Feed Drop: Why Only 10% of Companies Succeed With AI With Sam Ransbotham, Professor at Boston College
Me, Myself, and AI · 30 Jul 2024
Source: https://moderncto.io/
Transcript: https://podcasts.opennash.com/shows/me-myself-and-ai/feed-drop-why-only-10-of-companies-succeed-with-ai-with-sam-ransbotham-professor-9d64e9/ (via OpenNash Podcasts, machine-generated)

Today, we're talking to Sam Ransbotham, professor at Boston College and host of the Me, Myself, and AI podcast about why only 10% of companies succeed with AI. You're listening to Joel Beasley, Modern CTO.

All right, so your podcast, the the premise of it is why do only 10% of companies succeed with AI? That caught my attention. I was browsing the internet on LinkedIn or something, and I saw this Why do only 10% of companies succeed with AI? That's how I found you and all of this and all your show, Me, Myself, and AI. And I wanna know, like, is it really that big of a deal? You made an entire show about it?

Well, I think there's you you know how marketing works. I mean, I I think that's that that word, to, I mean, we have to we have to lead with some statistic that gets people interested. But that's a pretty interesting one, isn't it? Given the amount of stuff that we were hearing about artificial intelligence, we were hoping that that number was bigger than 10%.

Why are they failing?

I see, uh, there's there's the trap. I don't think that we're people are really failing. And so, you know, our our research, it looks at and says that about 10, or actually 11% are getting significant financial benefits. So it's not like they're not getting any benefits, it's not like they're failing. It's just maybe falling short of this, um, hey, AI is gonna change everything that we're we're hearing so much in society. So it's not don't cast it as failure.

There there's more than 2 options here. This is not Hobson's choice.

That's funny, yeah. So, w- of these companies that are achieving significant returns and investments from this AI, tell me about those.

Yeah, so we put together w- we tried to look at, alright, given given we have some have-nots and some haves, what's the difference? I mean, that's a natural question for an academic or for anyone to try to figure out those differences. And the first few are things that you might, I think, we would expect, right? They're got to get their technology house in order. I mean, you can't have something like artificial intelligence, complicated machine learning models, if you're basically working on an outdated copy of Excel that is running on a on a dated PC, right?

So, there's a certain sort of inform infrastructural element to that. And also there's talent. You know, you the there you have to have somebody use these tools here. Now, so what we found was that, you know, 10% 10% to get to be one of those 10%, you got to have some of those basic building blocks in place. And that's we think of those as talent, infrastructure, and strategy. And I could talk about each one, and more of those, but that doesn't get you all the way there.

There's a lot more after that, and that, w- I think, was one was more interesting for us. You can't say, for example, we're just going to take the same old thing and just do it with AI. That is not gonna get you into that 10%. Yeah, um, one of my fun examples is in the healthcare industry. So here's a question for you, Joel, when was the fax machine invented?

Ooh, I don't know. That's This is a a tough

bummer, earth, everyone. one. I'm putting you on the spot, literally.

Yeah, I'm gonna say somewhere between the 60s and s early 80s.

60s and early 80s. Now, I think you're thinking 1960s or 1980s, right? No, much closer to 1860s. So, which is interesting because, you know, if you think about that, predates telephone, and, you know, it was working across telegraph or whatever. So, here's the point. That's a really old technol- I'm gonna hit it somewhere with this story. Don't don't panic. Um, there's a lot of interesting stuff happening, and one of the thing the industries that uses faxes left and right is health care.

They'll fax stuff left, you know, back and forth. They're practically the only people still using faxes to this day. And so what I've read this story about people in health care using AI, optical character recognition, text parsing to take and scan an image from a fax machine and scan it and try to get all the information out of it. And that, on the one hand, seems like a great use of artificial intelligence, because nobody wants to retype everything that comes across the slit little fax paper, right?

So, again, there's value in artificial intelligence there. But what about just not sending a fax in the first place? What what about sending that information from one computer system to another computer system without a fax machine at all? And so that's the that's the point of you can't just slap AI on top of an existing process, which is faxing. Think of some new way to do a process. And I think that's the real difference in that 10%.

You were getting that.

by the way, I've, uh, my brother and and stepmom are both physicians, and I've seen in their offices, and it's it was fascinating when when I showed up and my brother was showing me his his office, and I saw this fax machine with just pages, like, they had they just faxed us over the whole medical report. And I just go through it, and I said, Man, at some point, someone's gonna need to create some technology that makes this better.

Yeah. Well, I mean, what's funny is we have the data in a computer system, we print it out, then we fax it, and then someone gets it into another computer system. The fax is an unimportant step here. And so that's the that's the key there is that just going in and saying, Hey, let's artificial intelligence up this whole system we got is not gonna get you into the, I there's value in it. I mean, you're gonna save money by somebody not typing in the facts, but there's a lot more money in, uh, in thinking of ways around that process.

How have you seen how companies are attempting to do this? Are they using internal talent, external talent? How are they approaching AI?

Yeah, all the above. I mean, so I think, you know, it'd be nice if I could say, Oh, no, man, always go internal, always go external, and in nothing in business is that clean. No, nothing has that pat an answer. I mean, sometimes you need expertise that's just not within your company. On the other hand, what's happening, I think, is technology is becoming much more of a commodity. Uh huh, I'll explain what I mean by that in just a second, but the point is, if it's a commodity, then what matters is what people know about your organization, and that's much harder to teach and buy.

And so that's a argument for using internal.

Have you seen the OpenAI Sora model?

Yo, I have not played with it yet.

Oh, have you seen it, though? You've seen the preview videos?

Yeah, this, it's crazy. And actually, that's what's so incredibly frustrating about being a professor teaching my I teach a I teach a class in machine learning and artificial intelligence. I am so jealous of the professors who get to use their slides from last semester, because I put up the slides from last semester and, like, Oh my gosh, that technology, I'm, like, talking about MySpace and Friendster up here or something. I mean, things are dated so quickly, it's amazing.

So, yeah, it's really hard. I mean, and it's progressing so quickly.

So tell me about this class that you teach.

So I that teach undergrads how to do machine learning and artificial intelligence. And actually, to tie it back to the point that I was making earlier about commoditization of technology, in our class, we will use an image classification, a number class, the handwriting, handwritten digits. And we can, within that class, using their laptops and code that we've downloaded from the internet that's freely available to everyone, we can beat what would have been a contest-winning performance 5 years ago, 6 years ago.

But we can do it with tools that they've just downloaded and laptops that they're running in class. That's really incredible. And actually, I'd like to think it's because I'm such an awesome teacher, but it's really the tools, and they're just becoming so crazy. And just the, you know, the image models, the video models that are coming out now are truly, truly amazing.

Are you teaching a 101 class? What type of competency level do they have entering your class?

Actually, well, um, they have to know a little bit about Python, and we have so we have an introduction to Python class so they know about coding. But this is really much more about scripting. And what people find in that class is that so much of this is about getting your data cleaned up and ready. So you press go on the model, and pressing go on the model is just a matter of kinda waiting for it to do its churning.

Um, so much of that is is, again, built into scripts that are available to people. You know, crank it up right now. You got your laptop, I can tell. I can see it in front of you. Let's, uh, let's let's download it and start going.

that that's that's pretty exciting. Yeah, I did a a couple interviews maybe 2 or 3 years ago with a few companies that were either growing or already public, and they were helping with the data organization, labeling, and modeling, just cleaning everything up. That was their entire business, was just helping people get their data structured and cleaned up.

Yeah, it's it's huge.

Yeah, are you teaching prompting?

Yeah, actually, we do play with some prompting there and going through that how to get a good prompt, how to get a bad prompt. And what's a little tricky for me on that is that it's feels like a very ephemeral knowledge. You know, you think about, I'd like for I'd like for somebody to retain something from class more more than more than 6 weeks or or, you know, uh, at end of the semester, uh, ideally. And prompting stuff is changing so quickly too.

It's really it's hard to elevate that to a principle level. But I think it's more important is is thinking about critical reasoning in this context. Cause I think we have a eh, you've played with these models, apparently, or you're talking about them. Actually, let me put you on the spot again. I mean, this is too,

for sure. This is fun. Yeah.

um, have you ever built a machine learning model in Python or any other coding language?

I have not. I've experimented with them, but I have not built them.

have you played with, uh, ChatGPT or any of these other large language models?

Ma- a significant amount of time invested in them. Yes, we we use them in our in the course of our business. I estimate it saves us about 100K a year at my company. that is huge. And Yeah,

what's huge about that is is, I mean, the magnitude is pretty amazing. But what's huge about that, too, is that how accessible that is. Because I'm I talk to people, and most people have not built their own ML models. But increasingly, a lot of people, practically everyone, is using these tools that are available now. And I think that's, again, towards that commoditization story that pretty much we're all gonna need to know a little bit about AI.

yeah, w- when we started, the way it emerged at our company is obviously I I had been tracking, um, for about 7 to 10 years. I'd been tracking the progression inside of models, the large language models, and and what they were doing. Cause I saw them fairly early, and I said, Well, this is gonna be interesting. So I checked in on it every year. When it became, oh, lack of a better term, public consciousness with GPT in February of last year, January or February of last year.

W- what I wasn't expecting is how much the world split in subject matter experts between the people that are just like, they essentially shunned it and shut their mind off and put their head in the sand. It can't do what a human can do. Or they did 1 prompt and they're like, It's not perfect, and they just completely disregarded the entire ecosystem. And then us, where and and to be honest with you, that that is one of the paths I leaned towards at first.

I was like, Eh, it's not that great. And then I saw some other people doing it m more advanced things with it. I said, Well, I just don't know how to use it then correctly. And so I went back and I said, I'm gonna go back and and revisit this. And that turned into, um, a phenomenal situation for our business. And we have a Slack channel dedicated to just what we're learning from GPT prompting. We pin specific prompts.

We share knowledge between the producers on how do we get, um, like, one one example, and and you might be able to to tell me there's a better way. Uh, but when we start a new GP- chat GPT conversation, we'll have these documents essentially that we paste into it, uh, to start, and then we can go from there. So we're like, Alright, this is the baseline knowledge you need to put into the new conversation. And then you can ask it questions like this, and it's gonna give you outputs like that.

And so that's what we're doing currently, uh, to to use it. But w- as I've started to see people come out with these custom, uh, iterations, essentially, I I haven't explored those yet.

Well, I think a lot of that's what the custom is doing, a lot of your documents doing, and just trying to do it at scale, right? You you get a company, and you say, Okay, let's pre-train it for everybody. And so they they have that information already within, or they have their specific knowledge. There's, like, 12 things cool in what you just said, though. Like, there's a Pew Research study that came out last fall, so it's already horrifically dated, but it's it says that the the number of people that have tried this tool is phenomenal.

And it breaks down inversely with age. If you're sort of p- m m post 65, very few people of those have played with the tool. Pew Pew couldn't ask anybody less than 18, and, you know, 75% or so of those people have used the tool. And I'm just based on my kids, if you went lower than the than 18, they're all over this tool. And that has really big implications about what changes in the future.

And it's it's also how you use the tool as well, like, for I'll give you another separate example. So I wrote a a fiction book, and I was 90% done. We were basically in the final editing in January, February of the of the previous year. So by the time GPT came out publicly, I was wondering, like, I is it possible to use this to help you with the book? And, yeah, I had a couple hypotheses. I was like, Alright, maybe if I put in, you know, how could I use it?

And and to fast forward through all of the examples of of how it didn't work, what I found it was really good at was helping me frame stories. Like frame a and if I was gonna, if the spy was gonna break into the facility, asking it for, like, 15 different ways that could happen. And then and sort of working with it as a as a another person in the room ended up being the most effective way for me to do it.

But you can't just say, write me a full I mean, it it's not gonna be what you want if you just say, write me a full book on this topic or whatever.

Well, you're I mean, you're right on exactly the problem right now. Um, or, you know, problem. That sounds too negative. I mean, what what's co- what's cool simultaneously cool and difficult about this is it is really just a tool, and it's yet another tool that we gotta figure out how to learn. And there are good ways to use a tool, and there are bad ways to use a tool. I mean, the first caveman that picked up a rock, they either used that rock to build a house, or they used the rock to bang grok on the head with it.

And the rock's the rock. And so I think that's a really an important part of what we try to bring up in the podcast. The podcast is me, myself and AI. 2 out of 3 of those words are about people. 1 is about com- technology, and I think we have yet another story where technology is important and it's critical, but how we use that tool is really important.

At what point do you think we'll be able to say that the AI is conscious?

Yeah, I would say this is this gets a into a scary thing. Actually, we've had some of these same sort of arguments here. I mean, it's it's a slippery slope, isn't it? I mean, we're so terrible about, you know, am I bald? Well, yeah, but what point did that happen? You know? There there was some planning years, and, well, calling these these very finite yes and no things on a gradual slope is really tough. But, I mean, you saw the same stories that I did about how, oh my gosh, it's conscious, it's, you know, sentient.

Um, it certainly seems that way, and certainly compare compared with a lot of people that you end up talking to, it's it seems a little more savvy than them, right? So

Yeah, and I I went down the rabbit hole on this and talked to anesthesiologists and people who are really close to consciousness, and we don't even have good answers for what consciousness is as a people. And so

is it a person?

I was like, Wow, this is still an open, there's not consensus here. There's consen- there's understandings of what consciousness is expected to be able to do to participate in society. But, um,

Well, that's one of my favorite things about artificial intelligence is you ask somebody for a definition, and it typically has this shape. Artificial intelligence is blah blah, intelligence. And so, you know, I don't know if you remember back in math days, that's a foul. You can't put the the thing on the left side of the equal sign on the right side of the equal sign. So what we're really phenomenally good at is defining artificial.

What we're really terrible about is defining that intelligence part, because it's changing all the time.

well, here's here's my base argument for this. ChatGPT is smarter than people that I know. I

I I'm gonna see pretend you're not looking at me right now.

No, but but yeah, that that w- I was talking, you know, I've I talk to all different types of people, from the people who are really low level designing the large language models into them. It's they have their picture in there. That goes, it's not until it's not consciousness, it's just not, it's just as I know I go, I know, but the result of what you're doing, my interaction with that, is equal to or greater than interaction with other humans.

And so, if that's the bar, then it's there.

Well, hey, let's call that bar stupid, just for right now. I mean, I think there's the the Turing test. I mean, what you're talking about here is that, can can this machine act in such a way that it fools you into thinking it's a person versus a machine? This is the Alan Turing test from long ago that we're all super familiar with. Well, we've been chasing that Turing test left and right for all these years, and, like a dog who's chased a car, we've caught the car, and we don't know what to do.

Like, what do we do now? We we can fool people into thinking that something is is human. Okay, now now what? I mean, is that was that really the goal? We've got something like, what, 9 billion people on the planet. I don't think we need to necessarily replace those. I mean, we need to be thinking about things that this technology can do that we can't do. Otherwise, we're just in this replacement replicating mode versus thinking about what we and the machines can do together better.

No, you're exactly right. And that Turing test, everybody, so I've been doing the show almost 10 years now, and when I was having the conversations in the earlier years, it was like, the Turing test, that's what we're going for, Turing. And then and then there was just this day when I remember doing some interview and the person was just like, Yeah, that's they were just super dismissive. They're just like, Yeah, but that doesn't really count. We haven't been working towards that as humanity for decades, but that doesn't really count.

Now it's the fact that it can't do A, B, or C. And I said, Whoa, how quickly we just went right past that Turing test as the point.

well, we're terrible about that. We, you know, one of the definitions back to the definitions of AI, it almost always has the word, like, phrase normally, th- you know, that humans normally do, or norm like something about normally or usually. So it just proves that this is a slippery slope. If you went back into the 1700s and you gave someone a quill, and that quill would turn red ink when you misspelled a word, that would be witchcraft, right?

You'd get burned.

We we we take you up to Salem and dunk you and and and leave you underwater for a while. But now, spell check, well, that's not artificial intelligence, right? And so we we just have this this ever expecting, you know, changing expectations of what the technology can do. And I don't I don't think that's gonna change, and we were always gonna want it to do more.

Yeah, I get mad at autocomplete. I'm like, You can't read my mind yet.

Exactly, come on.

Get out of here.

Back to your fiction book, though, one of the thing that that worries me in this scenario is I don't think you know, knowing, knowing what I know about you. You're not looking to get an average fiction book out there. Is your goal to wake up in the morning and say, Hey, what I'd really like is a statistically average fiction book?

Negative. I would never I do everything exceptionally.

Exactly, you're just like like all the kids at Lake Wobegon, you are you are better than average. Um, yeah, but that's the that's I think that's the crux of what we're grappling with right now is this, um, race to mediocrity. So what we've got is this phenomenal tool that's getting you to mediocre amazingly quickly. And there's 2, I'm gonna, you know, I I complained earlier, you made me pick A or B, and I'm gonna make you pick A or B here.

But is this a tool that, A helps us and gives us a huge head start so we get to mediocrity, and then we can build from it? Or B, is this a tool that is a crutch that gets us to mediocrity, leaving us without the tools and resources able to go beyond mediocre? And I think that's really what we're grappling with with our modern generative tools.

Yeah, I th- and I think that's gonna come down to an individual thing, you know? Like,

and and actually, maybe situational too, within individual. Like, I don't need to be awesome. Actually, one thing I do in class is I'll type it in there and have it make a theme song for the class, you know, little by a little poem. And it does phenomenally better than I will ever do at that, and I'm okay with that. Like, it's mediocre is so much better than I'm gonna do, and I'm gonna choose not to compete on that, you know, w- on that thing.

On the other hand, there's a lot of stuff that I'm hoping to do a lot better. Uh, you know, anything that I write, I want it to be not average. I don't want it to be mediocre. Eh, I I want it to be exceptional. And I think that's, you know, we're gonna have to within the individual pick, but also within situation pick, uh, which, you know, is this a situation that I wanna be awesome? Or is this where, eh, I mean, mediocre's okay?

How can leaders explore this? We got a lot of leaders that listen to this show, and and most of them are in technology, uh, all the way from first-time leaders through VPs, engineering, all the way to the CTO, CIO, CSO, the whole the whole stack. The reason why people listen is to become better at at leading their companies. And h- how how should a leader, like, begin to explore this within their organization?

What's really cool, and the the example I gave you earlier about, Have you built a machine learning model? No, there's a whole lot of setup and infrastructure you gotta get in place even be able to run that model in the first place. Um, you know, I think about that as a, uh, a learning curve, and you actually get, oh, I don't like the phrase steep learning curve, but cause it's not steep, it's actually very shallow. It takes you a whole lot of effort to get to a point where you can get something back out of that tool.

In contrast, the modern tools that we're seeing emerge now around generative are highly accessible. You can go out there and do them right now, and your your listeners, they ought to be playing with it because everybody in their organization is playing with it, and everybody is, you know, figuring out what they can do and not do. But when you play with it, I think one thing to remember, you know, back to your your example of how you played with it.

If you hired someone new in your company and they did not perform awesomely on the 1st day of the job, do you fire them or you give them a little help to get them better? I'm I'm hoping that it's give them a little help to get better, cause you'd have a pretty empty company if you just fired everybody who wasn't perfect on day 1. I think that's the model we have to think about when we're using these generative tools, is how can we help progress and get them better, and not just categorically reject what they do when they make a mistake or when they hallucinate.

Are you doing consulting for any companies?

Oh, not many. No, um, I do I do some engineering consulting and software on the side, and that that's what's fun for me.

Have you gotten involved in the policy aspect at all? Has the White House called you up and say, Hey, we need to know what's gonna happen? We're just gonna ban all models over 20 gigs type situation?

It's coming. They must have lost my phone number. I don't know what's happening here. Um,

That's okay.

I think the regulation stuff is interesting. Here's what I would say when they do call, uh, since you asked, is we have a good long history of being able to to handle new technologies, and this isn't new. We're treating this like it's a magical exception, and I don't think that it necessarily is. Um, I'll give you a couple stories. One is in 1906, Upton Sinclair wrote a book called The Jungle, and it was about the meatpacking industry in Chicago, and it was absolutely disgusting.

And what it did was it shown a whole bunch of light on a situation that was untenable. It was existing because nobody would ever went inside that meatpacking plant and looked at it, right? It was all hidden. And all we knew is that when we ate a stuff, we got sick. You fast forward to right now, you can open practically anything and eat it and not be worried about the supply chain that brought it to you.

We can go in any restaurant and be pretty comfortable that you're not gonna get sick. And in fact, once a year, we'll have an E. coli scare, or we'll have something, you know, you know, a restaurant that makes the headlines. These are headlines because they're unusual, because what's happened is that we built an infrastructure of oversight and trust around food packing and around restaurants that help us have trust in that. In contrast, we have none of that with the technology industry right now.

Every single one of these models is happening, you know, behind closed scenes. Uh, the What is OpenAI? Well, okay, one word is not open,

I know.

right? So so I don't know about the AI. We can debate about whether it's AI, but but the open part has has really disappeared on that. And so I think we're gonna have to think about how we treat these, uh, oversights, and it's gonna have to be, you know, we can buy stock in companies that we don't know, uh, what their books look like because we trust accountants to go in and look at it. You know?

So there's a model. So we have models in society. I used to work at the United Nations, uh, with the atomic energy agency and the weapons inspectors. We don't want people building bombs, okay? So we have an infrastructure around inspection and testing and a little bit of carrot and stick to where you get power reactor information to help you use these power reactors better to avoid them blowing up. So we we we built this infrastructure around that and, you know, knock on wood, and I'm not sure when this broadcast, but we've not had any huge, you know, nuclear incidents, um, you know, since that organization developed.

So it's not like we're the first time ever thinking about a technology that that is powerful and could do some harm, and we gotta figure out how to regulate it.

There is a a unique aspect to it from those, so you got those 2 examples, the jungle and the atomic, um, and those those are both, you know, hurting or or killing people, right? Those those are some of the examples. But, um, what about job loss? Like, one of the things that I've thought and and first of all, I look back at the s the stories of of the horses and the cars, you know, the the common ones, and, uh, the stories of the, uh, the mail system.

Everyone thinking that the internet was gonna put the the mail system out of business, but it's just exploded it cause of deliveries. And so I'm like, Alright, I I get those. However, when I see, like, job con consolidation, essentially, one of the the things that's different now than those others is the speed at which it can deploy. So, for example, there was a certain there's a lag time from the horses transitioning to the cars, and that's, let's say, a year or 2, right?

And there's a lag time of of of these technologies. We've compressed the la the lag time quite a bit. Um, do you th- do you think that we have the infrastructure in place to to handle that? Like, if we just wake up tomorrow and 30% of one industry's jobs is just gone because of this technology, like, how would we respond to that?

Yeah, that I mean, certainly if you wake up tomorrow, and I I think I think your point's valid about the increase in speed. I mean, I think it's one thing we're grappling with, and I you know, there was a call 6 months ago to pause, you know, that I think that's that was a that, you know, I mean, there there's too much in that prisoner's dilemma that lets people defect, um, you know, in in a shocking turn of events, the people who are ahead or the people who wanna pause, and the people who are behind are not interested in pausing.

And and, you know, in other ways, my analogy breaks down is that most of my analogies had physical goods involved, and this is purely information goods, so it moves across borders relatively quickly. Cause, I mean, those are not perfect analogies, but, I mean, I think there's 3 things that are gonna happen in terms of job loss, and the one that gets the most attention is jobs are gone. You come in tomorrow, and the machine machine is doing your job, and your this is your 30% example.

Job doesn't exist anymore. That's a scenario where machines have come in and taken the job entirely. And, yeah, I'm I'm pretty doubtful on that one to start with. I mean, I I get the I get the fear, but I'm pretty doubtful. I think the 2nd one is much more real, and that is that some other human out there takes your job cause they're better at using those tools. So there's a bunch of tools out there, and like you said, you are using these tools and you get better at them, and you learn how to use them, and you learn how to get more productive with them.

And suddenly, you are more attractive than than your com your human competitor. That's a very real risk, and that's the, you know, machines won't replace people, but people using machines will replace people not using machines. Uh, argument. I I think that's very real. Um I think the 3rd one is more, you know, and then get to go back to your example here. Let's say that your organization doesn't use these technologies at all, and another organization does.

Well, then you get a an organizational level, uh, wipeout. So the cost structure of an organization that is using these technologies drops, and they other organization not using these technologies is no longer cost competitive. And so that is a scenario for massive drop-offs, job loss too. It's just not at the individual level, it's more at the firm level. And I think that's another one to be so, the 1st one is something that catches our mind, but the number 2 and 3 are, I think, are the bigger things going on right now.

You mentioned, Joel, the $100,000 a year you think you save. Somebody out there is spending $100,000, and they won't be able to do it for too many more years before you put them out of business, right?

Well, we're always trying to put ourselves out of business,

So you know what somebody because else does, right?

yeah, yeah, well, it's like, I I we're 7 years or so into this business, and, you know, obviously, there's a hunger curve, right? You try to stay hungry the whole time, but you're never gonna be as hungry as before you made anything of yourself. And so those people are they're just looking at, unemotionally, they're looking at the marketplace, seeing what tools are available, grabbing them and trying to achieve outcomes. Versus, like, this is the proper way to do it, and this is the best way to do it, and this is the way the subject, you know, for example, Josh, Josh and I had these conversations, uh, a couple years ago, maybe a year or 2 ago, when the AI got

so good for post-production, uh, audio enhancements that it no longer made sense to just go manually do all of this stuff. And instead, you can just say, alright, AI is gonna do that section of my workflow, and then I'm gonna spend more time editing the conversation points and then and how and the flow of it. Um, and so, y- but there are a lot of people out there that are that are still like, Ah, this is the the right way to do it.

This is the real organic way to do it, and and, uh, all the real artists do it this way. It's like, well, look at outcomes.

Yeah, you too. Yeah. Well, I think there's let's pull 2 points out of there. One is most people are doing stuff that they don't wanna be doing in their job, for at least some of it. I mean, for me, I get an email that says, you know, Hey, professor, I miss class. Did I miss anything? And my snarky response is always, Nope. But I looked up and saw you weren't there. We just shut everything down. But no, I temper my snarky respon- response, and, you know, if I could have AI temper my response for me, so I could write my snarky and then have it tone it down and make it friendly, that's or answering questions around the syllabus, you

know, that's something I do a lot, and I would not say I'm adding a lot of value there. We did a stur- study a few years ago, and we asked people, Hey, what do you think about artificial intelligence? Are you do you hope that it's gonna do some of your tasks, or do you fear that it's gonna do some of your tasks? 73% of the people said they hoped it would do some of their task, 33% said fear.

And I think that's where we are now. I'm not saying that that number those numbers are not gonna change as we get more general or more, uh, knowledge-oriented tools, maybe those numbers change. But right now, we've got a lot of people doing stuff that they don't wanna be doing, and that probably not adding value. I'm I'm not gonna put people on the spot here, but I don't know about the post-production example. Is that fun work or not fun work?

It may just be tedious work.

Yeah, Josh, cleaning up cleaning up real bad audio. Yeah, I mean, where it excels is, is in the audio that sounds like it was qu- recorded in a trash can, and that's never fun to listen to or to work on. Um, and it's, uh, much more rewarding just to have a machine do it cause it sounds better. So

yeah, and you can get to focus on what content and what's important there, and what what makes a difference. And the second thing to pull out of that that you were saying, Joel, is that you you but you switched from the word job to t to skill or task, I can't remember exactly what you said, but I think that's the way to think about it. Our jobs right now are this composite of hundreds of things that we do on a daily basis.

Some of those are more amenable for computer, some are less, and we gotta figure out where those are. And and again, that comes down to management, because figuring that out is management. Figuring out where the ROI is for this task versus that task is the ex- crux of managing a scarce resource. You can't automate everything.

I wanna make sure we touch on Optimus. Have you seen Elon Musk's Bipedal robot Optimus?

No. Oh, oh, the, uh, yours are the actual robot? Uh, yeah, yeah, yeah. Okay, yeah, sorry.

The thing walking around, folding laundry, things like that. Have you seen it? That

Uh, yes, and I I see. What do you think? f- f- I can tell you're excited about it.

We're it's the e- well, we're we're suckers for convenience, humans. And so the moment that you s like, I would typically say, No, I'm not letting that bipedal robot in my house. But then the moment you say, Okay, for $200 a month, your house is gonna be cleaned all the time, it'll clean while you sleep, or when you're at work, or whatever it is. You're gonna your meals are gonna be made, your wife's gonna be happier, laundry's gonna like, everything's gonna get done So there's a for cheaper.

Right?

laundromat folded. You're living the life of Jay-Z right there, aren't you? He's

Right? And it it and I'd be like, Yeah, my disdain for the, uh, dystopian future, but my love for cheap convenience is they're at odds.

what I think is interesting about that is it it also speaks to our fascination with the biped. Like, when I when people think about artificial intelligence, they immediately gravitate towards these videos they've seen of the Boston Dynamics dogs or these, you know, these, and they all look like humans. I don't know who decided that was a good shape, like, why like, why is that our is that why is that what we want to look for in a in a machine?

I think there's lots of shapes of people, there's, you know, lots of shapes of things that would be more useful than people shape. Now, I admit you might need some people-shaped things to do jobs that were designed for people originally, but there's no reason to say that people shape is the right shape. And it gets us down into this humanoid robot thinking, which I think is kind of limit us in scope.

I is it you think it's the anthropomorphism? Yeah. And Like, we just we do it with dogs, we've been doing it with things forever, and we're just doing it with the technology now.

right, but there's no there's nothing that says that that's the right shape. And I think that's where, you know, back to the whole why have the machine read the facts? Well, why have a shape that is the shape of a human? Just because that's what I mean, there's some convenience of form factor in switching immediately from the ironing board at the height of me versus the ironing board that would be most optimal for a machine. But it won't be long before we we switch to that.

I wonder if those mirror neurons are partly to blame. We want thing we like things when they're like us, right? Yeah, yeah. Oh, wow. What what other so I don't I don't I think y- one of the interesting things about this interview is, in almost every section of questioning that I had for you, you brought up a completely new thought that I hadn't thought about. Which is

That's a lot of pressure, though.

I like you, uh, I like you quite a bit. I was I was really surprised with, um, the when you talked about the threat being the organizations that use the technologies outcompeting in a free market, the organizations who aren't, that's w- that's going to happen, they're gonna collapse because, yeah,

That's n- that's not even far-fetched to think about. He he's and again, we get so focused on that first example that we lose track of the 2 and 3 that are gonna spank us in the in the backside. Um,

uh, what are you what are you learning about? I just wanna talk with you just about, like, professor stuff for a minute. What are you learning about this next generation? How long have you been a professor? More than a decade?

gosh, yeah. Hm, 15 years.

Okay, what what's the trend that you've seen in changing with the students coming in?

You know, okay, that's something. No, man, you're you're just like, we're let me lay down on the couch and talk. No, I mean, I I think it's pretty fascinating. Kids are getting exposed to these technologies much, much earlier. And so you think about what that does to us in education is that it it it means that people are coming in with with a set of skills that they didn't come in with. We, in theory, used to provide those skills, and now they're coming in with those skills.

So then the question is, what do we provide now? Can we build on those? And so I think there's a a shift, and I think we can address the shift. But what I'm more sort of fascinated by is the dispersion, is the variance in that. So what I mean by that is that we have some people who've gotten super curious about technology and come in and are just amazing and on top of things already, and I'm not sure what I can teach them, right?

On the other hand, we also have some people who seem to be, you know, getting further and further. And so what that means is that that bell curve that we think about teaching to is changing. A bell curve is really nice in class if it's tall, because what that means is it's narrow, because then I in class can talk about the same topic and I'm gonna bore 2 or 3 and I'm gonna lose 2 or 3, but I'm gonna hit that sweet spot in the middle.

But when that thing spreads out, then I'm more in trouble, cause I can still cover the same sliver, but then my my tails get bigger, and I lose more and more people, either through boredom or not not being prepared. And I think that's actually what I'm most excited about with artificial intelligence right now, because I think it can help with that. We've spent so much time thinking about what we can teach the machines. That's back to your, you know, the Turing thing we were talking about earlier.

Can we teach this to be like a com- human? Can we teach it? And that's with us in this. I am the knower of all. I am the the presence which knows and bestows knowledge upon everyone, and that is just a bad mindset to be in, cause it it positions me as the giver of knowledge and students as the empty vessels to receive them. And there's a whole, you know, line of of research on this. But what I'm excited about artificial intelligence is being able to meet people where they are.

Like, ideally, we would have 1 on 1 teaching, but it just doesn't scale. To what degree can we actually use artificial intelligence to help us learn? And I'm not saying I'm not saying teach the machines, I'm saying, have the machines help us learn. If you think about something like well, you go back to your example of, you know, the ironing and and Jay-Z, you know, I guess if Jay-Z wants to get, you know, fit and have a personal trainer, they've got a per he's got a personal trainer on staff constantly, ready the moment that he's ready to exercise.

Whereas me, when I peel myself off the couch, chances are there's not a te- you know, a personal trainer hanging out and waiting. But introduce technology into this, and, you know, we've seen Peloton. I'm sure others I don't wanna particularly shout them out, but Peloton has a device that sits in your room and it can say, Um, you call that a plank, buddy. You know, that's not looking so or I thought we said 12 pushups. That looked a lot like 6 to me, or, you know, so these things that we can have the machine, it probably is not as good right now as an as a personal trainer, you know, as an individual human personal trainer.

But it's there, available, and it's customized to what you're doing at that moment. And I think that's very exciting. I don't know if you use something like Duolingo. The goal of Duolingo is not to translate language. The goal of Duolingo is for you to learn language. It's for most people to learn English. You can have customized language instruction at scale. It knows exactly what you know, it knows exactly what you don't know, it knows what you're good at, knows what you're weak at.

We can have human improvement that could be phenomenal, and I'm very excited about that. And, you know, you think about how you may have learned language growing up. You may have had a teacher who, through years of experience, learned good ways to teach, and they but how did those good ways get propagated? With Duolingo, if they figure out something that helps people learn faster, they can scale it across the world tomorrow. And that's pretty exciting. Uh, one of the examples I like is, uh, the Fosbury flop.

So, Dick Fosbury invented the high jump method where you instead of going and running and jumping forwards over the bar, you jump backwards over the bar. In about 2 years, the whole world high jumping world switched from forward over the bar people to backwards over the bar people. Alright, this is related. I'm not going a tangent here. Um, you think about there's companies putting sensors in ski boots, and they're monitoring in real time how you're skiing.

What's gonna happen is that somebody's gonna invent some crazy way of skiing that does better, performs better in some way. I don't know what it is. They're gonna record it, and the next day, their engineer is gonna look at it, and then the next day, it's gonna be in the advice that everyone else using that device is using. Does that make sense? So we have the ability to push out what we know at scale quickly. Again, these are ways that humans could be better, and I think I'm super excited about that.

yeah, my, uh, my kids use, uh, simply piano. Have you come across this application?

Hm, there you go. Hm.

And they have really mastered how to break it down so I can put a four-year-old or a seven-year-old in front of that thing and it'll walk they'll it'll hold their attention while teaching them, uh, step by step how to play this piano. It's fantastic.

That see, this stuff is amazing because, again, the goal of of that tool is not to play the piano or even to play music. The goal is to help you play music better. And I think that's just an untapped resource that we are we're really only getting started thinking about what we can learn from the machines. Thanks for listening today. We'll be back with new episodes of Me, Myself, and AI on September 17th. Please tune in.