Stephen Wolfram: How AI Works and How to Use It to Stay Ahead | Artificial Intelligence | AI Vault

24:52Or you can say, well, wait a minute, you know, 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, OK, we're looking at, well, roughly, a planet goes around the sun and 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.

25:39It worked pretty terribly in things like biology, in social sciences and so on. You know, people imagine there might be a social physics of how society works that never really panned out. So there was sort of this question of places where math had worked and it gave us a lot of modern engineering and so on, and their cases where it hadn't really worked. I got pretty interested in this at the beginning of the 1980s and 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, you know, calculus and so on give one. What I realized is that you kind of, you just think about, well, there are definite rules that describe how things work.

26:26And 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.

27:00Big 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 that'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.

27:42I wrote this big book back in 2002 called A New Kind of Science, which is, well, its title kind of says what it is. But it's that that was 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 going to take? How far is it from one city to another? or how do we make this image have this, how do we remove 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?

28:31And 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 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 describing things in the world, which might be, you know, this is a city. This is, you know, You might say 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 things about things in the world, so to speak.

29:30And 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 sort of the new way to make science really work is to make it computational. And so you see all these different fields, you know, call them X. You start seeing the computational X field start to come into existence.

30:09And kind of my, 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. But 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.

30:50And so similarly, what I've been trying to do the 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 kind of 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.

31:30And 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, really hop. And it's starting to be possible to do really, really interesting things. And that's going to be sort of an area of tremendous growth in the next how many years. 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.

32:11So I want to understand why you feel it's going to help humans evolve. Also curious to understand the practical ways that you're using the Wolfram language and how it relates to AI, if it does at all. Yeah, right. Well, so let's take the second thing first. Okay. So 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. The way that, for example, something like an LLM, like a chat GPT or something like that, 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.

33:06Chances 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. And so that's working kind of the way humans immediately think about things.

33:52Then 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. It'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.

34:31I 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 Wolfram Alpha system, which came out in 2009, is a system that has natural language understanding. We sort of had solved the problem of small sentence, you know, 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?

35:19And 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 to actually figure out, hey, this is the thing that's actually true, so to speak. And that's a mechanism. 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. 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.

36:20And 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, that's sort of the core part of 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. I think 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 sort of a separate branch. Yeah. Well, this is so fascinating. Honestly, you're teaching us so much.

37:05I 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 ChatGBT, right? So everybody has sort of embraced ChatGBT. 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 of questions about ChatGBT. You alluded to how it works a bit, but can you give us more detail about how neural networks work in general and what CHAPGBT is doing in the background to spit out something that looks like it's written by a human?

37:40Yeah, so I mean, neural networks. So, you know, 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, 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.

38:22And so this kind of the electrical machine, kind of lots of things connected to things. That was 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 these connections between sort of artificial neurons, usually called weights. You often hear about people saying, you know, 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 Chachi Biti 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.

39:11and 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 chat GPT-type behavior, a few hundred times.

39:56And then at the end, we get another number. Actually, we get 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 Chachupete 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.

40:45Now, 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 chat GPT, 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, it was like, well, you've got one arrangement of weights. OK, what next word does that predict? OK, that predicts turtle as the next word for the cat sat on thee. Turtle is wrong. Let's change that. Let's see what happens if we adjust these weights in that way.

41:28Oh, we finally got it to say mat. 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. You know, 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.

42:05It 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.

42:43That'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, the electron ate the moon. Okay, it's grammatically correct, but it probably doesn't really mean anything. except in some kind of poetic sense.

43:21So 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.

44:04And, you know, there'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 its 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.

44:38But 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. It's happened for things like, well, it happened for kind of visual object recognition around 2011, 2012 type timeframe. it's hard to know when these things are going to happen for different kinds of human activities. And I think, but the thing to realize is there are human-like activities, and then there are things that we have formalized where we've used, you know, math, we've used other kinds of things as a way to kind of work things out systematically.

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51:57Get all the big stuff for your small business right with Shopify. Sign up for your$1 per month trial period and start selling today at shopify.com slash profiting. Go to shopify.com slash profiting. Again, that's shopify.com slash profiting. This is so interesting. 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? 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, you know, I know what's going on in my mind.

52:42It's just some kind of guess what's going on in your mind, so to speak. And we've developed, you know, 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.

53:25When it comes to our AIs, I think that increasingly people will have the view that 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? You say, well, 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 conscious mind there, so to speak.

54:07So 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 the kind of computational stuff going on?

54:46And 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, 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 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.

55:25The 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 is 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's a commonality. The same level of computation is achieved by those different kinds of systems. And that's not obvious.

56:02It'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

56:21you might have thought that the sort of level of computation that will be achieved in those different cases will 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 the 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.

56:53Well, 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 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.

57:27And 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, the 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.

58:02And in fact, we already 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. 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 to 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?

58:43Well, I mean, more and more systems in the world will get automated. This has been a story of technology throughout history. AI is another step in the automation of things. It's kind of, 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, you know, it's like, well, then nobody's going to have anything to do.

59:24Well, 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, you know, for example, the thing that, you know, 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, you know, plugging wires in and so on. And people were saying, oh, gosh, you know, if we automate telephone switching, then all those jobs are going to go away.

1:00:02But 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.

1:00:41But 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 development of our civilization is a tiny, tiny, tiny slice.

1:01:33And this question 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 sort of will be automated, you know, one of the things that happened back several years ago now, the people were saying, oh, machine learning, the underlying area that leads to neural nets and AI and things like this, machine learning has put all these people out of jobs.

1:02:22The thing that was 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, you know, what things disappear, what things, 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.

1:03:02So in other words, the computational language we 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.

1:03:42You don't have to think that much. It's just like, oh, I turn the crank, I get the next, you know, 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.

1:04:16I 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++ programming or Python or Java or something like this. 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? This is so interesting because it's a good story, you know, because you're not saying, hey, we're doomed.

1:05:04You'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 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? 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.

1:05:42So 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.

1:06:19So 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, you know, 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.

1:06:54And 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.

1:07:26You 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, 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.

1:08:01And 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 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 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.

1:08:48But one of the things that that makes one realize is it's sort of computation all the way down. At the 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, in our brains. means the part that the computation that we care the most about is the part that we with our brains have and our civilization and our culture and so on have so far explored. That's the part we care the most about.

1:09:27Over 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 colonize more of the 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. And that was just not something within our human sphere. Now, the question is, as we go forward with automation, with the formalization of computational language, things like that, what more will be within our human sphere?

1:10:22It'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 there'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.

1:10:58and 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 in the past that might not even have been a thought of as an objective and then this this you know that there's a sort of whole chain of why are we doing this and that chain is is a is a thing of our time so to speak and and that will that will change over time and i think that that the the the 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.

1:11:41There will no doubt be people who, you know, you could ask the question, what will be the role of sort of the 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.

1:12:36And so we've kind of gotten used to it. 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. 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. Right. I think, I mean, you know, I'm an optimistic person.

1:13:10That's what happens if you, it's a, I've spent my life doing kind of large projects and building big things. You don't do that unless you have a certain degree of optimism. But I think also that 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 sort of 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. This is a terrible thing.

1:13:56And I think you see that in, there's a lot in the world where people are like, it's good the way it is. Let's not change it. Well, what's happening is in the sphere of ideas and in the sphere of technology, sort of things change. And I think to say, you know, the only things that, you know, 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, you know, I spent a lot of time learning how to do, I don't know, write. I don't know, I became, you know, a great programmer in some low-level programming language.

1:14:41and 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 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 the sort of superpower to to figure out to to go 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.

1:15:37The 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 what terrible scam will be made possible by what piece of AI, that's always a bit hard to predict. It's a kind of a computational irreducibility story of kind of this kind of thing of what will people figure out how to do, what will the computers let them do, and so on. But yes, in general terms, it is my nature to be optimistic.

1:16:23But I think also there is kind of an optimistic path through sort of the way the world is changing, so to speak. 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 profitors can do today to become more profitable tomorrow? And this is not just about money, but profiting in life.

1:16:59I 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, you know, 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 much more of these things because other people aren't doing it. And it'll be another decades before it gets done otherwise.

1:17:37But yes, learn computational thinking, learn the tools that are around that. That's a quick way to jump jump ahead 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. And where can people get resources from you to learn more about that? Where do you recommend? 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 I wrote a few years ago called Elementary Introduction to Wolfram Language, which is pretty accessible to people.

1:18:21But hopefully in another, well, certainly within a year, there should exist a thing that I'm working on right now, which is kind of a directly an introduction to computational thinking. But you'll find a bunch of resources around Wolfram Language that kind of sort of explain 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. 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.

1:18:59Stephen, thank you so much for your time. We really enjoyed having you on Young and Profiting Podcast. Thanks.