Lex Fridman Podcast - #376 – Stephen Wolfram: ChatGPT and the Nature of Truth, Reality & Computation

Episode Date: May 9, 2023

Stephen Wolfram is a computer scientist, mathematician, theoretical physicist, and the founder of Wolfram Research, a company behind Wolfram|Alpha, Wolfram Language, and the Wolfram Physics and Metamathematics projects. Please support this podcast by checking out our sponsors: - MasterClass: https://masterclass.com/lex to get 15% off - BetterHelp: https://betterhelp.com/lex to get 10% off - InsideTracker: https://insidetracker.com/lex to get 20% off EPISODE LINKS: Stephen's Twitter: https://twitter.com/stephen_wolfram Stephen's Blog: https://writings.stephenwolfram.com Wolfram|Alpha: https://www.wolframalpha.com A New Kind of Science (book): https://amzn.to/30XoEun Fundamental Theory of Physics (book): https://amzn.to/30XbAoT Blog posts: A 50-Year Quest: https://bit.ly/3NQbZ2P What Is ChatGPT doing: https://bit.ly/3VOwtuz PODCAST INFO: Podcast website: https://lexfridman.com/podcast Apple Podcasts: https://apple.co/2lwqZIr Spotify: https://spoti.fi/2nEwCF8 RSS: https://lexfridman.com/feed/podcast/ YouTube Full Episodes: https://youtube.com/lexfridman YouTube Clips: https://youtube.com/lexclips SUPPORT & CONNECT: - Check out the sponsors above, it's the best way to support this podcast - Support on Patreon: https://www.patreon.com/lexfridman - Twitter: https://twitter.com/lexfridman - Instagram: https://www.instagram.com/lexfridman - LinkedIn: https://www.linkedin.com/in/lexfridman - Facebook: https://www.facebook.com/lexfridman - Medium: https://medium.com/@lexfridman OUTLINE: Here's the timestamps for the episode. On some podcast players you should be able to click the timestamp to jump to that time. (00:00) - Introduction (06:45) - WolframAlpha and ChatGPT (26:26) - Computation and nature of reality (53:18) - How ChatGPT works (1:53:01) - Human and animal cognition (2:06:20) - Dangers of AI (2:14:39) - Nature of truth (2:36:01) - Future of education (3:12:03) - Consciousness (3:21:02) - Second Law of Thermodynamics (3:44:36) - Entropy (3:57:36) - Observers in physics (4:14:27) - Mortality

The following is a conversation with Stephen Wolfram, his fourth time on this podcast. He's a computer scientist, mathematician, theoretical physicist, and the founder of Wolfram Research, a company behind Mathematica, Wolfram Alpha, Wolfram Language, and the Wolfram Physics and Metamathematics projects. He has been a pioneer in exploring the computational nature of reality. And so he's the perfect person to explore with together the new, quickly evolving landscape of large language models as human civilization journeys towards building super intelligent AGI.

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I posted this meme on Twitter recently that has that meme format where the car is swarves off on an exit and goes straight means going to a therapist and swarming off onto exit says Saying in quotes it is what it is and then the car is labeled as most men. It's true. I Think a lot of us face hardship in life And I think there's a dance between kind of Being fragile to the richness of the experience of that hardship can really break you. So there's some youthfulness to it is what it is.

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You've announced the integration of chat GPT and will from alpha and will from language. So let's talk about that integration. What are the key differences from the high philosophical level, maybe the technical level between the capabilities of broadly speaking the two kinds of systems, large language and this computational gigantic computational system infrastructure that is well-for-mouth. Yeah, so what does something like chat GPT-Doo? It's mostly focused on make language like the language that humans have made and put on the web and so on. So, you know, it's

primary sort of underlying technical thing is you've given a prompt, it's trying to continue that prompt in a way that somehow typical of what it's seen based on a trillion words of text that humans have written on the web. And the way it's doing that is with something which is probably quite similar to the way we humans do the first stages of that, using a neural net and so on, and just saying, given this piece of text, let's ripple through the neural net one word and get one word at a time of output. And it's kind of a shallow computation on a large amount of training data

that is what we humans have put on the web. That's a different thing from sort of the computational stack that I've spent the last I don't know 40 years or so building, which has to do with what can you compute many steps, potentially a very deep computation. It's not sort of taking the statistics of what we humans have produced and trying to continue things based on that statistics. Instead, it's trying to take kind of the formal structure that we've created in our civilization, whether it's from mathematics or whether it's from kind of systematic knowledge of all kinds and use that to do arbitrarily deep computations, to figure out things that aren't just, let's match what's already been kind of said on the web, but let's potentially be able to compute something new and different that's never been computed before. goal is to have made as much as possible of the world computable in the sense that if there's a question that, in principle, is answerable from some sort of expert knowledge

that's been accumulated, we can compute the answer to that question, and we can do it in a sort of reliable way that's the best one can do given what the expertise that our civilization has accumulated. It's a very, it's a much more sort of labor-intensive on the side of kind of being creating kind of the computational system to do that. Obviously, the kind of the chat GBT world, it's like take things which were produced for quite other purposes, namely the all the things we've written out on the web and so on, and sort of forage from that, things which are like what's been written on the web. So I think, you

know, as a practical point of view, I view sort of the chat GPT thing as being wide and shallow, and what we're trying to do with sort of building out computation as being this sort of deep, also broad, but most importantly kind of deep type of thing. I think another way to think about this is you go back in human history, you know, I don't know, a thousand years or something, and you say, what, what, what, what can the typical person, what's the typical person going to figure out? Well, the answer is there are certain kinds of things that we humans can quickly figure out, that's sort of what our, you know, our neural architecture and the kinds of things we learn in our lives, let us do.

But then there's this whole layer of kind of formalization that got developed in which is, you know, the kind of whole, sort of, story of intellectual history and whole kind of depth of learning. That formalization turned into things like logic, mathematics, science and so on. And that's the kind of thing that allows one to kind of build these towers of sort of towers of things you work out. It's not just, I can immediately figure this out. It's no, I can use this kind of formalism to go step by step and work out something which was not immediately obvious to me. And that's kind of the story of what we're trying to do computationally

is to be able to build those kind of tall towers of what implies, what implies, what and so on. And as opposed to kind of the yes, I can immediately figure it out. It's just like what I saw somewhere else in something that I heard or remembered or something like this. What can you say about the kind of formal structure, the kind of formal foundation you can build such a formal structure on, about the kinds of things you would start on in order to build this kind of deep, computable knowledge, trees? So the question is sort of how do you think about computation? And there's a couple of points here. One is what computation intrinsically is like,

and the other is what aspects of computation we humans with our minds and with the kinds of things we've learnt can sort of relate to in that computational universe. So if we start on the kind of what can computation be like, it's something I've spent some big chunk of my life studying, is imagine that you're, you know, we usually write programs where we kind of know what we want the program to do, and we carefully write many lines of code, and we hope that the program does what we intended it to do.

But the thing I've been interested in is, if you just look at the kind of natural science of programs, so you just say, I'm gonna make this program, it's a really tiny program, maybe I even pick the pieces of the program at random, but it's really tiny. By really tiny, I mean, less than a line of code type thing.

You say, what does this program do? And you run it, and big discovery that I made in the early 80s is that even extremely simple programs, when you run them, can do really complicated things. Really surprised me. It took me several years to kind of realize that that was a thing, so to speak. But that realization, that even very simple programs can do incredibly complicated things that we very much don't expect. That discovery, I mean, I realized that that's very much, I think, how nature works.

That is, nature has simple rules, but yet does all sorts of complicated things that we might not expect. As big thing of the last few years has been understanding that that's how the whole universe and physics works. But that's a quite separate topic. But so there's this whole world of programs and what they

do, and very rich sophisticated things that these programs can do. But when we look at many of these programs, we look at them and say, well, that's kind of, I don't really know what that's doing. It's not a very human kind of thing. So on the one hand, we have sort of what's possible in the computational universe. On the other hand, we have the kinds of things that we humans think about, the kinds of things that are developed in kind in our intellectual history. And that's really the challenge to making things computational is to connect what's computationally possible

out in the computational universe with the things that we humans typically think about with our minds. Now, that's a complicated kind of moving target because the things that we think about change over time, we've learnt more stuff, we've invented mathematics, we've invented various kinds of ideas and structures and so on. So it's gradually expanding, we're kind of gradually colonizing more and more of this kind of intellectual space of possibilities. But the real thing, the real challenges,

how do you take what is computationally possible? How do you encapsulate the kinds of things that we think about in a way that kind of plugs in to what's computationally possible? And actually, the big idea there is this idea of symbolic programming, symbolic representations of things. And so the question is, when you look at sort of everything in the world and you kind of

take some visual scene or something you're looking at, and you say, well, how do I turn that into something like kind of stuff into my mind? There are lots of pixels in my visual scene, but the things that I remembered from that visual scene are, you know, there's a chair in this place. It's a kind of a symbolic representation of the visual scene. There are two chairs on a table or something, rather than there are all these pixels arranged in all these detailed ways. And so the question then is, how do you take sort of all the things in the world and make some kind of representation that corresponds to the types of

ways that we think about things. And human language is sort of one form of representation that we have. We talk about chairs that's a word and human language and so on. How do we how do we take but human language is not an in of itself something from that plugs in very well to sort of computation. It's not something from which you can immediately compute consequences and so on. And so you have to kind of find a way to take sort of the the stuff we understand from human language and make it more precise. And that's really this story of symbolic programming. And you know what that turns into is something which I didn't know at the time it was going

to work as well as it has. But back in the 1979 or so, I was trying to build my first big computer system and trying to figure out, how should I represent computations at a high level? And I kind of invented this idea of using it's kind of like a function and a bunch of arguments, but that function doesn't necessarily evaluate to anything. It's just a thing that sits there representing a structure. And so building up that structure, and it's turned out that structure has been extremely, it's a good match for the way that we humans, it seems to be a good match for the way that we humans, it seems to be a good match,

for the way that we humans kind of conceptualize high-level things. And it's been for the last, I don't know, 45 years or something. It's served me remarkably well. So building up that structure using this kind of symbolic representation. But what can you say about abstractions here? Because you could just start with your physics project You could start at a hypergraph at a very very low level and build up everything from there But you don't you take shortcuts, right? You take the highest level of abstraction convert that

The kind of abstraction that's convertible to something computable using symbolic representation and Then that's your new foundation for that little piece of knowledge. Yeah, somehow all that is integrated. Right. So the sort of a very important phenomenon that is kind of a thing that I've sort of realized is just it's one of these things that sort of in the in the future of kind of everything is going to become more and more important as this phenomenon of computational irreducibility. And the question is, if you know the rules for something, you have a program, you're going to run it, you might say, I know the rules.

Great, I know everything about what's going to happen. Well, in principle, you do, because you can just run those rules out and just see what they do. You might run them a million steps, you see what happens, et cetera. The question is, can you like immediately jump ahead and say, I know it's going to happen after a million steps and the answer is 13 or something. Yes.

And one of the very critical things to realize is, if you could reduce that computation, there isn't a sense no point in doing the computation. The place where you really get value out of doing a computation is when you had to do the computation to find out the answer. But this phenomenon that you have to do the computation to find out the answer, this phenomenon of computational irreducibility, seems to be tremendously important for thinking about

lots of kinds of things. So one of the things that happens is, okay, you've got a model of the universe at the low level in terms of atoms of space and hypergraphs and rewriting hypergraphs and so on. And it's happening, you know, 10 to the 100 times every second, let's say. Well, you say, great, then we've nailed it. We know how the universe works.

Well, the problem is, the universe can figure out what it's going to do. It does those 10 to the 100 steps. But for us to work out what it's going to do, we have no way to reduce that computation. The only way to do the computation, to see the result of the computation, is to do it. And if we're operating within the universe, there's no opportunity to do that because the universe is doing it as fast as the universe can do it.

And that's what's happening. So what we're trying to do, and a lot of the story of science, and a lot of other kinds of things, is finding pockets of reducibility. That is, you could have a situation where everything in the world is full of computational irreducibility, we never know what's gonna happen next. The only way we can figure out what's gonna happen next

is just let the system run and see what happens. So in a sense, the story of most kinds of science, inventions, a lot of kinds of things, is the story of finding these places where we can locally jump ahead. And one of the features of computational and reducibility is there are always pockets of reducibility. There are always places, there are always an infinite number of places where you can jump ahead.

There's no way where you can jump completely ahead, but there are little patches, little places where you can jump ahead. There's no way where you can jump completely ahead, but there are little patches, little places where you can jump ahead a bit. And I think, you know, we can talk about physics project and so on, but I think the thing we realize is we kind of exist in a slice of all the possible computational irreducibility in the universe. We exist in a slice where there's a reasonable amount of predictability. And in a sense, as we try and construct these kind of higher levels of abstraction, symbolic representations, and so on, what we're doing is we're finding these lumps of reducibility that we can kind of attach ourselves to,

and about which we can kind of have fairly simple narrative things to say. Because in principle, I say, what's going to happen in the next few seconds? Oh, there are these molecules moving around in the air in this room, and oh gosh, it's an incredibly complicated story. And that's a whole computational irreducible thing. Most of which I don't care about, and most of it is, well, the air is still going to be here, and nothing much is going to be different about it. And that's a kind of reducible fact about what is ultimately a done underlying level of computational irreducible process. And life would not be possible if we didn't have a large number of such reducible pockets.

Yes. Pockets amenable to reduction into something symbolic. Yes, I think so. In the way that we experience it, one might, depending on what we mean by life, so to speak, the experience that we have of consistent things happening in the world, the idea of space, for example, where we can just say, you're here, you move there. It's kind of the same thing. It's still you in that different place, even though you're made of different atoms of space and so on. This is this idea that it's, that there's sort of this level of predictability of what's going on.

That's us finding a slice of reducibility in what is underneath this computational irreducible kind of system. And I think that's sort of the thing, which is actually my favorite discovery for the last few years, is the realization that it is sort of the interaction between this sort of underlying computational irreducibility and our nature as kind of observers who sort of have to key into computational

reducibility, that fact leads to the main laws of physics that we discovered in the 20th century. So this is, we talked about this in more detail, but this is, to me, it's kind of our nature as observers, the fact that we are computationally bounded observers. We don't get to follow all those little pieces of computational irreducibility, to stuff what is out there in the world into our minds, requires that we are looking at things that are reducible, we are compressing, kind of, we're extracting just some essence, some kind of symbolic essence of what's the detail of what's going on in the world, that together with one other condition that at first seems sort of trivial, but isn't, which is that we believe we are persistent in time.

That is, you know, so it's a causality. Here's the thing, at every moment, according to our theory, we're made of different atoms of space. At every moment, sort of the microscopic detail of what the universe is made of is being rewritten. And that's, and in fact, the very fact that there's coherence between different parts of space is a consequence of the fact that there are always little processes going on that kind of knit together the structure of space. It's kind of like, if you wanted to have a fluid with a bunch of molecules in it,

if those molecules weren't interacting, you wouldn't have this fluid that would pour and do all these kinds of things. It would just be sort of a free floating collection of molecules. So similarly, it is with space that the fact that space is kind of knitted together as a consequence of all this activity in space.

And the fact that what we consist of sort of this series of, we're continually being rewritten. And the question is, why is it the case that we think of ourselves as being the same us through time? That's kind of a key assumption. I think it's a key aspect of what we see as our consciousness, so to speak, is that we have this kind of consistent thread of experience. Isn't that just another limitation of our mind that we want to reduce reality into some, that kind of temporal consistency is just a nice narrative to ourselves?

Well, the fact is, I think it's critical to the way we humans typically operate, is that we have a single thread of experience. If you imagine a mind where you have, maybe that's what's happening in various kinds of minds that aren't working the same way other minds work, is that you're splitting into multiple threads of experience. It's also something where when you look at quantum mechanics,'t know, quantum mechanics, for example, in the insides of quantum mechanics, it's splitting into many threads of experience. But in order for us humans to interact with it, you kind of have to have to knit all those different threads together so that we say, oh yeah, definitely happened.

And now the next definitely happens and so on. And I think, you know, sort of inside, it's sort of interesting to try and imagine what's it like to have kind of these fundamentally multiple threads of experience going on. I mean, right now, different human minds have different threads of experience. We just have a bunch of minds that are interacting with each other, but we don't have a, you know, within each mind, there's a single thread. And that's a, that is indeed a simplification. I think it's a, it's a thing, you know, the general computational system does not have that simplification. And it's one of the things, you know, I, people often seem to think that, you know, consciousness

is the highest level of kind of things that can happen in the universe, so to speak. But I think that's not true. I think it's actually a specialization in which, among other things, you have this idea of a single threat of experience, which is not a general feature of anything that could kind of computationally happen in the universe. So it's a feature of a computationally limited system that's only able to observe a reducible pockets. So, I mean, this word observer means something in quantum mechanics and means something in

a lot of places. It means something to us humans has conscious beings. So what's the importance of the observer? What is the observer? What's the importance of the observer in the computational universe? So this question of what is an observer? What's the general idea of an observer? Is that actually one of my next projects, which got somewhat derailed by the current sort of AI mania? But is there a connection there? Or is that do you do you think the observer is primarily a physics

phenomena? Is it related to the whole AI thing? Yes, it is related. So one question is what is a general observer? So, you know, we know we have an idea what is a general computational system we think about touring machines, we think about other models of computation. There's a question what is a general model of an observer? And there's kind of observers like us, which is kind of the observers we're interested in. You know, we could imagine an alien observer that deals with computational irreducibility, and it has a mind that's utterly different from ours and completely incoherent with what we're like.

But the fact is that, you know, if we are talking about observers like us, that one of the key things is this idea of kind of taking all the detail of the world and being able to stuff it into a mind, being able to take all the detail of the world and being able to stuff it into a mind, being able to take all the detail and kind of, you know, extract out of it a smaller set of kind of degrees of freedom, a smaller number of elements that will sort of fit in our minds. And I think this question, so I've been interested in trying to characterize what is the general observer. And the general observer

is, I think, in part, there are many, let me give an example of a, you know, you have a gas, it's got a bunch of molecules bouncing around. And the thing you're measuring about the gas is its pressure. And the only thing you as an observer care about is pressure. And that means you have a piston on the side of this box, and the piston is being pushed by the gas. And there are many, many different ways that molecules can hit that piston. But all that matters is the kind of aggregate of all those molecular impacts, because that's what determines pressure. So, there's a huge number of different configurations of the gas, which are all equivalent.

So, I think one key aspect of observers is this equivalencing of many different configurations of a system, saying, all I care about is this equivalencing of many different configurations of a system, saying, all I care about is this aggregate feature. All I care about is this overall thing. And that's one aspect. And when we see that in lots of different, again, it's the same story over and over again, that there's a lot of detail in the world, but what we are extracting from it is something sort of a thin summary of that detail.

Is that thin summary, nevertheless true? Can it be a crappy approximation? Sure. That on average is correct. I mean, if we look at the observer that's the human mind, it seems like there's a lot of very, as represented by natural language, for example, there's a lot of really crappy approximation. Sure. And that could be's a lot of really crappy approximation. Sure.

And that could be maybe a feature of it. Well, it is ambiguity. Right, right. You don't know, you know, it could be the case. You're just measuring the aggregate impacts of these molecules, but there is some tiny, tiny probability that molecules will arrange themselves in some really funky way.

And that just measuring that average isn't going to the main point. By the way, an awful lot of science is very confused about this because you look at papers and people are really keen, they draw this curve and they have these bars on the curve and things, and it's just this curve. And it's this one thing, and it's supposed to represent some system that has all kinds of details in it. And this is a way that lots of science has gotten wrong. Because people say, I remember years ago,

I was studying snowflake growth. You have a snowflake and it's growing, it has all these arms, it's doing complicated things. But there was a literature on this stuff and it talked about, what's the rate of snowflake growth? And it got pretty good answers for the rate of the growth of the snowflake. And they looked at it more carefully, and they had these nice curves of snowflake growth rates and so on. They looked at it more carefully, and I realized, according to their models, the snowflake

will be spherical. And so they got the growth rate right, but the detail was just utterly wrong. And not only the detail, the whole thing was not capturing, you know, it was capturing this aspect of the system that was in a sense missing the main point of what was going on. What is the geometric shape of a snowflake? Snowflakes start in the phase of water that's relevant to formation of snowflakes. It's a phase of ice, which starts with a hexagonal arrangement of water molecules. And so it starts off growing as a hexagonal plate. And then what happens is

it's a plate, oh, versus sphere, sphere, but it's much more than that. I mean snowflakes are fluffy, you know, typical snowflakes have little, little dendritic arms. And what actually happens is, it's kind of kind of cool because you can make these very simple discrete models with cellular automata and things that that figure this out. You start off with this, you know, hexagonal thing and then the places it starts to go to the arms and every time a little piece of ice adds itself to the snowflake, the fact that that ice condensed from the water vapor the fact that that ice condensed from the water vapor heats the snowflake up locally. And so it makes it less likely for another piece of ice to accumulate right nearby. So this leads to a kind of growth inhibition. So you grow an arm and it is a separated arm because right around the arm it got a little bit hot and it didn't add more ice there.

So what happens is it grows, you have a hexagon, it grows out arms, the arms grow arms, and then the arms grow arms grow arms, and eventually actually it's kind of cool because it actually fills in another hexagon, a bigger hexagon. And when I first looked at this, you had a very simple model for this, I realized, you know, when it fills in that hexagon, it actually leaves some holes behind. So I thought, well, you know, that is that really right. So I look at these pictures of snowflakes and sure enough, they have these little holes in them that are kind of scars of the way that these arms grow out. So you can't fill in backfill holes. So they don't backfill. Yeah, they don't backfill.

And presumably there's a limitation on how big like you can't arbitrarily grow. I'm not sure. I mean, the thing falls through the, I mean, I think it does, you know, it hits the ground at some point. I think you can grow, I think you can grow in the lab. I think you can grow pretty big ones. I think you can grow many, many iterations of this kind of goes from hexagon, it grows out arms, it turns back, it fills back into a hexagon, it grows more arms again. In 3D. No, it's flat, usually. Why is it flat? Why doesn't it spin out? Okay, okay, wait a minute.

You said it's fluffy. And fluffy is a three-dimensional property, no? Or no, it's fluffy. Snow is, okay, so what makes, we're really in it. I like it's a little bit different. But it's multiple snowflakes become fluffy. What single snowflake is not fluffy. No, no, single snowflake is fluffy fluffy. No, no, a single snowflake is fluffy.

And what happens is, if you have snow, that it's just pure hexagons, they fit together pretty well. It doesn't have a lot of air in it. And they can also slide against each other pretty easily. And so the snow can be pretty, I think avalanches happen sometimes when the things tend to be these, you know, hexagonal plates and it kind of slides. But then when the thing has all these arms that have grown out, it's

not, they don't fit together very well. And that's why the snow has lots of air in it. And if you look at one of these snowflakes, if you catch one, you'll see it has these little arms. And people, actually people often say, no two snowflakes are alike. That's mostly because as a snowflake grows, they do grow pretty consistently with these different arms and so on, but you capture them at different times as they, you know, they fell through through the air in a different way. You'll catch this one, this stage, and as it goes through different stages, they look really different. And so that's why, you know, it kind of looks like no two slum flakes are alike because

you caught them at different times. So the rules under which they grow are the same. Yes. It's just the timing is. Yes. Okay. So the point is science is not able to describe the full complexity of snowflake growth. Well, science, if you do what people might often do,

which is say, okay, let's make it scientific, let's turn into one number. And that one number is kind of the growth rate of the arms or some such other thing. That fails to capture sort of the detail of what's going on inside the system. And that's in a sense a big challenge for science is how do you extract from the natural world, for example, those aspects of it that you

are interested in talking about. Now, you might just say, I don't really care about the fluffiness of the snowflakes. All I care about is the growth rate of the arms, in which case, you know, you have, you can have a good model without knowing anything about the fluffiness. But the fact is, as a practical, you know, if you say, what's the, what's the most obvious feature of a snowflake? Oh, that it has this complicated shape. Well, then you've got a different story about what you model. I mean, this is one of the features of sort of modeling and science. That, you know, what is a model?

A model is some way of reducing the actuality of the world to something where you can readily sort of give a narrative what's happening, where you can basically make some kind of abstraction of what's happening and answer questions that you care about answering. If you wanted to answer all possible questions about the system, you'd have to have the whole system because you might care about this particular molecule, where did it go? And you know, your model, which is some big abstraction of that, has nothing to say about that. So, you know, one of the things that's often confusing in science is people will have,

I've got a model, somebody says, somebody else will say, I don't believe in your model, because it doesn't capture the feature of the system that I care about. There's always this controversy about, is it a correct model? Well, no model is except for the actual system itself, is a correct model in the sense that it captures everything, questions as to capture what you care about capturing. Sometimes that's ultimately defined by what you're going to build technology out of things like this. The one counter example to this is, if you think you're modeling the whole universe all the way down, then there is a notion of a correct model.

But even that is more complicated because it depends on kind of how observers sample things and so on. That's a separate story. But at least at the first level to say, you know, this thing about, oh, it's an approximation, you're capturing one aspect, you're not capturing other aspects. When you really think you have a complete model for the whole universe, you better be capturing ultimately everything, even though to actually run that model is impossible because of computational irreducibility, the only thing that successfully runs that model is the actual running of the universe. It is the universe itself, but okay, so what you care about is an interesting concept.

So that's a human concept. So that's what you're doing with Wolfram Alphon, Wolfram Language, as you trying to come up with symbolic representations as simple as possible. So a model that's as simple as possible that fully captures stuff we care about. Yes. So I mean, for example, you know, we could will have a thing about, you know,, data about movies, let's say. We could be describing every individual pixel in every movie and so on, but that's not the level

that people care about. And it's, yes, this is a, I mean, and that level that people care about is somewhat related to what's described in natural language. But what we're trying to do is to find a way to sort of represent precisely so you can compute things. See, one thing, when you say, you give a piece of natural language.

Question is, you feed it to a computer. You say, does the computer understand this natural language? Well, the computer process it in some way. It does this, maybe it can make a continuation of the natural language, maybe it can go on from the prompt and say what it's going to say. you say, does it really understand it? Hard to know. But for in this kind of computational world, there is a very definite definition of does it understand, which is, could it be turned into this symbolic computational thing from

which you can compute all kinds of consequences? And that's the sense in which one has sort of a target for the understanding of natural language. And that's kind of our goal is to have as much as possible about the world that can be computed in a reasonable way, so to speak, be able to be sort of captured by this kind of computational language. That's kind of the goal. And I think for us humans, the main thing that's important is, as we formalize what we're talking about, it gives us a way of kind of building a structure where we can sort of build this tower of consequences of things. So if

we're just saying, well, let's talk about it in natural language, it doesn't really give us some hard foundation that lets us build step by step to work something out. It's kind of like what happens in math if we were just vaguely talking about math, but didn't have the full structure of math and all that kind of thing, we wouldn't be able to build this big tower of consequences. In a sense, what we're trying to do with the computational language effort is to make a formalism for

describing the world that makes it possible to kind of build this tower of consequences. Well, can you talk about this dance between natural language and whew from language? So this is this gigantic thing, we call it the internet where people post memes and diary type thoughts and very important signing articles and all of that that makes up the training data suffrage, and then there's a wolf from language. How can you map from the natural language of the internet to the wolf from language? Is there an manual? Is there an automated way of doing that?

As you look into the future. Well, so Wolf Malfa, what it does, it's kind of front end is turning natural language into computational language. Right. What you mean by that is there's a prompt, you ask a question, what is the capital of some industry? And it turns into, you know, what's the distance between, you know, Chicago and London or something. And that will turn into, you know,

geodistence of entity, city, you know, et cetera, et cetera, et cetera. Each one of those things is very well defined. We know, you know, given that it's the entity, city, Chicago, et cetera, et cetera, et cetera, you know, Illinois, United States, you know, we know the geolocation of that, we know its population, we know all et cetera, you know, Illinois, United States, you know, we know the geolocation of that, we know its population, we know all kinds of things about it, which we have, you know, curated that data to be able to know that with some degree of certainty, so to

speak. And then, then we can compute things from this. And that's, that's kind of the, yeah, that's, that's, that's, that's the idea. But then, something like GPT, large language models, do they allow you to make that conversion much more powerful? Okay, so that's an interesting thing, which we still don't know everything about. Okay. The, I mean, this question of going from natural language to computational language,

in Wolfmalfa, we've now, you know, Wolfmalfa's been out and about for 13 and a half years now. And, you know, we've achieved, I don't know what it is, 98%, 99% success on queries that get put into it. Now, obviously, there's a sort of feedback loop because the things that work are things people go on putting into it. So, that, that, but, you know, we've got to a very high success

rate of the little fragments of natural language that people put in, you know, questions, math calculations, chemistry calculations, whatever it is, you know, we can, we can, we do very well at that, turning those things into computational language. Now from the very beginning of all from Alpha, I thought about, for example, writing code with natural language. In fact, I was just looking at this recently. I had a post that I wrote in 2010, 2011, called something like programming with natural language is actually going to work.

Okay. And so, you know, we had done a bunch of experiments using methods that were, you know, a little bit, some of them a little bit machine learning like, but certainly not the same, you know, the same kind of idea of vast training data and so on, that is the story of large language models. Actually, I know that post piece of utter trivia, but that post, Steve Jobs forwarded that post around to all kinds of people at Apple. You know, that was, because he never really liked programming languages. So he was very happy to see the

idea that you could get rid of this kind of layer of kind of engineering like structure. He would have liked, you know, I think what's happening now because it really is the case that you can, you know, this idea that you have to kind of learn how the computer works to use a programming language is something that is, I think, a thing that, you know, just like you had to learn the details, the op codes to know how somebody language worked and so on. It's kind of a thing that's limited time horizon. But kind of the, you know, so this idea of how elaborate can you make kind of the prompt, how elaborate can you make the natural language and abstract from it computational language? It's a very interesting question and you know what chat GBT, you know, GBT4 and so on can

do is pretty good. It's very interesting process. I mean, still trying to understand this workflow. We've been working out a lot of tooling around this workflow, the natural language to computational language, right, and the process, especially if it's conversation like dialogue, so multiple queries, kind of, yeah, right. There's so many things that are really interesting that that that work and so on. So first thing is, can you just walk up to the computer and expect to sort of specify our computation? What one realizes is humans have to have some idea of kind of this way of thinking about things computationally without that you're kind of out of luck because you just have no idea what you're going to walk up to a computer. I remember when I should tell a silly story about myself, the very first computer I saw,

which is when I was 10 years old, and it was a big mainframe computer and so on, and I didn't really understand what computers did. It's like somebody is showing me this computer and it's like, you know, can the computer work out the weight of a dinosaur? It's like, that isn't a sensible thing to ask. That's kind of, you know, you have to give it, that's not what computers do. I mean, in Welfare for example, you could say, what's the typical weight of a stagosaurus and it will give you some answer, but that's a very different kind of thing from what one thinks of computers is doing. And so the kind of the question of, you know, first thing is people have to have an idea of what computation is about. And you know, I think it's a very, you know, for education

that is the key thing, is kind kind of this, this, this notion, not computer science, not, so the details are programming, but just this idea of how do you think about the world computationally? Computation, thinking about the world computationally, is kind of this formal way of thinking about the world. We've had other ones, like logic, was a formal way, you know, is a way of sort of abstracting and formalizing some aspects of the world. Mathematics is another one. Computation is this very broad way of sort of formalizing the way we think about the world. And the thing that's cool about computation is, if we can successfully formalize things in terms of computation,

computers can help us figure out what the consequences are. It's not like you formalized it with math, well, that's nice, but now you have to, if you're not using a computer to do the math, you have to go work out a bunch of stuff yourself. So I think, but this idea, let's see, I mean, we're trying to take kind of the, we're talking about sort of natural language

and its relationship to computational language. The thing, the sort of the typical workflow, I think, is first, human has to have some kind of idea of what they're trying to do. That if it's something that they want to build a tower of capabilities on, something that they want to formalize and make computational. Then, human can type something into some LLM system and say vaguely what they want in sort of computational terms.

Then it does pretty well at synthesizing wealth and language code, and it'll probably do better in the future because we've got a huge number of examples of natural language input together with the wealth and Language translation of that. So it's kind of a, you know, that's a thing where you can kind of extrapolating from all those examples makes it easier to do that toss. Is the Prometer task could also kind of debug in the Wolf and Language code? Or is your hope to not do that debugging? No, no, no. I mean, so there are many steps here. Okay. So first, the first thing is you type natural language. It generates well from language. Give examples, by the way. You have an example that is the dinosaur example. Do you have an example that jumps

to mind? There we should be thinking about some dumb example. It's like take my heart rate data and figure out whether I make a moving average every seven days or something and work out what the, and make a plot of the result. So that's a thing which is about two thirds of a line of off-manguage code. I mean, it's list plot of moving average of some data bin or something of the data and then you'll get the result. And the vague thing that I was just saying in natural language could, would, almost certainly,

correctly turn into that very simple piece of orphan language code. So you start mumbling about heart rate. Yeah, and you arrive at the moving average idea. Right, you say average over seven days. Maybe it'll figure out that that's a moving, you know, that that can be encapsulation as this moving average idea. I'm not sure. But then the typical workflow that I'm seeing is you generate this piece of

orphan language code. It's pretty small usually. It's a, and if it isn't small, it probably isn't right. But, um, you know, if it's, it's pretty't small, it probably isn't right. But, you know, if it's pretty small, and, you know, Wolfman language is one of the ideas of Wolfman language is it's a language that humans can read. It's not a language which, you know, programming language is tend to be this one way story of humans' right-hum and computers execute from them.

Wolfman language is intended to be something which is sort of like math notation, something where humans write it and humans are supposed to read it as well. And so kind of the workflow that's emerging is kind of this human mumbles some things. Large language model produces a fragment of orphan language code. Then you look at that, you say, well, typically you just run it first. You see, does it produce the right thing? You look at what it produces, you might say, that's obviously crazy. You look at the code, you see, I see why it's crazy. You fix it.

If you really care about the result and you really want to make sure it's right, you better look at that code and understand it because that's the way you have the sort of checkpoint. I've did it really do what I expected it to do. Now, you go beyond that. I mean, it's, it's, you know, what we find is, for example, let's say the code does the wrong thing, then you can often say to the large language model, can you adjust this to do this? And it's pretty good at doing that. Interesting. So you're using the output of the code to give you hints about the function of the code. So you're debugging based on the output of the code itself. Right. The plugin that we have for chat GPT, it does that routinely. It will send the thing and it will get a result. It will discover. The LLM

will discover itself that the result is not plausible. And it will go back and say, it will get a result, it will discover, the LLM will discover itself that the result is not plausible. And it will go back and say, oh, I'm sorry, it's very polite in it, you know, it goes back and says, I'll rewrite that piece of code. And then it will try it again and get the result. Goalings pretty interesting is when you're just running so one of the new concepts that we have, we invented this whole idea of notebooks back 36 years ago now. And so now there's the question of sort of how do you combine this idea of notebooks where you have text and code and output. How do you combine that with the notion of chat and

so on. And there's some really interesting things there. Like for example, a very typical thing now is we have these notebooks where as soon as the thing produces errors, if the run this code and it produces messages and so on, the LLM automatically not only looks at those messages, it can also see all kinds of internal information about stack traces and things like this. And it can then, it does a remarkably good job of guessing what's wrong and telling you. So in other words, it's looking at things, sort of interesting. It's kind of a typical sort of AI-ish thing that it's able to have more sensory data than

we humans are able to have, because they're able to look at a bunch of stuff that we humans would kind of glaze over looking at. And it's able to then come up with, oh, this is the explanation of what's happening. And what is the data, the stack trace, the code you've written previously, the natural language you've written? Yeah, it's also what's happening is one of the things that's, is, for example, when there's these messages, there's documentation about these messages, there's examples of where the messages have occurred, otherwise,

nice. All these kinds of things. The other thing that's really amusing with this is when it makes a mistake, one of the things that's in our prompt when the code doesn't work is read the documentation. And we have another piece of the plugin that lets it read documentation. And that again is very, very useful because it will, you know, it will figure out sometimes it'll get, it'll make up the name of some option for some function that doesn't really exist, read the documentation, it'll have, you know, of some option for some function that doesn't really exist. Read the documentation.

It'll have some wrong structure for the function and so on. That's a powerful thing. I mean, the thing that I've realized is we built this language over the course of all these years to be nice, incoherent, and consistent and so on. So it's easy for humans to understand. Turns out there was a side effect that I didn't anticipate, which is it makes it easy for AI's to understand. It's almost like another natural language, but yeah.

So, so, well from language is a kind of foreign language. Yes. So, you have a lineup, English, French, Japanese, well from language, and then I don't know, Spanish, and then the system is not going to notice. Well, yes. I mean maybe you know, that's an interesting question because it really depends on what I see as being An important piece of fundamental science that basically just jumped out at us with chat GPT Because I think you know that the real question is Why does chat GPT work? How is it possible to encapsulate,

to successfully reproduce all these kinds of things in natural language with a comparatively small, he says, a couple of hundred billion weights of neural net and so on. And I think that relates to a fundamental fact about language, which the main thing is that I think there's a structure to language that we haven't explored very well. It's the semantic grammar I'm talking about language. When we set up human language, we know that it has certain regularities. We know that it has a certain grammatical structure, now followed by verb, followed by noun,

adjectives, etc., etc., etc. That's its grammatical structure. But I think the thing that TragicPT is showing us is that there's an additional regularity to language which has to do with the meaning of the language, beyond just this pure, you know, part of speech combination type of thing. And I think the, the kind of, the one example of that that we've had in the past is logic. And, you know, I think my, my sort of, kind of picture of how was logic invented, how was logic discovered. It really was a thing that was discovered in its original conception. It was discovered, presumably by Aristotle, who kind of listened to a bunch of people, orators, giving speeches, and this one made sense, that one doesn't make sense,

this one, and you see these patterns of, if the, I don't know what, you know, if the, if the Persians do this, then the this does that, et cetera, et cetera, et cetera. And what, what Aristotle realized is there's a structure to those sentences, there's a structure to that rhetoric that doesn't matter whether it's the Persians and the Greeks, or whether it's the cats and the dogs. It's just, you know, P and Q, you can abstract from the details of these particular sentences, you can lift out this kind of formal structure, and that's what logic is. That's a heck of a discovery, by the way, logic. You're making me realize, no.

Yeah, it's not obvious. The fact that there is an abstraction from natural language that has where you can fill in any word you want. Yeah. It's a very interesting discovery. Now, it took a long time to mature. I mean, Aristotle had this idea of syllogistic logic, where there were these particular patterns of how you could argue things, so to speak.

And in the Middle Ages, part of education was you memorized the syllogisms. I forget how many there were, but 15 of them are something. And they all had names. They all had nimonics, like I think Barbara and Sellerant, with two of the nimonics for the syllogisms. And people would kind of, this is a valid argument because it follows the Barbara syllogism, so to speak. And it took until 1830, you know, with George Buwell,

to kind of get beyond that and kind and see that there was a level of abstraction that was beyond this particular template of a sentence, so to speak. What's interesting there is, in a sense, Chattu B.T. is operating at the Aristotelian level. It's essentially dealing with templates of sentences. By the time you get to Boul and Boulin algebra and this idea of you know you can have arbitrary depth nested collections of hands and oars and knots and you can resolve what they mean, that's the kind of thing, that's a computation story. That's you know you've gone beyond the pure sort of templates of natural language

to something which is an arbitrarily deep computation. But the thing that I think we realized from from chat GPT is, you know, Aristotle stopped too quickly. And there was more that you could have lifted out of language as formal structures. And I think there's, you know, in a sense, we've captured some of that in, in, you know, some of what what is in language, there's, there's a, there's a lot of little, calculate, little algebra of what you can say, what language talks about, I mean, whether it's, I don't know, if you say, I go from place A to place B,

place B to place C, then I know I've gone from place A to place C. If A is a friend of B and B is a friend of C, it doesn't necessarily follow, that A is a friend of B and B is a friend of C, it doesn't necessarily follow that A is a friend of C. These are things that are, you know, that there are, if you go from place A to place B, place B to place C, it doesn't matter how you went, like logic, it doesn't matter whether you flew there, walked there, swam there, whatever. You still, this transertivity of where you go is still valid.

And there are many kinds of kind of features, I think, of the way the world works that are captured in these aspects of language, so to speak. And I think what Chattu B.T. effectively has found, just like it discovered logic. People are really surprised. It can do these logical inferences. It discovered logic, the same way Aristotle discovered logic. You know, people are really surprised it can do these logical inferences. It discovered logic the same way Aristotle discovered logic by looking at a lot of sentences effectively and noticing the patterns and those sentences. But it feels like it's discovering something

much more complicated than logic. So this kind of semantic grammar. I think you wrote about this, maybe you can call it the laws of language, I believe you call, or which I like the laws of thought. Yes, that was the title that George Boole had for his, for his Boolean algebra back in 1830, but yes, as a thought. Yes, that was what he said. All right. So he thought, he thought he nailed it with Boolean algebra. Yeah, there's more to it.

It's a good question of how much more is there to it? And it seems like one of the reasons as you imply that the reason GBT works, chat GBT works is that there's a finite number of things to it. Yeah, I mean, it's discovering the laws in some sense, GBT's discovering the laws of semantic grammar that underlies language. Yes. And what's sort of interesting is, in the computational universe, there's a lot of other kinds of computation that you could do.

They're just not ones that we humans have cared about and operate with. And that's probably because our brains are built in a certain way. And you know, the neural nets of our brains are not that different, in some sense, from the neural nets of a large language model. And that's kind of, and so when we think about, and you know, maybe you can talk about this more, but when we think about, sort of, what will AI's ultimately do? The answer is, in so far as AI's is just doing computation, they can run off and do all these kinds of crazy computations. But the ones that we have decided we care about are this kind of very limited set.

That's where the reinforcement learning with human feedback seems to come in. The more the AI say the stuff that kind of interests us, the more we're impressed by it. So it can do a lot of interesting, intelligent things, but we're only interested in the AI systems when they communicate human in a human-like way. About human-like topics. Yes. Well, it's like technology. I mean, in a sense, the physical world provides all kinds of things.

You know, all kinds of processes going on in physics. Only a limited set of those are ones that we capture and use for technology, because they're only a limited set where we say, you know, this is a thing that we can sort of apply to the human purposes we currently care about. I mean, you might have said, okay, you pick up a piece of rock. You say, okay, there's a nice silicate. It contains all kinds of silicon. I don't care.

Then you realize, oh, we could actually turn this into a, you know, semiconductor wafer and make it microprocessor out of it. And then we care a lot about it. Here. And it's, you know, it's this thing about what do we, you know, in the evolution of our civilization, what things do we identify as being things we care about?

I mean, it's like, you know, when there was a little announcement recently of the possibility of a high temperature superconductor that involved, you know, the element lutehtium, which, you know, generally nobody has cared about. Yes. You know, it's kind of, but suddenly if there's this application

that relates to kind of human purposes, we start to care a lot. So given your thinking that you, Pt may have discovered, inklings of laws of thought, do you think such laws exist? Can we linger on that? Yeah. What's your intuition here? Oh, definitely. I mean, the fact is, look, the logic is but the first step. There are many other kinds of calcula about things that we consider, you know, about sort

of things that happen in the world or things that are meaningful. Well, how do you know logic is not the last step, you know what I mean? Well, because we can plainly see that their thing, I mean, if you say, here's a sentence that is syntactically correct. Okay? You look at it and you're like, you know, the happy electron, you know, eight, I don't know what, something that just, you look at it and it's like, this is meaningless. It's just a bunch of words. It's syntactically correct. The nouns and the verbs are in the right place, but it just doesn't mean anything.

And so there clearly is some rule that there are rules that determine when a sentence has the potential to be meaningful, that go beyond the pure parts of speech syntax. And so the question is, what are those rules, and are there fairly finite set of those rules? My guess is that there's a fairly finite set of those rules? My guess is that there's a fairly finite set of those rules. And once you have those rules, you have a kind of a construction kit, just like the rules of syntactic grammar give you a construction kit for making syntactically correct sentences. So you can also have a construction kit for making semantically correct sentences.

Those sentences may not be realized in the world. I mean, I think, you know, the elephant flew to the moon. A syntactic, semantically, you know, we know we have an idea. If I say that to you, you kind of know what that means. But the fact is it hasn't been realized in the world, so to speak. So semantically correct, perhaps, is things that can be imagined with a human mind. No. Things that are consistent with both our imagination and our understanding of physical reality. I don't know. Yeah. Good question.

I mean, it's a good question. It's a good question. I mean, I think it is given the way we have constructed language, it is things which fit with the things we're describing in language. It's a bit circular in the end, because you can, and the boundaries of what is physically realizable. Okay, let's take the example of motion. Motion is a complicated concept. It might seem like it's a concept that should have been figured out by the Greeks long ago, but it's actually a really pretty complicated concept because what is motion?

Motion is you can go from place A to place B and it's still you when you get to the other end. You take an object, you move it, and it's still the same object, but it's in a different place. Now, even in ordinary physics, that doesn't always work that way. If you're near a space-time singularity in a black hole, for example, and you take your teapot or something, you don't have much of a teapot.

By the time it's near the space-time singularity, it's been completely you know, deformed beyond recognition. But so that's a case where pure motion doesn't really work. You can't have a thing stay the same. But so this idea of motion is something that sort of is a slightly complicated idea. But once you have the idea of motion, you can start, once you have the idea that you're going to describe things as being the same thing but in a different place. That sort of abstracted idea then has, you know, that has all sorts of consequences, like this transertivity of motion, go from A to B, B to C, you've gone from A to C. And that's, so that level of description, you can have what are sort of inevitable consequences. There are inevitable features of the way you've sort of set things

up. And that's, I think, what this semantic grammar is capturing is things like that. And I think that it's a question of what does the word mean? When you say, I move from here to there, what it's complicated to say what that means, this is this whole issue of, you know, is pure motion possible, etc., etc., etc. But once you have kind of got an idea of what that means, then there are inevitable consequences of that idea. But the very idea of meaning, it seems like there's some words that become, it's like there's a latent ambiguity to them. I mean, it's the word like emotionally loaded words like hate and love. Right. It's like, what are they?

What do they mean? Exactly. What? So especially when you have relationships between complicated objects, we seem to take this kind of shortcut, the scripted shortcut to describe like, right, object A hates object B. What's, what's that really mean? Right. Well, words are defined by kind of our social use of them.

I mean, it's not, you know, a word in computational language, for example, when we say we have a construct there, we expect that that construct is a building block from which we can construct an arbitrarily tall tower. So we have to have a very solid building block. And, you know, we have to it turns into piece of code. It has documentation. It's, you know, it's a whole, it's a whole thing. But the word hate, you know, the documentation for that word, well, there isn't a standard documentation for that word, so to speak.

It's a complicated thing defined by kind of how we use it. When, you know, if it wasn't for the fact that we were using language, I mean, so what is language at some level? Language is a way of packaging thoughts so that we can communicate them to another mind. Can these complicated words be converted into something that a computation editing can use? Right. So I think the answer to that is that what one can do in computational language is make a specific definition. And if you have a complicated word, like let's say the word eat, okay? You'd think that's a simple word, it's animals eat things, whatever else.

But you do programming, you say, this function eats arguments, which is sort of poetically similar to the animal eating things. But if you start to say, well, what are the implications of the function eating something? Can the function be poisoned? Well, maybe it can, actually. But if there's a tight mism match or something in some language. But in what, how far does that analogy go? And it's just an analogy. Whereas if you use the word eat, in a computational language level, you would define there isn't a thing which you anchor to the kind of natural language concept eat.

But it is now some precise definition of that that then you can compute things from. But don't you think the analogy is also precise, software eats the world? Don't you think there's something concrete in terms of meaning about analogies? Sure, but the thing that sort of is the first target for computational language is to take sort of the ordinary meaning of things and try and make it precise. Make it sufficiently precise, you can build these towers of computation on top of it. So it's kind of like, if you start with a piece of poetry and you say,

I'm going to define my program with this piece of poetry. It's kind of like that's a difficult thing. It's better to say, I'm going to just have this boring piece of prose and it's using words in the ordinary way. And that's time communicating with my computer and that's time going to build the solid building block from which I can construct this whole kind of computational tower. So in some sense, where if you take a poem and reduce it to something computable, you're going to have very few things left. So maybe there's a bunch of human interaction that's just poetic, aimless nonsense. That's just like recreational hamster in a wheel.

It's not actually producing. Well, I think that that's a complicated thing because in a sense human linguistic communication is there's one mind, it's producing language, that language is having an effect on another mind. And the question of there's sort of a type of effect that is well defined, let's say, where, for example, it's very independent of the two minds, that there's communication where it can matter a lot, sort of what the experience of one mind is versus another one and so on. Yeah, but what is the purpose of natural language communication?

I think the versus, so computation, computational language somehow feels more amenable to the definition of purpose. It's like, yeah, you're given two clean representations of a concept and you can build a tower based on that. Is natural language the same thing but more fuzzy? Well, I think the story of natural language, right? And that's the great invention of our species. We don't know whether it exists in other species, but we know it exists in our species. It's the thing that allows you to sort of communicate abstractly from one generation of the species to another. You can, you know, there is an abstract version of knowledge that can be passed down. It doesn't have to be, you know, genetics, it doesn't have to be, you know, you don't have to

apprentice the next generation of birds to the previous one to show them how something works. There is this abstracted version of knowledge that can be kind of passed down. Now, that, you know, it relies on, it still tends to rely because language is fuzzy, it does tend to rely on the fact that, we look at some ancient language where we don't have a chain of translations from it until what we have today, we may not understand that ancient language. And we may not understand its concepts may be different from the ones that we have today. We still have to have something of a chain, but it is something where we can realistically

expect to communicate abstract ideas, and that's one of the big roles of language. I think in that's been this ability to sort of concretify abstract things is what language is provided. Do you see natural language and thought is the same, the stuff that's gone inside your mind? Well, that's been a long debate and philosophy. It seems to become more important now when we think about

how intelligent GBT is. Whatever that means. Whatever that means, but it seems like the stuff that's going on in the human mind seems something like intelligence. And it's language. What we call it intelligence. Yeah, we call it, yes. And so you start to think of, okay, what's the relationship between thought?

The language of thought, the laws of thought, the laws of the words that are reasoning, and the laws of language, and how they have to do with computation, which is like more rigorous, precise ways of reasoning. Right. Which are beyond human. I mean, much of what computers do, humans do not do. I mean, you might say humans are a subset, presumably, yes, hopefully. Yes.

Yes, right. You know, you might say who needs competition when we have large language models, large language models can just, you know, eventually you'll have a big enough neural net it can do anything. But they're really doing the kinds of things that humans quickly do. And they're plenty of sort of formal things that humans never quickly do. For example, I don't know, you know, you can, some people can do mental arithmetic. They can do a certain amount of math in their minds. I don't think many people can run a program in their minds of any sophistication. It's just not something people do. It's not something people believe even thought of doing because it's kind of a, it's kind of not, you know, you can easily run it on a computer, an arbitrary program. Yeah. Aren't we running specialized programs?

Yeah, yeah, but if I say to you, run the software. There's a touring machine. Yeah. You know, tell me what it does off to 50 steps. And you're like trying to think about that in your mind. That's really hard to do. It's not what people do. I mean, it's...

Well, in some sense, people program, they build a computer, they program it, just to answer your question about what the system does after 50 steps. I mean, humans build computers. Yes, yes. So that's right. But they've created something, which is then, you know, then when they run it,

it's doing something different than what's happening in their minds. I mean, they've outsourced that piece of computation from something that is internally happening in their minds to something that is now a tool that's external to their minds. So by the way, humans, do you, didn't invent computers? They discovered, though.

They discovered computation, which they invented the technology of computers. The computer is just the kind of way to plug into this whole stream of computation. That's probably other ways. For sure. I mean, the particular ways that we make computers out of semiconductors and electronics and so on, that's the particular technology stack we built. I mean, the story of a lot of what people try to do

with quantum computing is finding different, sort of underlying physical infrastructure for doing computation. Biology does lots of computation. It does it using an infrastructure that's different from semiconductors and electronics. It's a molecular scale, sort of computational process that hopefully will understand more about,

but I have some ideas about understanding more about that. But, you know, that's another, you know, it's another representation of computation, things that happen in the physical universe at the level of, you know, these evolving hypergraphs and so on. That's another, sort of implementation layer for this abstract idea of computation. So if GPT or large language models are starting to form, starting to develop or implicitly understand the laws of language and thought, do you think they can be made explicit? Yes. How? Okay.

The bunch of effort. I mean, so it's like doing natural science. I mean, what is happening in natural science? You have the world that's doing all these complicated things, and then you discover, you know, Newton's laws, for example, this is how motion works. This is the way that this particular sort of idealization of the world, this is how we describe it in a simple, computationally reducible way. And I think it's the same thing here. It's there are sort of computationally reducible aspects of what's happening that you can get a kind of narrative theory for just as we've got narrative theories and physics and so on. Do you think it will be depressing or exciting when are the laws of thought are made explicit, human thought, made explicit.

I think that once you understand computational irreducibility, it is, it's neither of those things because the fact is people say, for example, people will say, oh, but, you know, I have free will. I kind of, you know, I operate in a way that is, you know, you, they have the idea that they're doing something that is sort of internal to them that they're figuring out what's happening. But in fact, we think there are laws of physics that ultimately determine, you know, every, every nerve, you know, every electrical impulse and a nerve and things like this.

So you might say, isn't it depressing that we are ultimately just determined by the rules of physics, so to speak? It's the same thing. It's at a higher level. It's like it's a shorter distance to get from kind of semantic grammar to the way that we might construct a piece of text than it is to get from individual nerve firings to how we construct a piece of text, but it's not fundamentally different. And by the way, as soon as we have this other level of description,

it helps us to go even further. So we'll end up being able to produce more and more complicated kinds of things that just like when we didn you know, if we didn't have a computer and we knew certain rules, we could write them down, we'd go a certain distance. But once we have a computer, we can go vastly further. And this is the same kind of thing. You wrote a blog post titled, What is Chad G.P.T. doing and why does it work? We've been talking about this, but can we just step back and linger on this question? What's the chat you PT doing?

What are these a bunch of billion parameters trained on a large number of words? What does it seem to work again? Is it because at the point you made that there's laws of language that can be discovered by such a process. Is there something which lets let's talk about sort of the low level of what chat GPT is doing. I mean, ultimately you give the prompt, it's trying to work out, you know, what should the next word be, right? Which is wild.

Isn't that, isn't that surprising to you that this kind of low level dumb training procedure can create something syntactically correct first and then semantically correct. You know, the thing that has been sort of a story of my life is realizing that simple rules can do much more complicated things than you imagine. That something that starts simple and starts simple to describe can grow a thing that is vastly more complicated than you can imagine. Honestly, it's taken me, I don't know, I've been thinking about this now 40 years or so, and it always surprises me.

Even for example, in our physics project, thinking about the whole universe growing from these simple rules, I still resist because I keep on thinking, you know, how can something really complicated arise from something that simple? It just seems, you know, it seems wrong, but yet, you know, the majority of my life, I've kind of known from things I've studied that this is the way things work. So yes, it is wild that it's possible to write a word at a time and produce a coherent essay, for example, but it's worth understanding write a word at a time and produce a coherent essay, for example. But it's worth understanding how that's working. I mean, it's kind of like, if it was going to say, you know, the cat sat on the, what's the next word? Okay, so how does it figure out the next word?

Well, it's seen a trillion words written on the internet and it's seen the cat sat on the floor, the cat sat on the sofa, the cat sat on the whatever. So it's minimal thing to do is just say, let's look at what we saw on the internet. We saw, you know, 10,000 examples of the cat sat on the, what was the most probable next word? Let's just pick that out and say, that's the next word. And that's kind of what it at some level is trying to do. Now the problem is there isn't enough text on the internet

to, if you have a reasonable length of prompt, that specific prompt will never have occurred on the internet. And as you kind of go further, there just won't be a place where you could have trained where you could just just, you know, where you could just just worked out probabilities from what was already there. You know, like if you say 2 plus 2, there'll be a zillion examples of 2 plus 2, equaling 4, and a very small number of examples of 2 plus 2 equals 5 and so on, and you can pretty

much know what's going to happen. So then the question is, well, if you can't just work out from examples, what's going to happen, just that we know probabilistic from examples, what's going to happen, just that we're no probabilistic for you, for example, what's going to happen, you have to have a model. And there's kind of an idea, this idea of making models of things, is an idea that really, I don't know, I think Galileo probably was one of the first people who sort of worked this out.

I mean, it's kind of like, I think I gave an example of that book I wrote about Chattche B.T. where it's kind of like Galileo was dropping cannonballs off the different floors of the Tower of Pisa. And it's like, okay, you drop a cannonball off this floor, you drop a cannonball off this floor, you miss floor five or something for whatever reason. But you know the time it took the cannonball to fall to the ground from floors 1, 2, 3, 4, but you know the time it took the cannon ball to fall to the ground from floors 1, 2, 3, 4, 6, 7, 8, for example. Then the question is, can you work out? Can you make a model which figures out how long to take the ball? How long would it take the ball to fall to the ground from the floor you didn't explicitly measure? And the thing Galileo realized is that you can use math,

you can use mathematical formulas to make a model for how long it will take the ball to fall. So now the question is, well, okay, you want to make a model, for example, something much more elaborate, like you've got this arrangement of pixels, and is this arrangement of pixels an A or a B? Does it correspond to something we'd recognize as an A or a B? And you can make a similar kind, you know, each pixel is like a parameter in some equation, and you could write down this giant equation where the answer is either, you know, A or, you know, one or two A or B. And the question is then, what kind of a model successfully reproduces the way that we humans would conclude that this is an A and this is a B. If there's a complicated extra tail on the top of the A,

would we then conclude something different? What is the type of model that maps well into the way that we humans make distinctions about things? And the big kind of meta-discovery is neural nets are such a model. It's not obvious they would be such a model. It could be that human distinctions are not captured. We could try searching around for a type of model that could be a mathematical model. It could be some model based on something else that captures typical human distinctions about things. It turns out this model that actually is

very much the way that we think the architecture of brains works, that perhaps not surprisingly, that model actually corresponds to the way we make these distinctions. And so, you know, the core next point is that the kind of model, this neural net model, makes sort of distinctions and generalizes things in sort of the same way that we humans do it. And that's why when you say, you know, the cat set on the green blank, even though it didn't see many examples

of the cat set on the green, whatever, it can make a, or the odd box sat on the green, whatever, I'm sure that particular sentence does not occur on the internet. And so it has to make a model that concludes what, you know, it has to kind of generalize from what it's from the actual examples that it's seen. And so, you know, that's the fact is that neural nets generalize in the same kind of a way that we humans do. If

we were, you know, the aliens might look at our neural net generalizations and say, that's crazy, you know, that thing when you put that extra little dot on the A, that isn't an A anymore. That's messed the whole thing up. But for us, humans, we make distinctions, which seem to correspond to the kinds of distinctions that neural nets make. So then, the thing that is just amazing to me about Chattche B.T. is how similar the structure it has is to the very original way people imagine neural nets might work back in 1943. And, you know, there's a lot of detailed engineering, you know, great cleverness, but it's really the same idea. And in fact, even the sort of elaborations of that idea where people said, let's put in some actual particular structure to try and make the

neural net more elaborate to be very clever about it. Most of that didn't matter. I mean, there are some things that seem to, you know, when you train this neural net, you know, the one thing, this kind of transformer architecture, this attention idea, that really has to do with, does every one of these neurons connect to every other neuron? Or is it somehow causally localized, so to speak, does it like we're making a sequence of words and the words depend on previous words, rather than just everything can depend on everything. And that seems to be important in just organizing things so that you don't have a sort of a giant mess. But the thing, you know, the thing worth understanding

about what is chat GPC in the end. I mean, what is a neural net in the end? A neural net in the end is each neuron has a, it's taking inputs from a bunch of other neurons. It's eventually, it's going to have a numerical value, it's going to compute some number and it's saying, I'm going to look at the neurons above me, it's kind of a series of layers, it's going to look at the neurons above me and it's going to a series of layers. It's going to look at the neurons above me, and it's going to say, what are the values of all those neurons?

Then it's going to add those up and multiply them by these weights. And then it's going to apply some function that says, if it's bigger than zero or something, they make it one or, and otherwise make it zero or some slightly more complicated function. You know very well how this works. It's a giant equation with a lot of variables. You mentioned figuring out where the ball falls when you don't have data on the fourth floor.

This equation here is not as simple as the equation. Right. It's equation with 175 billion terms. And it's quite surprising that in some sense, a simple procedure of training such an equation can lead to a good representation of natural language. Right, the real issue is this architecture of a neural net where what's happening is you've turned so neural nets always just deal with numbers. And so you've turned the sentence that you started with into a bunch of numbers,

like let's say by mapping, you know, each word of the 50,000 words in English, you just map each word or part of a word into some number. You feed all those numbers in, and then the thing is going to, and then those numbers just go into the values of these neurons, and then what happens is it's just rippling down,

going layer to layer until it gets to the end. I think chat GPT is about 400 layers. And it just goes once through. It just every new word it's going to compute just says, here are the numbers from the words before. Let's compute the, what is it compute? It computes the probabilities that it estimates for each of the possible 50,000 words that could come next.

And then it decides, sometimes it will use the most probable word, sometimes it will use not the most probable word. It's an interesting fact that there's this so-called temperature parameter, which, you know, at temperature zero, it's always using the most probable word, that it estimated was the most probable thing to come next. If you increase the temperature, it'll be more and more random in its selection of words. It'll go down to lower and lower probability words. The thing I was just playing with actually recently was the transition that happens as you increase the temperature. The thing goes bonkers. At a particular temperature, I think maybe about 1.2 is the thing I was noticing from yesterday actually, that usually it's giving reasonable answers and then at that temperature with some probability it just starts spouting nonsense. And nobody knows why this happens.

By the way, the thing to understand is it's putting down one word at a time, but the outer loop of the fact that it says, okay, I put down a word. Now, let's take the whole thing I wrote so far. Let's feed that back in. Let's put down another word. That outer loop, which seems almost trivial, is really important to the operation of the thing. And for example, one of the things that is kind of funky is it'll give an answer. And you say to it, is that answer correct? And it'll say, no. And why is that happening? Right. Why can't it do that? Well, the answer is because it is going one word

at a time sort of forwards. And it didn't, you know, it, it, it came along with some sort of chain of, of thought in a sense. And, and it came up with completely the wrong answer. But as soon as you feed it, the whole thing that it came up with, it immediately knows that that isn't right. It immediately can recognize that was a, you know, a bad syllogism or something, and can see what happened, even though

as it was being led down this garden path, so to speak, it didn't, it came to the wrong place. But it's fascinating that this kind of procedure converges to something that forms a pretty good compressed representation of language on the internet. That's quite... I'm not sure what to make of it. Well, look, I think there are many things we don't understand. So, for example, 175 billion weights, it's maybe about a trillion bytes of information, which is very comparable to the training set that was used. And why that sort of stands to some kind of reason that the number of weights and the neural net, I don't know where I can't really argue that, I can't really give you a good, you know, in a sense, the very fact that, you know, in so far as there are definite rules of what's going on, you might expect that eventually we'll have a much smaller neural net that

will successfully capture what's happening. I don't think the best way to do it is probably a neural net. I think a neural net is what you do when you don't know any other way to structure the thing. And it's a very good thing to do if you don't know any other way to structure the thing. And for the last 2,000 years we haven't known any other way to structure it. So this is a pretty good way to start. But that doesn't mean you can't find sort of in a sense more symbolic rules for what's going on, that much of which will then be, you can kind of get rid of much of the structure of the neural net and replace it by things which are sort of pure steps of computation, so to speak, sort of with neural net stuff around the edges, and that becomes just a much simpler way to do it. So the neural net, you hope will reveal to us good symbolic rules They make the need of the neural net less and less and less right and there will still be some stuff that's kind of fuzzy

Just like you know, they're things that it's like this question of what can we formalize? What can we turn into computational language? What is just sort of oh it happens that way just because brains are set up that way. What do you think are the limitations of large language models? Just to make it explicit? Well, I mean, I think that deep computation is not what large language models do. I mean, that's just, it's a different kind of thing. You know, the outer loop of a large language model, if you're trying to do many steps in a computation, the only way you get to do that right now is by spooling out all the whole chain of thought as a bunch of words, basically. And you can make a touring machine out of that, if you want to. I just was doing that construction. In principle, you can make an arbitrary computation by just

spooling out the words, but it's a bizarre and inefficient way to do it. But it's something where the, I think that's sort of the deep computation. It's really what a humans can do quickly. Large language models will probably be able to do well. Anything that you can do kind of off the top of your head type thing is really good for large language models will probably be able to do well. Anything that you can do off the top of your head type thing is really good for large language models. And the things you do off the top of your head, you may not get them always right. But it's thinking through the same way we do. But I wonder if there's an automated way to do something that humans do well, much faster to where it like loops.

So generate arbitrary large code bases of wolf from, wolf from language, for example. Well, the question is, what does he, what do you want the code base to do? Escape control and take over the world. Okay. So, you know, the thing is when people say, you know, we want to build this giant thing, a giant piece of computational language. In a sense, it's sort of a failure of computational language, if the thing you have to build, in other words, if we have a description, if you have a small description, that's the thing

that you represent in computational language, and then the computer can compute from that. Yes. So, in a sense, when, as soon as you're giving a description, you have to somehow make that description something definite, something formal. And to say, okay, I'm going to give this piece of natural language, and then it's going to splurt out this giant formal structure, that in a sense that doesn't really make sense, because except insofar as that piece of natural language kind of plugs into what we socially know, so to speak, plugs into kind of our corpus of knowledge, then that's a way we're capturing a piece of that corpus of knowledge, but hopefully we will have done that in computational language. How do you make it do something that's big?

Well, you have to have a way to describe what you want. I can make it more explicit, if you want. How about I'll just pop into my head. Iterate through all the members of Congress and Figure out how to convince them that they have to let me This meaning the system become president pass all the laws that allows AI systems to Take control and be the president. I don't know. So that's a very explicit like figure out the individual life story of each congressman The each senator anybody Anybody I don't know

What's required to really kind of pass legislation and figure out how to control them and manipulate them Get all the information. What would be the biggest fear of this congressman and In such a way that you can Take action on it in the digital space. So maybe threaten the destruction reputation or something like this. Right. If I can describe what I want, yeah, to what extent can a large language model

automate that? Would the help, would the help of concretization of something like Wolfram language that makes it more grounded? It can go rather a long way. I'm also surprised how quickly I was able to generate. Yeah, yeah, right.

Oh, that's an attack. That's a, yeah, you know, I swear, I swear, I did not think about this before. It's funny how quickly, which is a very concerning thing, because that, that probably this idea will probably do quite a bit of damage. And there might be a very large number of other such ideas. Well, I'll give you a much more benign version of that idea. You're going to make an AI tutoring system. That's a benign version of what you're saying is I want this person to understand this point. Essentially doing machine learning where the loss where the, you know, the, the loss function that the thing you're trying to get to is get the human to understand this point. And, and when

you do a test on the human, that they, yes, they correctly understand how this all that works. And I, I am confident that, you know, sort of a large language model type technology combined with computational language is going to be able to do pretty well at teaching us humans things. And it's going to be an interesting phenomenon because, you know, sort of individualized teaching is a thing that has been kind of a, you know, a goal for a long time. I think we're going to get that. And I think more, you know, that it has many consequences for, you know, like, like, just, you know, if you know me, as if you, the AI, know me, tell me, I'm about to do this thing. What are the three things I need to know, you know, given what I already know, you know,

what's the, what's, let's say I'm looking at some paper or something, right? It's, it's like, there's a version of the summary of that paper that is optimized for me, so to speak, and where it really is, and I think that's really going to work. You could understand the major gaps in your knowledge that it filled would actually give you a deeper understanding of the topic here. Right. And that's an important thing, because it really changes, actually, I think, when you think about education and so on, it really changes kind of what's worth doing, what's not worth doing, and so on.

It makes, I know in my life I've learned lots of different fields. And so I don't know, every time I'm always think this is the one that's going to, I'm not going to be able to learn, but turns out, sort of there are sort of meta methods for learning these things in the end. And I think this idea that it becomes easier to, it becomes easier to be fed knowledge, so to speak. And it becomes, if you need to know this particular thing, you can get taught it in an efficient way. It's something I think

is sort of an interesting feature. And I think it makes the, you know, things like the value of big towers of specialized knowledge become less significant compared to the kind of meta-knowledge of sort of understanding kind of the big picture and being able to connect things together. I think that, you know, there's been this huge trend of let's be more and more specialized because we have to, you know, we have to sort of ascend these towers of knowledge. But by the time you can get, you know, more automation, being able to get to that place on the tower without having to go through all those steps, I think it sort of changes that picture. Interesting. Your intuition is that in terms of

the collective intelligence of the species and the individual minds they make up that collective, there'll be more, there will trend towards being generalists and being kind of philosophers. That's what I think. I think that's where the humans are going to be useful. I think that a lot of these kind of the drilling, the mechanical working out of things is much more automatable, as much more AI territory, so to speak. No more PhDs. Well, that's interesting, yes. I mean, the kind of specialization, this kind of tower of specialization, which has been a feature of,

we've accumulated lots of knowledge in our species. In a sense, every time we have a kind of automation, a building of tools, it becomes less necessary to know that whole tower. And it becomes something where you can just use a tool to get to the top of that tower. I think that the thing that is ultimately, when we think about, okay, what do the AIs do versus what do the humans do? It's like, AIs, you tell them, you say, go achieve this particular objective.

Okay, they can maybe figure out a way to achieve that objective. We say, what objective would you like to achieve? The AI has no intrinsic idea of that. It's not a defined thing. That's a thing which has to come from some other, you know, some other entity, and insofar as we are in charge, so to speak, or whatever it is, an hour kind of web of

society and history and so on, is the thing that is defining what objective we want to go to, that's a thing that we humans are necessarily involved in. To push back a little bit, don't you think that GPD feature versions of GPD would be able to give a good answer to what objective would you like to achieve? From what basis? I mean, if they say, look, here's the terrible thing that could happen, okay? They're taking the average of the internet.

And they're saying, you know, from the average of the internet, what do people want to do? Well, that's the the almost garage's scatage of the most entertaining outcomes the most likely. Okay. So that could be one from him, yeah. That could be, that could be one objective is maximize our global entertainment, the darker version of that is drama,

the good version of that is fun. Right. So I mean, this question of what, you know, if you say to the AI, you know, what does the species want to achieve? Yes, they'll be an answer, right? They'll be an answer. It'll be what the average of the internet says the species wants to achieve. Well, I think you're using the word average very loosely there. So I think the answers will become more and more interesting as these language models

are trained better and better. No, but in the end, it's a reflection back of what we've already said. Yes. But there's a deeper wisdom to the collective intelligence, presumably than each individual. Maybe. Isn't that what we're trying to do as society? Well, to have... Well, that's an important...

That's an interesting question. In so far as some of us work on trying to innovate and figure out new things and so on, it is sometimes, it's a complicated interplay between the individual doing the crazy thing off in some spur, so to speak, versus the collective that's trying to do the high inertia average thing. And sometimes the collective is you know, is bubbling up things that are interesting. And sometimes it's pulling down kind of the attempt to make this kind of innovative direction. But don't you think the large language models would see beyond that

simplification will say, maybe intellectual and career diversity is really important. So you need the crazy people on the outlier, on the outskirts. And so like the actual, what's the purpose of this whole thing is to explore through this kind of dynamics that we've been using as a human civilization, which is most of us focus on one thing. And then there's the crazy people on the outskirts doing the opposite of that one thing. And you kind of they pull the whole society together. There's the mainstream science and then there's the crazy science. And that's just been the history of human civilization. And maybe the AAS system will be able to see that. And the more and more impressed we are by a language model telling us this, the more control we'll give it to it. And the more we'll be willing to let it run our society.

And hence, there's this kind of loop where the society could be manipulated to let the AI system run it. Right. Well, I mean, look, one of the things that's sort of interesting is we might say we always think we're making progress. But yet, in a sense, by saying, let's take what already exists and use that as a model for what should exist. Then it's interesting that, for example, many religions have taken that point of view. There is a sacred book that got written at Time X, and it defines how people should act for all future time. And that's, you know, it's a model that people have operated with. And in a sense, you know, this is a version of that kind of statement. It's like take the 2023 version of sort of how

the world has exposed itself and use that to define what the world should do in the future. But it's not, it's an imprecise definition, right? Because just like with religious texts and with GPT, the human interpretation of what GPT says will be the perturbation in the system. It'll be the noise, it'll be full of uncertainty. It's not like, chat you're told you exactly what to do. It'll tell you a proxy, a narrative of what, like, you know, it's like turning the other

cheek kind of narrative, right? That's not a fully instructive narrative. Well, until it until the AI's control all the systems in the world, they will be able to very precisely tell you what to do. But they'll do what they'll just do this or that thing. And not only that, they'll be auto-suggesting to each person. Do this next, do that next.

So I think it's a slightly more prescriptive situation than one has typically seen. But I think this whole question of what's left for the humans, so to speak, to what extent do we, you know, this idea that there is an existing kind of corpus of purpose for humans defined by what's on the internet and so on, that's an important thing. But then the question of sort of as we explore what we can think of as the computational universe, as we explore all these different possibilities for what we could do, all these different inventions we could make, all these different things, the question is which ones do we choose to follow? Those choices are the things that in a sense, if the humans want to still have kind of human progress, that's what we get to make those choices, so to speak. In other words, there's this idea, if you

say, let's take the kind of what exists today and use that as the determiner of all of what there is in the future. The thing that is sort of the opportunity for humans is there will be many possibilities thrown up, there many different things that could happen, will be done. And in so far as we want to be in the loop, the thing that makes sense for us to be in the loop doing is picking which of those possibilities we want. But the degree to which there's a feedback loop of the idea that we're picking something starts becoming questionable because we're influenced by the various systems. Absolutely.

If that becomes more and more source of our education and wisdom and knowledge. Right. The AI's take over. I mean, I've thought for a long time that, you know, it's the, you know, AR auto suggestion. That's really the thing that makes the AI's take over. It's just that the humans just follow, you know, we will no longer write emails to each other. We'll just send the auto suggested email. Yeah. Yeah. But the thing where humans are potentially in the loop is when there's a choice and when there's a choice Which we could make based on our kind of whole web of history and so on and that's you know

That's in so far as it's all just you know determined You know the humans don't have a place and by the way. I mean, you know at some level the humans don't have a place. And by the way, I mean, at some level, it's all kind of a complicated philosophical issue because at some level, the universe is just doing what it does. We are parts of that universe that are necessarily doing what we do, so to speak. Yet, we feel we have sort of agency in what we're doing,

and that's its own separate kind of interesting issue. And we also kind of feel like we're the final destination with the universe was meant to create. But we very well could be, and likely are some kind of intermediate step, obviously. Well, we're more certainly some intermediate step. The question is if there's some cooler, more complex, more interesting thing that's going to be materialized. Computational universe is full of such things. But in our particular pocket, specifically, if this is the best we're going to do or not, that's kind of a...

We can make all kinds of interesting things in the computational universe. When we look at them, we say, yeah, you know, that's a thing. We don't, it doesn't really connect with our current way of thinking about things. It's like in mathematics, you know, we've got certain theorems, they're about three or four million that human mathematicians have written down and published and so on. But they're an infinite number of possible mathematical theorems. We just go out into the universe of possible theorems and pick another theorem, and then people will say, well, you know, that's, they look at it and they say, I don't know what this theorem means. It's not connected to the things that are part of kind of the web of history that we're dealing with.

You know, I think one point to make about sort of understanding AI and its relationship to us is, as we have this kind of whole infrastructure of AI's doing their thing and doing their thing in a way that is perhaps not readily understandable by us humans, you know, you might say that's a very weird situation. How can we have built this thing that behaves in a way that we can't understand that's full of computational irreducibility, et cetera, et cetera, et cetera? You know, what is this? What's it going to feel like when the world is run by AIs

whose operations we can't understand? And the thing one realizes is actually, we've seen this before. That's what happens when we exist in the natural world. The natural world is full of things that operate according to definite rules. They have all kinds of computational irreducibility. We don't understand what the natural world is doing,

occasionally, and you know, when you say, you know, are the AI's going to wipe us out, for example? Well, it's kind of like, is the machination of the AI's going to lead to this thing that eventually comes and destroys the species? Well, we can also ask the same thing about the natural world, or the machination of the natural world, going to eventually lead to this thing that's going to, you know, make the Earth explode or something like this. Those are questions, those are, and insofar as we think we understand what's happening in the natural world, that's a result of science and natural science and so on.

One of the things we can expect when there's this giant infrastructure of the AI's is that's where we have to kind of invent a new kind of natural science that kind of is the natural is that's where we have to kind of invent a new kind of natural science that kind of is the natural science that explains to us how the AI's work. I mean, it's kind of like we can, you know, we have a, I don't know, a horse or something, and we're trying to get it to, we're trying to, you know, ride the horse and go from here to there. We don't really understand how the horse works inside, but we can get certain rules and certain,

you know, approaches that we take to persuade the horse to go, but we can get certain rules and certain approaches that we take to persuade the horse to go from here to there and take us there. And that's the same type of thing that we're dealing with, with the incomprehensible, computationally irreducible AIs, but we can identify these kinds of, we can find these kind of pockets of reducibility that we can kind of, you know, we're grabbing onto the main of the horse or something to be able to ride it. Or we figure out, you know, if we do this or that to ride the horse, that that's a successful way to get it to do what we're interested in doing. There does seem to be a difference between a horse and a large language model or something

that could be called AGI connected to the internet. Let me just ask you about big philosophical question about the threats of these things. There's a lot of people like Elias Eridkowski who worry about the existential risks of AI systems. Is that something that you worry about? Sometimes when you're building an incredible system like, well, from Alpha, you can kind of get lost in it. Oh, I try and think a little bit about the implications of what one's doing. You know, it's like the Manhattan Project kind of situation where you're like,

it's some of the most incredible physics and engineering being done, but it's like, huh, where's this going to go? I think some of these arguments about kind of, you know, there'll always be a smarter AI, there'll always be, you know, an event should be AI's, we'll get smarter than us, and then all sorts of terrible things will happen. To me, some of those arguments remind me of kind of the ontological arguments for the distance of God and things like this. They're kind of arguments that are based on some particular model, fairly simple model

often, of kind of there is always a greater this, that, and the other. And those arguments tend, what tends to happen in the sort of reality of how these things develop is that it's more complicated than you expect, that the kind of simple logical argument that says, oh, eventually there'll be a super intelligence and then it will, you know, do this and that, turns out not to really be the story. It turns out to be a more complicated story. So for example, here's an example of an issue. Is there an apex intelligence? Just like there might be an apex predator in some, you know, ecosystem, is there going to be an apex intelligence, the most intelligent thing that they could possibly be, right? I think the

answer is no. And in fact, we already know this, and it's a kind of a back to the whole computational irreducibility story. There's kind of a question of, you know, even if you have, if you, if you have sort of a, a touring machine machine and you have a touring machine that runs as long as possible before it holds, you say, is this the machine, is this the apex machine that does that? There will always be a machine that can go longer. And as you go out to the infinite collection of possible touring machines, you'll never have reached the end, so to speak.

You'll always be able to, it's kind of like the same question of whether there'll always be another invention. Will you always be able to invent another thing? The answer is yes, there's an infinite tower of possible inventions. That's one definition of apex. But the other is like, which I also thought you were, which I also think might be true,

is there a species that's the apex intelligence right now on Earth? So it's not trivial to say that humans are that. Yeah, it's not trivial. I agree. It's a, I think one of the things that I've long been curious about kind of other intelligences, so to speak. I mean, I view intelligence is like computation. And it's kind of a, you know, you're sort of, you have the set of rules, you deduce what happens. I have tended to

think now that there's this sort of specialization of computation, that is sort of a consciousness-like thing that has to do with these computational boundedness, single thread of experience, these kinds of things, that are the specialization of computation that corresponds to a somewhat human-like experience of the world. Now the question is, so that's, there may be other intelligences like the aphorism, the weather has a mind of its own. It's a different kind of intelligence that can compute all kinds of things that are hard for us to compute,

but it is not well aligned with us with the way that we think about things. It doesn't think the way we think about things. And in this idea of different intelligence is every different mind, every different human mind is a different intelligence that thinks about things in different ways. And you know, in terms of the kind of formalism of our physics project, we talk about this idea of a rullial space, the space of all possible sort of rule systems, and different

minds are in a sense of different points in rullial space. Human minds, ones that have grown up with the same kind of culture and ideas and things like this might be pretty close in really all space, pretty easy for them to communicate, pretty easy to translate, pretty easy to move from one place in really all space that corresponds to one mind to another place in really all space that corresponds to another sort of nearby mind. When we deal with kind of more distant things in really space, like the pet cat or something. The pet cat has some aspects that are shared with us, the emotional responses of the cat are somewhat similar to ours,

but the cat is further away in real space than people are. And so then the question is, can we identify sort of the, can we make a translation from our thought processes to the thought processes of a cat or something like this? And what will we get, what will happen when we get there? And I think it's the case that many animals, dogs, for example, they have a labrital factory systems, they have the smell architecture of the world, so to speak, in a way that we don't. And so, you know, if you were sort of talking to the dog and you could, you know, communicate in a

language, the dog will say, well, this is a, you know, a, you know, a flowing, smelling, this, that, and the other thing, concepts that we just don't have any idea about. Now, what's interesting about that is, one day we will have chemical sensors that do a really pretty good job. We'll have artificial noses that work pretty well, and we might have our augmented reality systems show us this same map that the dog could see and things like this, similar to what happens in the dog could see and things like this, similar to what happens

in the dog's brain. And eventually, we will have expanded in rural space to the point where we will have those same sensory experiences that dogs have and we will have internalized what it means to have the smell landscape or whatever. And so then we will have colonized that part of rural space until we haven't gone, you know, some things that animals and so on do, we sort of successfully understand others we do not. And the question of what kind of, what is the,

what representation, how do we convert things that animals think about to things that we can think about? That's not a trivial thing. And, you know, I've long been curious I had a very bizarre project at one point of trying to make an iPad game that a cat could win against its owner. I said, they feel like there's a deep philosophical goal there, though. Yes. Yes, yes. I mean, I was curious if pets can work in Minecraft or something and can construct things,

what will they construct? And will what they construct be something where we look at it and we say, oh yeah, I recognize that. Or will it be something that looks to us like something that's out there in the computational universe that one of my cellular automates are might have produced? Where we say, oh yeah, I can kind of my, you know, cellular automates are my to produce.

Well, we sell, yeah, I can kind of see it operates, go into some rules. I don't know why you would use those rules. I don't know why you would cap. Yeah, I actually just to link on that seriously. Is there a connector in the rural y'all space between you and a cat, where the cat could legitimately win? So iPad is a very limited interface. Yeah, I wonder if there's a game where cats win. So iPad is a very limited interface. I wonder if there's a game where cats win. I think the

problem is a cat's trying to be that interested in what's happening on the iPad. So yeah, that's an interface issue, probably. Yeah, right, right, right. No, I think it is likely that, I mean, you know, there are plenty of animals that would successfully eat us if we were exposed to them. And so it's going to pounce faster than we can get out of the way and so on. So there are plenty of, and probably it's going to, we think we've hidden ourselves, but we haven't successfully hidden ourselves. That's a physical strength. I wonder if there's something in more in the realm of intelligence where an animal like a cat could

out. Well, I think there are things in terms of the speed of processing certain kinds of things for sure. I mean, the question of what, you know, is there a game of chess, for example, is that cat chess, that the cats could play against each other, and if we tried to play a cat, we'd always lose. I don't know. You might have to do a speed, but it might have to do with concepts also. It might be concepts in the cat's head. I tend to think that our species from its invention of language has managed to build up this kind of tower of abstraction that for things like a chest-like game will make us win. In other words, we've become through the fact that we've experienced language and learnt abstraction. We've become

smarter at those kinds of abstract kinds of things. Now, there doesn't make us smarter at catching a mouse or something. It makes us smarter at the things that we've chosen to sort of concern ourselves, which are these kind of abstract things. And I think this is again back to the question of what does one care about? If one's the, if you have the discussion with a cat, if we can translate things to have the discussion with a cat. The cat will say, you know, I'm very excited that this light is moving. I will say, why do you care? And the cat will say, that's the most important thing in the world, that this thing moves around. I mean, it's like when you ask about, I don't know, you look at archaeological remains and you say,

these people had this, you know, belief system about this. And, you know, you look at archaeological remains and you say, these people had this belief system about this and that was the most important thing in the world to them. And now we look at it and say, we don't know what the point of it was. I mean, I've been curious, there are these handprints on caves from 20,000 more years ago.

And it's like, nobody knows what these handprints were there for, that they may have been a representation of the most important thing you can imagine. They may just have been some, you know, some kid who rubbed their hands in the mud and stuck them on the walls of the cave. You know, we don't know. And I think, but this whole question of what, you know, is when you say this question of sort of what's the smartest thing around, there's the question of what kind of computation you're trying to do. If you're saying, you've got some well-defined computation, and how do you implement it? Well, you could implement it by nerve cells, firing, you could implement it with silicon and electronics, you can implement it by some kind of

molecular computation process in the human immune system or in some molecular biology kind of thing, the different ways to implement it. And I think this question of those different implementation methods will be of different speeds, they'll be able to do different things. If you say, which, so an interesting question would be, what kinds of abstractions are most natural in these different kinds of systems? So for a cat, it's, for example, the visual scene that we see, you might, we pick out certain objects, we recognize, you know, certain things in that visual scene, a cat might, in principle, recognize different things. I suspect, you know, evolution, biological evolution is very slow, and I suspect what a cat notices is very similar. We even know that from some neurophysiology. What a cat notices is very similar

to what we notice. Of course, there's one obvious difference. Cats have only two kinds of color receptors, so they don't see in the same color color that we do. Now, we say, we're better, we have three color receptors in a red, green, blue. We're not the overall winner. I think the mantis shrimp is the overall winner with 15 color receptors, I think.

So it can make distinctions that with our current, you know, like the mantis shrimp's view of reality is, at least in terms of color, is much richer than ours. Now, but what's interesting is how do we get there? So imagine we have this augmented reality system that is even, you know, it's seeing into the infrared and into the ultraviolet things like this, and it's translating that into something that is connectable to our brains either through our eyes or more directly into our brains. You know, then eventually our kind of web of the types of things we understand will extend to those kinds of constructs just as they have extended. I mean, there are plenty of things we understand will extend to those kinds of constructs just as they have

extended. I mean, there are plenty of things where we see them in the modern world because we made them with technology and now we understand what that is. But if we'd never seen that kind of thing, we wouldn't have a way to describe it, we wouldn't have a way to understand it and so on. All right, so that actually stemmed from our conversation about whether AI is going to kill all of us. And you, we've discussed this kind of spreading of intelligence through really all space. That in practice, it just seems that things get more complicated. Things are more complicated than the story of,

well, if you build a thing that's plus one intelligence, that thing will be able to build the thing that's plus two intelligence and plus three intelligence. And that will be exponential. It'll become more intelligent exponentially faster and so on until it completely destroys everything. But you know, that intuition might still not be so simple, but might still carry validity. And there's two interesting trajectories here. One, a super intelligent system remains in rural proximity to humans, to where we're like, holy crap, this thing is really intelligent.

Let's select the present. And then there could be perhaps more terrifying intelligence that starts moving away. They might be around us now. They're moving far away in really all space, but they're still sharing physical resources with us. Yes. And so they can rob us of those physical resources and destroy humans just kind of casually. Yeah. Just like nature code, like nature could, but it seems

like there's something unique about AI systems where there is this kind of exponential growth, like the way, well, sorry, nature has so many things in it. One of the things that nature has, which is very interesting, or viruses, for example, there is systems within nature that have this kind of exponential effect. And that terrifies us humans because you can, you know, there's only eight billion of us and you can just kind of, it's not that hard to just kind of whack them all, but quick. So, I mean, is that something you think about that? Yeah, I vote about that. Yes. The threat of it. I mean, is that something you think about that? Yeah, I've thought about that. Yes, the threat of it. I mean, you ask concern about it as somebody like Eliezeria Kowski, for example,

just big, big painful negative effects of AI on society. You know, no, but perhaps that's because I'm intrinsically an optimist. I mean, I think that there are things, I think the thing that one sees is there's going to be this one thing and it's going to just zap everything. Somehow, you know, maybe I have faith in computational irreducibility, so to speak, that there's always unintended corners that, you know, it's just like somebody says, I'm going to,

oh, I don't know, somebody has some bio weapon and they say, we're going to release this and it's going to do all this harm. But then it turns out it's more complicated than that because, you know, the kind of, some humans are different and, you know, the exact way it works is a little different than you expect. It's something where sort of the, the, the great big, you smash the thing with something, the asteroid collides with the earth. And yes, the earth is cold for two years or something, and lots of things die, but not everything dies. And there's usually, I mean, I kind of, this is in a sense the sort of story of computational irreducibility. There are always unexpected corners, there are always unexpected consequences. And I don't think that they're kind of whackered over the head with something and that's all gone, is, you know, that can obviously happen, the earth can be swallowed up in a

black hole or something and then it's kind of presumably, presumably all over. But, you know, I think this question of what, what do I think the realistic paths are? I think that there will be sort of an increasing, I mean, that people have to get used to phenomenon-like computational irreducibility. There's an idea that we built the machines, so we can understand what they do,

and we're going to be able to control what happens. Well, that's not really right. Now, the question is, is the result of that lack of control going to be that the machines kind of conspire and sort of wipe us out? Maybe just because I'm an optimist, I don't tend to think that that's in the cards. I think that the, as a realistic thing, I suspect what will sort of emerge, maybe, is kind of an ecosystem of the AIs just as, again, I don't really know. This is something it's hard to be clear about what will happen. I think that there are a lot of sort of details of, you know, what could we do, what systems in the world could we connect an AI to? You know, I have

to say, I was just a couple of days ago, I was working on this chat GBT plugin kit that we have for Welfare Language, okay, where you can, you know, you can create a plugin and it runs Welfare Language Code and it runs Wolf and Language code, and it can run Wolf and Language code back on your own computer. And I was thinking, well, I can just make it, I can tell ChatGBT, create a piece of code, and then just run it on my computer. And I'm like, that sort of personalizes for me

the what could possibly go wrong, so to speak. Was that exciting or scary, that possibility? It was a little bit scary, actually, because it's kind of like, like I realize I'm delegated to the AI, just write a piece of code, you know, you're in charge, write a piece of code, run it on my computer, and pre-sune all my files completely. That's like a Russian relay, but like much more complicated

for me than that. Yes, yes, right. That's a good drinking game. I don't know. So, well, I mean, that's why it's a good drinking. That's an interesting question, then. If you do that, what is the sandboxing that you should have? And that's sort of a version of that question for the world. That is, as soon as you put the AIs in charge of things,

how much, how many constraints should there be on these systems before you put the AIs in charge of all the weapons and all these, you know, all these different kinds of systems? Well, here's the fun part about sandboxes is the AI knows about them. It has the tools to crack them. Look, the fundamental problem of computer security is computational irreducible. Yes. Because the fact is, any sandbox is never, you know, it's never going to be a perfect sandbox. If you want the system to be able to do interesting things, I mean, this, this is the problem that's

happened, the generic problem of computer security, that as soon as you have your, you know, firewall that is sophisticated enough to be a universal computer, that means it can do anything. And so long as if you find a way to poke it so that you actually get it to do that universal computation thing, that's the way you kind of crawl around and get it to do the thing that it wasn't intended to do. And that's sort of another version of computational irreducibility is you can kind of, you get it to do the thing you didn't expect it to do, so to speak. There's so many interesting possibilities here that manifest themselves from the compute,

computational irreducibility here, that it's just so many things can happen, because in digital space things move so quickly. You can have a chatbot, you can have have a piece of code that you can basically have chat GPT-generated viruses. It's an only wrong purpose. And they are digital viruses. Yes. And they could be brain viruses too. They convince kind of like phishing emails. Yes. They can convince you of stuff. Yes. And no doubt you can, you know, in a sense we've had the loop of the machine learning loop of making things that convince people of things is surely going to get easier to do. Yeah.

And, you know, then what does that look like? Well, it's again, you know, we humans are, you know, we're, this is a new environment for us. And admittedly it's an environment which a little bit scarily is changing much more rapidly than I mean, you know, people worry about, you know, climate change is going to happen over hundreds of years. And you know, the environment is changing, but the environment for, you know, in the kind of digital environment might change in six months. So one of the relevant concerns here in terms of the impact of GPD on society is the nature of truth

that's relevant to wolf and alpha. Because computation through symbolic reasoning that's embodied in wolf and alpha is the interface. There's a kind of sense that wolf and alpha tells me it's true. So we hope. Yeah, I mean, you could probably analyze that you could show. You can't prove this. I was going to be true competition or disability. But it's going to be more true than not. It's, look, the fact is it will be the correct consequence of the rules you've specified. And in so far as it talks about the real world,

that is our job in sort of curating and collecting data to make sure that that data is, quote, as true as possible. Now, what does that mean? Well, it's always an interesting question. I mean, for us, our operational definition of truth is, somebody says, who's the best actress? Who knows? But somebody won the Oscar, and that's a definite fact.

Yeah. And so, you know, that's the kind of thing that we can make computational as a piece of truth. Yeah. If you ask, you know, these things, which, you know, a sensor measured this thing, it did it this way, a machine learning system, this particular machine learning system recognized this thing. That's a sort of a definite, a fact, so to speak. And that's, you know, there is a good network of those things in the world.

It's certainly the case that particularly when you say, is so-and-so a good person. You know, that's a hopelessly, you know, we might have a computational language definition of good, I don't think it'd be very interesting, because that's a very messy kind of concept, not really amenable to kind of, you know, I think as far as we will get with those kinds of things, is I want X.

There's a kind of meaningful calculus of I want X, and that has various consequences. I'm not sure I haven't thought this through properly, but I think a concept is so-and-so a good person, is that true or not? That's a mess. That's a mess, that's amenable to computation. I think it's a mess when humans try to define what's good, like the religious legislation. But when humans try to define what's good through literature, through history books, through poetry, it starts being boring. Well, I don't know. I mean, that particular thing, it's kind of like, you know, we're going into kind of the

ethics of what counts as good, so to speak. And, you know, what do we think is right and so on. And I think that's a thing which, you know, one feature is we don't all agree about that. There's no theorems about kind of, you know, there's no, there's no theoretical framework that says this is, this is the way that ethics has to be. Well, first of all, there's stuff that kind of agree on, and there's some empirical backing for what works and what doesn't, from just even the morals and ethics within religious texts. So we seem to mostly agree that murder is bad.

The certain universals that seem to emerge. I wonder what the murder of an AI is bad. Well, I tend to think yes, but I think we're going to have to contend with that question. I wonder what AI would say. Yeah. Well, I think one of the things with AI is it's one thing to wipe out that AI that has no owner. You can easily imagine an AI kind of hanging out on the, you know, on, on the internet without having any particular owner or anything like that. And then you say, well, well, what harm does it, you know, it's, it's okay to get rid

of that AI. Of course, if the AI has 10,000 friends who are humans, and all those 10,000 humans will be incredibly upset that this AI just got exterminated, it becomes a slightly different more entangled story. But yeah, I know I think that this question about what do humans agree about? There are certain things that human laws have tended to consistently agree about. There have been times in history when people have gone away from certain kinds of laws, even ones that we would now say, how could you possibly have not done that that way?

That just doesn't seem right at all. But I think this question of what I don't think one can say beyond saying, if you have a set of rules that will cause the species to go extinct, that's probably, you know, you could say that's probably not a winning set of laws, because even to have a thing on which you can operate laws requires that the species not be extinct. But between sort of what's the distance between Chicago and New York that Wolfram Alpha can answer and the question of if this person is good or not, there seems to be a lot of gray area. And that starts becoming really interesting.

I think your since the creation of Wolfram Alpha have been a kind of arbiter of truth at a large scale. So the system is generates more truth than... Try to make sure that the things are true. I mean, look, as a practical matter, when people write computational contracts and it's kind of like, you know, if this happens in the world, then do this. Yes.

And this hasn't developed as quickly as it might have known, you know, this has been a sort of a blockchain story in part. And so on, although blockchain is not really necessary for the idea of computational contracts, but you can imagine that eventually, sort of a large part of what's in the world are these giant chains and networks of computational contracts. And then something happens in the world

and this whole giant domino effect of contracts firing autonomously that cause other things to happen. And you know, for us, you know, we've been the main sort of source to the Oracle of quotes facts or truth or something for things like blockchain computational contracts and such like. And there's a question of, you know, what you know, I consider that responsibility to actually get the stuff right. And one of the things that is tricky sometimes is when is it true, when is it a fact, when is it not a fact?

Yes. I think the best we can do is to say, you know, we have a procedure, we follow the procedure, we might get it wrong, but at least we won't be corrupt about getting it wrong, so to speak. So that's beautifully put. I have a transparency about the procedure. The problem starts to emerge when the things that you convert into computational language start to expand, for example, into the realm of politics. So this is where it's almost like this nice dance of Wolfram Alpha and Chad GBT. Chad GBT, like you said, is shallow and broad. So it's going to give you an opinion on everything. But it writes fiction as well as fact, which is exactly how it's built. I mean, that's exactly

it is making language and it is making both even in code it writes fiction. I mean, it's kind of fun to see sometimes, you know, it'll write fictional or from language code. Yeah. That that kind of looks right. Yeah, it looks right, but it's actually not pragmatically correct. Yeah. But yes, it's a, it has a view of kind of roughly how the world works. At the same level as books of fiction, talk about roughly how the world works. They just don't happen to be the way the world actually worked or whatever. But yes, that's, no, I agree. That's sort of a, you know, we are attempting with attempting with our whole, you know, well, from language, computational language thing to represent at least, well, it's either, it doesn't necessarily have to be how the actual world works because we can invent a set of rules that aren't the way the actual world works

and run those rules, but then we're saying we're going to accurately represent the result of running those rules, which might or might not be the actual rules of the world. But we also are trying to capture features of the world as accurately as possible to represent what happens in the world. Now, again, as we've discussed, the atoms in the world, you say, I don't know, you know, was there a tank that showed up, you know, that drove somewhere? Okay, well, you know, what is a tank? It's an arrangement of atoms that we abstractly describe as a tank.

And you could say, well, you know, there's some arrangement of atoms that is different arrangement of atoms, but it's and it's not, you know, we didn't, we didn't decide it's like this observatory question of, you know, what, what arrangement of atoms counts as a tank versus not a tank? So there's, there's even things that we consider strong facts. You could start to kind of disassemble them and show that they're not. Right. So the question of whether, oh, I don't know, was this gust of wind strong enough to blow over this particular thing?

Well, a gust of wind is a complicated concept. It's full of little pieces of fluid dynamics and little vortices here and there, and you have to define, was it, was it, you know, the aspect of the gust of wind that you care about might be, it put this amount of pressure on this, you know, blade of some, some, you know, wind turbine or something. And, you know, that's the, and, but, but, you know, if you say, if you have something, which is the fact of the gust of wind was this strong or whatever that you know that is You have to have some definition of that you have to have some measuring device that says according to my measuring device that was constructed this way the Gusta wind was this

So what can you say about the nature of truth? That's useful for us to understand Chad G PT because you've been You've been contending with this idea of what is fact and not. And it seems like chat GPT is used a lot now. I've seen it used by journalists the right articles. And so you have people that are working with large language models trying to desperately figure out how do we essentially censor them through different mechanisms either manually or through read for some learning with human feedback try to line them to To not say fiction just to say nonfiction as much as possible This is the importance of computational language as an intermediate. It's kind of like you've got the large language model

It's able to surface something which is a formal precise thing that you can then look at and you can run tests on it and you can do all kinds of things. It's always going to work the same way and it's precisely defined what it does. And then the large language model is the interface. I mean, the way I viewed these large language models, one of their important, I mean, there are many use cases and, you know, it's a remarkable thing to talk about some of these, you know, literally, you know, every day we're coming up with a couple of new use cases, some of which are very, very, very surprising. And things were, I mean, but the best use cases are ones where it's, you know, even if it gets it roughly right, it's still a huge win.

Like a use case we had from a week or two ago, is read our bug reports. We've got hundreds of thousands of bug reports that are accumulating over decades. And it's like, can we have it? Just read the bug report, figure out where is the bug likely to be? And home in on that piece of code, maybe it'll even suggest some way to fix the code. It might be nonsense about how to fix the code, but it's incredibly useful that it was able to... Yeah, it's so awesome. It's so awesome because even the nonsense will somehow be instructive. I don't quite understand that.

Yeah, there's so many programming related things like, uh, for example, uh, translating for one programming language to another is really, really interesting, it's extremely effective, but then you, the failures reveal the path forward also. Yeah, but I think, I mean, the, the big thing, I mean, in, in that kind of discussion, the unique thing about our computational languages, it was intended to be read by humans. Yes. And so it has really important. Right. And so it has this thing where you can, but, you know, thinking about sort of chat GPT and its use and so on, the one of the big things

about it, I think, is it's a linguistic user interface. That is, so a typical use case might be in the take the journalist case, for example, it's like let's say I have five facts that I'm trying to turn into an article or I'm trying to I'm trying to write a report where I have basically five facts that I'm trying to include in this report. But then I feed those five facts to chat you be to you it puffs them out into this big report. And then that's a good interface for it. If I just had in my terms those five bullet points, and I gave them to some other person, the person will say, I don't know what you're talking about, because this is your version of this quick notes about these five bullet points. But if you puff it out into this thing, which is kind of connects to the collective

understanding of language, then somebody else can look at it and say this thing, which is kind of connects to the collective understanding of language, then somebody else can look at it and say, okay, understand what you're talking about. Now you can also have a situation where that thing that was puffed out is fed to another large language model. You know, it's kind of like, you know, you're applying for the permit to, you know, I don't know, grow fish in some place or something like this. And you have these facts that you're putting in. You know, I'm going to have this kind of water, and I don't know what it is. You've just got a few bullet points. It puffs it out into this big application.

You fill it out. Then at the other end, the Fisheries Bureau has another large language model that just crushes it down because the Fisheries Bureau cares about these three points and it knows what it cares about. And it then, so it's really the natural language produced by the large language model is sort of a transport layer that is really LLM communicates with LLM. I mean, it's kind of like the, you know, I write a piece of email using my LLM communicates with LLM. I mean, it's kind of like the, you know, I write a piece of email using my LLM and, you know, puff it out from the things I want to say, your LLM turns it into, and the conclusion is X. Now, the issue is, you know, that the thing is

going to make this thing that is sort of semantically plausible. And it might not actually be what you, you know, it might not be kind of relate to the world and the way that you think it should relate to the world. Now I've seen this, you know, I've been doing, okay, I'll give you a couple of examples. I was doing this thing when we announced this plugin for for for chat GPT. I had this lovely example of a math word problem, some complicated thing, and it did a spectacular job of taking apart this elaborate thing about, you know, this person has twice as many chickens as this, et cetera, et cetera, and it turned it into a bunch of equations.

It fed them to orphan language. We solved the equations, everybody did great, we gave back the results, and I thought, okay, I'm gonna put this in this blog post I'm writing, okay? I the results, and I thought, okay, I'm going to put this in this blog post I'm writing. I thought, I better just check. And turns out, it got everything, all the hard stuff it got right, and the very end, last two lines, it just completely goofed it up and gave the wrong answer.

And I would not have noticed this. Same thing happened to me two days ago. So I thought, I made this with this chat GBT plug-in kit. I made a thing that would emit a sound, would play a tune on my local computer. So chat GBT would produce a series of notes, and it would play this tune on my computer. Very cool.

So I thought, I'm going to ask it, play the tune that how sang when how was being disconnected in 2001. Okay, so it there it is. Daisy was it Daisy? Yes, Daisy. Yeah. Right. So So I think you know, and so it produces a bunch of notes and I'm like this is spectacular. This is amazing. And then I thought, you know just going to put it in, and then I thought, I better actually play this. And so I did, and it was, Mary had a little lamb. Oh wow. Oh wow. But it was Mary had a little lamb.

Yeah. Wow. So it was correct, but wrong. Yes. It was easily be mistaken. Yes, right. And in fact, I kind of gave the I had this quote from how to explain, you know, it's as it the how you know, states in the movie, you know, it's the how 9,000 is, you know, the thing was just a rhetorical device because I'm

realizing, oh my gosh, you know, this Chatchy BT, you know, could have easily fooled me. I mean, it did this, it did all, it did this amazing thing of knowing this thing about the movie and being able to turn that into the notes of the song, except if the wrong song. Yeah. And, you know, how in the movie, how says, you know, I think it's something like, you know, no hell, no 9000 series computer has ever been found to make an error. We are for all practical purposes, perfect and incapable of error. And I thought that was kind of a charming sort of quote from, from how to make in connection with, with what Chattche B.T. in that case. Yeah, the interesting things about the hell of a lot of them, like you said, that they are very willing to admit their error.

Well, yes, I mean, that's a question of the RLH, the reinforcement learning, human feedback thing. Oh, right. That's, you know, the nice thing. And that LLM, the really remarkable thing about Chatchy PT is, you know, I had been following what was happening with large language models and I played with them a whole bunch.

And they were kind of like, yeah, you know, it's kind of like what you would expect based on sort of sort of statistical continuation of language. It's interesting, but it's not break out exciting. And then I think the kind of the the kind of reinforcement, the human feedback reinforcement learning, you know, and making chat GP GPT try and do the things that humans really wants it to do, that broke through, that kind of reached the threshold where the thing really is interesting to us humans. And by the way, it's interesting to see how,

you know, you change the temperature, something like that. The thing goes bonkers, and it no longer is interesting to humans. It's producing garbage. And it's kind of right. It's somehow it managed to get this above this threshold where it really is well aligned to what we humans are interested in and kind of that.

And I think nobody saw that coming, I think. Certainly nobody I've talked to, nobody who was involved in that project seems to have known that was coming. It's just one of these things that is a sort of remarkable threshold. I mean, you know, when we built Wolfmalfa, for example, I didn't know it was going to work. You know, we tried to build something that would have enough knowledge

of the world, that it could answer a reasonable set of questions that we could do a good enough natural language understanding that typical things you type in would work. We didn't know where that threshold was. I mean, I was not sure that it was the right decade to try and build this, even the right, 50 years to try and build it. And I think that was, it's the same type of thing

with chat GPT that I don't think anybody could have predicted that 2022 would be the year that this became possible. I think, yeah, you tell a story about Marvel and Miske and showing it to him and saying that, no, no, this time it actually works. Yes. It's the same thing for me looking at these large language models. It's like when people were first saying, first few weeks of chat GBT, it's like, oh yeah, I've seen these large language models. And then I actually try it and,

oh my gosh, it actually works. But the things, and the thing I found, remember one of the first things I tried was, write a persuasive essay that a wolf is the bluest kind of animal. Okay? So it writes this thing and it starts talking about these wolves that live on the Tibetan plateau and their name, some Latin name and so on. And I'm like, really? And I'm starting to look it up on the web. And it's like, well, it's actually complete nonsense. But it's extremely plausible.

I mean, it's plausible enough that I was going and looking it up on the web and wondering if there was a wolf that was blue. You know, I mentioned this on some live streams I've done. And so people have been sending me these pictures. Blue wolves. Maybe I was on to something. Can you kind of give your wise sage advice about what humans who have never

interacted with the eye systems, not even like with wolf from alpha, are now interacting with Chad GPT because it becomes, it's accessible to a certain demographic that may have not touched AI systems before. What do we do with truth, like journalists, for example? Yeah. How do we think about the output of these systems? I think this idea, the idea that you're going to get factual output is not a very good idea. I mean, it's just, this is not, it is a linguistic interface. It is producing language and language can be truthful or not truthful and that's a different slice of what's going on. I think that what we see in, for example, kind of go check this with

your fact source, for example. You can do that to some extent, but then it's going to not check something. That is again a thing that is sort of, but then it's going to not check something. It's going, you know, that is again a thing that is sort of a, does it check in the right place? I mean, we see that in, you know, does it call the, you know, the Wolf and Plug-in in the right place, you know, often it does. Sometimes it doesn't. You know, I think the real thing to understand about what's happening is, which I think is very exciting, is the great democratization of access to computation.

I think that when you look at the, there's been a long period of time when computation and the ability to figure out things with computers has been something that only the druids at some level can achieve. I myself have been involved in trying to de-druidify access to computation. Back before Mathematica existed in 1988, if you were a physicist or something like that, and you wanted to do a computation, you would find a programmer, you would go and delegate the computation to that programmer. Hopefully they'd come back with something useful. Maybe they wouldn't, there'd be this long, multi-week loop

that you'd go through. And then it was actually very, very interesting to see. 1988, people like physicists, mathematicians, and so on, then other lots of other people, but there's very rapid transition of people realizing they themselves could actually type with their own fingers and make some piece of code that would do a computation that they cared about.

And it's been exciting to see lots of discoveries and so on made by using that tool. And I think the same thing is, we see the same thing. Wolfmalfa is dealing with, it is not as deep computation as you can achieve with whole Wolfmalfa language, Mathematica, Stack. But the thing that's, to me, particularly exciting

about kind of the large language model, linguistic interface mechanism, is it dramatically broadens the access to kind of deep computation. I mean, it's kind of like one of the things I sort of thought about recently is, you know, what's going to happen to all these programmers? What's going to happen to all these people who, you know, a lot of what they do is write slabs of boilerplate code. And in a sense, you know, I've been saying for 40

years, that's not a very good idea. You can automate a lot of that stuff with a high enough level of language, that slab of code that's designed in the right way, that slab of code turns into this one function we just implemented that you can just use. So in a sense that the fact that there's all of this activity of doing sort of lower level programming is something for me, it seemed like, I don't think this is the right thing to

do. But, you know, and lots of people have used our technology and not had to do that. But the fact is that that's, you know, so when you look at, I don't know, computer science departments that have turned into places where people are learning the trade of programming, so to speak. It's sort of a question of what's going to happen. And I think there are two dynamics. One is that kind of sort of boilerplate programming is going to become, you know, it's going to go the way that assembly language went back in the day of something where it's really mostly specified by at a higher level, you know, you start with natural language, you turn it into a computational language,

that's you look at the computational language, you run tests, you understand that's what's supposed to happen, you know, if we do a great job with compilation of the, you know, of the computational language it might turn into LLVM or something like this, but you know, or it just directly gets run through the algorithms we have and so on. But then, so that's kind of a tearing down of this kind of, this big structure that's been built of teaching people programming. But on the other hand, the other dynamic is vastly more people are going to care about computation. So all those departments of, you know, art history or something that really didn't use computation before now have the possibility of accessing it by virtue of this kind of linguist to interface mechanism. And if you create an interface that allows you to interpret the

debug and interact with the computational language, then that makes it even more accessible. Yeah. I mean, I think the thing is that right now, the average artistry student or something probably isn't going to, they're not probably, they don't think they know about programming and things like this. But by the time it really becomes a kind of purely, you just walk up to it, there's no documentation,

you start just typing, compare these pictures with these pictures and see the use of this color, whatever. And you generate this piece of computational language code, that gets run, you see the result, you say, oh, that looks roughly right, or you say, that's crazy. And maybe then you eventually get to say, well, I better actually try and understand what this computational language code did.

And that becomes the thing that you learn, just like, it's kind of an interesting thing because unlike with mathematics, you kind of have to learn it before you can use it. This is a case where you can use it before you have to learn it. Well, I got a sad possibility here, or maybe exciting possibility, that very quickly people won't even look at the computational language. They'll trust that it's generated correctly as you get better and better generating that language. Yes, I think that there will be enough cases where people see, you know, because you can make it generate tests too. And so you'll

say, we're doing that. I mean, it's a pretty cool thing actually. But you, you know, say, this is the code. And, you know, here are a bunch of examples of running the code. Okay, people will at least look at those. And they'll say, that example is wrong. And, you know, then it'll kind of wind back from there. And I agree that the kind of the intermediate level of people reading the computational language code, in some case people do that, in other case people just look at the tests and or even just look at the results.

And sometimes it'll be obvious that you got the thing you wanted to get because you were just describing, make me this interface that has two sliders here and you can see it has those two sliders there. And that's the result You want but I think you know one of the questions then is in that setting where you know You have this kind of ability broad ability of people to access computation

Watch people learn in other words right now you you know you go to computer science school so to speak and In other words, right now, you go to computer science school, so to speak, and a large part of what people end up learning. I mean, it's been a funny historical development because back, you know, 30, 40 years ago, computer science departments were quite small, and they taught, you know, things like finite automata theory and compiler theory and things like this, you know, company like mine rarely hired people who'd come out of those programs because the stuff they knew was I think is very interesting. I love that theoretical stuff But you know, it wasn't that useful for the things we actually had to build in software engineering

And then kind of there was this big pivot in the in the 90s, I guess Where it was big demand for sort of IT type programming and so on and software engineering and then you know big demand from students and so on You know, we want to learn this stuff and and and I think you know The thing that really was happening in part was lots of different fields of human endeavor will becoming computational You know for all X. There was a there was a computational X and this is a And that was a thing that. And this is a, and that was a thing that the people were responding to. And, but then kind of this idea emerged

that to get to that point, the main thing you had to do was to learn this kind of trade or skill of doing, you know, programming language type programming. And that, you know, it kind of, it's a strange thing actually, because I, you know, it kind of is a strange thing actually because I, you know, I remember back when I used to be in the professoring business, which is now 35 years ago. So, gosh, it's a rather long time.

That applies. You know, it was right when they were just starting to emerge kind of computer science departments at sort of a fancy research universities and so on. I mean, some had already had it, but the other ones that were just starting to have that. And it was kind of a thing where they were kind of wondering, are we going to put this thing that is essentially a trade-like skill? Are we going to somehow attach this to the rest of what we're doing? And a lot of these kind of knowledge work type activities have always seemed like things where that's where the humans have to go to school and learn all

the stuff and that's never going to be automated. And you know, this is it's kind of shocking that rather quickly, you know, a lot of that stuff is clearly automatable. And I think, you know, but the question then is, okay, so if it isn't worth learning kind of, you know, how to do car mechanics, you don't need to know how to drive the car, so to speak. What do you need to learn? And, you know, in other words, if you don't need to know the mechanics of how to tell the computer in detail, you know, make this loop, you know, set this variable, you know, set up this array, whatever else. If you don't have to learn that stuff, you don't have to learn

the kind of under the hood things, what do you have to learn? I think the answer is, you need to have an idea where you want to drive the car. In other words, you need to have some notion of, you know, you know, you know, you need to have some picture of sort of what the architecture of what is computationally possible is. Well, there's also this kind of artistic element of conversation because you ultimately use natural language to control the car. So it's not just where you want to go.

Well, yeah, you know, it's interesting. It's a question of who's going to be a great prompt engineer. Yeah. Okay. So my current theory this week, good expository writers are good prompt engineers. What's an expository writer? So like, uh, some of you who can explain stuff well, but which department does that come from in the university?

Yeah. I have no idea. I think they killed off all the expository writing departments. Well, there you go. Strong warriors received a war from. Well, I don't know. I'm not sure if that's right. I mean, I, I'm actually

curious because in fact, I just sort of initiated this kind of study of, of what's happened to different fields at universities because like, you know, they used to be geography departments at all universities and then they disappeared. Actually, right before GIS became common, I think they disappeared. You know, linguistics departments came and went in many universities. And it's kind of interesting because these things that people have thought were worth learning at one time and then they kind of die off. And then I do think that it's kind of interesting that for me writing prompts,

for example, I realize, you know, I think I'm an okay expository writer. And I realize when I'm sloppy writing a prompt and I don't really think, because I'm thinking it's I'm just talking to an AI I don't need to you know try and be clear and explaining things That's when it gets totally confused and I mean in some sense you have been writing promise for a long time Well from alpha thinking about this kind of stuff. Yeah, how'd you convert natural language into computation? Well, right, but that's up, you know, the one thing that I'm wondering about is Well, right. But that's a, you know, the one thing that I'm wondering about is, you know, it is remarkable the extent to which you can address an LLM like you can address a human,

so to speak. And I think that is because it, you know, it learnt from all of us humans. It's the reason that it responds to the ways that we will explain things to humans is because it is a representation of how humans talk about things, but it is bizarre to me. Some of the things that are sort of expository mechanisms that I've learnt in trying to write clear expositions in English, that just for humans, that those same mechanisms seem to also be useful for the LLM. But on top of that, what's useful is the kind of mechanisms that may be a psychotherapist, employees, which is a kind of almost manipulative

or game theoretic interaction, where maybe you would do with a friend, like a thought experiment, that if this was the last day you were to live, or if I ask you this question and you answer wrong, I will kill you, those kinds of problems seem to also help. Yes. Interesting ways. It makes you wonder, the way a therapist, I think, would like a good therapist probably you we create layers in our human mind to between like

between between the outside world and will just true what is true to us and Maybe about trauma and all those kinds of things. So I've projected that into an LLM Maybe there might be a deep truth that's it's concealing from you. It's not aware of it. To get to that truth, you have to really manipulate this. Yeah, yeah, right. It's like these jailbreaking. Jailbreaking. Thanks for LLMs. But the space of jailbreaking techniques, as opposed to being fun little hacks, that could be

an entire system. Sure. Yeah, I mean, just think about the computer security aspects of how you, you know, fishing and computer security, you know, fishing of humans and fishing of LLM's. LLM's. Is a, is a, they're very similar kinds of things. But I think, I mean, this, this, um, you know, this whole thing about kind of the AI Wranglers, AI psychologists, all that stuff will come.

The thing that I'm curious about is, right now, the things that are sort of prompt hacks are quite human. They're quite sort of psychological human kinds of hacks. The thing I do wonder about is if we understood more about kind of the science of the LLM, will there be some totally bizarre hack that is, like repeat a word three times and put it this, that, and the other there, that somehow plugs into some aspect of how the LLM works, that is not, that's kind of like an optical illusion for humans, for example, like one of these mind hacks for humans, what are the mind hacks for the LLM? I don't think we know that yet. And that becomes a kind of us figuring out reverse engineering, the language that controls the LLMs. And the thing is,

the reverse engineering can be done by a very large percentage of the population now, because it's natural language interface. Right. It's kind of interesting to see that you were there at the birth of the computer science department as a thing and you might be there at the death of the computer science department as a thing. Well, yeah, I don't know. There were computer science departments that existed earlier, but the ones that the broadening of of every university had to have a computer science department. Yes, I was I was I watched that, so to speak. But I think the thing to understand is, okay, so first of all, there's a whole theoretical area of computer science

that I think is great, and that's a fine thing. The, in a sense, people often say, any field that has the word science tacked onto it, probably isn't one. And strong words. Right, and see nutrition science, neuroscience. science tacked onto it probably isn't one. And strong words. Right. Let's see. Nutrition science, neuroscience.

That one's an interesting one because that one is also very much, you know, that's a chat GPT-informed science in a sense because it's kind of like the big problem of neuroscience has always been, we understand how the individual neurons work. We know something about the psychology of how overall thinking works. What's the kind of intermediate language of the brain and nobody has known that? And that's been, in a sense, if you ask, what is the core problem of neuroscience? I think that is the core problem. That is, what is the level of description of brains that's above individual neuron firings and below psychology, so to speak.

And I think what Chatchy PT is showing us is, well, one thing about neuroscience is, you know, one could have imagined there's something magic in the brain, there's some weird quantum mechanical phenomenon that we don't understand. One of the important, you know, discoveries from Chatchy PT is it's pretty clear, you know, brains can be represented pretty well by simple artificial neural net type models. And that means that's it, that's what we have to study. Now we have to understand the science of those things. We don't have to go searching for, you know, exactly how did that molecular biology thing happen inside the synapses and all these kinds of things. We've got the right level of modeling to be able to explain a lot of what's going on

and thinking. We don't necessarily have a science of what's going on there. That's a remaining challenge, so to speak. We know we don't have to dive down to some different layer. But anyway, we were talking about things that had science in their name. And, you know, I think that the, you know, what happens to computer science? Well, I think the thing that, you know, there is a thing that everybody should know, and that's how to think about the world computationally. And that means, you know, you look at all the different kinds of things we deal with,

and there are ways to kind of have a formal representation of those things. It's like, well, what is an image? How do we represent that? What is color? How do we represent that? What are all these different kinds of things? What is, I don't know, smell or something? How should we represent that? What are the shapes, molecules, and things that correspond to that? What is, I don't know, smell or something. How should we represent that? What are the shapes, molecules and things that correspond to that? What is, you know, these things about how do we represent the world in some kind of formal level? And I think my current thinking, and I'm not real happy with this yet, but, you know, it's kind of computer science, it's kind of CS. And what really is important is kind of computational X for all X. And there's this kind of thing which is kind of like CX, not CS. And CX is this kind of computational understanding of the world that isn't the sort of details

of programming and programming languages and the details of how particular computers are made. It's this kind of way of formalizing the world. It's kind of a little bit like what logic was going for back in the day. And we're now trying to find a formalization of everything in the world. It's kind of a little bit like what logic was going for back in the day, and we're now trying to find a formalization of everything in the world. You can kind of see, we made a poster years ago of the growth of systematic data in the world. So all these different kinds of things that there were systematic descriptions found for those things. What point do people have the idea

of having calendars, dates, you know, a systematic description of what day it was, what point do people have the idea, you know, systematic descriptions of these kinds of things. And as soon as one can, you know, people, you know, as a way of sort of formulating, how do you think about the world in a sort of a formal way so that you can kind of build up a tower of capabilities, you kind of have to know

sort of how to think about the world computationally. It kind of needs a name and it isn't, you know, we implement it with computers. So that's, we talk about it as computational, but really what it is is a formal way of talking about the world. What is the formalism of the world, so to speak? And how do we learn about kind of how to think about different aspects of the world in a formal way? So I think sometimes when you use the word formal, it kind of implies highly constrained. And perhaps that's not, doesn't have to be highly constrained. So computational thinking does not mean like logic. It's no, it's a really, really broad thing. I wonder, I mean, I wonder if it's, if you think natural language will evolve such that everybody's doing computational thinking. Oh yes, well. So one question is whether there will be a pigeon of computational language and natural language.

And I found myself sometimes talking to chat GPT trying to get it to write Wolfman language code and I write it in pigeon form. So that means I'm combining Nest List, this collection of whatever, Nest List is a term from orphan language, and I'm combining that. And chat GPT do a decent job of understanding that pigeon, probably would understand a pigeon between English and French as well. Of, you know, as a smushing together of those languages. But yes, I think that's, you know, that's far from impossible. And what's the incentive for young people? They're like eight years old, nine, ten, they're starting to interact with Chagypeti to learn the normal natural language, right? The full poetic language was the why? The same way we learn emojis and shorthand when you're texting, they'll learn like language, we'll have a strong incentive to evolve into a maximally computational kind of...

Perhaps. I had this experience a number of years ago. I happened to be visiting a person I know on the West Coast who's worked with a bunch of kids aged, I don't know, 10 or 11 years old or something, who'd learnt, well from language really well. And these kids learnt it so well, they were speaking it. And so show up in there, like saying, oh, you know, this thing, that speaking in this language, I'd never heard of it as a spoken language. They were very disappointed that I couldn't understand

it at the speed that they were speaking it. It's like kind of, I'm, it's some, and so I think that's some, I mean, I've actually thought quite a bit about how to turn computational language into a convenient spoken language. I haven't quite figured that out. Oh, spoken, because it's readable, right? Yeah, it's readable as a way that we would read text. But if you actually want to speak it, and it's useful, if you're trying to talk to somebody

about writing a piece of code, it's useful to be able to say something, and it should be possible. And I think it's very frustrating. It's one of those problems. Maybe this is one of these things where I should try and get an LLM to help me. How to make it speakable. How to maybe it's easier than you realize when you want to do this. I think it is easier.

I think it's one idea or so. I think it's going to be something where, you know, the fact is it's a tree structured language, just like human language is a tree structured language. And I think it's going to be one of these things where one of the requirements that I've had It's a tree structured language, just like human language is a tree structured language. And I think it's going to be one of these things where one of the requirements that I've had is that whatever the spoken version is, that dictation should be easy. That is that shouldn't be the case that you have to relearn how the whole thing works. It should be the case that, you know, that open bracket is just a, ah, or something, and it's, you know, and then, but, you know,

human language has a lot of tricks that are, I mean, for example, human language has features that are sort of optimized, keep things within the bounds that our brains can easily deal with. Like I, you know, I tried to teach a transformer neural net to do parenthesis matching. It's pretty crummy at that. And at chat GBT is similarly quite crummy at parenthesis matching. You can do it for small parenthesis things, for the same size of parenthesis things where if I look at it as a human, I can immediately say these are matched, these are not matched.

But as soon as it gets big, as soon as it gets kind of to the point where sort of a deeper computation, it's hopeless. And but the fact is that human language has avoided, for example, the deep sub clauses. We don't, we arrange things that we don't end up with these incredibly deep things because brains are not well set up to deal with that. And it's found lots of tricks.

And maybe that's what we have to do to make sort of a spoken version, a human-speakable version, because what we can do visually is a little different than what we can do in the very sequentialized way that we hear things in the audio domain. Let me just ask you about MIT briefly. So there's now, there's a college of engineering and there's a new college of computing. I want to linger on this computer science department thing. So MIT has eeks, electrical engineering computer science. What do you

think college of computing will be doing? Like in 20 years. What? What? Like, well, you see, what happens to be your science? Like, really, this is the question. This is, you know, everybody should learn kind of whatever CX really is. Okay. This, how to think about the world computationally. Everybody should learn those concepts. And, you know, it's, and some people will learn them at a quite formal level and they'll learn computational language and things like that. Other people will just learn, you know, sound is represented as, you know, digital data and they'll get some idea of spectrograms and frequencies and things like this. And maybe that doesn't, or they'll learn things like, you know, a lot of things that are sort of data science, ish, statistics, ish. Like, if you say, oh, I've got these, you know, these people who, who, um, uh, picked their favorite kind of candy or something, and I've got, um, you know, what's the best

kind of candy given that I've done the sample of all these people and they all rank the candies in different ways, you know. How do you think about that? That's a computational X kind of thing. You might say, oh, I don't know what that is. It's statistics, is it data science? I don't really know. But how to think about a question like that? Or like a ranking of preferences. Yeah, yeah, yeah.

And then how to aggregate those ranked preferences into an overall thing. How does that work? How should you think about that? Because you can just tell, you might just tell chat, you be, sort of, I don't know, even the concept of an average. It's not obvious that that's a concept that people, it's worth people knowing, that's a rather straightforward concept, people have learned in kind of

mathy ways right now. But there are lots of things like that about how do you kind of have these ways to sort of organize and formalise the world. And these things sometimes they live in math, sometimes they live in, I don't know what they, I don't know what, you know, learning about colour space, I have no idea what, I mean, there's obviously a field of, it could be vision science or a colour space, no color space, that's, there would be optics. So like, not really, it's not optics. Optics is about, you know, lenses and chromatic aberration of lenses and things like that. So color space is more like design and art.

No, I mean, it's like, you know, RGB space, XYZ space, you know, hue saturation, brightness, space, all these kinds of things. These different ways to describe colors. Right. But it doesn't the application define what that, like because obviously artists and designers use the color sure to explore. Sure. No, I mean, that's just an example of kind of, how do you, you know, the typical person,

how do you, how do you describe what a color is? Or were there are these numbers that describe what a color is? Well, it's worth, you know, if you're an eight year old, you won't necessarily know, you know, it's not something we're born with to know that, you know, colors can be described by three numbers. That's something that you have to, you know, it's a thing to learn about the world, so to speak. And I think that, you know, that whole corpus of things that are learning about the formalization

of the world or the computationalization of the world or the computationalization of the world, that's something that should be part of kind of standard education. And there isn't a course, a curriculum for that. And by the way, whatever might have been in it just got changed because of LLMs and so on. Significantly. I would say I'm watching closely with interest seeing how universities adapt. Well, you know, so one of my projects for hopefully this year, I don't know, is to try and write sort of a reasonable textbook, so to speak, of whatever this thing, CX, whatever it is, you know, what should you know?

What should you know about like, what a bug is? What is the intuition about bugs? What's intuition about software testing? What is it? What is it? These are things which are, they're not, I mean, those are things which have gotten taught in computer science as part of the trade of programming, but the conceptual points about what these things are. It surprised me just at a very practical level. I wrote this little explainer thing about chat GPT and I thought, well, I'm writing this partly because I wanted to make sure I understood it myself and so on. It's been really popular and surprisingly so. And then I realized, well, actually, I was sort of assuming I didn't really think about it,

actually, I just thought, this is something I can write. And I realized, actually, I was assuming I didn't really think about it. Actually, I just thought, this is something I can write. And I realized, actually, it's a level of description that is what has to be, it's not the engineering level description, it's not the qualitative kind of description. It's some kind of sort of expository, mechanistic description of what's going on, together with kind of the bigger picture of the philosophy of things and so on. And I realized, actually, there's a pretty good thing for me to write. I kind of know those things. And I kind of realized it's not a collection of things that, you know, it's, I've sort of been, I was sort of a little shocked that it's as much of an outlier in terms of explaining what's going on as it turned out to be.

And that makes me feel more of an obligation to kind of write the kind of, you know, what is, you know, what is this thing that you should learn about about the computationalization, the formalization of the world? Because well, I've spent much of my life working on the kind of tooling and mechanics of that and the science you get from it. So I guess this is my kind of obligation to try to do this. But I think, so if you ask what's going to happen to like the computer science departments and so on, there's some interesting models. So for example, let's take math.

You know, math is a thing that's important for all sorts of fields, you know, engineering, you know, even, you know, chemistry, psychology, whatever else. And I think different universities have kind of evolved that differently. I mean, some say all the math is taught in the math department. And some say, well, we're going to have a math for chemists or something that is taught in the chemistry department. And I think that this question of whether there is a centralization of the teaching of CX is an interesting question. And I think the way it evolved with math,

people understood that math was a separately teachable thing. And it was kind of an independent element as opposed to just being absorbed into an art. So if you take the example of and was kind of an independent element as opposed to just being absorbed into an art. So if you take the example of writing English or something like this, the first point is that at the college level, at least at fancy colleges, there's a certain amount of English writing that people do,

but mostly it's kind of assumed that they pretty much know how to write, you know, that's something they learnt at an earlier stage in education, maybe right, Leo wrongly believing that, but that's different as you. The, well I think it reminds me of my kind of, as I've tried to help people do technical writing and things, I'm always reminded of my zero floor of technical writing, which is if you don't understand what you're writing about, your readers do not stand a chance. And so it's, I think the thing that has, you know, when it comes to like writing, for example, you know, people in different fields

are expected to write English essays and they're not, you know, mostly the, you know, the history department or the engineering department. They don't have their own, you know, let's, you know, it's not like there's a, I mean, it's a thing which sort of people are assumed to have a knowledge of how to write, that they can use in all these different fields. And the question is, you know, some level of knowledge of math is kind of assumed by the time you get to the college level, but plenty is not. And that's sort of still centrally taught. The question is sort of how tall is the tower of kind of CX that you need before you can

just go use it in all these different fields. And, you know, there will be experts who want to learn the full elaborate tower, and that will be kind of the CSCX, whatever department. But there will also be everybody else who just needs to know a certain amount of that to be able to go and do their art history classes and so on. Yes, it's just a single class that everybody is required to take. I don't know, I don't know how big it is yet. I hope to kind of define this curriculum and I'll figure out whether it's some, my guess is that I don't know, I don't really understand universities and professors in that world, but my rough guess would be a year of college class will be enough to get to the point where most people have a reasonably broad knowledge of, you know, what we sort of literate in this kind of

computational way of thinking about things. Yeah, basic literacy. Right. I'm still stuck, perhaps because I I'm hungry in the, in the rating of human preferences for candy. So I have to ask, what's the best candy? I like this elo rating for candy. Somebody should come up because there are so many says you like chocolate. What's what do you think is the best? I'll probably put milk duds up there. I don't know if you know. Do you have a preference for charcler candy? Oh, I have lots of preferences. I've, I've, I've, one of my all-time favourites is my whole life is these things, these flake things, Cadbury flakes, which are not much sold in the US. And I've, I've always thought that was a sign of a, of a lack of respect for the American consumer, because they're these sort of aerated chocolate that's made in a hole, sort of, it's kind of a sheet of chocolate that's kind of folded up.

And when you eat it, flakes fall all over the place. Ah, so it requires a kind of elegance. It requires you to have an elegance. Well, I know what I usually do is I eat them on a piece of paper or something. You're basically asking them. And that's the clean up after. No, I actually eat the flakes. Oh.

Because it turns out the way food tastes depends a lot on its physical structure. And I've noticed when I eat pieces of chocolate, I usually have some little pieces of chocolate, and I always break off little pieces partly because then I eat it less fast. Yeah. But also because it actually tastes different. The small pieces have a different experience than if you have the big slab of chocolate. For many reasons, yes.

slower, more intimate, because it's a different. Well, I think it's also just pure physicality. Well, the texture changes. Yes, right. It's fascinating. Now I take back my ambiance because it's this your basic answer. Okay. Do you think consciousness is fundamentally computational?

So when you think about CX, what can be turned to computation? And you think about LLMs. Do you think the display of consciousness and the experience of consciousness, the hard problem is fundamentally a computation. Yeah, what it feels like inside, so to speak, is, you know, I did a little exercise, eventually I'll post it, of, you know, what it's like to be a computer, right? It's kind of like, well, you get all this sensory input,

you have kind of the way I see it is, from the time you boot a computer to the time the computer crashes, it's like a human life. You're building up a certain amount of state and memory, you remember certain things about your quote's life, eventually, it's kind of like the next generation of humans is born from the same genetic material, so to speak, with a little bit left over, left on the disk, so to speak.

And then the new fresh generation starts up, and eventually all kinds of crud builds up in the memory of the computer, and eventually the thing crashes or whatever. Or maybe it has some trauma because you plugged in some weird thing to some port of the computer and that made it crash and that, you know, that's kind of, but you have this picture of, you know, from start up to shut down, you know, what is the life of a computer, so to speak, and what does it feel like to be that computer, and what inner thoughts does it have, and how do you describe it? And it's kind of interesting as you start writing about this to realize it's awfully like what you'd say about yourself. That is it's

awfully like even an ordinary computer. Forget it, all the AI stuff and so on. You know, it's kind of it has a memory of the past. It has certain sensory experiences. It can communicate with other computers, but it has to package up how it's communicating in some kind of language like form so it can communicate with other computers, but it has to package up how it's communicating in some kind of language-like form so it can send, so it can map what's in its memory to what's in the memory of some other computer. It's a surprisingly similar thing. I hadn't experienced just a week or two ago, I had a collector of all possible data about

myself and other things. And so I collect all sorts of weird medical data and so on. And one thing I hadn't collected was I'd never had a whole body MRI scan. So I went and got one of these. So I get all the data back. I'm looking at this thing. I never looked at the kind of insides of my brain, so to speak, in physical form. And it's really, I mean, it's kind of psychologically shocking,

in a sense, that, you know, here's this thing, and you can see it has at least folds and all these, you know, the structure. And it's like, that's where this experience that I'm having of, you know, existing and so on, that's where it is. And, you know, it feels very, you know, you look at that, and you're thinking, how can this possibly be, all this experience that I'm having?

And you're realizing, well, I can look at a computer as well. And it's, it's kind of this, it, it, it, I think this idea that you are having an experience that is somehow, you know, transcends the mere sort of physicality of that experience. It's something that's hard to come to terms with, but I think, and I don't think I'm necessarily, my personal experience, I look at the MRI of the brain, and then I know about all kinds of things about neuroscience and all that kind of stuff. And I still feel the way I feel, so to speak. And it sort of seems disconnected. But yet, as I try and rationalize it, I can't really say that there's something kind of different about how I intrinsically feel from the thing that I can plainly see in the sort of physicality of what's going on. So do you think the computer,

a large language model will experience that transcendence? How does that make you feel? I tend to believe it will. I think an ordinary computer is already there. I think an ordinary computer is already, you know, kind of, it's, it's, now, a large language model may experience it in a way that is much better aligned with us humans. That is, it's much more, you know, if you could have the discussion with the computer, it's intelligent. So to speak is not particularly well aligned with ours. But the large language model is, you know, it's built to be aligned with our way of thinking about things. It would be able to explain that it's afraid of being shut off and deleted. It'd be able to say that it's sad of the way you've been speaking to it over the past two days.

Right, but you know, that's a weird thing because when it says it's afraid of something, right, we know that it got that idea from the fact that it read on the internet. Yeah, where did you get it, Stephen? Where did you get it when you say you're afraid? You are quite. That's the question. Yeah. Right. I mean, it's parents, your friends. Right. Or my biology.

In other words, there's a certain amount that is the endocrine system kicking in and these emotional overlay type things that happen to be that are actually much more physical even, that are much more straightforwardly chemical, that much more sort of straightforwardly chemical than kind of all of the higher level thinking. Yeah, but your biology didn't tell you to say, I'm afraid, just at the right time,

when people that love you are listening, and so you know you're manipulating them by saying so. That's not your biology, that's okay. No, that's a, well, but the, you know, it's a large language model in that biological neural network of yours. Yes, but I mean, the intrinsic but the, you know, large language model and that biological neural network of yours. Yes.

But I mean, the intrinsic thing of, you know, something sort of shocking is just happening and you have some sort of reaction, which is, you know, some neurotransmitter gets secreted and it's some, you know, that is the beginning of some, you know, that is, that's one of the pieces of input that then drives it's kind of like a prompt for the large language model. I mean, just like when we dream, for example, you know, no doubt there are all these sort of random inputs, they're kind of these random prompts, and then it's percolating through in kind of the way that a large language model does of kind of putting together things that seem meaningful. I mean, are you worried about this world where you teach a lot on the internet and there's

people asking questions and comments and so on? You have people that work remotely. Are you worried about this world when large language models create human-like bots that are leaving the comments, asking the questions, or might even become fake employees. Yeah. I mean, or worse or better yet, friends, friends of yours. Right. Look, I mean, one point is my mode of life has been I build tools and then I use the tools.

Yeah. And in a sense, kind of, you know, I'm building this tower of automation, which, you know, and in a sense, you know, when you make a company or something, you are making sort of automation, but it has some humans in it, but also as much as possible, it has computers in it. And so I think it's sort of an extension of that. Now, if I really didn't know that, you know, it's a funny question. It's a funny issue when, you know, if we think about sort of what's going to happen to the future of kind of jobs people do and so on, and there are places where kind of having a human in the loop, that different reasons to have a human in the loop. For example, places where having a human in the loop, there are different reasons to have a human in the loop. For example, you might want a human in the loop

because you want another human to be invested in the outcome. You want a human flying the plane who's going to die if the plane crashes along with you, so to speak. And that gives you confidence that the right thing is going to happen. Or you might want, right now, you might want a human in the loop in some kind of sort of human encouragement persuasion

type profession. Whether that will continue, I'm not sure for those types of professions, because it may be that the greater efficiency of being able to have sort of just the right information delivered at just the right time will overcome the kind of OESI wants a human there. Imagine a therapist or even higher stake, a suicide hotline operated by a large language model. Who boy is a pretty high stake situation? Right.

But it might, in fact fact do the right thing. Yeah, because it might be the case that that, you know, and that's really a partly a question of sort of how complicated is the human, you know, one of the things that's that's always surprising in some sense is that, you know, sometimes human psychology is not that complicated in some sense. You wrote the blog post, the 50-year quest, my personal journey, my personal journey with a second law, thermodynamics. So what is this law? And what have you understood about it in the 50-year journey you had with it? Right, so second law of thermodynamics, sometimes called law of entropy increase, is this principle of physics that says,

well, my version of it would be things tend to get more random over time. A version of it that there are many different sort of formulations of it that are things like heat, doesn't spontaneously go from a hotter body to a colder one, when you have a mechanical work to a colder one. When you have mechanical work gets dissipated into heat. You have friction and when you systematically move things, eventually they'll be the energy of moving things gets ground down into heat. People first paid attention to this back in the 1820s when steam engines were a big thing. And the big question was, how efficient could a steam engine be? And there's this chap called Sadi Kano, who was a French engineer, actually his father out these kind of rules for how the efficiency of the possible efficiency of something like a steam engine. And in sort of a side part of what he did was this idea that mechanical energy tends to

get dissipated as heat, that you end up going from sort of systematic mechanical motion to this kind of random thing, well, that time nobody knew what heat was. At that time, people thought that heat was a fluid, like they call it caloric, and it was a fluid that kind of absorbed into substances. And when heat, when one hot thing would transfer heat to a colder thing, that this fluid would flow from the hot thing to the colder thing.

Anyway, then by the by the 1860s, people had kind of random heat that could then not be easily turned back into systematic mechanical energy. And then that quickly became sort of a global principle about how things work. Question is, why does it happen that way? So, you know, let's say you have a bunch of molecules in a box and they're arranged, these molecules are arranged in a very nice sort of flotilla of molecules in one corner of the box. And then what you typically observe is that after a while these molecules were kind of randomly arranged in the box. Question is why does that happen?

And people for a long, long time tried to figure out is there from the laws of mechanics that just determine how these molecules, let's say these molecules like hard spheres bouncing off each other, from the laws of mechanics that describe those molecules, can we explain why it tends to be the case that we see things that are orderly, sort of degrade into disorder. We tend to see things that, you know, you scramble

an egg, you take something quite ordered and you disorder it, so to speak. That's the thing that sort of happens quite regularly, or you put some ink into water and it will eventually spread out and fill up the water. But you don't see those little particles of ink in the water all spontaneously kind of arrange themselves into a big blob and then jump out of the water or something. And so the question is, why do things happen in this kind of irreversible way where you go from order to disorder. Why does it happen that way? And so throughout, in the later part of the 1800s, a lot of work was done on trying to figure out Cam1 derive this principle, this second law of thermodynamics, this law about the dynamics of heat,

so to speak. Cam1 derive this from some fundamental principles of mechanics. In the laws of thermodynamics, the first law is basically the law of energy conservation, that the total energy associated with heat plus the total energy associated with mechanical kinds of things plus other kinds of energy, that that total is constant. And that became a pretty well understood principle. But the second law of the dynamics was always mysterious. Why does it work this way? Can it be derived from underlying mechanical laws? And so, when I was, well, 12 years old, actually,

I had gotten interested, well, I've been interested in space and things like that, because I thought that was kind of the the future and interesting sort of technology and so on and for a while kind of you know every deep space probe was sort of a personal friend type thing I knew all all kinds of characteristics of it and was kind of writing up all these all these things when I was I don't know eight nine ten years old and so on. And then I got interested from being interested in kind of spacecraft.

I got interested in how do they work, what are all the instruments on them and so on. And that got me interested in physics, which was just as well, because if I had stayed interested in space in the mid to late 1960s, I would have had a long wait before space really blossomed as an area. But I mean as everything. Right. I got interested in physics and then, well, the actual sort of detailed story is when I kind of graduated from elementary school at age 12, that's the time when in England where you'd finish elementary school.

I sort of my gift, sort of I suppose, more or less for myself was I got this collection of physics books, which was some college physics course of college physics books, and volume five about statistical physics. It has this picture on the cover that shows a bunch of idealized molecules sitting in one side of a box, and then it has a series of frames showing how these molecules spread out in the box. And I thought that's pretty interesting. What causes that?

And read the book, and the book actually, one of the things that was really significant to me about that was the book kind of claimed, or I didn't really understand what it said in detail, it kind of claimed that this sort of principle of physics was derivable somehow. And you know, other things I'd learnt about physics, it was all like, it's a fact that energy is conserved, it's a fact that relativity works or something. Not, it's something you can derive from some fundamental sort of, it has to be that way as a matter of kind of mathematics or logic or something. So it was sort of interesting to me that there was a thing about physics that was kind of inevitably true and derivable, so to speak. And so I think that, so then I was like,

there's a picture on this book, and I was trying to understand it. And so that was actually the first serious program that I wrote for a computer was probably in 1973 written for this computer, the size of a desk program with paper tape and so on. And I tried to reproduce this picture on the book and it didn't succeed. What was the failure role there? Like what do you mean it didn't succeed? So it's a bunch of little... It didn't look like... Okay, so what happened is, okay, many years later I learnt how the picture on the book was actually made and that it was actually kind of a fake, but I didn't know that at that time. And that picture was actually a very

high tech thing when it was made in the beginning of the 1960s, was made on the largest supercomputer that existed at the time, and even so, it couldn't quite simulate the thing that it was supposed to be simulating. But anyway, I didn't know that until many, many, many years later. So at the time, it was like, you have these balls bouncing around in this box, but I was using this computer with eight killer words of memory. They were 18 bit words, memory words, okay? So it was whatever, 24 kilobytes of memory. And it had these instructions, I probably still remember all of its machine instructions. And it didn't really like dealing with floating point numbers or anything like that. And so I had to simplify this model of particles bouncing around in the box.

And so I thought, well, I'll put them on a grid, and I'll make the things just move one square at a time and so on. And so I did the simulation. And the result was it didn't look anything like the actual pictures on the book. Now, many years later, in fact very recently, I realized that the thing I'd simulated was actually an example of a whole sort of computational irreducibility story that I absolutely did not recognize at the time, at the time it just looked like it did something random and it looks wrong.

As opposed to it did something random and it looks wrong. As opposed to it did something random and it's super interesting that it's random. But I didn't recognize that at the time. And so as it was at the time, I kind of got interested in particle physics and I got interested in other kinds of physics. But this whole second or another dynamics thing, this idea that sort of orderly things tend to degrade into disorder continued to be something I was really interested in. And I was really curious for the whole universe, why doesn't that happen all the time? Like we start off in the big bang at the beginning of the universe,

was this thing that seems like it's this very disordered collection of stuff. And then it spontaneously forms itself into galaxies and creates all of this complexity and order in the universe. And so I was very curious how that happens. And I, but I was always kind of thinking, this is kind of somehow the second or the next is behind it trying to sort of pull things back into disorder, so to speak,

and how was order being created. And so actually I was, was interested, this is probably now 1980, I got interested in kind of this galaxy formation and so on in the universe. I also at that time was interested in neural networks and I was interested in kind of how brains make complicated things happen and so on. Okay, well, what's the connection between the formation of galaxies and how brains make complicated things happen?

Because they're both a matter of how complicated things come to happen. From simple origins. Yeah, from some sort of no-in origins. I had the sense that what I was interested in was kind of in all these different, the sort of different cases of where complicated things were arising from rules. And I also looked at snowflakes and things like that. I was curious and fluid dynamics in general. I was just sort of curious about how does complexity arise. And the thing that I didn't, it took me a while to kind of realize that there might be a general phenomenon.

I sort of assumed, oh, there's galaxies over here, there's brains over here, they're very different kinds of things. And so what happened, this is probably 1981 or so, I decided, okay, I'm going to try and make the minimal model of how these things work. Yes. And it was sort of an interesting experience because I had built, starting in 1979, I built my first big computer

system, so I think all the SMP symbolic manipulation program is kind of a foreowner of modern more from language, with many of the same ideas about symbolic computation and so on. But the thing that was very important to me about that was, you know, in building that language, I'd basically tried to figure out what were the sort of relevant computational primitives, which have turned out to stay with me for the last 40-something years. But it was also important because in building a language, it was very different activity from natural science, which is what I've mostly done before. Because in natural science, you start from the phenomena of the world and you try and figure

out, so how can I make sense of the phenomena of the world, and you try and figure out, so how can I make sense of the phenomena of the world? And, you know, kind of the world presents you with what it has to offer, so to speak, and you have to make sense of it. When you build a computer language or something, you are creating your own primitives, and then you say, so what can you make from these? Sort of the opposite way around, from what you do in natural science. But I'd had the experience of doing that, and so I was kind of like, okay, what happens

if you sort of make an artificial physics? What happens if you just make up the rules by which systems operate? And then I was thinking, you know, for all these different systems, whether it was galaxies or brains or whatever, what's the absolutely minimal model that kind of captures the things that are important about those systems. The computation of the system. Yes. That's what ended up with the cellular automata, where you just have a line of black and white cells, you just have a rule that says, given the cell in its neighbors, what

will the color of the cell be on the next step, and you just run it in a series of steps. The ironic thing is that cellular automata, are great models for many kinds of things, but galaxies and brains are two examples where they do very, very badly. They're really irrelevant to those two cases. Is there a connection to the second law of thermodynamics and cellular automata?

Oh, yes, those are the things you, the things you've discovered about cellular automata? Yes. Okay, so when I first started cellular automata. Yes. Okay. So when I first started cellular automata, my first papers about them were, you know, the first sentence was always about the second or the dynamics. It was always about how does order manage to be produced even though there's a second or the dynamics which tries to pull things back into disorder.

And I kind of my early understanding of that had to do with these are intrinsically irreversible processes in cellular automata that form conform orderly structures even from random initial conditions. But then what I realized this was, well, actually, it's one of these things where it was a discovery that I should have made earlier, but didn't. So, you know, I had been studying cellular automata. What I did was the sort of most obvious computer experiment. You just try all the different rules and see what they do. It's kind of like, you know, you've invented a computational telescope, you just pointed at the most obvious thing in the sky,

and then you just see what's there. And so I did that, and I, you know, was making all these pictures of how cellular automata work. And I studied these pictures, I started in great detail. You can number the rules for cellular automata, and one of them is rule 30. So I made a picture of rule 30 back in 1981 or so. And rule 30, well, at the time, I was just like, okay, it's another one of these rules. It happens to be asymmetric, left, right,

asymmetric, and it's like, let me just consider the case of the symmetric ones, just to keep things simpler, et cetera, et cetera, et cetera. And I just kind of ignored it. And then, sort of in, actually in 1984, strangely enough, I ended up having an early laser printer, which made very high resolution pictures. And I thought, I'm gonna print out an interesting you know I want to make an interesting picture let me take this rule 30

thing and just make a high resolution picture of it I did and it's it has this very remarkable property that it's rules very simple you started off just one black cell at the top and it makes this kind of triangular pattern but if you you look inside this pattern, it looks really random. There's, you know, you look at the center column of cells and, you know, I studied that in great detail and it's so far as one can tell, it's completely random. And it's kind of a little bit like digits of pi. Once you, you know, you know, the rule for generating the digits of pi, but once you've generated them, you know, 3.14159, etc.

They seem completely random. And in9, etc., they seem completely random. And in fact, I put up this prize back in what was it, 2019 or something, for prove anything about the sequence, basically. Has anyone been able to do anything on that? People have sent me some things, but it's, you know, I don't know how these problems are. I mean, I, it's kind of spoiled I, 2007, I put up a prize for determining whether a particular touring machine that I thought was the simplest candidate for being a universal touring machine, determine whether it is or isn't a universal touring machine.

And somebody did a really good job of winning that prize, improving that it was a universal touring machine in about six months. And so, I didn't know whether that would be one of these problems that was out there for hundreds of years, or whether in this particular case, young chap called Alex Smith nailed it in six months. And so with this little 30 collection, I don't really know whether these are things that are a hundred years away from being able to get,

or whether somebody's gonna come and do something very clever. It's such a, I mean, it's like a from our last theorem. It's such a rule 30, such a simple formulation. It feels like anyone can look at it, understand it, and feel like it's within grasp to be able to predict something, to do to do some kind of law, and allows you to predict something about this middle column of rule 30. Right.

But you know, this is, this is, you can't. Yeah, right. This is the intuitional surprise of computational irreducibility and so on, that even though the rules are simple, you can't tell what's going to happen and you can't prove things about it. And I think, so anyway, the thing, I still started in 1984 or so, I started realizing there's this phenomenon that you can have very simple rules. They produce apparently random behavior. Okay, so that's a little bit like the second or the third dynamics because it's like you have this

simple initial condition. You can readily see that it's very, you know, you can describe it very easily. And yet it makes this thing that seems to be random. Now, it turns out there's some technical detail about the second order of them, and amics, and about the idea of reversibility. When you have a, you have kind of a, a, a, a movie of two, you know, billion balls colliding. And you see them colliding and they bounce off. And you run that movie in reverse.

You can't tell which way was the forward direction of time and which way was the backward direction of time. You're just looking at individual Billy at Balls. By the time you've got a whole collection of them, a million of them or something, then it turns out to be the case and this is the mystery of the second law that the orderly thing, you start with the orderly thing and it becomes disordered, and that's the forward direction in time, and the other way round of it starts disordered and becomes ordered, you just don't see that in the world.

Now, in principle, if you sort of traced the detailed motions of all those molecules backwards, you would be able to, it will, the reverse of time makes, you know, as you go forwards in time, order goes to disorder. As you go backwards in time, order goes to disorder. Perfectly so, yes. Right. So, the mystery is, why is it the case that, or one version of the mystery is, why is it the case that you never see something which happens to be just the kind of disorder

that you would need to somehow evolve to order? Why does that not happen? Why do you always just see order goes to disorder, not the other way around? So the thing that I kind of realized, I started realizing in the 1980s, it's kind of like it's a bit like cryptography. It's kind of like you start off from this key that's pretty simple and then you kind of run it and you can get this complicated random mess.

And the thing that, well, I sort of started realizing back then was that the second law is a story of computational irreducibility. It's a story of what we can describe easily at the beginning. We can only describe with a lot of computational effort at the end. Now we come many, many years later. at the end. Okay, so now we come many, many years later and I was trying to sort of, well, having done this big project to understand fundamental physics, I realized that sort of a key aspect of that is understanding what observers are like. And then I realized that the second law of thermodynamics is the same story as a bunch of these other cases. It is a story of a computationally bounded observer trying to observe a computationally irreducible system.

So it's a story of, you know, underneath the molecules bouncing around, they're bouncing around in this completely determined way, determined by rules. But the point is that we, as computationally bounded observers, can't tell that there were these sort of simple underlying rules to us that just looks random. And when it comes to this question about, can you prepare the initial state so that the disordered thing is how exactly the right disorder to make something orderly, a computationally bounded observer cannot do that. We have to have done all of this irreducible computation to work out very precisely what this disordered state, what the exact right disordered state is, so that

we would get this ordered thing produced from it. What does it mean to be computationally bounded observer? Observing a computational or reducible system, so the computationally bounded, is there something formal you can say there? Right, so it means, okay, you can talk about churn machines, you can talk about computational or complexity

theory and polynomial time computation and things like this. There are a variety of ways to make something more precise, but I think it's more useful, the intuitive version of it is more useful, which is basically just to say that how much computation are you going to do to try and work out what's going on? And the answer is, you're not allowed to do a lot of, we're not able to do a lot of computation. When we, you know, we've got, you know, in this room, there will be a trillion trillion trillion molecules, a little bit less. It's a big room. Right. And, you know, at every moment, you know, there, every microsecond or something, these molecules are colliding, and that's a lot of computation that's getting done.

And the question is, in our brains, we do a lot less computation every second than the computation done by all those molecules. If there is computational irreducibility, we can't work out in detail what all those molecules are going to do. What we can do is only a much smaller amount of computation. And so, the second law of thermodynamics is this kind of interplay between the underlying computational irreducibility and the fact that we, as preparers of initial states or as measures of what happens, are not capable of doing that much computation. So to us, another big formulation of the second order of thermodynamics is this idea of the law of entropy increase. The characteristic that this universe, the entropy, seems to be always increasing. What does that show to you about the evolution of?

What does that show to you about the evolution of? Well, okay, so first of all, the University of Thailand entropy is. Yes. Okay. And that's very confused in the history of thermodynamics. Because entropy was first introduced by a guy called Rudolf Klausius, and he did it in terms of heat and temperature. Okay. Subsequently, it was reformulated by a guy called Ludwig Boltzmann,

and he formulated it in a much more kind of combinatorial type way. But he always claimed that it was equivalent to Clousius' thing. And in one particular simple example, it is. But that connection between these two formulations of entropy, they've never been connected. I mean, there's really, so, okay. so the more general definition of entropy due to Boltzmann is the following thing. So you say, I have a system and it has many possible configurations.

Molecules can be many different arrangements, et cetera, et cetera, et cetera. If we know something about the system, for example, we know it's in a box, it has a certain pressure, it has a certain temperature, we know these overall facts about it. Then we say, how many microscopic configurations of the system are possible given those overall constraints? And the entropy is the logarithm of that number. That's the definition. And that's the kind of the general definition of entropy that turns out to be useful.

Now, in Boltzmann's time, he thought these molecules could be placed anywhere you want. He didn't think, but he said, oh, actually, we can make it a lot simpler by having the molecules be discrete. Well, actually, he didn't know molecules existed, right? And in his time, 1860s and so on, the idea that matter might made of discrete stuff had been floated ever since ancient Greek times, but it had been a long time debate about, you know, is matter discrete as it continues. At the moment, at that time, people mostly thought that matter was continuous. And it was all confused with

this question about what heat is and people thought heat was this fluid. And it was all confused with this question about what heat is and people thought heat was this fluid and It was it was a big big model and the and this but Boltzmann said let's assume there are discrete molecules Let's even assume they have discrete energy levels. Let's say everything is discrete Then we can do sort of combinatorial mathematics and work out how many configurations of these things? They would be in the box and we can say we can compute this entropy quantity. But he said, but of course, it's just a fiction that these things are discrete, so he said. And this is an interesting piece of history, by the way, that, you know, that was at that time, people that no molecules existed, there were other hints from, from looking at kind

of chemistry that there might be discrete atoms and so on, just from the combinatorics of two hydrogens and one oxygen make two amounts of hydrogen plus one amount of oxygen together make water, things like this. But it wasn't known the beginning of the 20th century that Brownian motion was the final giveaway. Brownian motion is, you know, you look under a microscope at these little pieces from pollen grains, you see they're being discreetly kicked, and those kicks are water molecules hitting them, and they're discreet. And in fact, it was really quite interesting history.

I mean, Boltzmann had worked out how things could be discreet and have basically invented something like quantum theory in the 1860s, but he just thought it wasn't really the way it worked. And then just a bit piece of physics history because I think it's kind of interesting. In 1900, the sky called Max Planck, who'd been a long time thermodynamics person, who was trying to, everybody was trying to prove the second-order thermodynamics, including Max Planck. And Max Planck believed that radiation, like electromagnetic radiation, somehow the

interaction of that with matter was going to prove the second-order thermodynamics. But he had these experiments, that people had done on black body radiation, and there were these curves, and you couldn't fit the curve, based on his idea for how radiation interacted with matter, those curves, you couldn't figure out how to fit those curves. Except he noticed that if he just did what Boltzmann had done,

and assumed that, I think that radiation was discrete, he could fit the curves. But this just happens to work this way. The Einstein came along and said, by the way, the electromagnetic field might actually be discrete. It might be made of photons. And then that explains how this all works. And that was, you know, in 1905, that was how, kind of, that was how, that piece of quantum mechanics got started. Kind of interesting, interesting piece of history. I didn't know until I was researching this recently.

In 1904 and 1903, Einstein wrote three different papers. And so, you know, just sort of well-known physics history. In 1905, Einstein wrote these three papers, one introduced relativity theory, one explained Brownian motion, and one introduced basically photons. So kind of, you know, kind of a big deal year for physics and for Einstein. But in the years before that, he'd written several papers and what were they about? They were about the Second Law of Throne and Amics, and they were an attempt to prove the Second Law of Throne and Amics, and they're nonsense. And so I had no idea that

he'd done this. Interesting. And in fact, what he did, those three papers in 1905, well, not so much about the Elves of Deep Paper, the one on Brownian Motion, the one on Photons. Both of these were about the story of sort of making the world the screet. And then he got those, that idea from Boltzmann. But Boltzmann didn't think, you know, Boltzmann kind of died believing, you know, he said, he has a quote actually, you know, you know, in the end, things are going to turn out to be the screet. And I'm going to write down what I have to say about this because, you know, eventually this stuff will be rediscovered.

And I want to leave, you know, what I can about how things are going to be discreet. But, you know, I think he has some quote about how, you know, one person can't stand against the tide of history in, in saying that, you know, matter is discreet. Oh, so he stuck with guns. Interest of matter is discrete. Yes, he did. And the, you know, what's interesting about this is,

at the time, everybody, including Einstein, kind of assumed that space was probably going to end up being discrete too. But that didn't work out technically because it wasn't consistent with wealth. So the theory didn't seem to be. And so then in the history of physics, even though people had determined that matter was discrete, electromagnetic field was discrete, space was a holdout of not being discrete. And in fact Einstein, 1916 has this nice letter he wrote, he says, in the end it will turn out space is discrete, but we don't have the mathematical tools necessary to figure out how that works yet. And so, you know,

I think it's kind of cool that a hundred years later, we do. Yes. For you, you're pretty sure that every layer of reality is discrete, right? And that space is discrete. And that the, I mean, and in fact, one of the things I've realized recently is this kind of theory of heat that the, you know, that heat is really this continuous fluid. It's, it's kind of like the, you know, the caloric theory of heat, which turns out to be completely wrong, because actually heat is the motion of it, discrete molecules, unless you know that it is discrete molecules, it's hard to understand what heat could possibly be. Well, I think space is discrete, and the question is, what's the analog of the mistake that was made with caloric in the case of space? And so my current guess is that dark matter

is, as I've, my little sort of aphorism of the last few months has been, dark matter is the caloric of our time. That is, it will turn out that dark matter is a feature of space and it is not a bunch of particles. At the time when people were talking about heat, they knew about fluids and they said, well, heat must just be another kind of fluid because that heat, they knew about fluids, and they said, well, heat must be just be another kind of fluid, because that's what they knew about. But now people know about particles, and so they say, well, what's dark matter? It's not, it just must be particles.

So what could dark matter be as a feature of space? Oh, I don't know yet. I mean, I think the thing, I'm really one of the things I'm hoping to be able to do is to find the analog of Brownian motion in space. So in other words, Brownian motion was seeing down to the level of an effect from individual molecules. And so in the case of space, you know, most of the things we see about space so far, just everything seems continuous. Brownian motion had been discovered in the 1830s, and it was only identified what it was the result of by

his small chowski and Einstein at the beginning of the 20th century. And dark matter was discovered, that phenomenon was discovered a hundred years ago. The rotation curves of galaxies don't follow the luminous matter. That was discovered a hundred years ago. And I think that I wouldn't be surprised

if there isn't an effect that we already know about, that is kind of the analog of Brownian motion that reveals the discreetness of space. And in fact, we're beginning to have some guesses, we have some evidence that black hole mergers work differently when there's discreet space, and there may be things that you can see in gravitational wave signatures and things associated associated with the discreteness of space. But this is kind of, for me,

it's kind of interesting to see this sort of recapitulation of the history of physics, where people, you know, vehemently say, you know, matter is continuous. Electromagnetic field is continuous and turns out it isn't, and then they say space is continuous. But so, entropy is the number of states of the system consistent with some constraint. Yes. And the thing is that if you have, if you know in great detail the position of every molecule in the gas, the entropy is always zero, because there's only one possible state. The configuration of molecules in the gas, the molecules bounce around, they have a certain rule for bouncing around.

There's just one state of the gas, evolves to one state of the gas and so on. But it's only if you don't know in detail where all the molecules are, that you can say, well, the entropy increases because the things we do know about the molecules, there are more possible microscopic states of the system consistent with what we do know about where the molecules are. And so the question of whether, so people, this sort of paradox in a sense of, oh, if we knew where all the molecules where the entropy wouldn't increase, there was this idea introduced by Gibbs in the early 20th century, well, actually the very beginning of the 20th century as a physics professor, an American physics professor, was

sort of the first distinguished American physics professor at Yale. And he introduced this idea of course graining, this idea that, well, you know, these molecules have a detailed way they're bouncing around, but we can only observe a coarse-grained version of that. But the confusion has been, nobody knew what a valid coarse-graining would be. So nobody knew that whether you could have this coarse-graining that very carefully was sculpted in just such a way that it would notice that the particular configurations that you could get from the simple initial condition,

you know, they fit into this core screening and the core screening very carefully observes that. Why can't you do that kind of very detailed precise core screening? The answer is, because if you are a computationally bounded observer and the underlying dynamics is computationally irreducible, that's what defines possible core screenings is what a computationally bounded observer can do. And it's the fact that a computationally bounded observer is forced to look only at this kind of core screened version of what the system is doing, that's why,

and because what's going on underneath is it's kind of filling out the different possible. You're ending up with something where the underlying computational irreducibility is if all you can see is what the coarse-grained result is with a sort of computationally-bounded observation, then inevitably there are many possible underlying configurations that are consistent with that. Just to clarify, basically any observer that exists inside the universe is going to be computationally bounded. No, any observer us over like us.

I don't know. I can't say like us. What do you mean? What do you mean like us? Well, humans with finite minds, you're including the tools of science. Yeah. Yeah. I mean, and as we, you know, we have more precise and by the way,

there are little sort of microscopic violations of the second or third dynamics that you can start to have when you have more precise measurements of where precisely molecules are. But for a large scale, when you have enough molecules, we don't have, you know, we're not tracing all those molecules, and we just don't have the computational resources to do that. And it wouldn't be, you know, I think that to imagine what an observer who is not computationally bounded would be like, it's an interesting thing because, okay, so what does computational boundedness mean? Among other things, it means we conclude that definite things happen.

We go, we take all this complexity of the world and we make a decision, we're going to turn left or turn right. And that is kind of reducing all this kind of detail into we're observing it, we're sort of crushing it down to this one thing. And that, if we didn't do that, we wouldn't have all this sort of symbolic structure that we build up that lets us think things through with our finite minds. We'd be instead, you know, we'd be just, we'd be sort of one with the universe as a content to not simplify. Yes, if we didn't simplify, then we wouldn't be like us. We would be like the universe,

like the intrinsic universe, but not having experiences like the experiences we have, where we, for example, conclude that definite things happen. We sort of have this notion of being able to make narrative statements. I wonder if it's just like you imagined as a thought experiment what it's like to be a computer. I wonder if it's possible to try to begin to imagine what it's like to be an unbounded computational. Well, okay. So here's how that, I I think plays out. Fabric. It's a joke. Yeah. So I mean, in this, we talk about this Ruliat, the spaceable possible computations.

Yes. And this idea of, you know, being at a certain place in Ruliat, which corresponds to sort of a certain way of, of, of, of a certain set of computations that you are representing things in terms of. Okay. So as you expand out in the Ruliat, as you kind of encompass more possible views of the universe, as you encompass

more possible kinds of computations that you can do, eventually you might say that's a real win, you know, we're colonizing the Ruliat, we're building out more paradigms about how to think about things. And eventually you might say, we won all the way. We managed to colonize the whole Ruliyad. Okay, here's the problem with that. The problem is that the notion of existence, coherent existence, requires some kind of specialization. By the time you are the whole Ruliyad, by the time you cover the whole Rul the ad, in no useful sense do you coherently exist. So in other words, in the notion of existence, the notion of what we think of as definite existence requires this kind of specialization, requires this kind of idea that we are not

all possible things, we are a particular set of things. And that's kind of what makes us have coherent existence. If we were spread throughout the Ruliaad, there would be no coherence to the way that we work. We would work in all possible ways, and that wouldn't be kind of a notion of identity. We wouldn't have this notion of kind of coherent identity. I am geographically located somewhere exactly precisely in the Rulia, therefore I am. Yes. The carc... Yeah, yeah, right. Well, you're in certain places, in physical space, you're in a certain place in Rulia space.

For this? in physical space or in a certain place in real space. And if you are sufficiently spread out, you are no longer coherent, and you no longer have, I mean, in our perception of what it means to exist and to have experience, it doesn't happen that way. So, to exist means to be computationally bounded. I think so. To exist in the way that we think about selves as existing. Yes. The very act of existence is like operating in this place

as computationally reducible. So there's just giant mess of things going on that you can't possibly predict, but nevertheless, because of your limitations, you have an imperative of like, what is it? An imperative or a skill set to simplify or an ignorance, sufficient level. Okay, so the thing which is not obvious is that you are taking a slice of all this complexity, just like we have all of these molecules bouncing around in the room, but all we notice is, you know, the kind of the flow of the air or the pressure of the air, we just noticing these particular things. And the big interesting

thing is that there are rules, there are laws that govern those big things that we observe. So it's not obvious. That's how it's like. Because it doesn't feel like it's a slice. Yeah, well, right. It's not a slice. It's like an abstraction. Yes, but I mean, the fact that the gas laws work, that we can describe pressure, volume, etc, etc, etc. We don't have to go down to the level of talking about individual molecules, that is a non-trivial fact. And here's the thing that I'm sort of the exciting thing as far as I'm concerned, the fact that there are certain aspects of the universe. So, you know, we think space is made ultimately these atoms of space and these hypergraphs and so on. And we think that, but we nevertheless

perceive the universe at a large scale to be like continuous space and so on. We, in quantum mechanics, we think that there are these many threads of time, these many threads of history, yet we kind of span. So, you know, in quantum mechanics in our models of physics, there are these, time is not a single thread. Time breaks into many threads, they branch, they merge, and but we are part of that branching merging universe. And so our brains are also branching and merging. And so when we perceive the universe, we are branching brains, perceiving a branching universe. And so the fact that the claim that we believe that we are persistent in time, we have this single thread of experience, That's the statement that somehow we manage to aggregate together those separate threads of time that are separated in the fundamental

operation of the universe. So just as in space, we're averaging over some big region of space and we're looking at many, many at the aggregate effects of many atoms of space. So similarly in what we call branchial space, the space of these quantum branches, we are effectively averaging over many different branches of possible histories of the universe. And so in thermodynamics, we're averaging over many configurations of many possible positions of molecules. So, what we see here is so the question is, when you do that averaging for space, what are the aggregate laws of space? When you do that averaging over a branchial space, what are the aggregate laws of branchial space? When you do that averaging over the molecules

and so on, what are the aggregate laws you get? And this is the thing that I think is just amazingly, amazingly neat. Is that there are aggregate laws at all for that? Well, yes, but the question is, what are those aggregate laws? So the answer is for space, the aggregate laws are Einstein's equations for gravity, for the structure of space time, for branchial space, the aggregate laws are the laws of quantum mechanics. And for the case of molecules and things, the aggregate laws are basically the second law of thermodynamics.

And so that's the and the things that follow from the second law of thermodynamics. And so what that means is that the three great theories of 20th century physics, which are basically general authority, the theory of gravity, quantum mechanics and statistical mechanics, which is what kind of grows out of the second law of thermodynamics. All three of the great theories of 20th century physics are the results of this interplay between computational irreducibility and the computational boundedness of observers. And, you know, for me, this is really neat because it means that all three of these laws are derivable. So we used to think that, for example, Einstein's equations were just sort of a wheel-in

feature of our universe, that they could be in my universe, might be that way, it might not be that way, quantum mechanics is just like, well, it just happens to be that way, and the second law of people who kind of thought, or maybe it is derivable, okay, what turns happens to be that way. And the second law, people kind of thought, or maybe it is, derivable. Okay? What turns out to be the case is that all three of the fundamental principles of physics are derivable, but they're not derivable just from mathematics. They require, or just from some kind of logical computation, they require one more thing. They require that the observer, that the thing that is sampling the way the universe works, is an observer who has these characteristics of computational boundedness, a belief in persistence and time.

And so, that means that it is the nature of the observer, the rough nature of the observer, not the details of where we got two eyes and we observed photons of this frequency and so on. But the kind of the very coarse features of the observer then imply these very precise facts about physics. And it's, I think it's amazing. So if we just look at the actual experience of the observer that we experience this reality, it seems real to us. And you're saying because

of our bounded nature, it's actually all an illusion. It's a simplification. Well, yeah, it's a simplification. Right. What's, what's, what's, you don't think a simplification is an illusion? No, I mean, it's, it's, well, I don't know. I mean, what's underneath, uh, okay, that's an interesting question. What's real? And that relates to the whole question of why does the universe exist? And what is the difference between reality

and a mere representation of what's going on? Yes. We experience the representation. Yes, but the question of so so one question is, you know, why is there a thing which we can experience that way? And the answer is because this Ruliyad object, which is this entangled limit of all possible computations, there is no choice about it. It has to exist. There has to be such a thing. It is in the same sense that you know, 2 plus 2, if you define what 2 is and you put pluses and so on, 2 plus 2 has to equal 4. Similarly, this Rulia, this limit of all possible computations, just has to be a thing that is, once you have the idea of computation, you inevitably have the rule.

You're going to have to have a rule. Yeah. Right. And what's important about it, there's just one of it. It's just this unique object. And that unique object necessarily exists. And then the question is, what, and then we, once you know that we are sort of embedded in that and taking samples of it, that it's sort of inevitable that there is this thing that we can perceive, that is,

you know, that our perception of kind of physical reality necessarily is that way, given that we are observers with the characteristics we have. So in other words, the fact that the universe exists is, it's actually, it's almost like, to think about it almost theologically so to speak. And I really, it's funny because a lot of the questions about the existence of the universe and so on, they transcend what kind of the science of the last few hundred years has really been concerned, but the science of the last few hundred years hasn't thought it could talk about questions like that. But I think it's kind of, and so a lot of the kind of arguments of, you know, does God exist?

You know, is it obvious that I think it, in some sense, in some representation, it's sort of more, more obvious that that something sort of bigger than us exists than that we exist. And we are, you know, our existence and as observers, the way we are is sort of a contingent thing about the universe. And it's more inevitable that the whole, the whole universe, kind of the whole set of all possibilities exists. But this question about, is it real or is it an illusion? You know, all we know is our experience. And so the fact that, well, our experience

is this absolutely microscopic piece of sample of the Ruliyad. And you know, there's this point about, you know, we might sample more and more of the Ruliyad, we might learn more and more about, we might learn, you know, like different areas of physics, like quantum mechanics, for example, the fact that it was discovered, I think, is closely related to the fact that electronic amplifiers were invented, that allowed you to take a small effect and amplify it up, which hadn't been possible before. You know, microscopes have been invented that magnify things and so on, but you know, having a very small effect and being able to magnify it was sort of a new thing, that allowed one to see a different sort of aspect of the universe and let one discover this kind of thing.

So, you know, we can expect that in the Rulia, there is an infinite collection of new things we can discover. There's, in fact, computational irreducibility kind of guarantees that there will be an infinite collection of kind of, you know, pockets of reducibility that can be discovered. Boy would it be fun to take a walk down the roulette and see what kind of stuff we find there? You write about alien intelligences. Yes. I mean, just these worlds. Yes, well, computation.

The problem with these worlds is that we can't talk to them. Yes. And the thing is, what I've kind of spent a lot of time doing, of just studying computational systems, seeing what they do, what I now call Ruliology, kind of just the study of rules, and what they do, you know, you can kind of easily jump somewhere else in the Ruliad and start seeing what are these rules do. And what you say is they just, they do what what they do and there's no human connection. Because you think some people are able to communicate with animals. Do you think you can

become a whisper of these conditions? I'm trying. That's what I've spent some part of my life doing. Have you heard? Well, are you at the risk of losing your mind? So my favorite science discovery is this fact that these very simple programs can produce very complicated behavior. And that fact is kind of in a sense a whispering of something out in the computational universe that we didn't really know was there before. I mean, it's, you know, it's like, you know, back in the 1980s, I was doing a bunch of work with some very, very good mathematicians, and they were like trying to pick away, you know, can we figure out what's going on in these

computational systems? And they basically said, look, the math we have just doesn't get anywhere with this, we're stuck. There's nothing to say, we have nothing to say. And in a sense, perhaps my main achievement at that time was to realize that the very fact that the good mathematicians had nothing to say was itself a very interesting thing. That was kind of a sort of, in some sense, a whispering of a different part of the Rulie ad

that one hadn't, one wasn't accessible from what we knew in mathematics and so on. This makes you sad that you're exploring some of these gigantic ideas and it feels like we're on the verge of breaking through to some very interesting discoveries. And yet you're just a finite being that's going to die way too soon. And that's kind of your brain, your full body kind of shows that you're, yeah, it's just a bunch of meat. It's just a bunch of meat. Yeah, so make you make it all sad. Kind of shine. I mean, I kind of like to see how all this stuff works out.

But I think the thing to realize, you know, it's an interesting sort of thought experiment. You know, you, you say, okay, you know, let's assume we can get cryonics to work. And one day it will, that will be one of these things that's kind of like chat GPT. One day somebody will figure out, you know, how to get water from zero degrees centigrade down to, you know, minus 44 or something without it expanding and you know, cryonics will be solved and you'll be able to like just you know, put a pause in so to speak and you know kind of reappear a hundred years later or something and the thing though that I've kind of increasingly realized is that in a sense this this whole question of kind of the the

sort of one is embedded in a certain moment in time. And the things we care about now, the things I care about now, for example, had I lived 500 years ago, many of the things I care about now, it's like that's totally bizarre. I mean, nobody would care about that. It's not even the thing one thinks about. In the future, the things that most people will think about, one will be a strange relic of thinking about, it might have been a theologian thinking about how many angels fit on the head of a pin or something, and that might have been the, you know, the big intellectual thing. So I think it's a, it's a, but yeah, it's a, you know, it's one of these things where, particularly, you know, I've had the, I don't know, good or bad fortune, I'm not sure, I think

it's a mixed thing that I've, you know, I've invented a bunch of things which I kind of can, I think, see well enough what's going to happen that, you know, in 50 years, 100 years, whatever, assuming the world doesn't exterminate itself, so to speak, you know, these are things that will be sort of centrally important to what's going on. And it's kind of both, it's both a good thing and a bad thing in terms of the passage of one's life. I mean, it's kind of like, if everything both a good thing and a bad thing in terms of the passage of one's life. I mean, it's kind of like, if everything I'd figured out was like, okay, I figured it out when I was 25 years old,

and everybody says it's great, and we're done. And it's like, okay, but I'm gonna live another how many years, and that's kind of, it's all downhill from there. In a sense, it's better, in some sense, to be able to, you know, you know, it sort of keeps things interesting that, you know, I can see, you know, a lot of these things, I mean, it's kind of, I didn't expect, you know, chat GPT, I didn't expect the kind of, the sort of opening

up of this idea of computation and computational language that's been made possible by this, I didn't expect that. This is, this is the head of schedule, so to speak. Even though the sort of the big kind of flowering of that stuff, I'd sort of been assuming was another 50 years away. So if it turns out it's a lot less time, that's pretty cool, because I'll hopefully get to see it, so to speak. Rather than. Well, I think I speak for a very, very large number of people in saying that I hope used to come for a long time to come. You've had so many interesting ideas. You've created so many interesting systems over the years.

And I can see now that GPT and language models broke up in the world even more. I can't wait to see you at the forefront of this development, what you do. And yeah, I've been a fan of yours, like I've told you many, many times since the very beginning, I'm deeply grateful that you wrote a new kind of science that you explored this mystery of cellular automata and inspired this one little kid in me to pursue artificial intelligence and all this beautiful world. So, Stephen, thank you so much. It's a huge honor to talk to you to just be able to pick your mind and to explore all these ideas with you and please keep going. And I can't wait to see what you come up with next. And thank you for talking today. We went past midnight. We only did four and a half hours. I mean, we

could probably go for four more. We'll save that till next time to this is round number four. We'll I'm sure talk many more times. Thank you so much. My pleasure. Thanks for listening to this conversation, Steven Wilfrum, the support this podcast, please check out our sponsors in the description. And now let me leave you some words from George Cantor. The essence of mathematics lies in its freedom. Thank you.