Stephen Wolfram — Productivity Systems, Richard Feynman Stories, Computational Thinking, and More

Tim Ferriss: Stephen, it's nice to see you. Thank you for taking the time today. Stephen Wolfram: It's good to be here. Tim Ferriss: I was impressed that right before we started recording, when I tried to remember when we sat next to each other, you quickly said 2011 Wired Health Conference and mentioned that you had a file. So how do you search for something like that? Stephen Wolfram: I keep a lot of things. I have all my emails from 30 years ago. I have a habit of writing trip reports every time I go to an event or something. And I also have scans of paper documents.

You were not a paper document. You are newer than the paper document, so to speak. But I have scans of, well, it's like a quarter of a million pages or so, paper documents that I generated at the time of my life before I went completely digital, so to speak. And I also usually record. I record every keystroke I type and screenshots and all kinds of stuff like that. And then... Tim Ferriss: How would you use? I'm sorry. I was just going to ask, how do you use keystroke logging? How would that be used? Stephen Wolfram: I usually don't.

Tim Ferriss: Not so. Stephen Wolfram: I usually don't. From time to time I will want to know; Occasionally, for example, some computer crashes in some horrible way and it'll be like, oh, I just lost a bunch of stuff. Well, no, I didn't, because I had it recorded. Tim Ferris: Oh. Good. Stephen Wolfram: That's what made me start recording them 25 years ago, it was an accident. And then I decided it was cheap to record everything like that. And every once in a while I do things like, oh, I'm using a new keyboard. Do I type faster or slower on my new keyboard?

That kind of things. Tim Ferriss: Got it. And you can track those kinds of things. Stephen Wolfram: It's easy to answer that question if you have that data. Tim Ferriss: It is one thing to record or ingest information and quite another to structure thought. And I, in the process of researching this conversation, came across a discussion on Reddit about creating arrays. That's why I wanted to explore using arrays for different projects. I'm just going to read a very, very short section here, which says: "I actively avoid

thinking

about things that I don't have a 'matrix' for.

I don't like having 'disembodied ideas' floating around." What is my current state of affairs, so I ask this selfishly. "Of course, when something is important enough to me, I try to build a 'matrix' for it." Could you give us an example of what such an array would look like? Stephen Wolfram: Yes. What I mean by this is some kind of framework in which I'm doing something. So, for example, if I have a little idea about molecular biology, I don't really have a good place to express it. If I'm doing a big project on molecular biology where I'm building a complete structure, then I have a place to put it.

So it's that kind of thing. I've been very lucky, the main work of my life is building our

computational

language, the Wolfram Language, which is this language that is supposed to represent everything in the world

computational

ly. So a lot of ideas I have about how to represent things computationally can end up in the matrix, which is the Wolfram Language. Similarly, I've found, for example, that writing my kind of blog, sort of like writings, is another kind of matrix that I can put, when I do historical studies of things, I'll write an article about that historical topic.

Study and that's a place to put it. Tim Ferriss: I see. Stephen Wolfram: But when it's something that's too small, I just don't have a place to put it and it tends to die on the branch. Since you asked about this, I have been exposing myself to a very strange experience. I mean, I just finished a project I started 50 years ago. Tim Ferriss: Wow. Stephen Wolfram: And in the process of doing this, something that I started, that interested me when I was 12, is a question about physics and about the second law of thermodynamics and why randomness is generated in the world, etc. .

I've made several advances on this question over the years, but finally now, with a lot of things we've done recently to understand the fundamental theory of physics, I think I can really solve this question. So I wrote a whole article about the scientific answer to that question, but then I thought I should write an article describing my 50-year journey trying to answer this question. And that made me look again at all my calendars from 1983, and all these paper documents that I've scanned, etc. It's a very interesting experience, going back and seeing what mattered from 1983 and what didn't.

How did I get to the things that were important in the end? How did they arise? What were the types of steps I had to follow? One of the things that I really noticed, that really stood out to me, is that there is often a large amount of time where I was building a conceptual framework for something. Sometimes I had clues about how that framework should work, I didn't even recognize the clues. I just didn't understand it. Finally, through a very slow process, I build this intellectual framework and then get some other clue. I do some other computer experiments, something else happens.

And then I can literally trace, because I have all the file creation dates, all this kind of stuff, I can trace. It took 15 minutes from the time I saw this to the time I started typing this and so on. It's really surprising how many years can pass, but you slowly build up the kind of conceptual framework needed and then it's often very sudden to take the next step. That was something surprising to me. Tim Ferriss: I want to ask some follow-up questions about conceptual frameworks and maybe just ask for an example of what such a conceptual framework would look like, for people listening and, frankly, for me.

But before we get to that, writing a blog post or article that records your search for answers or exploration of these open questions is quite a task. Going through calendars from 1983 and so on, I imagine it takes a good amount of time. Why do you do it? Or is it the reward in the writing process? Is it something rewarding in the writing process? Do you hope to impart anything to those who read this article? Why do that? Stephen Wolfram: That's a good question. I was wondering myself. I thought it would be very easy, but it wasn't that easy.

No. I have done quite a few historical biographies, usually of other people. I find that when I'm really trying to understand an idea, I need to know where that idea came from. So, for example, the things I'm doing now about the second law of thermodynamics, the second law of thermodynamics was developed in the 1860s. I think people took a wrong turn shortly after. So I think what I've discovered now is a little bit different from what people had discovered at the time. So when I say I think they took a wrong path, I really want to know how they came to take that wrong path.

And that's my next project for next week. I went back and collected the material for it. I can go back and read all the original sources for how people came to think about those things. But I don't feel confident that I know what was going on until I can trace that this person made that move because he thought this and understood that and so on. And I thought to myself, I was mostly, well, a little curious. I thought it was an interesting story. It is a very rare case in the history of science that one has really precise and detailed data about how an idea developed.

That's why I thought it was an interesting example of essentially computational history. One thinks of computational X for all X. This is the computational story. And it's like, what can you do in computational history? What kind of things can you expose in computational history? The other thing that's interesting to me is when I go back and think about things I did or discovered 40 years ago, let's say, I lived it, but then when I looked back I realized there were threads that could be tied together. which I didn't see at all at the time. I give you an example.

I was working on these simple programs. What do simple programs do? The big surprise was that even the simplest programs can do very complicated things. That was something I didn't expect. It was a violation of my intuition. It took me a couple of years to accept the fact that that was possible. At the same time, several of the best mathematicians who were friends of mine were trying to work on the things I had discovered. They said, "Let's do the math on these things. Let's find out

more

about them." And they worked for a while. Actually, I just found a bunch of his notes, going through this stuff from 1984 and so on.

And they did a lot of very sophisticated calculations and they couldn't solve anything. What I realized is that at that moment I thought, okay, well, I'm figuring my stuff out. His methods didn't work, so what? What I realized then, right now, is that my big innovation was realizing that the fact that they couldn't discover anything was in itself a super interesting fact. In other words, there's this phenomenon I call computational irreducibility, which is basically the big picture of why they couldn't solve anything. Generally you say, "I know the rules by which some system operates." Then you could say, "Okay, I'm clear.

I know everything about what's going to happen in that system." Well, that's not true. Because if the rules define some calculation, to know what the system does, you can find out by running the calculation, the question is can you beat that calculation? Can you say, "Okay, system, you went through a million steps of that calculation, but I don't need to do that. I'm smarter than the system. I can only say that the answer is 42," or something like that. What computational irreducibility tells you is that you are generally stuck with having to follow every step in the evolution of the system.

And that's a really important fact about science. It is a way for science to explain from within science that science has questions it cannot easily answer. What I realized only, I don't know what, 35 years after the fact, is that, in a certain sense, if I had an achievement, an intellectual achievement in that whole process, it was realizing that the fact that one had gotten Stuck It was in itself the most important thing to know. Not: "Oh, we're stuck. Let's quit." But the fact that one was stuck meant that a paradigmatic shift had to be made in the way of

thinking

about these types of questions in science.

So that's an example that, to me, is quite useful, to go back and see what really happened there, what was really important there, that I hadn't realized at the time. Tim Ferriss: It makes me... I was going to say, it makes me think a little bit about Sherlock Holmes, the case of the dog that wouldn't bark at night. The term I'd love for you to explain a bit

more

, and I apologize, it's going to be a muggle question, but for non-technical people, I'll put myself in that audience, when you say, for example, computational history or computational Think of that term someone who might be afraid of the term "computational"?

Computational history as an example? Stephen Wolfram: We humans like to find abstract and formal ways to describe things. Language itself is an example of this. We see things in the world and say, "That's a tree, that's a dog, that's whatever." The fact that we can symbolically describe these things in the world (there are many different types of dogs and many different details, but we just say "It's a dog") is a way of organizing the things we see, in that case, simply using natural language. There have been a variety of types of organizational approaches in history. Logic, for example, was one of antiquity.

Mathematics is another kind of organizational approach to saying, "This is how you structure the way you talk about the world." As far as I'm concerned, the importance of computing is that it's another way of structuring the way we talk about the world. And a big part of what I've spent my life doing is building this kind of computational language, which provides a precise way to take something like, I don't know, some description of some food or some description of some position. on Earth or anything else, and represent those things computationally in a precise way that has the characteristic that, well, a human could read it and say, "Oh, I know what that means." But we also have the added boost of the fact that a computer can read it as well, and then the computer can help us move forward with it.

I mean, in a sense, one of the great achievements that we, as a species, you know, is human language. You can take things about the world and describe them in a somewhat precise and abstract way. I think of computational language as another level in that evolution, except that we can share the burden of seeing what happens, not only with other humans, but with computers as well. And so, for me, the most important thing is to describe the world computationally. Now, when I talk about simple programs and that kind of thing, what I tend to say is a kind of meta-model of the world.

There are models of real trees and dogs and trajectories on Earth and things like that. And then how do you break that down into something?even more primitive? And then what you end up with are these sets of rules that say, well, you could describe what they are about in many different ways. But, for example, one type that I've studied a lot, the technical name is cellular automaton, and the typical configuration is that you have a row of cells, and each cell can be white or black, let's say. And then the computational rule is: you look at each cell and say what is the color of that cell and its two neighbors, let's say.

Based on that you say, okay, I'm going to change the color of the cell to be white or black or whatever. You keep running that rule over and over again. The big surprise is, and this is what I finally discovered around 1984, the big surprise is that even with such a simple ruler, you can start with a black dot and it creates this incredibly complicated pattern. Such a complicated pattern that if you were to just look at a part of the pattern and say, is it random or does it have some regularity? You'd just say it seems completely random to me.

Although the rule that created it is a very, very simple rule that you can easily describe, write or send to a computer or anything else. So to me, this notion of computation is having this way of structuring the way you talk about the world, and then there's this kind of meta-modeling of that, that is, what are the simplest elements of that computational process? And then talk about what can be done with them. I mean, I think maybe a good analogy for the computational description of the world comes from the mathematical notation that is used to talk about mathematics.

I mean, it's kind of an interesting evolution that, if you look at the mathematics done in ancient times and things like that, people didn't have a symbol for more. They only used words. And then about 500 years ago, people started inventing a plus sign, an equal sign, things like this. And that's when that kind of simplification of the way we talk about mathematics came online, that's where mathematics really took off and algebra was invented and then calculus was invented and then we have this whole mathematical approach to science that could be done. I guess my personal effort over the last 40 years has been to try to make a computational notation for talking about the world that is a kind of parallel for calculus to what mathematical notation is for the mathematical way of talking about the world.

Tim Ferriss: I have a question about natural languages. I don't think I'm misquoting, but feel free to check. You are deeply aware of and able to work with language in multiple, I would say, dimensions. I read at one point that you were considering a job at CERN, and I think I read that you said you'd practiced French but never had the courage to use it, or something like that. And I don't know if that was the ironic comment, but... Stephen Wolfram: Well, no, no. That's from ancient times. I went to posh schools in England as a child.

I learned three languages, Latin, Greek and French. Okay, you can't speak Latin or Greek or ancient Greek, at least not in most places. But in principle you could speak French. I can read scientific French quite fluently. But if you say, if I'm in France and I say, "Can I order that piece of food or something?" No way. I can not do this. It's one of these things. I should get over it someday. Because I think I have the knowledge of the vocabulary, I think, and all that. I've never really gotten into that. I have been very involved in computational language.

I have not been deeply interested... I have been interested in human language, but not from the point of view of the practice of learning many human languages. It's one of those skills that I could have put a lot of effort into, but it's like machine translation is getting to a point where, for many types of things, it's not as important anymore. Just as he might have been a champion map reader. I'm glad I didn't put much effort into it, because now I just use a GPS. Tim Ferriss: Yes, that's true. I guess I'm the complete opposite in the sense that I've spent a lot of time on natural languages, partly because I get a lot of pleasure from it and I think it's a cognitive exercise in doing it, but if I look at the progression in, say, Google Translate From my last trip to Japan, which was pre-COVID, until just six weeks ago, is amazing.

Stephen Wolfram: That's interesting. Tim Ferriss: – how much it has improved. Fortunately, I already speak, read and write Japanese reasonably well, because I went there as an exchange student when I was 15, but it's remarkable the extent to which someone can now use their voice quite synchronously to communicate with someone with machine translation. Where do you see that or how do you think that will play out in, say, the next few years or in the near term? I mean, this is something I imagine you probably have an opinion on. But how do you think we will use this type of technology in the next five years?

Stephen Wolfram: I think one of the things it really digs into is the question of whether you can really express the same thoughts in different human languages. And that is a deep topic. I think what we realize is that language is a representation of organized human thoughts. In a sense, it's a social construct that we all know what a chair is. So when we use the word "chair," we know what we're talking about. But if you have a language that comes from a place where the environment and culture are very different, you're going to end up with words for which there really just isn't a translation for that word.

Because there is simply no shared cultural understanding of what is being talked about there. So I think what will be very interesting to see is, as we see the tightening of the structural aspects of translation, at what point do we really realize that in that culture there are thoughts that we simply don't have in some other culture? And that's something, when you start generalizing that, it's like, okay, how do we communicate with alien intelligences? How do you communicate with dogs and cats? How do you communicate with AIs? Things like this. These are all examples of extraterrestrial intelligences with which we share certain types of things.

We share some emotional responses to pets and things like that, but we probably don't share some kind of deep philosophical convictions, etc. It is interesting to see how this translation process can work and to what extent things can be translated. I guess in the case of computers, what I've been most involved in is how do you get from the things we think about in our minds to the things we can represent to a computer? And how we do it? Computers can calculate all kinds of things. Many of the things they can calculate, at least we humans don't currently care about.

There is a small set of possible things that computers can do that relate to the things that we humans, in the current state of our civilization and so on, have decided we care about. So an interesting question to understand is: to what extent can we translate the things we think we care about into something that can be represented computationally? And it comes back again, I guess after you spend your life working on something, everything somehow relates to these questions like, how do you make this computational language represent human thoughts computationally? Now, when you talk about natural language translation and so on, what we've done when we make our Wolfram|Alpha system and our smart assistant use that and so on, what it does is it answers a question in natural language, like what is the Population? of India divided by China in 1960 or something?

And you take that and turn it into a precise computational question, a precise symbolic representation from which we can then calculate the answer. But whatever poetry there may be in that question, can the population of, I don't know, tell me some poetic name for some country and something else? We squashed all the poetry of that. We're just converting it, so what is the precise computational representation that is a good computer language, so to speak? While it might have been that the appreciative way someone described some country translated into some other human language, that notion of appreciation would have been the most important part of what one is asking.

But to the computer it says, "I don't care about that. I'm just here to provide a symbolic representation and give the answer," so to speak. I think the thing to understand about translation, ultimately, is that the target mind is not necessarily constructed in the same way as the source mind, so there may be no way to change that. Now, you know you can see that. If you imagine you have two machine learning neural network

systems

and both have been trained to distinguish cats from dogs, for example, the internal methods by which they will typically do this will be quite different.

The details of how they will have reacted to that training will be quite different. And so there is no direct translation. System A does it like this on the inside. The most important thing is that the tail has this shape, or something like that. So there is no such direct internal translation. Just like with humans, even if we could perform a brain-to-brain thought transfer, it wouldn't actually work. Just like when you have two machine learning

systems

, the details of how they learned things inside will be different. And that thought experiment, so to speak, on thoughts, of direct thought transference, that also applies.

What constitutes the strong transference of thoughts is basically language. The thoughts themselves are not directly transferable, but we package them into a language, which is this formal representation of thoughts that we can transfer from one mind to another, so to speak. I think that's at least my way of thinking about it. Tim Ferris: Yes. I think this underlines part of the appeal that learning these languages ​​has for me, even though they actually have very little use. I was studying Romanian, which has very limited use. Part of the fascination is, as you mention, these concepts or labels that take the form of language, even if the translations, the literal translation, can be conveyed to the target mind, the nuance can be much more difficult to convey.

And I find that exploring the language is a way to better understand the thinking of a target population, whether Japanese or Romanian, and it's a lot of fun. Because, for example, in Japanese there are at least 40 or 50 ways to say "no." But they could take the form of "Well, that's very difficult" or "Maybe it's possible. Let me ask Mr. Takahashi." And they all mean "no." But the translation won't necessarily convey that. And I'm also thinking, and I'll leave my mini TED talk here in a second, but the structure of language, let's say something as simple as subject, verb, object. "I eat the apple" vs. "I, the apple eat", which can be found in Japanese and then in German, but only in certain cases where it is a relative clause.

And I think all of that represents, often represents, fundamental differences in how people process reality. So I really enjoy it. Now, let me ask you a question about the type of forensic scientific analysis that you have performed. Where you're looking at how someone took a left turn, in, say, thermodynamics at some point. I haven't read this book of yours, but Idea Makers. It is a collection of essays. How did you choose the players on the field for this? How did you choose the people you included? Stephen Wolfram: Oh, he was always opportunistic. I'm afraid part of it was because someone died and I knew them and wanted to write an obituary.

Others said that someone was celebrating a big anniversary and there was a big revelry associated with it, quite opportunistically. But it turned out that it covered a pretty nice collection of different types of people, from Dick Feynman and Steve Jobs, who were people I knew, to people like Ada Lovelace and Ramanujan, who are people who died long before I was born. Tim Ferriss: Dick Feynman, I would love, if you'll allow me, to tell me a little bit about your experience with Dick Feynman. I own the Encyclopedia Britannica set that he bought when he was, I think, 43 years old.

I ended up buying it on my 43rd birthday as a reminder to never stop searching and learning. Stephen Wolfram: That's great. Tim Ferriss: And I also drew some of the diagrams for him, which I appreciate. And I wish I knew more physics. I really enjoyed it when I was younger. I didn't pursue it to an advanced level at all. I really wish I could appreciate his genius more faithfully. But what was it like spending time with him and how did you meet him? What was he like in person? Stephen Wolfram: Yes, I met him when I was 18 and he was 60.

Okay? Tim Ferris: Yeah. Stephen Wolfram: And he always said, "I was as fast as you, but now I'm three times your age." No, he could be quite competitive at that sort of thing. But what I liked about him, every time I see him, is that he would think of anything. It is as if the thinking apparatus was activated and would remain activated, whatever the topic, so to speak. And he liked to dig deeper until he knew: "What is the real point? What is the essence of whatThese three theories come from the same place and they are all derivable from each other.

And they are all derivable in a really interesting way. Ultimately, all of them can be derived from this strange thing we call rullation, which is the limit of all possible calculations. The intertwined limit of all possible calculations. And what it turns out is that those three theories are the result of observers like us taking samples of this ruliada object. And what matters is that we have certain attributes as observers. For example, we are computationally limited. We can only fit a limited amount of computational material into our minds. We can't describe, Oh, this is where every atom in the universe went.

In our minds, the narrative we have to describe the universe is a far cry from describing where every atom went. We're just talking about these much more filtered versions of what's happening in the universe. That turns out to be one of the important things. The other is that we believe that we are persistent over time. In other words, although at each moment we are made of different atoms of space, we are being the atoms of space that we were at one moment are being destroyed, new ones are being created and so on. Despite this, we believe that we are persistent over time.

It's like the little whirlpool in the water. The molecules that form that whirlpool are different molecules at each moment. However, there is something definite that, if the whirlpool had a mind, so to speak, it might think to be persistent over time. Those two characteristics that our minds are computationally limited and we believe that we are persistent over time, those two characteristics determine how we test this rulliated thing, which is the ultimate limit of all possible processes. And the sampling that we can do gives us those three characteristics, those three great theories of 20th century physics. Tim Ferriss: How do you spell ruliad?

Stephen Wolfram: R-U-L-I-A-D. Tim Ferriss: Oh, I got it. Check it out. Incredible. Now, is there a secular explanation or exploration of ruliada that is not completely corrupted? Someone like... Stephen Wolfram: Yeah, I think so. Tim Ferriss: — Could I digest it myself? Stephen Wolfram: The first thing to think about is that there's a little story leading up to it. One of the things I've been interested in for a long time is: is there a simple rule that you just run long enough to get everything that happens in the universe? In other words, earlier I was talking about how simple computational rules that run do really complicated things.

The most complicated thing we know is the entire universe, so could we find a rule? Or we just wrote this rule and we could run it long enough, so the entire universe would form. Okay, so you start thinking, well, let's imagine we had that rule. Let's imagine that we had discovered that; Let me go first to another place, which is how quantum mechanics works. In classical mechanics there are laws that describe how things move or what happens in the world. There may be something that says: I throw a ball with a certain speed, it will move in a certain trajectory.

Tim Ferriss: And by classical, do you mean Newtonian in this case? Stephen Wolfram: Newtonian physics, yes. Tim Ferriss: Got it. Stephen Wolfram: Actually, relativistic physics works the same way. That's the distinction between classical and non-quantum and quantum. The dividing line is around 1920, it's about 100 years old. And so, in the quantum view of the world, it is not the case that definite things happen. Instead, the quantum view is that there are many paths that can be followed. That was Dick Feynman's idea, this idea of ​​path integrals and following many quantum trajectories. But the idea is that a lot of different things happen in quantum mechanics.

The ball follows many different possible trajectories. We, as observers of what happened, can sample those possibilities and just say, "Oh, there was a certain chance that this would happen. There's a certain chance that it would happen." That is the traditional view of quantum mechanics. In our models you have this giant graph that represents this giant network, which represents the structure of the universe. And it is continually rewritten according to some rule. The thing is that there are many different possible rewrites that could occur. Those different possible rewrites give you these different story paths. They give you essentially different threads of time, so to speak.

Different possible things that could happen in the universe. Those story threads sometimes branch, because two different things could happen next. Sometimes they merge, because two things end up producing essentially the same universe. So you end up with this whole complicated branching and merging structure of possible

stories

for the universe. So now the question is, how do we perceive what is happening in that universe? And why don't we see the universe as branching out everywhere? And how can we know what is happening? Well, what we have to realize is that we ourselves are embedded in this branching universe.

So our minds are branching like everything else in the universe is branching. It turns out that the central question of how quantum mechanics is perceived is: how does a branching mind perceive a branching universe? So, this thing that I mentioned that is a characteristic of us is that we believe that we are persistent over time. And so, although from some kind of external view of God, so to speak, the universe is branching like crazy, we believe that our minds are passing through a single thread of experience. And that means that when we impose that belief on what's really happening in the universe, we combine many different paths that from the outside would look like the universe is doing different things.

But we know that, in reality, in some sense they are all the same, because that is what we have to believe in order to have the presumption that we have a definite thread of experience. And that process is what drives the understanding of how quantum mechanics works. And actually, going back to Dick Feynman. He always used to say that having worked all his life on quantum mechanics, he always really liked to say that nobody understands quantum mechanics. And I talked to him for years and years about it. And I wish he was still around because I think I can finally say that I really understand quantum mechanics.

And it is simply this idea of ​​the branched mind that perceives the branched universe. He hadn't seen it coming at all. And I think it's a strange idea that reveals how it works. But you know, in quantum mechanics we have all these different possible things that could happen in the universe, which for us combine into a defined path. Well, okay, let's say we have this model and we say we found this rule, and this rule represents everything the universe does. So we could imagine this day when this rule comes out of our computer, we've done some searching and we have rule number 713 which is our universe.

For a long time I was really uncomfortable with that idea. Because let's say we are universe 713, the next question is, why did we get the number 713? Why didn't we get the number seven billion, whatever it is? Because this? One of the great lessons of science of the last 500 years is the lesson of Copernicus. We are not very special. We might have thought that the Earth was the center of the universe. We might have thought this kind of thing, but it's not true. We are simply on a random planet somewhere in this random space that makes up the universe.

Even the idea of ​​our government being a simple rule, as opposed to an incredibly complicated rule, seems very anti-Copernican. This really bothered me for a long time. And I realized that something even stranger might actually be happening. That is, perhaps the universe is not choosing any particular rule, but rather applying all possible rules. So, what ruling is is this computational process that executes all possible rules. So imagine you have all possible computers, you start them from all possible starting points, and you run them all. You might say, "How could that make anything interesting?" The critical point is that sometimes those computers will end up doing the same thing.

In other words, two different computers could end up producing something that is the same, has the same structure. And then you could say, "Well, everyone is going to do their independent thing." Well, they don't do independent things because there are all these equivalences between the things they do. And so you end up building this rich structure, and that structure that you build is what we call the ruliada. It is the intertwined limit of all possible calculations. And the really interesting thing is that there is only one and it is a necessary object. In other words, as soon as you define that, you're talking about the notion of computation.

As soon as you define your terms, you'll have control. It is not the case that we say, oh, it happens that this feature of the world is like this. It's as inevitable as once you define what integers are and what plus signs are, etc. Two plus two equals four. There's no way out of it. It is not a random fact about the world that humans have two eyes and one nose, which could be seen as a coincidence. It's something that is a necessary feature of the formal structure of what you've set up. So the ruliada is this necessary object.

And now the interesting thing is, okay, so you have this object that is these limited possible calculations. How do we experience that? Well, we are also part of that object. So it's the same story as the branching mind perceiving the branching universe, except there's an even more abstract version of that. It is, how do we, as elements within this ruliad, perceive all ruliad? One of the things that one starts to talk about is this notion of what we call rulial space, which is the space of possible different views of how the universe works. So we could say that we have a view of the universe: "Oh, it works this way, it follows this rule." And then some other person, alien or whatever, says, "No, no, no, you're so wrong.

Instead, the universe really works by this other rule." Now, the reason, what brings all of that together, is a kind of technical fact that's been known for about a hundred years, which is this idea of ​​universal computing. You may have thought that if you wanted to have a computer that was a word processor, you would buy a word processing computer. If you want to have a computer with spreadsheets, you would buy a computer with spreadsheets. But the big fact that emerged in the 1920s and 1930s is that you can have only one type of hardware object.

I mean, it wasn't put into practice until the 1940s and 1950s, but it was, you can have a single hardware object that can be programmed to be a word processor, a spreadsheet, whatever. And it's pretty much the same thing with the universe, that you can attribute different rules to the workings of the universe, but they are convertible, in the same way that your computer can function to run a spreadsheet instead of a word processor, so to speak. say it. So, Tim Ferriss: Yeah, finish your thought and then I want to pause for a second and ask for a follow-up.

Stephen Wolfram: So one of the things I really like about this is the notion of where are you in rulial space? What kind of description do you have of what is happening in the world? And you can imagine that each different mind is in a different place in rulial space. So the fact that you and I have different internal models of the world is an affirmation of the fact that we are some distance apart in governing space. And so what you realize is that when we think about the universe, we have the exploration of the universe through spaceships or anything that goes out into physical space.

We also have the exploration of the universe in rulial space. And this is how, the different minds and the different ways of describing the universe represent a kind of journey through rural space. And just as when we send spaceships into physical space, we explore different parts of the physical universe, when we come up with different ways of thinking about things and different ideas, we are like traveling in rulial. space. And that's kind of a way to start representing that kind of thing. Tim Ferriss: Very good. I'm going to ask a series of questions that will undoubtedly put me at risk of embarrassment, but I knew that when...

Stephen Wolfram: I have to say one more thing. Tim Ferriss: Oh, yeah, just... Stephen Wolfram: Because you asked about languages ​​and different human languages. That is an example of being in different places in rulial space. So you can imagine two languages ​​where the way of thinking about the world is very similar, they correspond in a way to close places in rulial space, where it's quite easy to translate, to travel from one to another, while things that are very different, Very different types of worldviews are further away in rulial space. And that's just one way to conceptualize maybe what this is about.

Please go ahead. Tim Ferriss: Yes, whenyou said that, I was just thinking about gendered languages ​​versus gendered languages ​​or certain languages ​​that don't conjugate, say, the past tense, like Chinese, Mandarin, and how that affects maybe your position in rulial space. . So how does a branched mind perceive a branched universe or how does branched power perceive the branched universe? I think when many people hear this, they imagine these multiple or infinite possibilities branching out in some way to evoke the image of the whirlpool, meaning these shifting atoms, but if you took a snapshot of the whirlpool, minute after minute, it would have some resemblance.

But there is a fork that I think for many people listening will take place in linear time, so there is some past to future in this fork. I've tried to push the boundaries of how I consider or define time by reading and listening to Carlo Rovelli, who I think focuses quite a bit on quantum gravity. I don't know his research very well. How do you think about time? Is the way humans experience or think about time simply a very convenient collective illusion in terms of its linear nature from past to future? Stephen Wolfram: I mean, the first thing is, what is time?

Tim Ferriss: Exactly. estebanWolfram: That's something that I think we really nailed down in the way we think about our theory of physics. I mean, time is the inexorable progress of computing. In other words, the universe is in some state, then the universe will transform to another state, and to another. This progressive process of transformation is the passage of time. And this phenomenon of computational reducibility that I mentioned before and that in some ways cannot be anticipated, is the fact that time is significant. There's something you can't just say, "Oh, I didn't have to go through those moments.

I could always just jump forward." Now, in most of the universe, time simply moves forward. This is how the universe updates itself, which corresponds to the passage of time. We are now part of the universe, so we are also being updated. If the universe simply stopped, we wouldn't know that it had stopped because we would be stopped too. So, for example, one place where that happens, in the simplest type of black hole, in the center of the simplest type of black hole, is the space-time singularity, which has the property of being a place where time is stops.

And so in our physics model, what happens is this universe is updating, this network is updating, it's updating, but if you're in the center of the black hole, it just stops. There are no more updates that can be applied. Actually, if you're doing math, you'll want to get to that point. If you're doing a calculation, you say, "Oh, we're calculating. These things are happening," and eventually you get to the answer and that's a place where everything is fixed, nothing changes anymore. That's what happens at the center of a black hole. In a sense, it's bad news if you want to have a future, so to speak, because time just stopped.

So as far as I'm concerned, time is this inexorable progress of computing, and time, in the actual form in which it manifests itself in the universe, has many complicated characteristics. So, for example, in the theory of relativity and gravitation, etc., there are all kinds of ways in which the notion of "When is the same time as somewhere else?" It's complicated. As we say, let's say, have a colony on Mars someday and we define Earth Standard Time. Well, it's 12 noon right now. Well, Mars is 20 light minutes away, for example. Shall we say that 12 noon is the time in which the light signal from our clock that indicated 12 noon on Earth reaches Mars?

Or do we try to recalculate that and say, "Well, that's the time that would have arrived if the clock had arrived 20 minutes earlier?" Etc. That whole question of how these portions are placed in the universe to define what counts as simultaneity in time, is a kind of history of the theory of relativity and the theory of gravitation, etc. And that's another kind of twist on this whole thing. But in quantum mechanics, the big problem is: is there only one threat: the thread of time or are there many threads of time? Now, humans normally only perceive a thread of time.

I've wondered if there is some type of trance that people can go into that is a kind of multi-track trance, where they actually have multiple threads of experience happening at the same time. But for most of us, most of the time, it's just that we have a defined thread of experience. And that's... Tim Ferriss: If I may interrupt for a second, what caused that wonder or question about whether there are people? Stephen Wolfram: Because one of the features of our physics model is what ultimately drives the mathematical structure of quantum physics. mechanics, it's this assumption that we have that we are persistent over time and that we can combine things to the point where we have a single thread of experience.

If that's not the case, then we have a different theory of quantum mechanics, because quantum mechanics ends up being something that says, "If you do this whole quantum mechanics thing, it has many parts of the story." But in the end we want to get an answer. We don't mean to say that we have two different answers in mind. We're going to say, "We're saying something definitive happened." And so, for example, when people talk about making quantum computers, the most important thing you hope for is that you can use these multiple threads of the story so that each one runs a different calculation, and so you can do all these things in parallel. .

Now the big problem, again and again, it seems like I'm mentioning Dick Feynman too much here, but he and I both work in quantum physics. Tim Ferriss: It doesn't bother me. Stephen Wolfram: – back in 1981 or so. It was kind of a fun experience because he did all the calculations by hand and I was using a computer. In fact, I found one of the calculations I did recently, and he was doing these calculations by hand, and I had no idea why the answers he got were correct. Because it's like you do this calculation and say, "You could have done this or this or that, you could have made this or that assumption right now.

I don't know why that assumption is correct." And he would look at the things he did on a computer and say, "I have no idea why that's all right." So it was an interesting challenge, if you will. But actually, even at that point, we came to the conclusion that the big question about using quantum mechanics to calculate things is, "How do you determine the answer?" In the formal theory of quantum mechanics, how does measurement work in quantum mechanics? How do you actually measure what happened in the quantum process? Well, now what we see is that there are all these story threads and in the end, we humans, if we want to get a definitive answer, we have to tie all those story threads together.

And the big question is, "How difficult is it to tie those story threads together?" And if it is as difficult to put them together as what is gained by having several threads, in the end there is no advantage. And that's something that's hard to understand, and it's something that we're trying to figure out. I'm not hopeful about the true quantum advantage. I think the formalism of quantum mechanics is super interesting. And it's very, this whole idea of ​​what we call multi-way graphs and all these multiple story threads, etc. This is something very interesting and relevant to many fields.

But the idea that you can actually create an engineered system from it and gain this kind of quantum advantage is less compelling. Also, as a practical matter, the whole quantum computing effort has made people think, "Oh, can we make computers with things other than electronics, semiconductors, etc.?" And that is also something that is worth it. So both extremes are worth it. I'm not sure the medium is worth that much. So that is our notion of time. Now, in terms of people's perception of time, it's this process where we're going through these calculations, our minds are going through these calculations and so is the rest of the universe.

And that's pretty much it, it's the alignment of the calculations that are done in our minds with the calculations that are done in the universe that leads to these different forms of time, time in the thermodynamics of things that somehow they break down into heat, or time into the expansion of the universe, things about cosmology, etc. The fact that all those different arrows of time line up is a consequence of the fact that they are actually the same thing, they are just the kind of inexorable process of calculation that is happening in the universe. Tim Ferriss: I'm going to use a term that can be frustratingly undefined and overused to the point that it is at least often undefined.

But I'm going to ask this question anyway: do you have any ideas about what constitutes consciousness? It can be defined any way you want or it can simply be discarded. Or whether it is an emergent property or a subjective experience with certain foundations that can currently be explained? How do you think this can be a terrible question, if you think so? Stephen Wolfram: Yes. Well, I've always avoided it because it's always seemed deeply slippery to me. But I was literally faced with, “I need to apply the idea of ​​consciousness.” It may not be necessary, and this is how it is done.

In the universe, ultimately, there are all these possible calculations that can happen, but our minds don't do all the possible calculations. Our minds are something else, they are much more filtered in what they do. And in particular, they have these characteristics of computational limitation, belief and persistence, etc. To me, those are the things we need to use about consciousness to derive things in physics. So those are characteristics of consciousness that distinguish us from the rest of the universe. It's actually a little disappointing because we might have thought, "Oh, there's inanimate matter and there's this and that, and we have this big stack that we're building that goes through life.

And eventually, we get to intelligence." , awareness. We are the best in the universe," so to speak. But in reality, I have realized that that is not true at all, that the universe has much more capacity than we do. And what we call consciousness is a filtering of that capacity towards something specific, where we believe, for example, that there is a single thread of experience that we have And that is something like consciousness, the application of consciousness to science, is something where it is not about everything in the universe. , it's just about the particular things that are the way our minds perceive things.

I think it's an exercise that people talk about: "Well, how can you talk about this in such a materialistic way? Isn't there something magical about consciousness that is a kind of spark that is different from everything else in the universe? "Well, for us, on the inside, there absolutely is. For us, on the inside, we are this single point in rulial space where this set of things happen. That is our experience of the universe and it is completely unique. And there may be some other point in rulial space, some other mind that is close enough where we can say, "We are experiencing these things.

We can say that they are similar to what is being experienced here." But each type of consciousness is unique in that sense. Now, recently I was doing an exercise that I need to finish, which is to describe what it is like to be a computer. And you imagine that we We humans live our lives, we remember a lot of things throughout our lives. In the end, everything is lost when we die. And the question is, for a computer, from the moment it boots up to the moment it dies. operating system fails, that is a period of time during which the computer has some kind of life experiences.

And how do those life experiences compare to the "life experiences" that we humans have? is happening in the computer. How does that compare to us humans? There is a kind of communication with other computers, the experience of the outside world, etc. How does that compare to what it is like to be: even a current computer, forget about sci-fi AI of the future, just talk about a current computer? What would it be like to be that kind of insider who experiences things from a machine's point of view, so to speak? Tim Ferriss: Let's move on to personal

productivity

.

So I imagine this is something you still think about quite a bit, and I've read a fair amount of your writing about personal infrastructure hacks and such. And it seems like there are, I guess you could describe them, nerdy

productivity

tricks that then become more common or more accepted, more widely distributed. Are there any personal or infrastructure productivity tools or tricks that you're using now that you think will see wider adoption in some form in the not-too-distant future? Stephen Wolfram: So I started live streaming a bunch of work meetings that I do and I started this thing about when was it? 2017, a few years ago.

And it's really an interesting process.was I doing when I got sick? And was it, oh, I went out and met a group of people, or was it whatever? And the only correlation, and I haven't been completely scientific about this, the only correlation was that it often occurred two days after I was on a flight, on a plane. Well. And in some cases, it wasn't a commercial plane. It was a private plane, so there weren't many other people on it, so it's interesting. And then I asked my friends from medical research and so on, "Hey, what's going on here?" And here is the theory.

The theory would be that a big part of the upper respiratory defense, so to speak, is the innate immune system operating in the nose, etc. And if your nose gets dry and stuff, from being in dry air, on airplanes and things like that, your little innate immune system doesn't stand a chance. So my trick has been to take things like wheat germ and stuff that stimulate, right before I get on a plane, take that and a couple of other things, and so far, we only have an N of about eight or something like that on the side.

From the trip. time. So far I haven't gotten sick Tim Ferriss: So far, so good. It also makes me think of ways that you could not necessarily humidify, but also maintain the kind of moisture integrity of the nasal lining, with a spray or something like that. Stephen Wolfram: Yeah, I thought about that. My top medical researcher friend claimed that it's easier to just take choline than to try to do it. Tim Ferriss: Try to keep your nose well hydrated. Stephen Wolfram: Right. It's something strange. You're on some flight, going somewhere, and you're constantly sticking things up your nose or whatever.

No, that's... Tim Ferriss: So, Stephen... Stephen Wolfram: One of the things that I've found, for me, is to keep the list of things to do when I'm tired because, for me, in terms of motivation , etc. , it's always nice. If I'm sick, I might say, "Oh my God, I'm sick. That's so terrible." But in a sense I think, "Great, now I have the opportunity to do these things that I knew I had to do somehow." I do the same when I drive. I always keep a list of phone calls while I'm driving that are a little more, "I have to do this sometime.

I don't need to be in front of a computer." This is something I can do then. And it's actually good for a lot of interactions that I do, where I would never do it. It's like if there is a person who lives in the same city as you, you would never see them. But if they live in a completely different place, "Oh, I'm going to go wherever for a day," and you end up seeing them. And the same thing has happened to me with calls while driving. It's like, "I'm going to call someone," and this is the process.

Tim Ferriss: Stephen, I am continually impressed not only with the breadth of your thinking, but also with the way you record, track, and interpret so much data. I think I take a lot of notes, but you mentioned, at the beginning of this conversation, a quarter of a million pages, something like that. It's just amazing. Stephen Wolfram: Actually, that's what's written. I also have three million emails, so yeah, a lot of stuff over a long period of time. Tim Ferriss: So, you have a lot of things, over a long period of time, and I would love to, at some point, do a second round.

I'm sure we could have topical conversations on probably several dozen different topics. Is there anything else you would like to mention in this conversation or bring to the attention of my audience? Stephen Wolfram: Oh my God. Tim Ferriss: - anything, in terms of final comments, comments, any complaints you'd like to express publicly, really anything you'd like to mention before us - Stephen Wolfram: Makes me want to ask you for a bunch of personal productivity tricks, etc., and the kind of things I'm missing. Tim Ferriss: Oh wow. Yes. Stephen Wolfram: — because little by little one accumulates these things.

And I find that I will try things and probably two-thirds of the things I try work and a third don't. But you have to keep trying them, but anyway. Tim Ferris: Yes. Stephen Wolfram: I don't think so, we've covered all kinds of things. Tim Ferriss: I hope to see you again in person at some point, but this has been lovely and a lot of fun for me. I've taken a lot of notes, so I'll be doing a lot of follow-up on my own. And it seems to be doing pretty well on the productivity front. If I think of anything that is a serious omission, I'll be sure to send it to you.

Stephen Wolfram: Okay. Yes. Tim Ferriss: People can find you on Twitter, Stephen, that's a phone number, Stephen_Wolfram, then Facebook/

stephen

wolfram

, linkedin.com, also your name and then the website

stephen

wolfram

.com. And we'll link to everything you've mentioned. Is there anything you would like to point people to that is a priority for you right now, or any resources that people can't find on their own? Stephen Wolfram: Well, let's see. What I write ends up at writings.stephenwolfram.com. And I put a lot of effort into writing these things, so I hope some people find them fun to read.

Although even the process of writing them, as I was explaining, is a useful process in itself. Also something recent, for me, is that we just launched our Wolfram Institute, that is, I guess, an attempt to hack productivity. My company, which I started 36 years ago, is my machine for turning the ideas I have into real things. And there are 800 people who are really good at doing that and also generating their own ideas. But that's been one thing, where we primarily make products, but one of the problems I've been trying to solve is, if you're doing basic science, what is the machine that does that?

And I've set aside some company resources and so on to do that, but we recently launched the Wolfram Institute, which is an entity whose goal is to do basic science. And that's something new in recent weeks, so stay tuned for interesting things happening there. And I guess there will be even more live streams of science in action and such. So, those are some things. And I guess the other thing is that I have to connect my life's work, which is building the Wolfram Language and Wolfram|Alpha and Mathematica, etc., which are all part of the same idea of ​​making the world computational.

And I guess the only thing I would do is that what we've built, I can inexorably see is an artifact of the future. And in other words, the direction things are going is to represent the world computationally and actually be able to make use of that. But there are a few million people who actually use it in our technology stack, but there are many millions of people who don't and this is an inexorable piece of the future. And it's a big advantage if you can harness the magic of the future. I have worked a lot with children, who are learning our things and doing projects, etc., and I have begun to refer to the fact that learning computer language is a superpower.

It's something you can do and then you can do all kinds of magical things with it. Anyway, learn that superpower and more people should do it. And it's one of those things that you can see in the world, when things involve big ideas, there is a certain inexorable slowness in the way they are adopted. And there are always some early adopters, who are the ones in the lead, so my parting speech would be: if you don't understand computer language and Wolfram Language, etc., try to understand it because it is, to me, you talk about tricks of productivity.

The biggest amplifier and biggest productivity trick is the whole idea of ​​computer language. That's what I've done in science and technology, they're all based on that idea and the technology tower that we built around that. So that's my ultimate productivity hack. Tim Ferriss: Wonderful. And for everyone listening, Mathematica, Wolfram|Alpha, Wolfram Language, we'll link all of these things in the show notes, at tim.blog/podcast. And you can just search Wolfram, W-O-L-F-R-A-M, and it will appear right away. Stephen, I really enjoy learning from you because not only are you an incredible thinker and technologist (I'm sure there are many multi-hyphen labels I could apply), but you are a very talented communicator and teacher.

Therefore, the practical impact of what one does is manifested not only through products used by millions of people and that will be used, in one way or another, by many, many millions more, but also in systematic thinking. and of principles that one I can and do share with people, including children, even with non-technical Muggles, who are nevertheless very curious, like me and, without a doubt, with many, many millions of listeners on this podcast. So thanks. I really appreciate the time you take to do what you do and the time you also take to have this conversation.

Stephen Wolfram: Thank you. Tim Ferriss: Thank you very much. And for everyone listening, I'll plug it in one more time. You can go to tim.blog/podcast to see the show notes on everything we've talked about in this episode and every episode. And until next time, be a little nicer than necessary. Be very curious. Definitely row early for superpowers that you can get ahead of, in terms of early adoption, like the ones Stephen was mentioning. And thanks for tuning in.