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Stephen Wolfram: How AI Works and How to Use It to Stay Ahead | Artificial Intelligence | AI Vault
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- [00:03]
A
Yeah, fam. I have to admit, Revolve takes all my money.
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B
I spent all my money at Revolve.
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A
It is by far my favorite place to shop. I've been shopping there for years and.
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B
I recently moved from Jersey City to Austin.
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A
And when I got here I realized that I needed a wardrobe upgrade. In New York. I went out, it was always like really fancy mini dresses and heels out here. It is cool girl Austin vibes. People are wearing baggy pants, they're more casual and and I seriously needed new clothes. I recently did a fall pant haul and I got all new pants and I just can't wait to wear them. I totally fit that Austin girl vibe now. And I'm so obsessed with Revolve because they've got all the amazing trends and brands that you could ever imagine. They've got inclusive sizing from extra extra small and also they've got men's clothes. So I have been buying my man a bunch of clothes on Revolve cause I'm also trying to have him match my style. And, and since it's that time of year, you've got to check out the Revolve holiday shop. They've got sparkly dresses, cozy sets and the cutest gifts and every holiday look that you need all curated in one place. And for you young and profiting men out there looking to upgrade your wardrobe, you've got to check out Revolve for men. If you don't think you have good fashion sense, just go on Revolve and buy some stuff. You'll be fashionable after that. Whether it's a weekend away or a big night out or a holiday party, your dream wardrobe is just one click away. Head to Revolve.com profiting shop my edit and take 15% off your first order. With code profiting fast two day shipping, easy returns. It's literally the only place you need to shop from. That's Revolve.com profiting to shop my favorites and get 15% off your first order with code profiting offer available for a limited time.
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C
So happy shopping AI is another step in the automation of things. This is the coming paradigm of the 21st century.
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A
Dr. Steven Stephen Wolfram.
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B
He's an award winning renowned computer scientist.
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A
Mathematician, a theoretical physicist and the founder of Wolfram Research.
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C
More and more systems in the world will get automated when things get automated. Things humans used to have to do with their own hands, they don't have to do anymore. Of all the things that are out there to compute the set that we humans have cared about so far in the developments of our civilization is a tiny, tiny, tiny slice. The AI left to its own devices is an infinite set of things that it could be doing.
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B
The question is AI potentially, potentially being sentient or having any sort of agency? How does this make you feel about human consciousness?
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C
The thing that we learned from sort of the advance of AI is, well, actually, there's not as much distance between sort of the amazing stuff of our minds and things that are just able to be constructed computationally. I mean, one of the things to realize is.
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A
Hello, young and profiters. If you've been following along, you already know our AI Vault series is well underway, and it's been filled with insights from the greatest minds in artificial intelligence. In the last episode, we featured the godmother of AI, Dr. Fei Fei Li, and we talked about human centered AI. And this week we're featuring Stephen Wolfram. Stephen is the founder of Wolfram Research, the creator of Mathematica and Wolfram Alpha, and he's one of the greatest scientific thinkers of our time when it comes to computational science and the nature of intelligence. If you've ever wondered what's really happening inside ChatGPT, well, or how AI systems actually think, this is a conversation you won't want to miss. Stephen will break down the mechanics of neural networks, large language models, and his groundbreaking concept of computational thinking, a powerful way to solve problems that could transform not just technology, but the way we approach life and work itself. We'll cover the future of AI, its impact on jobs, creativity, and society, and why intelligence may be far bigger and more universal than we've ever imagined. And guys, if you're new here, don't just listen. Make sure you subscribe so you can stay plugged into all these different AI conversations. Coming up. Without further ado, here's my conversation with Stephen Wolfram.
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B
Stephen, welcome to Young and Profiting Podcast.
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C
Hello there.
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A
Hi.
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B
I am so excited for today's interview. We love the topic of AI, and I wanted to talk a little bit about your childhood before we got into the meat and potatoes of today's interview. So, from my understanding, you started as a particle physicist at a very young age. You even started publishing scholarly papers as young as 15 years old. So talk to us about how you first got interested in science and what.
- [04:21]
A
You were like as a kid.
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C
Well, let's see. I grew up in England in the 1960s when space was the thing of the future, which it is again now, but wasn't for 50 years, and so I was interested in those kinds of things. And that got me interested in kind of how things like spacecraft work. And that got me interested in physics. And so I started learning about physics. And so happened that the early 1970s were a time when lots of things were happening in particle physics. Lots of new particles getting discovered, lots of fast progress and so on. And so I got involved in that. It's always cool to be involved in fields that are in their kind of golden age of expansion, which particle physics was at the time. So that was how I got into these things. It's funny you mentioned AI and I realized that when I was a kid, sort of machines that think were right around the corner, just as colonization of Mars was right around the corner too. But it's kind of an interesting thing to see what actually happens over a 50 year span and what doesn't.
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Go ahead.
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B
I was gonna say, yeah, it's so crazy to think how much has changed over the last 50 years and how much has not.
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C
I mean, I've been in science, for example, I have just been finishing some projects that I started basically 50 years ago. And it's kind of cool to finish something that one. There's a big science question that I started asking when I was 12 years old about how a thing that people have studied for 150 years now works. The second law of thermodynamics. And I was sort of interested in that when I was 12 years old. And I finally, I think, figured that out. I published a book about it last year. And it's kind of nice to see that one can tie up these things. But it's also a little bit shocking how slowly kind of big ideas move. I mean, for example, the neural nets that everybody's so excited about now in AI, neural nets were invented in 1943. And the original kind of conception of them is not that different from from what people use today. Except that now we have computers that run billions of times faster than things that were imagined back in the 1950s and so on. But.
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It'S interesting, some things. Occasionally things happen very quickly. Oftentimes it's shocking how slowly things happen and how long it takes for the world to kind of absorb ideas. And sometimes there'll be an idea and finally some technology will make it possible to execute that idea in a way that wasn't there before. Sometimes there's an idea and it's been hanging out for a long time and people just ignored it for one reason or another. And I think some of the things that are happening with AI today probably could have happened a bit earlier the. Some things have depended on sort of the building of a. Of a big technology stack. But it's. It's always interesting to see that, to.
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B
Me at least, it's so fascinating. And this actually dovetails perfectly into. My next question is about your first experiences with AI. So now everybody knows what AI is, but really most of us really started to understand it and using this term maybe five years ago, max. But you've been kind of studying this for decades. Even before people probably called it AI. So can you talk to us about.
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C
The beginnings of how it predates me? AI was that term was invented in 1956.
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I knew that they're dead now, but the couple of characters who invented that term.
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It'S funny because the idea that we're going to. As soon as computers were invented. So electronic computers invented basically in the late 1940s. And they started to become sort of things that people had seen by the beginning of the 1960s. Like probably I first saw a computer when I was 10 years old, which was 1969ish. And at the time a computer was a very big thing, tended by people in white coats and so on. And so when I first sort of got my hands on a computer in 1972, and that was a computer that was kind of the size of a large desk and programmed with paper tape and so on, and was rather primitive by today's standards, but kind of the elements were all there by that time. But most people had. It's true, most people had not seen a computer until probably the beginning of the 1980s or something, which was when PCs and things like that started to come out. But you know, it was from the very first moments when electronic computers came on the scene. People sort of assumed that computers would automate thought as bulldozers and things like forklift trucks had automated kind of mechanical work. And that was the.
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Giant electronic brains was a typical characterization of computers in the 1950s. So this idea that one would automate thought was a very early idea. That was. Now the question was how hard was it going to be to do that. And people in the 1950s and beginning of the 1960s, they were like, this is going to be easy. Now we have these computers, it's going to be easy to replicate what brains do. In fact, a good example back in the beginning of the 1960s, a famous incident was.
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During the Cold War, and people were worried about us, Russian, Soviet communication and so on. They said, well, you know, maybe the people are in a room, there's some interpreter, the Interpreter is going to not translate things correctly. So let's not use a human interpreter. Let's teach a machine to do that translation. Beginning of the 1960s, and of course, machine translation, which is now finally in the 2000s, pretty good, took an extra 60 years.
- [10:38]
To actually happen. And people just didn't have the intuition about what was going to be hard, what wasn't going to be hard. So the term AI was sort of in the air already very much by the 1960s, I'm sure. When I was a kid, I'm sure I read sort of books about the future in which AI was a thing, and it was certainly in movies and things like that. I think then this question of, okay, so how would we get computers to do sort of thinking like things. You know, when I was a kid, I was interested in kind of taking sort of the knowledge of the world and somehow cataloging it and so on. I don't know why I got interested in that, but that's something I've been interested in for a long time. And so I started thinking, you know, how would we take kind of the knowledge of the world and make it automatic to be able to answer questions based on sort of the knowledge that our civilizations accumulated? And by the time. So I started building things along those lines and I started building whole technology stack that I started in the late 1970s. And well, now it's turned into a big thing that lots of people use. But that kind of the idea there, the first idea there was let's be able to compute things like math and so on, and let's sort of take what has been something that humans have to do and make it automatic to have computers do it. So that was the thing when it came to, for example, people had said for a while, when computers can do calculus, then we'll know that they're intelligent. Okay, so things I built solved that problem. By the mid-1980s, that problem was pretty well solved. And then people said, well, it's just engineering. It's not really a computer sort of being intelligent. I would agree with that. But then actually, at the very beginning of the 1980s, when I was working on kind of automating things like mathematical computation, I got curious about sort of the more general problem of sort of doing the kinds of things that we humans do. Like we match patterns, we say, that's a picture. We see this image and it's got a bunch of pixels in it, and we say, that's a picture of a cat or that's a picture of a dog. And this question of how do we do that kind of pattern matching, I got interested in and started trying to figure out how to make that work. I knew about neural nets. I started trying to get. This must have been 1980, 81, something like that. I started trying to get neural nets to do things like that, but they didn't work at all at the time. Hopeless, actually, as it turns out, in a bizarre. You say things happen quickly, and I say things sometimes happen very slowly.
- [13:21]
I was just working on something that is kind of a potential new direction for how neural nets and things like that might work. And I realized, you know, I did this once, I worked on this once before, and I pulled out this paper that I wrote in 1985 that has kind of the same basic idea that I was just very proud of myself for having figured out just last week. And it's like, well, I started on it in 1985. Well, now I understand a bunch more. And we have much more powerful computers. Maybe I can make this idea work. But so.
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This notion that one could do. There are things that people thought would be hard for computers, like doing calculus and so on, we crushed that, so to speak, a long time ago. Then there were things that are super easy for people, like tell that's a cat, that's a dog, which wasn't solved, and I wasn't involved in the solving of that. That's something that people worked on for a long time, and nobody thought it was going to work. And then suddenly, in 2011, sort of through a mistake, some people who'd been working on this for a long time kind of left a computer trying to train to tell things like cats from dogs for a month without paying attention to it. They came back, they didn't think anything exciting would have happened, and by golly, it had worked. And that's what kind of started the current enthusiasm about neural nets and deep learning and so on. And when ChatGPT came out in late 2022, again, the people who'd been working on it, they didn't know it was going to work. There had been previous. We had worked on previous kinds of language models, things that try to do things like predict what the next word will be in a sentence, those sorts of things. And they were really pretty crummy. And suddenly, for reasons that we still don't understand, we kind of got above this threshold where it's like, yes, this is pretty human like, and it's not clear what caused that threshold. It's not clear whether we, in our human languages, for example, we might have, I don't know, 40,000 words that are common in a language like most languages, English as an example, and it's kind of, you know, there's probably that number of words is somehow related to how big an artificial brain you need to be able to deal with language in a reasonable way. And, you know, if our brains were bigger, maybe we would routinely have languages with 200,000 words in them. We don't know. And maybe, you know, it's this kind of match between what we can do with an artificial neural network versus what our human sort of biological neural nets managed to do. We managed to reach enough of a match that people say, by golly, the thing seems to be doing the kinds of things that we humans do.
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But I mean, this question, what's ended up happening is.
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There'S sort of what us humans can quickly do, like tell a cat from a dog or figure out what the next word in the sentence is likely to be. Then there are things that we humans have actually found really hard to do, like solve this math problem or figure out this thing in science or do this kind of simulation of what happens in the natural world. Those are things that with sort of the unaided brain doesn't manage to do very well on. But the big thing that's happened last 300 years or so is we built a bunch of kind of formalization of the world. First with things like logic, that was back in antiquity, and then with math and most recently with computation, where we're kind of setting up things so that we can talk about things in a more structured way than just the way that we think about them. Sort of off the top of our head, so to speak.
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A
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- [23:16]
B
So that's so interesting. And I know that you work on something called computational thinking, and I think what you're saying now really relates to that. So help us understand the Wolfram Project and computational thinking and how it's related to the fact that humans, we need to formalize and organize things, like you said, like mathematics and logic. Why do we, like, what's the history behind that? Why do we need to do that as humans? And then how does it relate to computational thinking in the future?
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C
Well, so.
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There'S kind of things one can immediately figure out. One just sort of intuitively knows, oh, that's a cat, that's a dog, whatever. Then there are things where you have to kind of go through a process of working out what's true and, or working out sort of how to construct this or that thing. When you're going through that process, you've got to have kind of solid bricks to start building that tower. And it's not something. So. So you have to. So what are those bricks going to be made of? Well, you have to have something which has sort of definitive structure. And that's something where, for example, back in antiquity, when logic got invented, it was kind of like, well, you can think vaguely. Yeah, that sentence sounds kind of right. Or you can say, well, wait a minute, this or that. If one of those things is true, then that or that has to be true, et cetera, et cetera, et cetera. You've got some structured way to think about things. And then in 1600s, math became sort of a popular way to think about the world. And then you could say, okay, we're looking at, well, roughly a planet goes around the sun in roughly an ellipse. But let's put math into that, and then we can have this kind of way to actually compute what's going to happen. And then. So for about 300 years, kind of this idea of math is going to explain how the world works at some level was kind of a dominant theme. And that worked pretty well in physics. It worked pretty terribly in things like biology, in social sciences and so on. People imagined there might be a social physics of how society works that never really panned out.
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So there was sort of this question of things, that things were places where math had worked and it gave us a lot of modern engineering and so on, and the cases where it hadn't really worked. I got pretty interested in this at the beginning of the 1980s, in sort of figuring out how do you sort of formalize thinking about the world in a way that goes beyond what math provides one things like calculus and so on. Give one. What I realized is that you just think about, well, there are definite rules that describe how things work. And those rules are more stated in terms of, oh, you have this arrangement of black and white cells, and then this happens and so on. They're not things that you necessarily can write in mathematical terms, in terms of multiplications and integrals and things like this. And so, as a matter of science, I kind of got interested in. So what do these simple programs that you can describe as these kind of systems as rules of being, what do they typically do? And kind of what one might have assumed is you have a program that's simple enough, it's going to just do simple things. This turns out not to be true. Big surprise to me, at least, I think, to everybody else as well. It took people a few decades to kind of absorb this point. It took me a solid bunch of years to absorb this point. But you just do these experiments, computer experiments, and you find out, yes, you use a simple rule, and no, it does a complicated thing. And that turns out to be pretty interesting if you want to understand how nature works. Because it seems like it's kind of the secret that nature uses to make a lot of the complicated stuff, that we see the same phenomenon of simple rules, complicated behavior. So that turns into a whole big direction and kind of new understanding about how science works. I wrote this big book back in 2002 called a new Kind of Science, which is. Well, its title kind of says what it is.
- [27:27]
So that's one kind of branch is sort of understanding the world in terms of sort of computational rules. Another thing has to do with taking the things that we normally think about, whether that's, you know, how long is it going to take, how far is it from one city to another, or, you know, how do we, you know, make this image, have this, how do we remove, you know, this thing from this image or something like this, Things that we would normally think about and talk about. And how do we take those kinds of things and think about them in a structured computational way? And so that has turned into a big enterprise in my life, which is building our computational language, this thing now called Wolfram Language, that powers a lot of kinds of, well, research and development kinds of things, and also lots of actual practical systems in the world. Although when you are interacting with those systems, you don't see what's inside them, so to speak. But kind of the idea there is to make a language for. For describing things in the world which might be, this is a city.
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You might say.
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It's both the concept of a city and the actuality of the couple of hundred thousand cities that exist in the world, where they are what their populations are, lots of other data about them, and being able to compute.
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Things about things in the world, so to speak. And so that's been sort of a big effort to build up that computational language. And the thing that's kind of exciting, that we're sort of on the cusp of, I suppose, is people who study things like science and so on for the last 300 years. It's like, okay, to make this science really work, you have to make it somehow mathematical. Well, the thing that's sort of now the case is that the new way to make science really work is to make it computational. And so you see all these different fields, call them X. You start seeing the computational X field start to come into existence. And kind of, I suppose one of my big life missions has been to provide this sort of language and notation for making computational X for all X possible. It's kind of a similar mission to what people did maybe 500 years ago when people invented mathematical notation. I mean, there was a time when if you wanted to talk about math, it was all in terms of just regular words at the time in Latin.
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And then people invented things like plus signs and equal signs and so on. And that kind of streamlined the way of talking about math. And that's what led to, for example, algebra and then calculus and then all the kind of modern mathematical science that we have. And so similarly, what I've been trying to do last 40 years or so is build kind of a computational Language, a notation for computation, a way of talking about things computationally that lets one sort of build computational X for all X. And one of the great things that happens when you make things computational is not only do you have a clearer way to describe what you're talking about, but also your computer can help you figure it out, so to speak. And so you get this sort of superpower. As soon as you can express yourself computationally, you kind of tap into the superpower of actually being able to compute things. And that's amazingly powerful. And when I was a kid, as I say in the 1970s, and physics was sort of hopping at the time because various new methods have been invented, not related to computers at this time, sort of all the computational X fields are just starting to kind of really hop and it's starting to be possible to do really interesting things. And that's going to be sort of an area of tremendous growth in the next however many years.
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B
It sounds really, really exciting. So I have a few follow up questions to that. So you say that computational thinking is another layer in human evolution. So I want to understand why you feel it's going to help humans evolve. Also curious to understand, like, the practical ways that you're using the Wolfram language and how it relates to AI, if it does at all.
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C
Yeah, yeah, right. Well, so let's take the second thing first. Okay. So.
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Wolfram language is about representing the world computationally in a sort of precise computational way. It also happens to make use of a bunch of AI. But let's put that aside.
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The way that, for example, something like an LLM, like a ChatGPT or something like that.
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What it does is it makes up pieces of language. So if we have a sentence like the cat sat on the blank, what it will have done is it's read a billion web pages. Chances are the most common next word is going to be mat. And it has kind of set itself up so that it kind of knows that the most common next word is mat. So let's write down mat. So the big surprise is that it doesn't just do simple things like that, but having kind of built the structure from kind of reading all these web pages, it can write sort of plausible sentences. Those sentences might make. They sort of sound like they make sense. They're kind of typical of what you might read. They might or might not actually have anything to do with reality in the world, so to speak.
- [33:20]
But that's working kind of the way humans immediately think about things. Then there's the separate whole idea of Kind of formalized knowledge, which is the thing that led to kind of modern science and so on. That's a different branch from kind of things humans just can quickly and naturally do. So in a sense, Wolfram language, the big contribution right now to sort of the world of the emerging kind of AI language models, all this kind of thing, is that we have this computational view of the world which allows one to do precise computations and build up these kind of whole towers of consequences. So the typical setup, and you'll see more and more coming out along these lines. I mean, we built something with OpenAI back, oh, gosh, a year ago now, that was sort of an early version of this is you've got the language model and it's trying to make up words. And then it gets to use as a tool our computational language, if it can formulate what it's talking about. Well, we have ways to take the natural language that it produces. We've had our WolframAlpha system, which came out in 2009, is a system that has natural language understanding. We sort of had solved the problem of small sentence, one sentence at a time, kind of. What does this mean? Can we translate this natural language in English, for example, into computational language, then compute an answer using potentially many, many steps of computation? And then that's something that is sort of a solid answer that was computed from knowledge that we've curated, et cetera, et cetera, et cetera. So kind of the typical mode of interaction is there's sort of a linguistic interface provided by things like LLMs and that using our computational language as a tool that to actually figure out, hey, this is the thing that's actually true, so to speak, and that's a mechanism. It's sort of. It's the. Just as humans don't necessarily immediately know everything, but with tools they can get a long way. I suppose it's been sort of the story of my life, at least is, you know, I discovered computers as a tool back in 1972 and I've been using them ever since and managed to figure out, well, a number of interesting things in science and technology and so on by using this kind of external, to me, kind of superpower tool of computation, so to speak. And it's kind of, well, the LLMs and the AIs get to do the same thing. So that's kind of how we sort of.
- [36:01]
That's sort of the core part of the. How the technology I've been building for a long time kind of most immediately fits into the current sort of expansion, of excitement about AI and language models and so on.
- [36:20]
There are other pieces to this which have to do with how, for example, science that I've done relates to understanding more about how you can build other kinds of AI like things. But that's. That's sort of a separate branch.
- [36:34]
B
Yeah. Well, this is so fascinating. Honestly, you're teaching us so much. I feel like a lot of people tuning in are probably learning a lot of this stuff for the first time. But one thing that we all are using right now is chat GPT. Right. So everybody has sort of embraced chatGPT. It kind of feels like it's magic, right. When you're just getting something that is giving you something that a human could potentially write. So I have a couple questions about ChatGPT. You alluded to how it works a bit, but can you give us more detail about how neural networks work in general and what Chap GPT is doing in the background to spit out something that looks like it's written by a human?
- [37:12]
C
Yeah, so, I mean, neural networks. So the original inspiration for neural networks was understanding something about how brains work. You know, in our brains, we have about roughly 100 billion neurons. Each neuron is a little electrical device, so to speak. And they're kind of connected with things that kind of look under a microscope a bit like wires. So one neuron might be connected to 1,000 or 10,000 other neurons in one's brain. And these neurons kind of. They'll have a little electrical signal, and then they'll pass on that electrical signal to another neuron. And pretty soon one's gone through a whole chain of neurons, and one says the word next word or whatever the. And so this kind of the electrical machine kind of lots of things connected to things. That's kind of how people imagine that brains work. And that's how neural nets are kind of an idealization of that set up in a computer, where one has.
- [38:13]
These connections between sort of artificial neurons, usually called weights. You often hear about people saying this thing has a trillion weights or something. Those are kind of the connections between artificial neurons, and each one has a number associated with it. And so what happens when you ask ChatGPT something? What will happen is it will take the words that it's seen so far, the prompt, and it will grind them up into numbers, and it will take that sequence of numbers and feed that in as input to this network. So it's just saying here, it takes the words more or less. Every word in English gets a number or every part of a Word gets a number. You have the sequence of numbers. That sequence of numbers is given as input to this essentially mathematical computation that goes through and says, okay, here's this arrangement of numbers. We multiply each number by this weight, then we add up a bunch of numbers, then we take sort of a threshold of those numbers and so on, and we keep doing this, and we do it a sequence of times, like a few hundred times for typical kind of chatgpt type behavior, a few hundred times. And then at the end, we get out another number. Actually, we get out another collection of numbers that represent the probabilities that the next word should be this or that. So in the example of the cat sat on the. The next word has probably very high probability. 99% probability to be MAT and 1% probability or 0.5% probability to be floor or something. And then what ChatGPT is doing is it's saying, well, usually I'm going to pick the most likely next word. Sometimes I'll pick a word that isn't the absolutely most likely next word. And it just keeps doing that. So, and the surprise is that just doing that kind of thing, a word at a time gives you something that seems like a reasonable English sentence. Now, the next question is, well, how did it figure out all those weights that. How did it get all those. In the case of the original ChatGPT, I think it was 180 billion weights. How did it get those numbers? And the answer is, what it tried to do was it was trained. And it was trained by being shown all this text from the web. And what was happening was like, well, you've got one arrangement of weights. Okay, what next word does that predict? Okay, that predicts turtle as the next word for the cat sat on the turtle is wrong. Let's change that. Let's see what happens if we adjust these weights in that way. Oh, we finally got it to say, Matt, great. That's the correct version of that particular weight. Well, you keep doing that over and over again. That takes huge amounts of computer effort. You keep on bashing it and trying to get it. No, no, no, you got it wrong. Adjust it slightly to make it closer to correct. Keep doing that long enough, and you get something which is a neural net which has the property that it will typically reproduce the kinds of things it's seen. Now, it's not enough to reproduce what it's seen because if you keep going writing a big, long essay, a lot of what's in that essay will never have been seen before. It will be something that those particular combination of words will never have been produced before. So then the question is, well, how does it extrapolate? How does it figure out something that it's never seen before? What words is it going to use when it never saw it before? And this is the thing which nobody knew what was going to happen. This is the thing where the big surprise is that the way it extrapolates is similar to the way we humans seem to extrapolate things. And presumably that's because its structure is similar to the structure of our brains. We don't really know why it sort of when it figures things out that it hasn't seen before, why it does that in a kind of human like way. That's kind of a scientific discovery. Now we can say, you know, can we get an idea why this might happen? I think we have an idea why it might happen. And it's more or less this that you say, how do you put together an English sentence? Well, you kind of learn basic grammar. You say it's a noun, a verb, a noun. That's a sort of typical English sentence. But there are many noun, verb, noun, English sentences that aren't really reasonable. Sentences like, I don't know.
- [42:43]
The electron, eight, the moon. Okay, it's grammatically correct, but probably doesn't really mean anything except in some kind of poetic sense. So the question then is, then what you realize is there's a more elaborate construction kit about sentences that might mean something. And people have been intending to kind of create that construction kit for a couple of thousand years. I mean, Aristotle started the time when he created logic. He started thinking about that kind of construction kit. Nobody got around to doing it. But I think, you know, ChatGPT and LLMs kind of show us there is a construction kit of, oh, you know, that word. If it's blah, ate, blah, the first blah better be a thing that eats things. And there's a certain category of things that eat things. And, you know, that's like animals and people and so on. And so that's sort of part of the construction kit. So you end up with this kind of notion of a kind of semantic grammar, of a way, a construction kit of how you put words together. My guess is that's essentially what ChatGPT has discovered. And once we understand that more clearly, we'll probably be able to build things like ChatGPT much more simply than it's very indirect way to do it, to have this neural net and keep bashing it and say, you know, make it, you know, predict words better and so on. There's probably a more direct way to do the same kind of thing. But that's sort of what's happened. And this moment when it becomes sort of human level performance, you know, very hard to predict when that will happen.
- [44:22]
It's happened for things like, well, it happened for kind of visual Object recognition, around 2011, 2012 type, time frame.
- [44:33]
It's hard to know when these things are going to happen for different kinds of human activities. But the thing to realize is there are human like activities and then there are things that we have formalized where we've used math, we've used other kinds of things as a way to kind of work things out systematically. And that's a different kind of direction than the direction that things like neural nets are going in. And that's, you know, happens to be the direction that I've spent a good part of my life trying to, trying to build up. And these things are very kind of complementary in the sense that.
- [45:10]
A
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- [47:45]
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- [49:09]
C
Yap the kind of things like the linguistic interface that are made possible by neural nets kind of feed into this kind of precise computation that, that we that we can do on that side.
- [49:22]
A
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- [51:23]
B
And my next question for you is, how does this make you feel about human consciousness and AI potentially being sentient or having any sort of agency?
- [51:34]
C
Yeah, well, I mean, it's always a funny thing because we have an internal view of the fact that there's something going on inside for us. We experience the world and so on. Even when we're looking at other people, it's like, it's just a guess that I know what's going on in my mind. It's just some kind of guess what's going on in your mind, so to speak. And we've developed. The big discovery of our species is language. This way of packaging up the thoughts that are happening in my mind and being able to transmit them to you and having you unpack them and make similar thoughts, perhaps in your mind, so to speak. So this idea of where can you sort of imagine that there's a mind that's operating? That's something which we already. It's not obvious, you know, between different people. We kind of always make that assumption. When it comes to other animals, it's like, well, we're not quite sure, but maybe we can kind of tell that, you know, our cat had some emotional reaction which sort of reminded us of some human emotion and so on. When it comes to our AIs, you know, I think that increasingly people will have the view that that kind of the AIs are a bit like them. So when you say, well, is there a there. There. Is there a thing inside? It's like, okay, is there a thing inside another person? It's, you know, if you say, well, but, you know, but we can tell that the other person is kind of thinking and doing all this stuff. Well, if we were to look inside the brain of that other person, all we'd find is a bunch of electrical signals going around. And those add up to something where there's kind of. You know, we have the assumption that there's a. There's a conscious mind there, so to speak. So I think in the thing that is sort of a. We have always felt that sort of our thinking and minds and so on are very far away from other things that are happening in the world. I think the thing that we learn from sort of the advance of AI is, well, actually, there's not as much distance between sort of the amazing stuff of our minds and things that are just able to be constructed computationally. I mean, one of the things to realize is that something that's come out of a bunch of science that I've Done. This whole question of sort of what thinks, where is there kind of computational stuff going on? And you might say, well, humans do that. Maybe our computers do that. Well, actually, nature does that too. When people will have this thing, the weather has a mind of its own. Well, what does that mean? Well, it means typically, operationally, it means it seems like the weather is acting with free will. We can't predict what it's going to do. But if we say, well, what's going on in the weather? Well, it's a bunch of kind of fluid dynamics in the atmosphere and this and that and the other, and we say, well, how do we compare that with the electrical processes that are going on in our brains? They're both sort of computations that operate according to certain rules. The ones in our brains we're kind of familiar with. The ones in the weather we're not familiar with. But in some sense, both of these cases, there's a computation going on. And one of the things that kind of was a big piece of bunch of science I've done is this thing called the principle of computational equivalence, which is kind of this discovery, this idea that if you look at different kinds of systems operating according to different rules, whether it's a brain or the weather, there is a certain. There's a commonality. There's. The same level of computation is achieved by those different kinds of systems. And that's not obvious. It's kind of like you might say, well, I've got the system, and it's just a system that's made from physics as opposed to the system that's the result of lots of biological evolution or whatever. Or I've got the system and it just operates according to these very simple rules that I can write down. It's kind of.
- [55:31]
You might have thought that the sort of level of computation that will be achieved in those different cases would be very different. The big surprise is that it isn't. It's the same. And that has all kinds of consequences. Like, if you say, okay, I've got this system in nature. Let me predict what's going to happen in it. Well, essentially, what you're doing by saying, I'm going to predict what's going to happen is you're somehow setting yourself up as being smarter than the system in nature. It will take it all these computational steps to figure out what it does. But you are going to just jump ahead and say, this is what's going to happen in the end. Well, the fact that there's this principle of computational equivalence implies this thing I call computational irreducibility, which is the realization that there are many systems where to work out what will happen in that system. You have to do kind of an irreducible amount of computational work. And that's a, that's a surprise because we kind of have been used to the idea that kind of science lets us kind of jump ahead and just say, oh, this is what the answer is going to be. And this is kind of showing us from within science, it's showing us that there's a fundamental limitation where we can't do that. And that's important when it comes to thinking about things like AI. When you say things like, well, let's make sure that AIs never do the wrong thing. Well, problem with that is there's this phenomenon of computational irreducibility. The AI is doing what the AI does. It's doing all these computations and so on. We can't know in advance. We can't just jump ahead and say, oh, we know what it's going to do. We are stuck kind of having to follow through these steps. We can try and make an AI where we can always know what it's going to do. Turns out that AI will be too dumb to be a serious AI. And in fact, we already, we see that happening in recent times of people saying, let's make sure they don't do the wrong thing. We put enough constraints, it can't really do the things that a computational system should be able to do. And it doesn't really achieve the kind of this level of capability that you might call sort of real AI, so to speak.
- [57:35]
B
Next, I want to talk about how the world is going to change now that AI is here being more adapted by people. It's becoming more commonplace. How is it going impact jobs? And also, if you can touch on, you know, the risks of AI, like, what are the biggest fears that people have around AI.
- [57:53]
C
Well, I mean, more and more systems in the world will get automated. This has been a story of technology throughout history. You know, AI is another step in the automation of things.
- [58:07]
As kind of, you know, when things get automated, things humans used to have to do with their own hands, they don't have to do anymore. The typical pattern of economies like in the US or something is 150 years ago in the US most people were doing agriculture. You had to do that with your own hands. Then machinery got built that let that be automated. And the people who, it's like, well, then nobody's going to have anything to do. Well, it turned out they did have things to do because that very automation enabled a lot of new types of things that people could do. And I think, for example, the thing that we're doing, the podcasting thing we're doing right now, is enabled by sort of the fact that we have video communication and so on. There was a time when all of that automation that has now led to the kind of telecommunications infrastructure we have wasn't there? And there had to be telephone switchboard operators plugging wires in and so on. And people were saying, oh, gosh, if we automate telephone switching, then all those jobs are going to go away. But actually, what happened was, yes, those jobs went away, but that automation opened up many other categories of jobs. So the typical thing that you see, at least historically, is a big category. There's a big chunk of jobs that are something that people have to do for themselves, that gets automated, and that enables what becomes many different possible things that you end up being able to do. And I think the way to think about this is really the following, that once you've defined an objective, you can build automation that does that objective. Maybe it takes 100 years to get to that automation, but you can in principle do that. But then you have the question, well, what are you going to do next? What are the new things you could do? Well, that question. There are an infinite number of new things you could do. The AI left to its own devices, there's an infinite set of things that it could be doing. The question is, which things do we choose to do? And that's something that is really a matter for us humans, because it's like you could compute anything you want to compute. And in fact, some part of my life has been exploring kind of the science of the computational universe. What's out there that you can compute? And the thing that's a little bit kind of sobering is to realize of all the things that are out there to compute, the set that we humans have cared about so far in the developments of our civilization is a tiny, tiny, tiny slice.
- [60:40]
And so that's the. And this question of sort of, where do we go from here, so to speak, is, well, what other slices which are now been. Now they're possible? Which things do we want to do? And I think that the typical thing you see is that a lot of new jobs get created around the things which are still sort of a matter of human choice. What you do, eventually it kind of gets standardized, and then it gets automated, and then you go on to another stage. So I think that the spectrum of what jobs.
- [61:13]
Sort of will be automated. One of the things that happened back several years ago now, the people were saying, oh, machine learning, kind of the sort of underlying area that leads to sort of neural nets and AI and things like this. Machine learning is going to put all these people out of jobs. The thing that was sort of amusing to me was that I knew perfectly well that the first category of jobs that would be impacted were machine learning engineers. Because machine learning can be used to automate machine learning, so to speak. And so it was what things disappear? What things?
- [61:50]
It's not quite as. It's not something where, oh, kind of. Well, there's a lot of, you know, once the thing becomes routine, then it can be automated. And for example, a lot of people learn to do programming, low level programming. You know, I've spent a large part of my life trying to automate low level programming. So in other words, the computational language we've built, which people like, oh my gosh, I can do this. I can get the computer to do this thing for me by spending an hour of my time. If I were writing kind of standard programming language code, I'd spend a month trying to set my computer up to do this. That's kind of the thing we've already achieved is to be able to automate out those kinds of things. What you realize when you automate out something like that is people say, oh my gosh, things have become so difficult now because if you're doing kind of low level programming, some part of what you're doing is just routine work. You don't have to think that much. It's just like, oh, I turn the crank, I get the next, I show up to work the next day, I get this piece of code written. Well, if you've automated out all of that, what you realize is most of what you have to do is figure out, so what do I want to do next? And that's where this kind of being able to do real computational thinking comes in, because that's where it's like, so how do you think about what you're trying to do in computational terms so you can define what you should do next. And I think that's an example of the low level kind of turn the crank programming. I mean, that should be extinct already because I've spent the last 40 years trying to automate that stuff. And in some segments of the world it is kind of extinct because we did automate it. But there's an awful lot of people where they said, oh, we can Get a good job by learning C code, C programming or Python or Java or something like this. That's what we'll. That's a thing that we can spend our human time doing. It's not necessary. And that's being more emphasized at this point. The thing that is still very much the human thing is, so what do you want to do next? So to speak.
- [64:07]
B
This is so interesting because it's a good story, you know, because you're not saying, hey, we're doomed. You're saying, AI is going to actually create more jobs. It's going to automate the things that are repetitive and the things that we still need to make decisions on or decide the direction that we want to go in. That's what humans are going to be doing, sort of shift shaping all of it. But do you feel that AI is going to like, supersede us in intelligence and have this apex intelligence one day where we are not in control of the next thing?
- [64:38]
C
Right. Well, so I mentioned the fact that lots of things in nature compute. Our brains do computation. The weather does computation. The weather is doing a lot more computation than our brains are doing. So if you say, what's the apex intelligence in the world already? Nature has vastly more computation going on than happens to occur in our brains. The computation going on in our brains is computation where we say, oh, we understand what that is and we really care about that. Whereas the computation that goes on in the babbling brook or something, we say, well, that's just some flow of water and things. We don't really care about that. So we already lost that competition of are we the most computationally sophisticated things in the world? We're not. I mean, we're all, many, many things are equivalent in their computational abilities. So then the question is, well, what will it feel like when AI gets to the point where routinely it's sort of doing all sorts of computation beyond what we manage to do? I think it feels pretty much like what it feels like to live in the natural world. The natural world does all kinds of things. There are, occasionally a tornado will happen, occasionally this will happen. We can kind of make some prediction about what's going to happen, but we don't know for sure what's going to happen, when it's going to happen, and so on. And that's kind of, I think, what it will sort of feel like to be in a world where most things are run with AI and we'll be able to do some science of the AI saying, just like we can do science of the natural world and say, this is what we think is going to happen, but there's going to be this kind of infrastructure of kind of AI society. There already is to some extent, but that will grow of more and more things that are sort of happening automatically as a sort of computational process. But in a sense, that's no different from what happens in the natural world. The natural world is just automatically doing things that are not where we can try and divert what it does, but it's just doing what it does, so to speak. You know, for me, it's kind of one of the things I've long been interested in is kind of how is the universe actually put together? If we kind of drill down and look at the sort of smallest scales of physics and so on, what's down there and what we've discovered in the last few years is that it looks like we really can understand the whole of what happens in the universe as a computational process that sort of underneath them.
- [67:01]
People have been arguing for a couple of thousand years whether the world is made of sort of continuous things or whether it's made of little discrete things like atoms and so on. And about a bit more than 100 years ago, it got nailed down. Matter is made of discrete stuff. There are individual atoms and molecules and so on. Then light is made of discrete stuff, photons and so on. Space people had still assumed was somehow continuous, was not made of discrete stuff. And the thing we kind of, well, kind of nailed down, I think, in 2020, was the idea that space really is made of discrete things. There are discrete elements, discrete kind of atoms of space. And we can really think of the universe as made of a sort of giant network of atoms of space. And we hopefully in the next few years, maybe if we're lucky, we'll get sort of direct experimental evidence that space is discrete in that way. But one of the things that that makes one realize is it's sort of computation all the way down at this lowest level. The universe consists of this sort of discrete network that keeps on getting updated, and it's kind of following these simple rules and so on. It's all rather lovely, but that kind of makes one, in a sense, kind of. There's computation everywhere in nature, in our AIs and our brains.
- [68:25]
The computation that we care the most about is the part that we, with our brains and our civilization and our culture and so on, have so far explored. That's the part we care the most about. Over progressively, we should be able to explore more. And as sort of the computational X fields come into existence and so on, and we get to use our computers and computational language and so on. We get to kind of colonize more of the sort of computational universe, and we get to bring more things into. Oh, yes, that's the thing we humans talk about. I mean, if you go back even just 100 years, nobody was talking about all these things that we now take for granted about computers and how they work and how you can compute things and so on. That was just not something within human. Kind of within our human sphere. Now the question is, as we go forward with automation, with kind of the formalization of computational language, things like that, what more will sort of be within our kind of human sphere? It's hard to predict. It is, to some extent, a choice. There are things where we could go in this direction, we could go in that direction. These are things we will eventually humanize, so to speak, and they'll be. You know, it's also. If you look at the course of human history and you say, what did people think was worth doing a thousand years ago? A lot of things that people think are worth doing today, people absolutely didn't even think about. Like, you know, a good example perhaps is, you know, walking on a treadmill. That would just seem just completely stupid to somebody from even a few hundred years ago. And you kind of. It's like, why would you do that? Well, I want to live a long life. Why do you even want to live a long life? Well, you know, that's. Because whatever that wasn't, you know, in the past, that might not even have been thought of as an objective. And then this, you know, there's a sort of whole chain of, why are we doing this? And that chain is a thing of our time, so to speak. And that will change over time. And I think that.
- [70:36]
The kind of this. I think what is possible in the world will change. What we get to have sort of explored out of the sort of computational universe of all possibilities will change. There will no doubt be people who.
- [70:55]
You could ask the question.
- [70:59]
What will be the role of the biological sort.
- [71:03]
A
Of.
- [71:06]
C
Biological intelligence, so to speak, versus all the other things in the world? And as I say, we're already somewhat in that situation. There are things about the natural world that just happen, and some of those things are things that are much more powerful than us. We don't get to sort of stop the earthquakes and so on. They're things that. So we already are in that situation. It's just that the things that we are doing with AI and so on, we happen to be building a layer of that infrastructure that is sort of of our own construction rather than something which has been there all the time in nature. And so we've kind of gotten used to it.
- [71:47]
B
But this is so mind blowing, honestly. It's so mind blowing. But I love the fact that you seem to have like a positive attitude towards it. Like you're not, you know, we've had other people on the show that sort of are worried about AI, but you don't have that attitude towards it. It seems like you're more accepting of the fact that this is, it's coming whether we like it or not. Right. And to your point, we're already living in nature, which is way more intelligent than us anyway. And so maybe this is just an additional layer.
- [72:16]
C
Right. I think, I mean, the, you know, I'm an optimistic person. That's what happens if you, it's, it's a. I've spent my life doing kind of large projects and building big things. You don't, you don't do that unless you have a certain degree of optimism. But I think also that.
- [72:37]
What will always be the case, as things change, things that people have been doing will stop making sense. You see this in the intellectual sphere with paradigms in science. It's like, well, somebody, I built some new things in science where people at first say, oh my gosh, this is terrible. I've been doing this other thing for 50 years. I don't want to learn this new stuff.
- [73:04]
This is a terrible thing. And I think you see that.
- [73:10]
There'S a lot in the world where people are like, it's good the way it is, let's not change it. Well.
- [73:18]
What'S happening is in the sphere of ideas and in the sphere of technology sort of things change. And I think to say the only things that if you ask, is it going to wipe our species out? I don't think so. But that would be a thing that we would probably think is definitively bad if we say, well, I spent a lot of time learning how to do. I don't know. Right. I don't know. I became a great programmer in some low level programming language and by golly, that's not a relevant skill anymore. Yes, that can happen. I mean, for example, in my life I got interested in physics when I was pretty young. And when you do physics, you end up having to do lots of mathematical calculations. I never liked doing those things. I always thought. But there were other people who were like, that's what they're into. That's what they like doing. I never liked doing those things, so I taught computers to do them for me. And me plus a computer did pretty well at doing those things. But it's kind of. One had automated that away and that might have been seen as. To me, that was a big positive because it let me do a lot more. It let me kind of take what I was thinking about and get this sort of superpower to figure out to go places with that to other people. That's like, oh my gosh, the thing that we really were good at doing, of doing all these kind of mathematical calculations by hand and so on, that just got automated away. The thing that we like to do isn't a thing anymore. So that's a dynamic that I think continues. But having said that, there are plenty of ridiculous things that sort of get made possible by whenever there's powerful technology, you can do ridiculous things with it. And the question of exactly what kind of, you know, what, what terrible scam will be made possible by what piece of AI? That's, that's always a bit hard to predict. That's a kind of a computational irreducibility story of, of kind of this, this kind of thing of what will people figure out how to do, what will the computers let them do? And so on. But I'm. Yes, in general terms, it is my nature to be optimistic, but I think also there is a. There is kind of an optimistic path through sort of the way the world is changing, so to speak.
- [75:41]
B
Yeah. Well, it's really exciting. I can't wait to have you back on maybe in a year to hear all the other exciting updates that have happened with AI. I end my show asking two questions. Now, you don't have to use the topic of today's episode. You can just use your life experience to answer these questions. So one is, and you can be very quick. What is one actionable thing our young and profits can do today to become more profitable tomorrow? And this is not just about money, but profiting in life.
- [76:09]
C
I mean, understand computational thinking. This is the coming paradigm of the 21st century. And if you understand that, well, it gives you a huge advantage. And if you say unfortunately, it's not like you go sign up for a computer science class and you'll learn that unfortunately, kind of the educational resources for learning about computational thinking aren't really fully there yet. And it's something which, frustratingly, after many years, I've decided I have to really build a bunch more of these things because other people aren't doing it. And It'll be another decades before it gets done otherwise. But, yes, learn sort of computational thinking. Learn the tools that are around that.
- [76:55]
That's a kind of.
- [76:58]
A quick way to kind of jump ahead in. In whatever you're doing. Because there's kind of a. As you make it computational, you get to think more clearly about it and you get the computer to help you kind of jump forward.
- [77:11]
B
And where can people get resources from.
- [77:13]
A
You to learn more about that?
- [77:15]
B
Where do you recommend?
- [77:16]
C
Well, I mean, our kind of computational language, Wolfram Language, is kind of the main example of sort of where you get to do computational thinking. There's a book I wrote a few years ago called elementary Introduction to Wolfram Language, which is pretty accessible to people. But hopefully in another. Well, certainly within a year, there should exist a thing that I'm working on right now which is kind of directly an introduction to computational thinking. But you'll find a bunch of resources around Wolfram Language that kind of sort of explain more, although I don't think quite in the right way yet, kind of how one can think about things computationally. That's kind of the raw material for doing that.
- [78:00]
B
Okay, well, whatever links that we find, I'll stick them in the show notes. And next time, if you have something and you're releasing it, make sure that you contact us so you can come back on Young and Profiting podcast. Stephen, thank you so much for your time. We really enjoyed having you on Young and Profiting podcast.
- [78:13]
C
Thanks.