trying to attempt to move, say, my hand. I try to attempt to move my hand to the right, to the left, forward and back. Um, and that's all attempted, attempt to, you know, like, lift my finger up and down, attempt to kick or something. Um, I'm physically trying to do all of those things, even if you can't see it like I'm. This would be like me attempting to, like, shrug my shoulders or something.

That's all attempted movement, um, that all. That's what I was doing for the 1st couple of weeks when they were going to give me cursor control, when I was doing body mapping, it was attempt to do this, attempt to do that. When, um, Neir was telling me, um, to, like, imagine doing it, it, like, kind of made sense to me. But it's not something that people practice, like if you started school, um, as a child. And they said, Okay, write your name with this pencil, and so you do that, like, Okay, now imagine writing your name with that pencil.

Kids would think, uh, like, I, I guess, like that kinda makes sense, and they would do it. Um, but that's not something we're taught. It's all like how to do things physically, we think about, like HM, thought experiments and things, but that's not like. That's not like a physical action of doing things, it's more like what you would do in certain situations. So imagine movement. It it never really connected with me. Like, I guess you could maybe describe it as like a professional athlete, like, uh, swinging a baseball bat or swinging like a golf club.

Like, imagine what you're supposed to do, but then you go right to that and physically do it like you. Then you get a bat in your hand and then you do what you've been imagining. And so I don't have that like connection. So telling me to imagine something versus attempting it, it just there wasn't a lot that I could do there. Um, mentally, I just kinda had to accept what was going on and try, um, but the attempted moving thing, it all made sense to me.

Like, if I try to move, then there's a signal being sent in my brain. And as long as they can pick that up, then they should be able to map it to what I'm trying to do. And so when I 1st moved the cursor like that, it, it was. It was like, Yes, this should happen, like, I'm, I'm not surprised by that.

But can you clarify, is there supposed to be a difference between imagined movement and a attempted movement?

Yeah, just that in imagined movement, you're not attempting to move at all, so it's You're like visualizing visualizing.

yourself doing, and then theoretically, is that supposed to be a different part of the brain that lights up in those two different situations?

Yeah, not necessarily. I think all these signals can still be represented in motor cortex, but the the difference, I think, has to do with the naturalness of imagining something versus, uh, attempting it, and sort just of the fatigue of that over time.

and by the way, on on the mic is bliss. Uh, so, like, this is just different ways to prompt you to kind of get to the thing that you're. A around that attempted movement does sound like the right thing to try.

yeah, I mean, it makes sense to me.

Cause, imagine for me, I would be s. I would start visualizing like in my mind, visualizing attempted, I would actually start trying to like, there's a. I mean, I, you know, I did, like, combat sports my whole life, like wrestling. When I'm imagining a move, see, I'm like, moving my muscle, like, like, there's a. There is a bit of an activation, almost versus like visualizing yourself, like a picture doing it.

yeah. It's something that I feel like naturally anyone would do. If you try to tell someone to imagine doing something, they might close their eyes and then start physically doing it. Um, but it, it's just just didn't it. click, Yeah, it, it's, it's hard. It was very hard at the beginning,

but attempted worked.

attempted worked, it worked just like it should work, like Hm, worked like a charm. Um,

Remember, there was like, one Tuesday we were messing around. And I think I forget what swear word you used, but there was a swear word that came out of your mouth. When you figured out, you could just do the direct cursor control,

yeah, that's it. It blew my mind, like, no pun intended, blew my mind when I 1st, um, moved the cursor just with my thoughts and not attempting to move. It's something that I found, um, like, over the couple of weeks, like building up to that, um, that. As I get better cursor control, as like, the model, uh, gets better, um, then it gets easier for me to like, um, like, I don't have to attempt as much to move it.

And part of that is something that I'd even talked with them about, um, when I was watching the signals of my brain, one day I was watching when I, like, attempted to move to the right and I watched the screen as like, I saw the spikes, s like I was seeing the spike. The signal was being sent before I was actually attempting to move. Um, I imagine, just because, you know, when you go to say, move your hand or any body part, that signal gets sent.

Before you're actually moving, has to make it all the way down and back up before you actually do any sort of movement. So there's a delay there, and I noticed that there was something going on in my brain before I was actually attempting to move that. Um, my brain was like anticipating what I wanted to do, and that all started sort of, um, I don't know, like percolating in my brain, like it just, it was just sort of there, like, always in the back.

Like, that's so weird that it could do that. It kinda makes sense, but I wonder what that means, um, as far as like, using the Neuralink, and, um, you know. And then, as I was playing around with the attempted movement and playing around with the cursor, and I saw that, like, as the cursor control got better, that it was anticipating my movements. Um, and what I wanted it to do, like cursor movements, what I wanted it to do a bit better and a bit better.

And then one day, I just randomly, as I was playing web grid, I, um, like, looked at a target before I had started, like attempting to move. I was just trying to, like, get over, like, train, um, my eyes to start looking ahead, like, okay, this is the target I'm on, but if I look over here to this target, I know I can, like, maybe be a bit quicker getting there. And I looked over and the cursor just shot over it.

It was wild, like I. I had to take a step back, like I was like, this should not be happening. All day. I was just smiling, I was so giddy. I was like, Guys, do you know that this works? Like, I can just think it and it happens? Which like? They'd all been saying this entire time, like, I can't believe, like you're doing all this with your mind. I'm like, Yeah, but is it really with my mind?

Like, I'm attempting to move and it's just picking that up, so it doesn't feel like it's with my mind. But when I moved it for the 1st time like that, it was, Oh man, it like. It made me think that this technology, that what I'm doing is actually way, way more impressive than I ever thought. It was way cooler than I ever thought. And it just opened up a whole new world of possibilities of like, what could possibly happen with this technology and what I might be able to be capable of with it.

because you had felt for the 1st time like this was digital telepathy, like you're controlling a digital device with your mind. I mean, this is a that's a real moment of discovery, that's really cool, like you've discovered something. I've seen, like scientists talk about, like a big aha moment, you know, like Nobel Prize winning. They'll have this like, holy crap, like, whoa,

that's what it felt, that's what it felt like, like. I didn't feel like, like. I felt like I had discovered something. But for me, maybe not necessarily for like the world at large, or like this field at large, it just felt like an aha moment for me. Like, Oh, this works, like, obviously it works. Um, and so that's what I do like all the time now. Uh, I kind of intermix, um, the attempted movement and, uh, imagined movement.

I do it all like together. Because I've found that there is some interplay with it that maximizes efficiency with the cursor. So it's not all like one or the other, it's not all just. I only use attempted, or I only use like imagined movements, it's more I use them. Um, in parallel, and, uh, I can do one or the other. I can just completely think about, um, whatever I'm doing, but um, I don't know, I. I like to play around with it.

I also like to just experiment with these things. Like every now and again, I'll get this idea in my head, like, Hm, I wonder if this works, and I'll just start doing it. And then afterwards I'll tell them, by the way, I wasn't doing that like you guys wanted me to. Um, I was I. I thought of something and I wanted to try it, and so I did. It seems like it works, so maybe we should, like, explore that a little bit.

so I think that discovery is not just for you, at least from my perspective, that's a discovery for everyone else who ever uses a Neuralink. That this is possible, like, I don't think that's an obvious thing that this is even possible. It's like, Uh, I was saying to Bliss earlier, Uh, it's like the 4 min mile. People thought it was impossible to run a mile in 4 min. And once the 1st person did it, then everyone just started doing it.

So, like, just to show that it's possible, that paves the way to like anyone can now do it. That's a thing that's actually possible. You don't need to do the attempted movement, you can just go direct, that's crazy.

it is crazy. It is crazy. Yeah,

Uh, for people who don't know, can you explain how the Link app works? You have an amazing stream on the topic, your 1st stream, I think on X, uh, describing the app, uh, can can you just describe how it works?

yeah. So it's just an app that Neuralink created, um, to help me interact with the computer. So on the Link app, uh, there are a few different settings and different, uh, modes and things I can do on it. So there's like the body mapping, which we kinda touched on, um, there's a calibration. Um, calibration is how I actually get cursor control, so calibrating what's going on in my brain to, uh, translate that into cursor control. So it will pop out, um, models, what, what they use, I think, is like time.

So it would be, you know, 5 min. And calibration will give me, um, so good of a model. And then if I'm in it for 10 min and 15 min, uh, the models will progressively get better. And um, so, you know, the longer I'm in it generally, the um, better the models will get.

That's really cool, cause you often refer to the models, so the model is the thing that's constructed once you go through the calibration step. And then you also talked about sometime. Sometimes you'll play like a really difficult game, like Snake, just to see how good the model is.

yeah, yeah. So snake is kinda like my litmus test for models. If I can control snake decently well, then I know I have a pretty good model. So, yeah, the link app has all of those, it has web grid in it now. Um, it's also how I like connect to the computer, just in general. Um, so, uh, they've given me a lot of like voice controls with it at this point. So I can, you know, say like, connect or implant, disconnect, and um, as long as I have, uh, that charger, uh, handy, then I can connect to it.

So the charger is also how I connect to the link app to connect the computer. I have to have, um, the implant charger, uh, over my head when I wanna connect to have it wake up, cause the implant's in hibernation mode. Uh, like always when I'm not using it. Um, I think there's a setting to, like, wake it up every, you know, so long. So we could set it to half an hour, 5 h or something, if I just want it to wake up, uh, periodically.

Um, so yeah, I'll like, connect to the link app and then go through all sorts of things, uh, calibration for the day, maybe body mapping. I have, like, I made them give me like a little homework tab. Um, because I am very forgetful and I forget to do things a lot. Um, so I have, like, a lot of data collection things, uh, that they want me to do.

Is the body mapping part of the data collection, or is that also part of the

Yeah, it is.

calibration?

It's something that they want me to do, um, daily, which I've been slacking on, cause I've been doing so much media and traveling and so You've much. So I've been, I've gotten super been, yeah, famous. I've. I've been a terrible, um, 1st candidate for how much I've, uh, been slacking on my homework. Um, but yeah, it's just something that they want me to do every day. To, you know, track, um, how, uh, well, the Neuralink is performing over time and have something to give.

I imagine to give to the FDA. To, you know, create all sorts of fancy charts and stuff and show like, Hey, this is what the Neuralink, this is how it's performing. You know, day one versus day 90 versus day 180 and things like that.

What's the calibration step like? is it? is it like, move left, move right?

It's, uh, a bubble game, so there will be like yellow bubbles that pop up on the screen. At 1st, it is open loop, so open loop. This is something that I still don't fully understand. The open loop and closed loop thing.

Me and Bliss talked for a long time about the difference between the two from the on the technical side. So it'd be great to hear your okay, so open your side of the story.

open loop is basically, um, I have no control over the cursor, um, the cursor will be moving, uh, on its own across the screen. And I am following by intention, um, the cursor to different bubbles, and then my, um. The algorithm is training off of what, like, the signals it's getting are as I'm doing this. There are a couple different ways that they've done it. They call it center out target. So there will be a bubble in the middle and then eight bubbles around that.

And the cursor will go from, uh, the middle, uh, to one side, so say middle to left, back to middle, to up to middle, like up right. And they'll do that all the way around, uh, the circle. And I will follow that cursor, um, the whole time, and then it will train off of my intentions, what it is expecting my intentions to be. Um, throughout the whole process.

Can you actually speak to when you say follow, you don't mean with your eyes, you mean with your intentions?

yeah, so, uh. Generally for calibration, I'm doing attempted movements, uh, cause I think it works better. I think the better models, as I progress through calibration, um, make it easier, um, to use. Imagined, Wait, wait, uh, movements.

wait, wait, wait. So calibrated on attempted movement will create a model that makes it really effective for you to then use the force.

Yes, I've tried, um, doing calibration with imagined movement, and it just doesn't work as well. Um, for some reason. So that was the center out targets. There's also one where, you know, a random target will pop up on the screen and it's the same. I just like, move, I follow along, um, with wherever the cursor is to that target all across the screen. Um, I've tried those with imagined movement, and for some reason, the models just don't, um, they don't give as high levels quality when we get into closed loop.

Um, I haven't played around with it a ton. So maybe, like, the different ways that we're doing calibration now might make it a bit better. But what I've found is there will be a point in calibration where I can use, uh, imagined movement before that point, it doesn't really work. So if I do calibration for 45 min, the 1st 15 min, I can't use imagined movement. It just like, doesn't work for some reason. Um, and after a certain point, uh, I, I can just sort of feel it, I can tell it moves different.

Uh, that's the best way I can, I can describe it. Like it's almost as if it is anticipating what I am going to do again before I go to do it. Um, and so using attempted movement for 15 min. At some point, I can kind of tell when I like, move my eyes to the next target that the cursor is starting to, like, pick up, like it's starting to understand, it's learning, like, what I'm going to do.

So 1st of all, it's really cool that I mean, you are a true pioneer in all of this. You're like exploring how to do every aspect of this most effectively, and there's just, uh, I imagine so many lessons learned from this. So thank you for being a pioneer in all these kinds of different, like, super technical ways. And it's also cool to hear that there's like a different, like feeling to the experience when it's calibrated in different ways.

Like just cause, I mean, I imagine your brain is doing something different, and that's why there's a different feeling to it. And then trying to find the words and the measurements to those feelings would be also interesting. But at the end of the day, you can also measure the your actual performance on whether it's snake or web grid, you can see like what actually works well. And you're saying, for the open loop calibration, the attempted movement works best for now.

So the this is the open loop. You don't get the feedback that's something that you did. Something yeah, I'm is that frustrating?

just no. Or No, it makes sense to me. Like, uh, we've done it with a cursor and without a cursor in open loop. So sometimes it's just, um, say, for like, the center out, the um. You'll start calibration with a bubble lighting up and I push towards that bubble, and then when that bubble, you know, when it's pushed towards that bubble. For say, 3 s, a bubble will pop and then I come back to the middle.

Um, so I'm doing it all, Ju, just by my intentions, like, that's what it's learning anyways. So it makes sense that as long as I follow what they want me to do, you know, like, follow the yellow brick road, that it'll all work out. Um,

you're full of great references. Uh, is the is the bubble game fun? Like,

yeah. They always feel so bad making me do calibration, like, Oh, we're about to do, you know, a 40 min calibration. I'm like, all right, like, do you guys want to do two of them? Um, like, I'm always asking to like whatever they need, I'm more than happy to do, and it's not. It's not bad. Like I get to lie there and um, or sit in my chair and like, do these things with some great people.

I get to have great conversations, I can give them feedback, um, I can talk about all sorts of things. Uh, I could throw something on on my TV in the background and kinda like, split my attention between them. Um, like, it's not bad at all, I don't is there a score mind it.

that you get? Like, if can you do better on the bubble game?

no, I would love that. Um, I, I, I would love, yeah, writing I would love to play. down, uh, SUG, suggestions That's from Nolan, th that's, uh,

make it more fun, gamified.

yeah. That's one thing that I really, really enjoy about web grid is, cause I'm so competitive. Um, like, the higher the BPS, the higher the score, I know, the better I'm doing. And so if I I think I've asked at 1.1 of the guys, like, if he could give me some sort of numerical feedback for calibration? Like, I would like to know what they're looking at, like, Oh, you know, it is, um, we see like this number while you're doing calibration.

And that means, at least on our end, that we think calibration is going well. Um, and I would love that because I would like to know if what I'm doing is going well or not. But then they've also told me, like, Yeah, not necessarily like one to one. It doesn't actually mean that calibration is going well in some ways. Um, so it's not like 100. And they don't wanna like skew what I'm experiencing or want me to change things based on that.

If that number isn't always accurate to like how the model will turn out, or how like the end result, that's at least what I got from it. Uh, one thing I do, uh, I have asked them, and something that I really enjoy, um, striving for is. Towards the end of calibration, there is like a time between targets, um, and so I like to keep like at the end, uh, that number as low as possible. So at the beginning, it can be, you know, four, five, six seconds between me popping bubbles.

But towards the end, I like to keep it below like one, five. Or if I could get it to like one second between like bubbles. Because in my mind, that translates, um, really nicely to something like web grid, where I know if I can hit a target, uh, one every second, that I'm doing real, real well.

there you go. That's the way to get a score on the calibrations, like the speed, how quickly can you get from bubble to bubble? Uh, so there's the open loop, and then it goes to the closed loop, closed and the closed loop, loop. C can already start giving you a sense, cause you're getting feedback of like, how good the model is.

yeah. So closed loop is when I, um, 1st, get cursor control. And how they've, uh, described it to me, someone who does not understand this stuff. I am the dumbest person in the room. Every time I'm I love with any of these the humility guys. Yeah, appreciation. um, is that I am closing the loop? So I am actually now, um, the one that is like finishing the loop of whatever this loop is. I don't even know what the loop is.

They've never told me. They just say there is a loop. And at one point it's open and I can't control, and then I get control and it's closed, so I'm finishing the loop.

So how long did calibration usually take? You said, like 1015 minutes Well, yeah, they're, that's quick.

they're trying to get that number down pretty low. Um, that's what we've been working on a lot recently, is getting that down as, uh, low as possible. So that way, you know, if this is something that people need to do on a daily basis, or if some people need to do on a, um, like every other day basis, or once a week. They don't want people to be sitting in calibration for long periods of time. I think they wanted to get it down 7 min or below.

Um, at least where we're at right now. It'd be nice if they you never had to do calibration, um, so we'll get there at some point, I'm sure. The more we learn about the brain. And, um, like, I think that's, you know, the dream. Um, I think right now for me to get like, really, really good models. Um, I'm in calibration 40 or 45 min. Um, and I don't mind. Like I said, they always feel really bad.

But if it's gonna get me a model that can, like, break these records on web grid, I'll stay in it for flipping 2. H.

Let's talk business. So web grid, um, I saw a presentation that where Bliss said, by March, you selected 89 0 targets in web grid. Can you explain this game? what, what? what is web grid? and what does it take to be a world class performer in web grid as you continue to break world records? It's like a gold medalist talk. Well, what about me?

you know, I'd like to thank, I'd like to thank everyone who's helped me get here. My coaches, my parents for driving me to practice every day at five zero in the morning. Um, like to thank God, um, and just overall my dedication to my yeah, the craft. the. The interviews Yeah,

with athletes are always like that, exact, it's like that template.

um, yeah, so so, um, So web grid is, web grid is uh, it's a grid a yeah,

of cells.

it, it's. It's literally just a grid. They can make it as big or small as you can make a grid. A single box on that grid will light up and you go and click it. And it is a way for them to benchmark how good a BCI is. So it's, you know, pretty straightforward, you just click targets.

Only one blue cell appears, and you're supposed to move the mouse to there and click on it.

so I I like playing on like bigger grids, cause it the bigger the grid, the like more BPS. It's bits per second. Um, that you get every time you click one. So I'll say I'll play on like a 35 by 35 um grid. And then one of those little squares, uh, cell, call it target, whatever, will light up. And you move the cursor there and you click it, and then you do that, um, forever.

And you've been able to achieve at 1st eight bits per 2nd, and you've recently broke that.

yeah, I'm, I'm at eight, five right now. I would've beaten that literally the day before I came to Austin. Um, but I had like, a, I don't know, like a 5 s lag right at the end, and um, I just had to wait until the latency calmed down and then I kept clicking. But um, I was at like eight, zero, one and then 5 s of lag, and then the next like three targets I clicked all stayed at eight, zero, one.

So if I would've been able to click, um, during that time of lag, I probably would've hit, I don't know, I might've hit nine. So I'm there, I'm like, I'm really close. And then this whole Austin trip has really gotten in the way of my web grid playing Frustrating. ability. Yeah, So that's all that's, I you can think think that's changed. about right now Yeah, I know, I was just, I just want, I want to do better,

at nine.

I want to do better, I wanna hit nine. I think, well, I know nine is very, very achievable. I'm right there. Um, I think ten I could hit, maybe in the next month, like I could do it, probably in the next few weeks if I really push.

I think you and Elon are basically the same person. Cause last time I did a podcast with him, he came in extremely frustrated that he can't beat Uber Lilith as a

I don't know.

druid. That was like a year ago, I think I forget, like, solo and he. I could just tell there's some percentage of his brain the entire time was thinking, like, I wish I was right now attempting yeah, I to beat think he did it the game. He

that night.

did it that night, he stayed up and did it that night, which is crazy to me. I mean, it's, uh, in a, in a, in a fundamental way. It's really inspiring, and what you're doing is inspiring in that way. Cause, I mean, it's not just about the game. Everything you're doing there has impact. By striving to do well on web grid, you're helping everybody figure out how to create the system. All along, like the the coding, the software, the hardware, the calibration, all of it, how to make all of that work so you can do everything else really well.

yeah, it's just really fun. Um,

Well, that's also that's part of the thing is like, making it fun.

yeah, it's addicting. I'm. I've joked about, um, like, what they actually did when they went in and put this thing in my brain? They must have flipped a switch to make me, uh, more susceptible to these kinds of games, to make me addicted to, like, web grid or something. Do you know? Blisse's high score?

yeah, he said, like, 14 or something. Oh 171 or boy, something, 710117. Yeah, he told me. He like, does it on the floor with peanut butter, and he like, fasts. It's, it's, it's weird. That sounds like cheating, sounds like performance enhancing. Uh,

Nolan, like the 1st time Nolan, uh, played this game, he asked, You know, how good are we at this game? And and he, I think he told me right then, you're gonna, you're gonna try to beat me, and that's I'm gonna get great. there someday. I Yeah, I think I can, fully believe you.

I think I can, I

I'm excited for that.

think, yeah. So I've been playing 1st off with the dual cursor, which really hampers my web grid playing ability. Basically, I have to wait. Point three seconds for every click.

oh, so you can't do the click, so you have to, you have to, so you click by dwelling. You said, point three,

point three seconds, which which sucks. It really slows down how much I'm able to like, how high I'm able to get. I still hit like 50, I think I hit like 50 something trials, net trials per minute in that, um, which was pretty good, um, cause I'm able to, like, um, there's. One of the settings is also like how slow you need to be moving in order to initiate a click, to start a click. So I can tell sort of when I'm on that, um, threshold to start initiating a click.

Just a bit early, so I'm not fully stopped over the target when I go to click, I'm doing it like on my way to the target, so a little, um, to try to time it just right. Yeah,

wow. So you're slowing down.

yeah, just a just a hair right before the target.

This is like elite performance, though, okay, but the that's still, it's. It sucks that there's a ceiling of the point three.

Well, there, I can get down to point two and point one. Point one's Oh, good, what I've, yeah, and I've played with that a little bit, too. Um, I have to adjust a ton of different parameters in order to play with point one, and I don't have control over all that on my end. Yet. It it also changes, like how the models are train, like if I train a model, like in web grid, uh, like I bootstrap on a model, which basically is them, uh, training models.

As I'm playing web grid, um, based off of, like, the web grid data that I'm so like, if I play web grid for 10 min, they can train off that data specifically. Um, in order to get me a better model, um, if I do that with point three versus point one, the models come out different. Um, the way that they, um, interact is, it's just much, much different. So I have to be really careful. I found that doing it with point three is actually better in some ways, unless I can do it with point one and change all of the different parameters, then that's more ideal.

Cause obviously point three is faster than point one. So, uh, I could, I could get there, I can get there

can you click using your brain?

for right now. It's the hover clicking with the dual cursor. Um, we before all the thread retraction stuff happened, we were calibrating clicks left click, right click. That was, um, my previous ceiling, um, before I broke the record again with the dual cursor. Was, I think, on a 35 by 35 grid with left and right click. And you get more, um, BPS, more bits per second, using multiple clicks, cause it's more difficult.

Oh, because what is it? The BL you g? You're supposed to do either a left click or a like right click. Is it different colors different colors, that point to this? yeah, blue targets Cool, for left click, orange cool,

targets for right click is what they had done. So, uh, my previous record of seven, five was with the two was with the blue clicks. and the orange targets. Yeah, which, um, I think if I went back to that now, um, doing the click calibration, I would be able to. And being able to, like, initiate clicks on my own, I think I would break that ten ceiling, like in a couple days max. Like,

Yeah, you'll start making Bliss nervous about his 17. I mean, you should why do you think be, yeah, Uh,

we haven't given him the? The?

uh. So what? What did it feel like? When there were attractions that there was, uh, some of the threads were attracted?

it sucked it. It was really, really hard. The day they told me was the day of my big Neuralink tour at their Fremont facility, and they told me, like, right before we went over there, it was really hard to hear. My initial reaction was all right, go in, fix it, like, go in, take it out and fix it. The 1st surgery was so easy. Like, like, I went to sleep a couple hours later, I woke up and here we are.

Um, I didn't feel any pain, didn't take like any, um, um, pain pills or anything. So I just knew that if they wanted to, they could go in and put in a new one, like next day, if that's what it took. Cause I just wanted, I wanted it to be better and I wanted not to lose the capability. I had so much fun, um, playing with it for a few weeks, for a month I had like.

It had opened up so many doors for me, it had opened up so many more possibilities that I didn't want to lose it. After a month, I thought it would've been a cruel twist of fate if I had gotten to see the view from like the top of this mountain and then have it all come crashing down after a month. And I knew, like, say, the top of the mountain, but, uh, like, I. How I saw it was.

I was just now starting to climb the mountain and I was it. Like, there was so much more than I knew was possible. And so to have all of that be taken away was really, really hard. Um, but then on the drive over to the facility, I don't know, like 5 min drive, whatever it is. Um, I talked with my parents about it, I prayed about it. I was just like, you know, I'm not gonna let this ruin my day, I'm not gonna let this, um, ruin this amazing like tour that they have set up for me.

Like, I wanna go show everyone how much I appreciate all the work they're doing, I wanna go like, meet all of the people who have made this possible. And I wanna go have one of the best days of my life. And I did, and it was amazing, and it absolutely was one of the best days I've, uh, ever been privileged to experience. And then for a few days, uh, I was pretty down in the dumps, but, uh, for like, the 1st few days afterwards, I was just like, I didn't know if it was gon ever gonna work again.

And then I just, I made the decision that it. Even if I lost the ability to use the Neuralink, even if I lost, um, even if I like, lost out on everything to come. Um, if I could keep giving them data in any way, then I would do that. If I needed to, just do, um, like some of the data collection every day or body mapping every day for a year, then I would do it. Um, because I know that everything I'm doing helps everyone to come after me, and that's all I wanted.

I guess the whole reason that I did this was to help people, and I knew that anything I could do to help, I would continue to do. Even if I never got to use the cursor again, then, you know, I was just happy to be a part of it and everything that I had done was just a perk. It was something that I got to experience and I know how amazing it's gonna be for everyone to come after me.

So might as well just keep trucking along, you know?

Uh, that said, you were able to get to work your way up to get the performance back. So this is like going from Rocky I to Rocky II. So when did you 1st realize that this is possible, and what gave you sort of the strength, the motivation, the deter, determination to do it? To increase back up and beat your previous R record?

Uh, yeah, it was within a couple weeks, like,

Again? This feels like I'm interviewing an athlete. This is great. I like the thing. the My parents

the road, the road back was long and hard, like a fraught with many difficulties. movie. There were dark days. Um, uh, it wa. It was a couple weeks, uh, I think. And then there was just a turning point. I think they had switched how, um, they were measuring, um, the neuron spikes in my brain, like the bliss. Help me out. Uh,

Yeah, the way in which we're measuring, uh, the behavior of individual neurons. So we're switching from, uh, sort of individual spike detection to something called spike band power. Which, uh, if you've watched the previous segments with either me or DJ, you probably have some concept.

So when they did that, it was kinda like, a, you know, light over the head, like light bulb moment, like, Oh, this works, and um, this seems like like we can run with this. And I saw the UM uptick in performance immediately, like I could feel it. When they switched over, I was like, this is better, like this is good, like everything up till this point, for the last few weeks, last like whatever three or four weeks.

Cause it was before, they even told me, like everything before this sucked, like, let's keep doing what we're doing now. And at that point, it was not like, Oh, I know, I'm still only at like, say, in web grid terms, like four or five BPS compared to my seven, five before. But I know that if we keep doing this, then, like, I can, I can get back there. And then they gave me the dwell cursor and the dwell cursor sucked.

At 1st, it's not obviously not what I want, but it gave me a path forward to be able to continue using it. And, um, hopefully, to continue to help out. And so I just ran with it, never looked back. Like I said, I'm just kinda person I roll with the punches anyway. So

What was the process? What was the feedback loop on? The figuring out how to do the spike detection in a way that would actually work well for Nolan?

Yeah, it's a great question. So maybe just describe 1st how the actual update worked. It was basically an update to your implant, so we just did an over the air software update to his implant, same way you'd update your Tesla or your iPhone. And, uh, that firmware change enabled us to record sort of averages of populations of neurons nearby individual electrodes. So we have, uh, sort of less resolution about which individual neuron is doing what, but we have a broader picture of what's going on nearby an electrode overall.

And, uh, that feedback, I mean, basically, as Nolan described, it was immediate when we flipped that switch. Uh, I think the 1st day we did that, you had three or four BPS right out of the box. And that was a a lightbulb moment for, Okay, this is the right path to go down. And from there, there was a a lot of feedback around, like how to make this useful for independent use. So what we care about ultimately is that you can use it independently to do whatever you want.

And, uh, to get to that point, it required us to reengineer the UX, as you talked about, the dwell cursor, to make it something that you can use independently without us needing to be involved all the time. And, uh, yeah, this is obviously just the start of this journey. Still, hopefully we get back to the places where you're doing multiple clicks. And, uh, using that to control much more fluidly everything, and much more naturally, the applications that you're trying to interface with.

And, most importantly, get that web grid number up. So how is the on the hover? Click? Do you accidentally click stuff sometimes? like, what's? how hard is it to avoid accidentally clicking?

I have to continuously keep it moving, basically, so, like I said, there's a threshold where it will initiate a click. So if I ever, um, drop below that, it'll start. And I have 03 seconds to move it before it clicks anything. Oh, good, Um, and if I don't want it to ever get there, I just keep it moving at a certain speed. And like, just constantly, like doing circles on screen, moving it back and forth to keep it from clicking stuff.

Um, I actually noticed, uh, a couple weeks back that I was. When I was not using the implant, I was just moving my hand back and forth or in circles. Like I was trying to keep the cursor from clicking. And I was just doing it, like, while I was trying to go to sleep, and I was like, Okay, this is a problem. Like,

to avoid the clicking, I guess. Does. Does that create problems? Like when you're gaming, accidentally click a thing like,

Yeah, yeah, it happens in chess. Um, I've lost, I've lost a number of games because I'll accidentally click something.

I think the 1st time I ever beat you was because of an accidentally misclick. Yeah,

it's it's a nice excuse, right? You can always, it's, it's anytime you lose, great, it's you can just say perfect. Yeah, that was accidental. You said the app improved a lot from version one when you 1st started using it, it was very different. So can you just talk about the trial and error that you went through with the team? Like 200 plus pages of notes, like, what's that process like, of work, going back and forth and working together to improve the thing?

It's a lot of me just using it, like, day in and day out and saying, like, Hey, can you guys do this for me? Like, give me this, I wanna be able to do that. Um, I need this. Um, I think a lot of it just doesn't occur to them, maybe until someone is is actually using the app, using the implant. It's just something that y they just never would've thought of. Or, um, it's very specific to even like me, maybe what I want.

It's something I'm a little worried about with the next people that come is, you know, um, maybe they will want things much different than how I've set it up or what. The advice I've given the team and they're gonna look at some of the things I've they've added for me. I'm like, that's a dumb idea, like, why would he ask for that? Um, and so I'm really looking forward to get the next people on. Because I guarantee that they're going to think of things that I've never thought of, they're gonna think of improvements.

I'm like, Wow, that's a really good idea, like I wish I would've thought of that. Um, and then they're also gonna give me some pushback about, like, yeah, what you are asking them to do here? Um, that's a bad idea. Let's do it this way. And I'm more than happy, um, to have that happen. But it's just a lot of like, you know, uh, different interactions with different games or applications, um, the internet, just with the computer in general.

Um, there's tons of bugs, um, that end up popping up left, right, center. Um. So it's just me trying to use it as much as possible and showing them what works and what doesn't work and what I would like to be better. And um, then they take that feedback and they usually create amazing things for me. They solve these problems in ways I would have never imagined. Uh, they're so good at everything they do. Um, and so I'm just really thankful that I'm able to give them feedback and they can make something of it.

Cause a lot of my feedback is like, really dumb. It's just like, I want this, please do something about it. And they'll come back. Super well thought out. And it's way better than anything I could have ever thought of or implemented myself. So they're just great, they're really, really cool.

As the BCI community grows, would you like to hang out with the other folks with Neuralinks? Like, what? What relationship, if any, would you wanna have with them? Cause you said, like, they might have a different set of like ideas of how to use the thing. Uh, would you be intimidated by their web grid performance? no, no. I hope, Compete?

I hope day one. They like, wipe the floor with me, I hope they beat it, um, and they crush it, you know, double it if they can, um, just because, on one hand, it's only gonna push me to be better, um, cause I'm super competitive. I want other people to push me. Um, I think that is important for anyone trying to, um, achieve greatness is they need other people around them who are going to push them to be better.

And I even made a joke about it on X once, like, once the next people get chosen, like cue buddy cop music. Like, I'm just excited to have other people to do this with and to, like, share experiences with. I'm more than happy to interact with them as much as they want, more than happy to give them advice. I don't know what kind of advice I could give them, but if they have questions, I'm more than happy.

What advice would you have for, uh, the next participant in the clinical trial?

That they should have fun with this, um, because it is a lot of fun, um, and that I hope they work really, really hard. Because it's not just for us, it's for everyone that comes after us. Um, and you know, come to me if they need anything, and to go to Neuralink if they need anything, man. Neuralink moves mountains like they do absolutely anything for me that they can, and it's an amazing support system to have.

Um, it, it puts my mind at ease, um, for like, so many things that I, uh, have had like questions about, or so many things I wanna do. Um, and they're always there and that's really, really nice, um, and so I just. I would tell them not to be afraid to go to Neuralink with any questions that they have, any concerns, uh, anything that you know they're looking to do with this and any help that Neuralink is capable of providing.

I know they will. Um, and I don't know, I don't know. Just work your ass off, because it's it's really important that we try to give our all to this.

So have fun and work hard.

Yeah, yeah, there we go. Maybe that's what I'll just start saying to people, have fun, work hard.

Now you're a real pro athlete, just keep it short. Um, maybe it's good to talk about what you've been able to do now that you have a Neuralink implant. Like the the freedom you gain from this way of interacting with the outside world, like you play video games all night and you do that by yourself, and that's a kind of freedom. Can you speak to that freedom that you gain

yeah. It's what all I don't know people in my position want. They just want more independence. The more load that I can take away from people around me, the better. Uh, if I'm able to interact with the world, uh, without using my family, without going through any of my friends, um, like needing them to help me with things, the better. Um, if I'm able to sit up on my computer all night and not need someone to, like, sit me up.

Uh, say, like on my iPad, like in a position where I can use it and then have to have them wait up for me all night until I'm ready to be done using it. Um, like that, it takes a load off of all of us, and it's it's really like all I can ask for. Um, it's something that, you know, I could never thank Neuralink enough for, and I know my family feels the same way. Um, you know, just being able to have the freedom to do things on my own, uh, at any hour of the day or night, it m means the world to me.

And, um, I don't know,

when you're up at two zero a m playing web grid by yourself? I just imagine like, it's darkness and there's just a light glowing and you're just focused. What? What's going through your mind? Or you're like, in a state of flow, where it's like the mind is empty, like those like Zen masters.

yeah. Generally, it is me playing music of some sort. I have a massive playlist and so I'm just like rocking out to music. And then it's also just like a race against time. Because I'm constantly, constantly looking at how much battery percentage I have left on my implant. Like, all right, I have 30, which equates to, you know, x amount of time, which means I have to break this record in the next, you know, hour and a half, or else it's not happening tonight.

Um, and so it's it's a little stressful, uh, when that happens, when it's like, when it's above 50, I'm like, okay, like, I got time. Then it starts getting down to 30 and then 20, and it's like, all right, uh, ten. A little pop up's gonna pop up right here and it's gonna really screw my web grid flow. It's gonna tell me that, you know, like, there's a like, a low battery, low battery. Uh, pop up comes up and I'm like, it's really gonna screw me over.

So if I have to, if I'm gonna break this record, I have to do it in the next like 30 s, or else that pop up's gonna get in the way. Like, cover my web grid, um, and then it. After that, I go click on it, go back into web grid and I'm like, all right, that means I have, you know, 10 min left before this thing's dead. That's what's going on in my head generally, that and whatever song is playing, um, and I just, I just want, I want to break those records so bad, like it's all I want.

When I'm playing web grid, it it has become less of like, oh, this is just a leisurely activity, like I just enjoy doing this because it just feels so nice and it puts me at ease. It is no. Once I'm in web grid, you better break this record, or you're gonna waste like 5 h of your life right now. And um, I don't know, it's just fun, it's fun, man,

Uh. Have you ever tried web grid with like two targets and three targets? Can you get higher BPS with that?

can you? Can you do that?

you mean, like, different color targets, or you mean, Oh, d, uh, multiple targets. Does yeah, so

that change the thing?

yeah. So BPS is a log of number of targets times correct minus incorrect, divided by time. And so you can think of like different clicks as basically doubling the number of active targets. So, you know, b B, you? Basically, higher BPS. The more options there are, the more difficult the task. And, uh, there's also, like, Zen mode you've played in before, which is like infinite, canvas yeah, it covers, mode.

it covers the whole screen with a grid. And, um, I don't

what? know. Yeah, No, yeah, and so you can go like, that's, everywhere, yeah, that's insane.

He doesn't like it cause it didn't show BPS. So, like, you know, Oh yeah, that's

I had them, I had them put in a giant BPS in the background. So now it's like the opposite of Zen mode. It's like it's like super hard mode, like just metal mode, if it's just like a giant number in the back, counting we should just name up.

that metal mode is, I'm just Yeah, metal gonna name that. mode.

so. You also play Civilization VI.

I love CIV VI. Yeah,

Um, usually go with Korea, you I do, said,

yeah. So the great part about Korea is they, um, focus on like science, tech victories, which was not planned. Like I've been playing Korea for years and then all of the neuralink stuff happened. Um, so it, it kinda aligned, um, but what I've noticed with tech victories is if you can just rush tech, rush science, um, then you can do anything. Like at one point in the game, you will be so far ahead of everyone technologically that you will have.

Like musket men, infantry men, planes sometimes, and people will still be fighting with like bows and arrows. And so if you want to win a domination victory, you just get to a certain point with the science and then go and wipe out the rest of the world. Or, um, you can just take science all the way and win that way. And you're gonna be so far ahead of everyone, cause you're producing so much science that it's not even close.

Um, I've accidentally won in different ways, just by focusing on science. I was, yeah, I like, I, I was playing only science, obviously, like just science all the way, just tech. And I was trying to get like every tech in the tech tree and stuff. And then I accidentally won, uh, through a diplomatic victory, and I was so mad, I was so mad. Uh, it cause it just like ends the game. One turn, it was like, Oh, you won, you're so diplomatic.

I'm like, I don't wanna do this. I should've declared war on more people or something. Um, it was terrible. But you don't need like giant civilizations with tech, especially with Korea, you can keep it pretty small. So I generally, just, you know, get to a certain military unit and put them all around my border to keep everyone out, and then I will just build up. So very isolationist. Um, nice, just yeah,

work on the science, the tech. yep, that's it, You're making it sound so fun.

it's so much fun.

And I also saw a Civilization VII trailer.

Oh man, I'm so pumped.

Yeah, and that's probably coming out. Come on Maybe

Civ seven, hit me up. I'll alpha, beta test, whatever that'd be,

wait, when, when is it coming out? In in next? Yeah, yeah, next year, yeah. What other stuff would you like to see improved? Uh, about the Neuralink app and just the entire experience?

I would like to, like, I said, get back to the UM. Like, click on demand, like the regular clicks, that would be great. Uh, I would like to be able to connect to more devices. Uh, right now it's just the computer. I'd like to be able to use it on my phone or use it on different consoles, different, uh, platforms. Um, I'd like to be able to control as much stuff as possible, honestly. Um, like, an optimus robot would be pretty cool, that would be sick if I could control an optimus robot.

Uh, the link app itself, um, it seems like we are getting pretty, um, dialed in to what, um, it might look like. Down the road seems like we've gotten through a lot of what I want from it, at least. The only other thing I would say is like, more control over all the parameters that I, um, can tweak, uh, with my like cursor and stuff. There's a lot of things that, you know, go into how the cursor moves in certain ways.

Um, and I have, I don't know, like three or four of those parameters, and they're like, gain my

and friction and all that.

gain friction. Yeah, and there's maybe double the amount of those with just like velocity, and then with the actual dwell cursor. Um, so I would like all of it. I want as much control over my environment as possible. Um, especially,

So you want, like, advanced mode, you know, like, and like, there's menus, usually there's a basic mode and you're like one of those folks like the

it's like, I go into the power user, the game, man, yeah, advanced. that's, that's, that's what I want. I want as much control over this as possible. Um, so yeah, that's, that's really all I can ask for. Just give me, give me everything.

yeah, uh. Has speech been useful, like just being able to talk also, in addition to everything else,

Yeah, you mean, like, while I'm using it,

while you're using it, like speech to text, or do you type? Or like? Cause there's also a keyboard, that's really nice.

yeah. So there's a virtual keyboard. That's another thing I would like to work more on is finding some way to, um, type or text in a different way. Right now, it is, um, like a dictation, basically, and a virtual keyboard that I can use with the cursor. But we've played around with, um, like finger spelling, like sign language, finger spelling, um, and that seems really promising. So I have this thought in my head that it's going to be a very similar learning curve that I had with, um, the cursor.

Where I went from attempted movement to imagined movement at one point, I have a feeling, um, this is just my intuition. That at some point I'm going to be doing finger spelling and I won't need to actually attempt to finger spell anymore. That I'll just be able to think the like letter that I want and it'll pop up,

that would be epic. yeah, and not That's challenging,

just, yeah.

though, that's hard. That's a lot of work for you to kinda take that leap, but that would be awesome.

And then, like, going from letters to words is another step, like you would go from. You know, right now it's finger spelling of like, just the sign language alphabet. But if it's able to pick that up, then it should be able to pick up like the whole sign language like language. Um, and so then if I could do something along those lines, or just the sign language, um, spelled word. If I can, you know, spell it at a reasonable speed and it can pick that up, then I would just be able to think that through and it would do the same thing.

I don't see why not. After what I saw with the UM, with the cursor control, I don't see why it wouldn't work, but we'd have to play around with it more.

What was the process in terms of, like, training yourself to go from attempted movement to imagined movement? How long did that take? So, like, how long would this kind of process take well, you?

it was a couple weeks before it, just like, happened upon me. But now that I know that that was possible, I think I can make it happen with other things. I think it would be much, much simpler.

Would you get an upgraded implant device? So,

whenever, whenever they'll let me.

uh, so, you don't have any concerns for you? With the surgery experience, all of it was, um, like, no regrets, so everything's

not at all. been good so far, Yep,

so you just keep getting upgrades.

yeah, I mean, why not? I've seen how much it's impacted my life already and I know that everything from here on out is just gonna get better and better. So, um, I would love to, I would love to get the upgrade.

what, uh, future capabilities, uh, are you excited about? Sort of beyond this kind of, uh, telepathy, uh, is vision interesting. So for folks who, for example, who are blind, so Neuralink, uh, enabling people to see, or or for speech.

yeah, there's a lot that's very, very cool about this. I mean, we're talking about the brain, so there's like, this is just motor cortex stuff. There's so much more that can be done. The vision one is fascinating to me. I think that is going to be very, very cool. To give someone the ability to see for the 1st time in their life would just be, I mean it. It might be more amazing than even helping someone like me.

Like that just sounds incredible. Um, the speech thing is really interesting, being able to have some sort of like real time translation, and, um, cut away that language barrier would be really cool. Um, any sort of like actual impairments, um, that it could solve, like with speech, would be very, very cool. And then also there are a lot of different disabilities that all originate in the brain, and you would be able to hopefully be able to solve a lot of those.

Um, I know there's already stuff to help people with seizures, um, that can be implanted in the brain. This would do, I imagine, the same thing, and so you could do something like that. I know that, you know, even someone like Joe Rogan has talked about, uh, the possibilities with being able to, um, stimulate the brain in different ways. Um, I'm not sure, I'm not sure what you know like how ethical a lot of that would be.

That's beyond me, honestly. But I know that there's a lot that can be done when we're talking about the brain and being able to go in and physically make changes to help people or to improve their lives. So I'm really looking forward to everything that comes from this, and I don't think it's all that far off. Um, I think a lot of this can be implemented within my lifetime, um, assuming that I live a long life.

What you were referring to is things like people suffering from depression or things of that nature, potentially getting help.

yeah, flip a switch like that, make someone happy. Um, I know, I think Joe has talked about it more. In terms of like, you want to experience, like what a drug trip feels like, like you wanna experience what it'd be like to be on.

of course.

Uh, yeah, mushrooms or something like that, DMT, like, you can just flip that switch in the brain. Uh, my buddy Bane has talked about being able to, like, wipe parts of your memory and re experience things that like for the 1st time, like your favorite movie or your favorite book. Like, just wipe that out real quick and then re fall in love with Harry Potter or something. Um, I told him, I was like, I don't know how I feel about, like people being able to just wipe parts of your memory.

Um, that seems a little sketchy to me. He's like, they're already doing it, so

sounds legit. Uh, I would love memory replay, just like actually, like high resolution replay of all memories.

yeah. I saw an episode of Black Mirror about that once. I don't think I want it.

Yeah, so Black Mirror, uh, always kind of considers the worst case, which is important. I think people don't consider the best case or the average case enough. I don't know what it is about us humans. We wanna think about the worst possible thing. We love drama. It's like, how is this new technology gonna kill everybody? well, We just love that. We'll get hopefully, like, Yes, let's watch.

yeah. Hopefully people don't think about that too much. With me, or it'll ruin a lot of my plans.

Yeah, yeah, I assume you're gonna have to take over the world. I mean, you're, I love your Twitter, you, uh, you tweeted. I'd like to make jokes about hearing voices in my head since getting the Neuralink, but I feel like people would take it the wrong way. Plus the voices in my head told me not to. Yeah, please never stop. So you're talking about Optimus. Um, is that something, uh, you would love to be able to do to control the robotic arm or the entirety of Optimus?

oh yeah, for sure, for sure, absolutely

You think there's something like, fundamentally different about just being able to physically interact with the world?

yeah. Oh, 100, um, um, this, I, I know. Another thing with like, being able to like, give people the ability to like, feel sensation and stuff too. By going in with the brain and having the Neuralink, maybe do that. That could be something that, um, could be translated through, transferred through the optimus as well. Like, there's all sorts of really cool, um, interplay between that. And then also, like you said, just physically interacting. I mean, ninety nine of the things that I can't do myself, um, obviously need.

I need a caretaker for someone to physically do things for me. If an optimus robot could do that, like, I could live an incredibly independent life and not be such a burden on those around me. Um, and that would, it would change the way people like me live, um, at least until whatever this is gets cured. Um, but being able to interact with the world physically like that would just be amazing. Um, and and they're not just like for being for having to be a caretaker or something, but something like.

I talked about just being able to read a book, imagine an optimus robot, just being able to hold a book open in front of me. Like, get that smell again. I might not be able to feel it at that point, um, or maybe I could again with the sensation and stuff, but being there's something different about reading like a physical book than staring at a screen or listening to an audiobook. I actually don't like audiobooks. I've listened to a ton of them at this point, but I don't really like them.

Um, I would much rather like read a physical copy.

So one of the things you would love to be able to experience is opening the book, bringing it up to you and to feel the touch of the paper.

yeah, oh man, the touch, the smell. I mean, it's just like just something about the words on the page. And you know, they've, they've replicated, you know, that page color on, like the Kindle and stuff. Yeah, it's just not the same. Yeah, so just something as simple as that

So one of the things you miss is touch.

I do, yeah, a lot of, a lot of things that I interact with in the world, like clothes, or literally any physical thing that I interact with in the world. A lot of times. What people around me will do is they'll just come like, rub it on my face. They'll like, lay something on me so I can feel the weight, they will rub, you know, a shirt on me so I can feel fabric. Like, there's something very profound about touch, and, uh, it, it is, it's something that I miss a lot, um, and something I would love to do again.

Uh, we'll see.

What would be the 1st thing you do with a with a hand that can touch? Give your mom a hug after that, right?

Yeah, yeah, I know that's. It's one thing that I've, that I've asked, um, like God, for basically every day since, uh, my accident. Was just being able to, like, one day move, even if it was only like my hand, so that way, like I could squeeze my mom's hand or something. Just to like, show her that, you know, like, how much I care and how much I love her and everything. Um, something along those lines, um, being able to just interact with the people around me, handshake, give someone a hug, um, I don't know anything like that.

Being able to help me eat like, I'd probably get really fat, um, which would be a terrible, terrible thing. Um,

Also, beat bliss in chess on a physical chessboard.

yeah, yeah, I mean, there are just so many upsides, you know, and any, any way to find some way to feel like I'm bringing bliss down to my level. Cause, um, he's just such an amazing guy and everything about him is just so above and beyond. Um, that anything I can do to take him down a notch, I'm more than happy. Yeah,

yeah, humble him a bit, he needs it, okay, as he's sitting next to me. Uh, did you ever make sense of why God puts good people through such hardship?

oh man, um, I think it's all about understanding how much we need God. And I don't think that there's any light without the dark. I think that if all of us were happy all the time, um, there would be, you know, no reason to turn to God ever. I feel like there would be no concept of, you know, good or bad. And I think that as much of, like the darkness and the evil that's in the world, it makes us all appreciate the good and the things we have so much more.

And I think, you know, like when I had my accident, the 1st, one of the 1st things I said to one of my best friends was. And this was within like the 1st month or two after my accident. I said, you know, everything about this accident has just made me understand and believe that, like, God is real and that there really is a God, basically, and that, um, like, my interactions with him have all been, you know, real and worthwhile.

And he said, if anything, seeing me go through this accident, he believes that there isn't a God. And it's a very different reaction, um, but I believe that it is. It is a way for God to test us, to build our character, to, um, um, send us through trials and tribulations. To make sure that we understand, um, how precious, you know, he is, and the things that he's given us, and the time that he's given us.

And then, um, hopefully, grow from all of that. Um, I think that's a huge part of being here is to, um, not just, you know, have an easy life and do everything that's easy. But to step out of our comfort zones and really challenge ourselves. Uh, because I think that's how we grow.

What gives you hope about this whole thing we have going on, human civilization?

Oh man, um, I think people are my biggest, uh, inspiration. Uh, even just being at Neuralink, um, for a few months. L looking people in the eyes and hearing their motivations for why they're doing this, it's it's so inspiring. And I know that they could be other places, um, at cushier jobs, um, working somewhere else, doing X, Y or Z that doesn't really mean that much, um. But instead, they're here and they want to better humanity, and they wanna better just the people around them, the people that they've interacted with in their life.

They wanna make better lives for their own family members who might have disabilities. Or they look at someone like me and they say, you know, I can do something about that, so I'm going to. And it's always been what I've connected with most in the world are people. I'm. I've always been a people person and I love learning about people and I love learning. Like how people developed and where they came from, and to see, like, how much people are willing to do for someone like me.

When they don't have to, and they're going out of their way to make my life better. It gives me a lot of hope for just humanity in general. How much, how much we care and how much we're capable of when we all kinda get together and try to make a difference. And I know there's a lot of bad out there in the world, but there always has been and there always will be. Um, and I think that that is.

It shows human resiliency, and it shows what we're w able, what we're able to endure, and how much, how much. We just want to be there and help each other, and how much satisfaction we get from that. Because I think that's one of the reasons that we're here is just to help each other. And, um, I don't know that. That always gives me hope is just realizing that there are people out there who still care and who wanna help.

And thank you for being one such human being and continuing to be a great human being through everything you've been through. And being an inspiration to many people, to myself for many reasons, including your epic, unbelievably great performance on web grid. I will be training all hey night tonight to try to man, you can to try to do it, you catch up.

can do it.

And I believe in you that you can, uh, once you come back, so sorry to interrupt with the Austin trip. Once you come back, uh, eventually beat Bliss.

Yeah, yeah, for sure, absolutely.

I'm rooting for you, the the the

thanks man.

whole world is rooting for you.

thank you.

Thank you for everything you've done, man.

thanks. thanks man.

Thanks for listening to this conversation with Noland Arbaugh, and before that with Elon Musk, DJ, Sah, Matthew MacDougall and Bliss Chapman. To support this podcast, please check out our sponsors in the description. And now let me leave you with some words from Aldous Huxley in The Doors of Perception. We live together, we act on and react to one another, but always, and in all circumstances, we are by ourselves. The martyrs go hand in hand into the arena, they are crucified alone.

Embrace the lovers desperately try to fuse their insulated ecstasies into a single self transcendence in vain. But its very nature, every embodied spirit is doomed to suffer and enjoy its solitude. Sensations, feelings, insights, fancies, all these are private and, except through symbols and a 2nd hand, incommunicable. We can pool information about experiences, but never the experiences themselves. From family to nation, every human group is a society of island universes. Thank you for listening and hope to see you next time.