Please enjoy this transcript of my interview with Stephen Wolfram (@stephen_wolfram), the creator of Mathematica, Wolfram|Alpha, and the Wolfram Language; the author of A New Kind of Science; the originator of the Wolfram Physics Project; and the founder and CEO of Wolfram Research. Over the course of more than four decades, he has been a pioneer in the development and application of computational thinking and has been responsible for many discoveries, inventions, and innovations in science, technology, and business.
Transcripts may contain a few typos. With many episodes lasting 2+ hours, it can be difficult to catch minor errors. Enjoy!
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Tim Ferriss: Stephen, it’s nice to see you. Thank you for making the time today.
Stephen Wolfram: Nice to be here.
Tim Ferriss: I was impressed that just before we started recording, when I fumbled an attempt to recall when it was that we sat next to each other, you very quickly said 2011 Wired Health Conference and mentioned that you have an archive. So how do you search for something like that?
Stephen Wolfram: I keep an awful lot of stuff. I’ve got all my email going back 30 years. I have a habit of writing trip reports for myself whenever I go to some event or something like that. And I have also scans of paper documents. You weren’t a paper document. You’re more recent than paper document, so to speak. But I have scans of, well it’s like a quarter million pages or so, paper documents that I generated in the time in my life before I went fully digital, so to speak. And I also tend to record. I record all the key strokes I type and screen captures and all kinds of things like that. And then —
Tim Ferriss: How would you use — I’m sorry to — I was just going to ask, how do you use the logging of the key strokes? How would that be used?
Stephen Wolfram: I don’t usually.
Tim Ferriss: You do not.
Stephen Wolfram: Usually I don’t. Occasionally I’ll want to know — occasionally, for example, some computer will crash 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 Ferriss: Ah. Right.
Stephen Wolfram: That was the thing that caused me like 25 years ago to start recording those, is one crash. And then I decided it’s cheap to just record everything like that. And occasionally I’ll do things like, oh, I’m using a new keyboard. Do I type faster or slower on my new keyboard? Those kinds of things.
Tim Ferriss: Got it. And you can track that type of thing.
Stephen Wolfram: It’s easy to answer that question if you have that data.
Tim Ferriss: It’s one thing to record or ingest information, it’s quite another to structure your thinking. And I, in the process of doing research for this conversation, came across some discussion on Reddit involving creating matrices. So I wanted to explore your use of matrices for different projects. I’m just going to read a very, very short section here, which reads, “I actively avoid thinking about things where I don’t have a ‘matrix.’ I don’t like to have ‘disembodied ideas’ floating around.” Which is my current state of affairs, so I’m selfishly asking this. “Of course, when something is important enough to me, I try to build a ‘matrix’ for it.” Could you give an example of what such a matrix might look like?
Stephen Wolfram: Yeah. What I mean by that is, some kind of framework in which I’m doing something. So for example, if I have a small idea about molecular biology, I don’t really have a good place to put that idea. If I’m doing a big project about molecular biology where I’m building up a whole structure, then I have a place to put that. So it’s that kind of thing.
I’ve been lucky enough, my main life work is building our computational language, Wolfram Language, which is this language that’s supposed to represent everything in the world computationally. So a large number of ideas that I have about how to represent things computationally can wind up in the matrix, which is the Wolfram Language. Similarly, I’ve found, for example, writing my blog-like, kind of writings-thing, that’s another kind of matrix into which I can put, when I do historical studies of things, I’ll write a piece about that historical study and that’s a place to put it.
Tim Ferriss: I see.
Stephen Wolfram: But when it’s too small a thing, I just don’t have a place to put it, and it tends to die on the vine. Since you’re asking about this, I’ve just been exposing myself to a very bizarre experience. Which is, I’ve just been finishing a project that I started 50 years ago.
Tim Ferriss: Wow.
Stephen Wolfram: And in the process of doing this — something that I started, I got interested in when I was 12 years old, it’s a question about physics and about the second law of thermodynamics and why randomness gets generated in the world and so on. I’ve made various pieces of progress on this question over the years, but finally now with a bunch of the things that we’ve done recently in understanding fundamental theory of physics, I think I can actually really nail this question. So I’ve written a whole thing about the scientific answer to that question, but then I thought I should write a piece describing my 50-year journey of trying to answer this question. And so that got me back into, I’m looking at all my calendars from 1983, and I’m looking at all of these paper documents that I have scanned and so on. 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 those come to be? What were the kinds of steps that I had to go through?
One of the things I really noticed, really striking to me, is there’s often a large amount of time in which I was building some conceptual framework for something. Sometimes I had clues about how that framework should work, I didn’t even recognize the clues. Just didn’t understand it. Finally, through the very slow process, I build this intellectual framework, and then I get some other clue. I do some other computer experiments, something else happens. And then literally I can trace, because I have all the file creation dates, all this kind of thing, I can trace. It was 15 minutes from the time when I saw this to the time when I started writing this and so on. It’s really remarkable how much years can go by, but one’s slowly building up the kind of conceptual framework needed, and then it’s often very sudden to take the next step. That was a striking thing to me.
Tim Ferriss: I want to ask a few follow up questions about conceptual frameworks and perhaps just request an example of what such a conceptual framework might look like, for people listening and for me, frankly. But before we get to that, putting together a blog post or an article that chronicles your search for answers or exploration of these open questions is quite an undertaking. Reviewing calendars from 1983 and so on, I would imagine requires a good amount of time. Why do you do it? Or is it the reward in the process of writing? Is it something gratifying in the process of writing? Are you hoping to impart something to those who read this piece? Why do that?
Stephen Wolfram: That’s a good question. I was wondering it myself. I thought it would be really easy, but it wasn’t quite so easy. No. I have done quite a lot of historical biography, 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 right now on the second law of thermodynamics thing, second law of thermodynamics was developed in the 1860s. I think people took a wrong turn sometime shortly thereafter. So I think the thing that I’ve now figured out is a little different from what people had figured out at that time.
And so when I’m saying I think they took a wrong turn, I really want to know how did they come to take that wrong turn? And so that’s my next project for the next week or so is going back and I collected the material for it. I get to go back and read all the original sources of 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 this person took that move because they thought this and they understood that and so on.
And I thought for myself, I was mostly, well, a bit curious. I thought it was sort of an interesting story. It’s a very rare case in history of science that one actually has really precise, detailed data on how some idea got developed. And so I thought it’s kind of an interesting example of essentially computational history. One thinks about computational X for all X. This is computational history. And it’s like what can you do in computational history? What kinds 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 that I did or figured out 40 years ago, let’s say, I lived through it, but then when I looked back I realized there were threads that could be joined that I absolutely did not see at the time. Give you an example. I was working on these simple programs. What do simple programs do? The big surprise was, even very simple 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 come to terms with the fact that that was possible.
Same time, several of actually top mathematicians who were friends of mine were trying to work on the things that I had uncovered. They said, “Let’s go do math on these things. Let’s figure out more stuff about them.” And they worked for a while. I just found a bunch of their notes, actually, from going through these things from 1984 and so on. And they did a bunch of very sophisticated math, and they couldn’t figure out anything.
What I realized is, at the time I was just like, okay, well I’m figuring out my things. Their methods didn’t happen to work, so what? What I then realized, just now, is the big innovation of mine was realizing that the fact that they couldn’t figure out anything was itself a super interesting fact. So in other words, there’s this phenomenon I call computational irreducibility, which is basically the big picture of why they couldn’t figure anything out.
Usually you say, “I know the rules by which some system operates.” So then you might say, “Okay great, I’ve got it nailed. I know everything about what’s going to happen in that system.” Well, that’s not true. Because if the rules define some computation, to know what the system does, you can find out by running the computation, the question is can you outrun that computation? Can you say, “Okay, system, you went through a million steps of that computation, but I don’t need to do that. I’m smarter than the system. I can just say the answer is 42,” or something. What computational irreducibility tells you, is that in general you are stuck having to follow each step in the evolution of the system.
And that’s a really important fact about science. It’s a way in which science explains from within science that science has questions that it can’t readily answer. The thing that I realized only, I don’t know what, 35 years after the fact, is that, in a sense, if I had an achievement, intellectual achievement in that whole process, it was realizing that the fact that one had gotten stuck was itself the most important thing to know. Not, “Oh, we got stuck. Let’s give up.” But the fact that one got stuck meant that there was a paradigmatic change that had to be made in the way that you think about these kinds of questions in science. So that’s an example that, for me, is quite useful, to go back and see what really happened there, what was really important there, which I had not realized at the time.
Tim Ferriss: Makes me — I was just going to say, it makes me think a bit of the Sherlock Holmes, the case of the dog that didn’t bark in the night. The term that I’d love for you just to elaborate on a little bit, and I apologize this is going to be a muggle question, but for people who are non-technical, I’ll put myself in that audience, when you say, for instance, computational history or computational X, how should someone who may have fear around the term “computational” think of that term? Computational history as an example?
Stephen Wolfram: We humans like to come up with abstract, formal ways to describe things. Language itself is an example of that. We see things out there in the world and we say, “That’s a tree, that’s a dog, that’s whatever.” The fact that we are able to symbolically describe these things in the world — there are many different kinds of dogs and many different details, but we just say “It’s a dog” — so it’s a way of organizing the things that we see, in that case, just using natural language. There’ve been in history a variety of kinds of organizational approaches. Logic, for example, was one from antiquity. Mathematics is another kind of organizational approach to say, “This is how you structure the way that you talk about the world.”
As far as I’m concerned, the importance of computation is, it’s another way to structure how you 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 piece of food or some description of some position on the Earth or whatever else, and represent those things computationally in a precise way that has the feature that, well a human could read it and say, “Oh, I know what that means.” But also we have the extra boost from the fact that a computer can read it too, and then the computer can help us to get further with that.
I mean, in a sense, one of the big things that we, as a species, you know, big achievement is human language. You can take of things about the world and describe them in a somewhat precise, abstract way. I see computational language as being another level in that evolution, except that we get to share the burden of seeing what happens, not just with other humans, but with computers. And so, for me, that’s the big thing is describing the world computationally.
Now, when I talk about simple programs and those kinds of things, what I tend to mean is kind of a meta model of the world. So there are models of actual trees and dogs and trajectories on the Earth and things like this. And then how do you break that down to something even more primitive? And so 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 automata, and the typical setup is you have a row of cells, and each cell can be either black or white, let’s say. And then the computational rule is, you look at every cell and you 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 to be black or whatever. You just keep running that rule over and over again.
The big surprise is, and this is the thing I finally discovered in around 1984, the big surprise is that even with a rule that simple, you can just start it off with one black dot and it makes this incredibly complicated pattern. A pattern so complicated that if you were to just look at a piece of the pattern and say, is this random or is there some regularity to it? You would just say, it looks completely random to me. Even though the rule that made it is this very, very simple rule that you can easily describe or write down or feed to a computer or whatever else. So for me, this notion of computation is having this way to structure the way that you talk about the world, and then there’s this kind of meta modeling of that, which is to say what are the very simplest elements of that computational process? And then talk about what one can do with those.
I mean, I think a good analogy perhaps for the computational description of the world comes from the mathematical notation that one uses to talk about mathematics. I mean, it’s sort of an interesting evolution that if you look at mathematics done in antiquity and things like that, people didn’t have a symbol for plus. They just used words. And then some time around 500 years ago, people started inventing a plus sign, an equals sign, things like this. And it’s when that sort of streamlining of the way to talk about math came online, that’s where math really took off and algebra got invented and then calculus and then we have this whole mathematical approach to science that was able to be done. I guess my own personal last 40 years of effort has been to try to make a computational notation for talking about the world that is kind of a parallel for computation of 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 fact check this. You are so deeply aware of and able to work with language in multiple, I would say, dimensions. I did read at one point you were considering a job with CERN and I believe I read that you said you had practiced French but had never built up the nerve to use it, or something along those lines. And I don’t know if that was the tongue-in-cheek comment, but —
Stephen Wolfram: Well no, no. That’s from ancient times. I went to fancy schools in England when I was growing up. I learned three languages, Latin, Greek, and French. Okay, you don’t get to speak Latin or Greek, ancient Greek, at least not in most places. But French, you could in principle speak. I can read scientific French pretty fluently. But if you say — if I’m in France and I’m like, “Can I order that piece of food or something?” No way. I can’t do that. It’s one of these things. I should get over it one day. Because I think I have the vocabulary knowledge, I think, and all that. I just have never really gotten into that.
I’ve been very deeply involved in computational language. I’ve not been deeply — I’ve been interested in human language, but I’m not from the point of view of the practice of learning lots of human languages. It’s one of those skills where I could have put a lot of effort into it, but it’s like, automatic translation is now getting to the point where, for many kinds of things, not so important anymore. Just like I could have been a champion map reader. I’m glad I didn’t put huge effort into that, because I just use a GPS now.
Tim Ferriss: Yeah, that’s true. I suppose I am the opposite in the sense that I’ve spent a lot of time on natural languages, in part because I derive so much pleasure and I think cognitive exercise from pursuing it, but looking at the progression, say, in Google Translate from my last trip to Japan, which was pre-COVID, to just about six weeks ago, it is astonishing —
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 the extent to which someone can now use their voice pretty synchronously to communicate with someone with automatic translation is remarkable. Where do you see that, or how do you see that developing, say, in the next handful of years or in the near term? I mean, this is something I would imagine you probably have a view on. But how do you think we’ll be using this type of technology in the next, call it five years?
Stephen Wolfram: I think one of the things that it really drills into is the whole question of can you actually express the same thoughts in different human languages? And that’s a deep issue. I think what we realize is that, language is one representation of organized human thoughts. In a sense, it is a societal construction of we all know what a chair is. So when we use the word “chair,” we know what each other is talking about. But if you have a language that comes from a place where the environment, the culture is very different, you’ll end up with words where there really just isn’t a translation for that word. Because there just isn’t the shared cultural understanding of what one’s talking about there. So I think the thing that will be pretty interesting to see is, as we see the tightening up of the structural aspects of translation, at what point do we really realize, in that culture there are thoughts that we just don’t have in some other culture?
And that’s something, as you start generalizing that, it’s like, okay, how do we communicate with the alien intelligences? How do you communicate with cats and dogs? How do you communicate with AIs? Things like this. These are all examples of alien intelligences with which we share certain kinds of things. We share some emotional responses with pets and things like this, but we don’t share, probably, some sort of deep philosophical convictions and so on. It’s interesting to see how this process of translation can work and how far out can you translate things.
I guess for computers, the thing I’ve been most involved in, is how do you go from the things we think about in our minds to the things that we can represent for a computer? And how do we — computers can compute all kinds of things. Many of the things they can compute, we humans don’t at least currently care about. There’s a certain small set of possible things computers can do that are things that relate to the things that we humans in the current state of our civilization and so on have decided we care about. And so an interesting question to understand, to what extent we can translate the things we think we care about into something which can be represented computationally? And it comes back again, I suppose after one’s spent one’s life working on something, everything somehow relates to these questions like, how do you make this computational language to represent human thoughts in a computational way?
Now, when you talk about natural language translation and so on, what we’ve done when we make our Wolfram|Alpha system and intelligent assistant uses of that and so on, what it’s doing is it’s taking a natural language question, like what’s the population of India divided by China in 1960 or something? And it’s taking that and it is turning that into a precise computational question, precise symbolic representation that we can then compute the answer from. But whatever poetry there might have been in that question, can you tell me the population of the, I don’t know, some poetic name for some country and some other thing. We crush all the poetry out of that. We’re just turning it into, so what is the precise computational representation that is good computer speak, so to speak. Whereas it could have been that the very appreciative way that somebody described some country translated into some other human language, that notion of appreciation would’ve been the most important part of that thing that one’s asking. But for 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 the destination mind isn’t built the same way the source mind is necessarily built, and so there may just be no way to change that. Now, you know can see that. If you imagine you have two machine-learning neuralnet systems and they’ve both been trained how to tell cats from dogs, for example, the internal methods by which they will do it will typically be quite different. The details of how they will have reacted to that training will be quite different. And so there isn’t a direct translation. System A does it this way inside. Its most important thing is that the tail has this form, or something like this. So there isn’t this direct internal translation. Just as for humans, even if we could do brain-to-brain transfer of thoughts, it’s not really going to work. Just like when you have two machine-learning systems, the details of how they learnt things inside will be different.
And that thought experiment, so to speak, about thoughts, of direct transfer of thoughts, that also applies. The thing that is the robust transfer of thoughts is basically language. The thoughts themselves are not directly transferable, but we package them into language, which is this formal representation of thoughts that we can transfer from one mind to another, so to speak. I think that’s my way of thinking about that at least.
Tim Ferriss: Yeah. This underscores, I think, part of the appeal for me in learning these languages, even when they really have very little utility. I was just studying Romanian, which has very limited use. A part of the fascination is, as you’re mentioning, these concepts or labels that take the form of language, even if the translations, literal translation, can be conveyed to the target mind, the nuance can be much more challenging to convey. And I find exploring the language is a way to better understand the thinking of a target population, whether Japanese or Romanian, to be a lot of fun. Because for instance in Japanese, there are at least 40 or 50 ways to say “no.” But they might take the form of, “Well, that’s very difficult,” or, “Maybe that’s possible. Let me ask Mr. Takahashi.” And those all mean “no.” But the translation won’t necessarily convey that.
And also thinking about, and I’ll stop my mini TED Talk here in a second, but the structure of the language, let’s just say something as simple as subject verb object. “I eat the apple” versus “I, the apple eat,” which you would find in Japanese and then in German you would find it, but only in certain cases where it’s a relative clause. And all of that, I think, 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 scientific forensic analysis that you’ve done. Where you’re looking at how someone took a left turn, in say, thermodynamics at some point in time. I have not read this book of yours, but Idea Makers. It’s a compilation of essays. How did you choose the players on the field for this? How did you choose the people you included?
Stephen Wolfram: Oh, it was always opportunistic. I’m afraid some of it was somebody died and I knew them and I wanted to write an obituary post. Others were, somebody was having a big anniversary and there was a big shindig associated with that, so rather opportunistically. But it happened to cover a rather nice collection of different types of folks from Dick Feynman and Steve Jobs, who were both people I knew, to people like Ada Lovelace and Ramanujan, who are people who died long before I was born.
Tim Ferriss: So Dick Feynman, I’d love to, if you would indulge me, just to tell me a bit of your experience with Dick Feynman. I own the set of Encyclopedia Britannica 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 cool.
Tim Ferriss: And I drew a few diagrams of his as well, which I prize. And I wish I knew more physics. I really enjoyed it when I was younger. I did not pursue it to an advanced level at all. I really wish I could appreciate his genius in a higher fidelity way. But what was it like to spend time with him, and how did you know him? What was he like in person?
Stephen Wolfram: Yeah, I met him when I was 18 and he was 60. Okay?
Tim Ferriss: Yeah.
Stephen Wolfram: And he would always say, “I was as quick as you were, but now I’m three times older than you are.” No, he could be quite competitive in those kinds of things. But the thing that I liked about him, whenever I see it, is he just would think about anything. It’s like the thinking apparatus is engaged and will stay engaged, whatever the topic, so to speak. And he liked drilling down to really get, “What is the real point? What’s the essence of what’s going on?” And sometimes he would play a little trick on the world, which was one of the things he was really good at was calculating things by hand. He used computers a little bit, but mostly hand calculation, complicated math, these kinds of things. And so he would do, he’d have some question, he would do all this complicated math to work out the answer. Then he would get this answer, and then he would think, “Well, how can I have figured out that answer by just some intuitive argument without having to go through all the complicated math?”
I remember talking to him about this. He thought that everybody can do the complicated math, but it’s really impressive if you can figure this out by intuition. And so he would then figure out this intuitive answer, throw away the math, tell people only the intuition. There’s some fields where he did that. And people are like, “We don’t know how on Earth he figured this out.” And people have been trying to reverse engineer the math for years, but that was one of his little clicks on the world, so to speak.
Tim Ferriss: Oh, that’s incredible. Intellectual sleight of hand. Would Ramanujan — this is someone I know even less about. I personify the intuition that Feynman was referring to, or on some level —
Stephen Wolfram: Well, Ramanujan is a different story. Ramanujan was a slightly decently educated person just hanging out in India and producing remarkable mathematical results. Ramanujan, like Feynman, was a very good calculator. And it really confused the mathematicians because he would say, “I’ve got this amazing formula for Pi. Nobody’s ever seen anything like it.” And he’d say, “Here it is.” And then when he started corresponding with mathematicians in England in 1913, or whatever, they were like, “Well, how did you prove this? How do you know this?” And eventually he got so fed up, I think, that he told them, “Well, the goddess so and so told it to me in a dream.”
But the truth of it was, he was a really good calculator. And so he just worked out, this particular series is the same as Pi to this number of decimal places. And he had good enough mathematical intuition to say, “And it’s just going to be correct. It’s not just going to be an approximation, it’s going to be exactly right.” Occasionally, that intuition failed him. He had a result about prime numbers, for example, where he had done many, many, many cases and it looked like it was true. Turns out the result isn’t true. But the first exception is that 10 to the 10 to the hundred or something, some huge place which he couldn’t reach with calculation. So in a sense he was, I think, a great experimental mathematician. Had he used computers, he would’ve had a whole different set of things he could’ve discovered. But even with himself as the computer, so to speak, he was discovering all kinds of things.
And literally, the mathematicians of the time had never seen anything like it. And so for them, it just seemed to be this magical thing where he was just pulling formulas from nothing. A bit like Dick Feynman. He was often pulling formulas from a lot of hard work of computation. And I remember when I was younger, I happened to start using computers to do physics very early on when I was a teenager, and so on. And for some reason the tools existed, but to be able to do some mathematical computations by computer, but people weren’t using them.
One of the experiences that I had when I was a kid was I discovered the fact that computers can be really powerful for doing science and so on when I was 13, 14, 15 years old. And I started using them to do that, and I started being able to derive all kinds of complicated mathematical formulas and so on by computer. For whatever crazy reason, other people just weren’t doing that. And so I could write physics papers where I would have these very elaborate formulas that I derived, and so on. And people were like, “You must be amazingly good at doing all this calculational stuff.”
And it’s like, well no, actually. I’m actually pretty bad at it, but me with a computer we’re pretty good. But they didn’t really even understand the fact that that was a thing back in those days. Yeah, I’ve seen this interesting phenomenon of when you use tools where people don’t necessarily even understand that those tools exist it has some interesting consequences. I find, for me —
Tim Ferriss: I was just going to say, the attribution, I’m going to skip. But what was it, any sufficiently advanced form of technologies indistinguishable from magic? Something like that. I can’t remember what the attribution would be.
Stephen Wolfram: — Arthur C. Clarke.
Tim Ferriss: I think that sounds like Arthur C. Clarke.
Stephen Wolfram: Right. Yeah. No, I think that the thing that I’ve spent a lot of my scientific effort getting intuition from doing computer experiments. In another age I would’ve been there with test tubes and other things doing physical experiments. Thank goodness I can get away with doing computer experiments, because it’s a lot better for me, so to speak. But you do these experiments and there’s a certain art to doing a good computer experiment, but you can discover things that you never thought were there, and they inform your intuition and allow you to build things up. And it’s this thing that comes from nowhere. Because it’s just coming from not the natural world, but the computational world. You’re just turning over this rock in the computational world and suddenly you discover that there’s this whole crazy thing going on underneath it, so to speak.
Tim Ferriss: I would love to pose a question that was posed at a group dinner I attended not too long ago, and it’s related to what the moderator called heresies. And I’ll unpack what that means. He asked each of us to present a heresy.
And the heresy in this context was something that the people in this small group, about six to eight people, would disagree with you about. And these were a lot of technical folks. And I think each person in the group could have put out many things they believed that the broader population would disagree with them about. But are there any particular beliefs that you have or scientific insights, computational insights, this could be related to the Physics Project, could be related to other things? Things you believe that you have high conviction in that many people would disagree with you on now, but let’s just say hopefully 10 years from now looking back they would say, “Ah, yes, that was actually, in fact, had some grounds to it.” Does anything come to mind?
Stephen Wolfram: Yeah, there’s several, but there’s one example of a big one. But this one, the resistance is crumbling rapidly, maybe it doesn’t quite count as well. But I think it would still be the case that if you poll, let’s say physicists, the resistance would not have completely crumbled. And that’s the question of, “What is space? And is space made of anything?” So go back, let’s say 120 years. People said, “What’s water made of?” Water isn’t made of anything. Water is just a fluid that flows in the way it flows. Turns out what became clear than the late 19th century, is actually water is made of something. People have guessed it much earlier, it’s made of molecules. So the question now is, “What is space made of? This thing that we move around in, is it just a thing where we get to place something wherever we want? Or does it have an inner structure?”
And I’ve been pretty sure for quite a while that space has an inner structure, it’s just made of these discrete atoms of existence. You can think of in the case of space, atoms of space. And all that one can say is how these atoms of space are connected to other ones. So there’s this giant network that defines the structure of space, and that’s what space ultimately is. And everything that exists in the universe is a feature of the way those connections work in the underlying structure of space. That’s kind of like, if you have a fluid, there’s a bunch of these molecules bouncing around. But let’s say you just look at little vortex, little eddy on the surface of the fluid, little whirlpool type thing. We can say, there’s a whirlpool, you can see it go by. And we can talk about it and so on.
But ultimately, it’s just made of a bunch of molecules moving in a particular organized fashion. And it’s my strong belief that everything we know in the universe, all the electrons and photons, and the things we that get made up of those, they’re all just things like eddies in the structure of this giant network that is the underlying data structure of the universe, and the underlying thing that space is made of. So that’s something —
Tim Ferriss: Where would dark matter fit into this, be vetoed by this, be compatible or incompatible with this?
Stephen Wolfram: It’s kind of a detail. The bigger picture is something which is a more embarrassing feature of current physics. Dark matter is the rotations of galaxies look like there’s more stuff inside the galaxy than you can account for by looking at the luminous stars and so on. But there’s a bigger embarrassment, which is that in quantum field theory, the standard theory of the way small scale stuff happens in physics, there is this phenomenon called zero point fluctuations. And there are an infinite collection of zero point fluctuations in the universe that essentially produce energy, or are associated with energy that has a gravitational effect that will roll the universe up into a tiny ball.
That is not what we see. We see a universe that’s a big universe, not a universe that’s just rolled up into a tiny ball. And so that’s, in terms of missing energy, that is a much bigger by a factor of a hundred orders of magnitude or more. That’s a bigger problem, so to speak, than the dark energy problem. But that’s a dark matter problem. Dark energy is yet a different problem. But these are all features of, there’s aspects of the universe where there’s energy, but where the energy doesn’t seem to have the effect that we would expect to have, or we don’t know where the energy is coming from, and so on. One feature of our of model of physics is that the very processes that are leading to those vacuum fluctuations and so on, those are the processes that knit together the structure of space.
So in the usual theory, it’s like there’s space and that’s a thing. And then there’s all this matter that is doing all these weird quantum fluctuations in space, and that matter should have more of an effect on space. But in our models, those quantum fluctuation-like things, those are what make space. And so it’s not surprising, and the math works out this way, that the thing that makes space doesn’t itself have the effect on space of doing something like curling the universe up into a tiny ball. That’s an example of how something like that works out in our model of physics.
Tim Ferriss: I do have a question about time, which I’ll get to in a second. But any other heresies that would be on the short list of things that come to mind?
Stephen Wolfram: Oh, boy. Here’s another one, it’s a little bit more detailed. See, what’s happened is, as a result of the Physics Project we have come to understand a different paradigmatic way of thinking about a bunch of things. And so there are a bunch of fields that you can then apply this new paradigmatic way of thinking to, and start to make foundational changes in those fields. I’ll mention another thing, I don’t know how much of a heresy this is. But in physics, the two big theories of physics — well, there are really three big theories of 20th century physics: general relativity, Einstein’s theory of gravity, quantum mechanics, and statistical mechanics. Which is what brings us things like the second law of thermodynamics, the law of entropy increase, and so on. And it’s the theory of heat, so to speak. Those are the three big theories of 20th century physics.
One of the things that I think is just super amazingly cool, is it’s turned out that all three of those theories are basically come from the same place. They’re all in a sense versions of the same statement. They’re all in some sense the same theory. Which, to me, is really remarkable. And in fact, one of the things that has just become clear to me now is that the people have thought — well, okay, so statistical mechanics is about when you put lots of molecules together, what do they typically do? For example, you can have molecules that make a gas, and you say there are certain gas laws that determine the pressure and volume of the gas, and so on. These are typical things that happen when you just throw a bunch of molecules altogether. That’s what statistical mechanics is about. And people have believed that the most significant thing in statistical mechanics, the second law of thermodynamics, which is the law that says things tend to get more random. When you have mechanical work that’s doing things, eventually that’s dissipated as heat.
And once it’s heat, that’s microscopic motion of molecules, you never get back that large scale mechanical motion. It’s turned into heat, it’s random, you don’t get anything back from it. People have believed that it’s possible to derive the second law of thermodynamics in some kind of almost mathematical way. You don’t really need to know physics to be able to derive that principle that things tend to, for example, go from mechanical work to heat. But people have believed that general relativity and quantum mechanics are both wheel-in features of our universe. They’re both things where you could have made a universe that didn’t have one of those things, they’re just things where it happened to be that way. What has become clear from our Physics Project is that all three of these theories of come from the same place, and they’re all as derivable with each other. And they’re all derivable in a really interesting way.
They’re all derivable ultimately from this strange thing we call the ruliad, which is this limit of all possible computations. The entangled limit of all possible computations. And what turns out to happen is all three of those theories are the results of observers like us sampling this ruliad object. And what matters is that we have certain attributes as observers. For example, we are computationally bounded. We can only fit a limited amount of computational stuff into our minds. We can’t describe, oh, here’s where every atom in the universe went. In our minds, the narrative that we have for describing the universe is far away from, let’s describe where every atom went. We’re just talking about these much more filtered versions of what’s going on in the universe. That turns out to be one of the important things.
The other one is that we believe we are persistent in time. In other words, even though at every moment we are made from 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 that, we believe that we are persistent in time. It’s like the little eddy on the water. The molecules that make that eddy, it’s different molecules at every moment in time. Yet there is a definite thing, which if the eddy had a mind, so to speak, it could think it is persistent in time. Those two features that our minds are computationally bounded and we believe we are persistent in time, those two features determine how we sample this ruliad thing, which is the ultimate limit of all possible processes. And the sampling that we get to do is one that gives us those three features, those three big 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. Look at that. Incredible. Now, is there a lay explanation or exploration of the ruliad that isn’t completely corrupted? That someone like —
Stephen Wolfram: Yeah, I think so.
Tim Ferriss: — myself might digest?
Stephen Wolfram: The first thing to think about is, there’s a little bit of a story that gets to it. One of the things I’ve long been interested in is, is there a simple rule from which where you just run that rule long enough and you’ll get everything that happens in the universe? In other words, I was talking before about how simple computational rules you run them, they do really complicated things. The most complicated thing we know about is the whole universe, so could we find a rule? Or we just write down this rule and we could run it long enough, it would just make the whole universe. Okay, then you start thinking, well, let’s imagine that we had that rule. Let’s imagine that we’d found that — let me go actually one other place first, which is how quantum mechanics works. In classical mechanics you have laws that describe how things move, or what happens in the world. There might be something that says, I throw a ball with a certain velocity, it will move in a certain trajectory.
Tim Ferriss: And by classical, you mean Newtonian in this case?
Stephen Wolfram: Newtonian physics, yes.
Tim Ferriss: Got it.
Stephen Wolfram: Actually, the relativistic physics works the same way. That’s the distinction between classical as in not quantum and quantum. The dividing line is around 1920, it’s about 100 years old, the thing. And so, in the quantum view of the world, it isn’t the case that definite things happen. Instead, the quantum view is there are many paths that get followed. That was Dick Feynman’s idea, this idea of path integrals and following many quantum paths. But the notion is that in quantum mechanics lots of different things happen. The ball goes on many different possible trajectories. We, as observers of what happened, we get to sample across those possibilities and just get to say, “Oh, there was a certain probability that this would happen. There’s a certain probability that would happen.”
That’s the traditional view of quantum mechanics. In our models you have this giant graph that represents this giant network, that represents the structure of the universe. And it’s continually being rewritten according to some rule. What turns out to happen is, there are many different possible rewrites that could occur. Those different possible rewrites give you these different paths of history. They give you essentially different threads of time, so to speak. Different possible things that could happen in the universe. Those threads of history, sometimes they 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 structure of branching and merging of possible histories for the universe. So now the question is, how do we perceive what’s going on in that universe? And why do we not see the universe as this thing where it’s branching all over the place? And how can we tell what’s happening?
Well, the thing we have to realize is that we ourselves are embedded in this branching universe. So our minds are branching just like everything else in the universe is branching. It turns out the core question of how one perceives quantum mechanics is, how does a branching mind perceive a branching universe? And so then this thing that I mentioned that’s a feature of us, is we believe that we are persistent in time. And so even though in some sort of external God’s eye view, so to speak, the universe is branching like crazy, we believe that our minds are going through a single thread of experience. And so that means as we impose that belief on what’s actually going on in the universe, we conflate lots of different paths that from the outside would look like the universe is doing different things.
But we so know, actually, those are all in some sense the same thing, because that’s what we have to believe in order to have this conceit that we have a definite thread of experience. And so that process is what drives the understanding of how quantum mechanics works. And actually, returning to Dick Feynman again. He always used to say that having worked his whole life on quantum mechanics, he always was very fond of saying nobody understands quantum mechanics. And I talked to him for ages and ages about that. And I wish he was still around because I think I can finally say I think I actually understand quantum mechanics. And it’s just this idea of the branching mind perceiving the branching universe. I hadn’t seen that coming at all. And it’s a bizarre idea that turns out, I think, to unlock how that works.
But okay, in quantum mechanics we have all these different possible things that could happen in the universe, which to us get conflated together into a definite path. Well, okay, let’s say we’ve got this model and we say we find this rule, and this rule represents everything the universe does. So then we might imagine this day where we’ve got this rule comes out of our computer, we’ve done some search and we have rule number 713 is our universe. For a long time I was just really uncomfortable with that idea. Because let’s say we’re universe 713, the next question is, why did we get number 713? Why didn’t we get number seven trillion, whatever? Why this one? One of the big lessons of science over the last 500 years is the Copernicus lesson. We’re not very special.
We might have thought the Earth was the center of the universe. We might have thought these kinds of things, but it isn’t true. We’re just on a random planet somewhere in this random space that makes up the universe. Even the idea that our rule is 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, actually, something even more bizarre may be going on. Which is, maybe the universe is not picking any particular rule, it’s running all possible rules. And so what the ruliad is is this computational process that runs all possible rules. So imagine you had all possible computers and you start them off from all possible starting points, and you run them all. You might say, “How could that do anything interesting?”
The critical point is that sometimes those computers will end up making the same thing. In other words, two different computers might end up having being producing something which is the same, has the same structure. And so you might say, “Well, they’re just all going to do their independent things.” Well, they don’t do independent things because there are all these equivalences between things that they do. And so you end up building up this rich structure, and that structure you build up is what we call the ruliad. It’s the entangled limit of all possible computations. And what’s really interesting about it is, there’s only one of it, 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 have the ruliad. It’s not the case that it’s like, oh, it so happens that this feature of the world is this way.
It is as inevitable as, once you define what integers are and what plus signs are, and so on. Two plus two equals four. There’s no way of getting out of that. It’s not something that is a random fact about the world that humans happen to have two eyes and a nose type thing, which might be seen to be more coincidental. It’s something that is a necessary feature from the formal structure of what you’ve set up. So the ruliad is this necessary object. And now the thing which is interesting is, okay, so you have this object that is this limitable possible computations. 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 an even more abstracted version of that. It’s, how do we as elements inside this ruliad perceive the whole ruliad? One of the things one starts talking about is this notion of what we call rulial space, which is the space of kind of possible different views of how the universe works. So we might say we’ve got one view of the universe, “Oh, it works this way, it follows this rule.” And then some other person, alien, whatever, says, “No, no, no, you’re quite wrong. The universe really works according to this other rule instead.”
Now the reason, what knits 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 computation. You might have thought that if you wanted to have a computer that was a word processor, you’d buy a word processing computer. You want to have a spreadsheet computer, you’d buy a spreadsheet computer. But the big fact that emerged in the 1920s and 1930s is that you can have a single sort of hardware object. I mean, it wasn’t put to practice until the 1940s and ’50s, but it was, you can have the single hardware object that can just be programmed to be a word processor, be a spreadsheet, whatever.
And it’s kind of the same thing with the universe, that you can attribute different rules to the operation of the universe, but they’re convertible, in the same way as your computer can be made to run a spreadsheet rather than a word processor, so to speak. So —
Tim Ferriss: Yeah, finish your thought and then I want to pause for a second, ask a follow-up.
Stephen Wolfram: So one of the things that I really kind of like about this is there’s this notion of where are you in rulial space? Where what kind of mode of description of what’s going on in the world do you have? And you can imagine that every different mind is at a different place in rulial space. So the fact that you and I have different internal models of the world is a statement of the fact that we are some distance apart in rulial space.
And so what you realize is, as we think about the universe, we have the exploration of the universe by spacecraft or whatever going out in physical space. We also have the exploration of the universe in rulial space. And that’s kind of, the different minds and different ways of describing the universe represent kind of travel through rulial space. And just as we kind of, when we send out spacecraft in physical space, we’re exploring different parts of the physical universe, when we come up with different ways of thinking about things and different ideas, we’re kind of traveling in rulial space. And that’s kind of a way to start representing those kinds of things.
Tim Ferriss: All right. I’m going to ask a number of questions that will no doubt put me at risk of embarrassing myself, but I knew that I when I —
Stephen Wolfram: I have to say one more thing.
Tim Ferriss: Oh, yeah, just —
Stephen Wolfram: Because you were asking about languages and you were asking about different human languages. That’s 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 kind of correspond to nearby places in rulial space, where it’s pretty easy to translate, to travel from one to the other, whereas things which are very different, very different sort of views of the world are further away in rulial space. And that’s just a way of perhaps conceptualizing what this thing is about. Please, go ahead.
Tim Ferriss: Yeah, when you said that, I was just thinking about gendered languages versus gendered languages or certain languages that don’t conjugate, say, past tense, like Chinese, Mandarin, and how that affects maybe where you stand in rulial space.
So how does a branching mind perceive a branching universe or the branching might perceiving the branching universe? I think as many people hear this, they imagine these multiple or infinite possibilities branching out in some form of to conjure the image of the eddy, so these changing atoms, but if you took a snapshot of the eddy, minute after minute, it would have some resemblance. But there is a branching that I think for many folks listening will take place in linear time, and so there’s some past to future to this branching.
I have tried to stretch the boundaries of how I consider or define time by reading and listening to say, Carlo Rovelli, who I think focuses a fair amount on quantum gravity. I don’t know his research very well. How do you think about time? Is how humans experience or think about time Just a very convenient collective delusion in terms of its linear past to future nature?
Stephen Wolfram: So I mean, first thing is, what is time?
Tim Ferriss: Exactly.
Stephen Wolfram: That’s something that I think we really kind of nailed in the way we think about our theory of physics. I mean, time is the inexorable progress of computation. So in other words, the universe is in some state, then the universe is going to be transformed to another state, and another one. That progressive process of transformation is the passage of time. And this phenomenon of computational reducibility that I was mentioning before that you kind of can’t jump ahead, that is the fact that time is meaningful. There is something, you can’t just say, “Oh, I didn’t have to go through those moments of time. I could always just jump ahead.”
Now, in most of the universe, time is just progressing. It’s just as the universe is sort of updated, so that corresponds to the passage of time. Now, we are part of the universe, so we are being updated too. If the universe just stopped, we wouldn’t know it had stopped because we’d be stopped too. So for example, one place where that happens, in the simplest kind of black hole, at the center of the simplest kind of black hole, is the space-time singularity, which has the property that it’s a place where time stops. And so in our model of physics, what’s happening is this universe is being updated, this network is being updated, being updated, but if you’re at the center of the black hole, it just stops. There’s no more update that can be applied.
Actually, if you’re doing math, you kind of want to get to that point. If you’re doing a calculation, you do, “Oh, we’re calculating. These things are happening,” and eventually, you get to the answer and that’s a place where it’s fixed, nothing changes anymore. That’s what happens at the center of a black hole. It’s kind of bad news in a sense, if you want to have a future, so to speak, because time just stopped.
So time, as far as I’m concerned, is this inexorable progress of computation and time, in the actual way that it manifests in the universe, has many complicated features. So for example, in relativity and gravitation theory and so on, there are all kinds of ways in which the notion of, “When is it the same time as somewhere else?” Is complicated. Like we say, let’s say, have a Mars colony one day and we define Earth standard time.
Okay, it’s 12 noon at this point. Well, it’s 20 light minutes away to Mars, for example. Do we say that the 12 noon is the time when the light signal from our clock that said it was 12 noon on Earth reaches Mars? Or do we try and back calculate that and say, “Well, it’s the time when it would’ve reached if the clock had been 20 minutes early?” And so on. That whole question of the way in which you kind of put these slices across the universe to define what counts as simultaneity in time, that’s kind of the story of relativity theory and gravitation theory and so on.
And that’s another kind of twist in this whole thing. But in quantum mechanics, the big issue is, is there just one threat— thread of time or are there many threads of time? Now, we humans normally only perceive one thread of time. I’ve sort of wondered whether there’s some trance that people can go into that’s kind of a multi-way trance, where they actually have multiple threads of experience that are going on at the same time. But for most of us, most of the time, it’s just there’s a definite thread of experience that we have. And that’s a —
Tim Ferriss: If I may interrupt for a second, what prompted that wonder or question about whether there are people through —
Stephen Wolfram: Because one of the features of our model of physics is what ultimately drives the mathematical structure of quantum mechanics, is this assumption that we have that we are persistent in time and that we can conflate things to the point where we have a single thread of experience. If that isn’t the case, then we’ve got a different theory of quantum mechanics, because quantum mechanics ends up being something which says, “You do all this quantum mechanical stuff, it has these many parts of history.” But in the end, we want to get an answer. We don’t want to be saying we’ve got two different answers in mind. We’re going to say, “We say a definite thing happened.”
And so for example, when people talk about making quantum computers, the big thing that one hopes for is that one can use these multiple threads of history to each run a different computation, and so then, you can do all these things in parallel. Now the big problem, and again, I seem to be mentioning Dick Feynman too much here, but he and I worked on quantum physics —
Tim Ferriss: Doesn’t bother me.
Stephen Wolfram: — back in 1981 or so. It was kind of a funny experience because he did all his calculation by hand, and I was using a computer. I actually found one of the computations that I did from that just recently, and he would do these calculations by hand, and I had no idea why the answers he got were right. Because it’s just like you do this calculation and it’s like, “You could have done this or this or this, you could have made this or this assumption at this point. I don’t know why that assumption is right.”
And he’d look at the stuff I did on a computer and he’d say, “I have no idea why any of that’s right.” So that was an interesting challenge, so to speak. But actually, even at that time, we kind of concluded that the big question about making use of quantum mechanics to compute things is, “How do you determine the answer?”
In the formal theory of quantum mechanics, how does the measurement work in quantum mechanics? How do you actually measure what happened in the quantum process? Well now, what we see is there are all these threads of history, and at the end, us humans, if we want to get a definite answer, have to knit together all those threads of history. And the big question is, “How hard is it to knit together those threads of history?” And if it’s as hard to knit them together as what you gain by having multiple threads, then you don’t get an advantage in the end.
And that’s a difficult thing to figure out, and it’s something we’re trying to figure out. I’m not hopeful about the true quantum advantage. I think that the formalism of quantum mechanics is super interesting. And it’s very, this whole idea of these, what we call multiway graphs and this whole multiple threads of history and so on. That’s a very interesting formal thing relevant to many fields. But this idea that you’re actually going to be able to make an engineering system out of it and get this sort of quantum advantage, less convincing.
It’s also, as a practical matter, the whole quantum computing effort has caused people to think, “Oh, can we make computers out of things other than electronics and semiconductors and so on?” And that’s a completely worthwhile thing, as well. So the two ends are worthwhile. I’m not sure that the middle is so worthwhile. So that’s our notion of time. Now, in terms of people’s perception of time, it is this process of, we are undergoing these computations, our minds are undergoing these computations, and so is the rest of the universe.
And that’s kind of, it’s the alignment of the computations going on in our minds with the computation going on in the universe that leads these different forms of time, the time in thermodynamics of things sort of decaying down to heat, or the time in the expansion of the universe, things about cosmology and so on. The fact that all those different arrows of time align is a consequence of the fact that they’re actually the same thing, they’re all just the sort of inexorable process of computation that’s happening in the universe.
Tim Ferriss: I’m going to use a term that is, might be frustratingly undefined, overused to the point that it’s often undefined, at least. But I’m going to ask this question anyway, which is, do you have any thoughts on what constitutes consciousness? It can be defined any way you want, or it can just be tossed. Or if that is an emergent property or a subjective experience with certain underpinnings that can be currently explained? How do you think about, if at all, this may be a terrible question —
Stephen Wolfram: Yeah. Well, I’ve always sort of avoided it because it’s always seemed like a deeply slippery thing. But I was literally confronted with, “I need to apply the idea of consciousness.” I may not have to — and here’s how. In the universe, ultimately there are all these possible computations that can happen, but our minds don’t do all possible computations. Our minds are somewhat more, they’re much more filtered in what they do.
And in particular, they have these features of computational boundedness, belief, and persistence and so on. For me, those are the things we need to use about consciousness to derive things in physics. So those are features of consciousness that distinguish us from the rest of the universe. It’s kind of actually a little disappointing because we might have thought, “Oh, there’s inanimate matter and there’s this and that, and we’ve got this big stack we’re building and it goes through life. And eventually, we get to intelligence, consciousness. We are the tippy-top. We are the best thing in the universe,” so to speak.
But actually, what I’ve come to realize is that that’s not true at all, that the universe has much more capability than we have. And this thing we call consciousness is a filtering of that capability to something specific, where we believe, for example, that there’s a single thread of experience that we have. And that that’s kind of the thing that consciousness, the application of consciousness to science is this thing where it’s not about everything in the universe, it’s just about the particular things that are sort of the way that our minds perceive things.
I think an exercise people talk about, “Well, how can you talk about this in this kind of very materialistic way? Isn’t there some magic thing in consciousness that is this sort of spark that is different from everything else in the universe?” Well, to us, inside, there absolutely is. To us, inside, we are this one point in rulial space where there is this set of things going on. That is our experience of the universe, and it’s completely unique. And there may be some other point in rulial space, some other mind that is fairly close by where we can say, “We’re experiencing these things. We can tell that they’re similar to what’s being experienced here.” But each sort of consciousness is unique in that sense.
Now, I was kind of doing an exercise recently, which I need to finish, which is to describe what it’s like to be a computer. And you imagine we humans, we live our lives, we remember a bunch of stuff through our lives. Eventually, it’s all lost when we die. And the question is, for a computer, from the time it boots up to the time the operating system crashes, that’s a period of time over which the computer has sort of its life experiences.
And how do those life experiences compare to the “life experiences” that we humans have? There is a whole inner thinking that’s going on for the computer. How does that compare with us humans? There’s sort of the communication with other computers, the experience of the outside world and so on. How does that compare? How can we describe that in what it’s like to be — even a current computer, forget of the science fiction AI of the future, just talk about a current computer. What would it be like to be the sort of inside that, experiencing things from the point of view of the machine, so to speak?
Tim Ferriss: Let’s segue to personal productivity. So this is something I imagine you do still think about a fair amount, and I’ve read a fair amount of your writing on personal infrastructure hacks and so on. And it seems like there are, is I think you might describe them, nerdy productivity hacks that then later become more mainstream or more accepted, more widely distributed. Are there any personal productivity or infrastructure tools or hacks that you are using now that you think will gain more adoption in some form in the not-so-distant future?
Stephen Wolfram: So I started live streaming a bunch of working meetings that I do, and started this about, when was it? 2017, so a few years ago. And it’s really an interesting process because a large fraction of software design reviews that we do are live streamed. And that means people who are in the meeting are a little bit more paying attention because they kind of know it’s going out to the world, but it’s also really nice because there’s kind of an immediate feedback.
If we announce some particular topic we’re going to work on, we’ll get — some world experts in that topic often will show up because, gosh, they know they’re going to be stuck using our tools and they might as well contribute to getting them to be designed right. And also, people who are just energetic users about technology. And it’s really a wonderful kind of immediate feedback. To me it’s a little bit, it helps me feel that the time I’m spending doing very detailed, grinding away, trying to figure out how software should be designed and so on, it feels like the fact that I’m leaving some record of this, feels like it makes it more meaningful. And I know people have used some of our live streams for software engineering classes and things like this. And so it helps me make it seem more meaningful, so to speak.
The even more extreme version of that is that I’ve been doing video work logs, which means I’m just sitting by myself and I’m working on something and I’m writing some document or something like that, and I just record it. Like last night, I probably recorded five hours of video work logs. Basically was working on this sort of personal journey history thing, and I’m recording my screen and I’m just recording what I’m doing. Now, why am I doing this? Because it’s easy to do and the thing that — and somehow, it makes it feel a little bit more meaningful to me. It sort of makes me think, “Oh, I’m not going to goof off in that way because I’m screen recording everything here.” And okay, I admit, I have a secondary screen, so I can goof off on the secondary screen. And the other thing is, when I’m doing science stuff, what has happened, a number of times, is people say, “Well, how did you figure that out?” I’m saying, “You can really find out how I figured that out. Just go look at this video —
Tim Ferriss: Just watch it.
Stephen Wolfram: — and you can find the minute where I figured this out.” And maybe I got it wrong, and you’ll see, “Look, he did something really stupid there.” And you can see that moment, and you can then unwind it. Same thing, by the way, for our software design meetings. The people who do project management routinely go back and look at all kinds of pieces of the meetings. “What actually happened? How did we decide this? Oh, a bug showed up there. Let’s go carve that piece of the video out and send it to people,” and so on. So I think this thing about doing actual production meetings, so to speak, live streamed is — I don’t know anybody else who does that. I think nobody else is crazy enough to do it.
Stephen Wolfram: And part of why we can do it is that we’re in a unique place and technology space, where it’s not like — I know people who wanted to compete with us have watched live streams. It’s like, good luck. We just spent 35 years building all this stuff. One live stream is not going to let you rebuild that tower. So, that’s one thing. The other thing that I myself have become confident enough that even though I know I’m going to say really stupid things on these live streams, I don’t care. These are real life things where, yes, I’m going to make mistakes. People are going to say, “You’re wrong. You’re wrong.” And we’re going to have a little argument, and eventually, I’m going to say, “Hey, okay, you’re right.”
And I don’t have an internal ego issue with either that process happening or that being something that people can go back and find it. I’m sure it’s possible to find all kinds of terrible, stupid things that I said in these live streams and so on. Okay. So, another productivity hack, I suppose, related to live streaming is this. So, at the beginning of the pandemic, I thought, “Oh, all these kids are going to be out of school. I know a bunch of stuff about science.” Every so often I’ll do a Q&A. Once a week I’ll do a Q&A about science and tech for kids and others. So I started doing this at the beginning of the pandemic, and I’ve still been doing it. I’ve done 100 episodes or more now of this hour, hour and a half, on Fridays, of science and technology Q&A for kids and others.
Okay, so what has that done for me? People ask all kinds of crazy things, and it makes me think about stuff. And I find that this process of I’m just sitting here, looking at the camera and no notes, no looking anything up, just how can I figure out the answer to that question. And from a couple of days ago, there was a question somebody asked that made me realize some piece of physics that is relevant to our Physics Project, that I’d never realized before. And the process of explaining it, particularly with the forced feature that I’m just going to go on talking at the camera, so to speak, it’s that forcing function of, “So now you’ve got to figure something out,” I found really useful for understanding things.
And I’ve been doing actually two other alternating, alternate weeks Q&As, one about history of science and technology and the other about business innovation and managing life, more your territory. And people there, again, I just find it really helpful in crystallizing my own thinking that I’m trying to answer these questions, particularly in this real-time format, where I don’t get to say, “Oh, let me think about that, and let me think some more about it and so on.” And then I never get around to answering it. I’m kind of on the spot and having to answer something, and that’s been a very useful process. I know that, in terms of explaining things, science things, I’m pretty sure I can feel that I’ve gotten better through hundreds of hours of people ask random questions, and I try and answer them. The one thing that always trips me up is when somebody asks a question, and I think to myself, “That’s really easy. I have that one absolutely nailed.” Those are the ones that I trip up on.
And actually, there’s a curious reason, which I did realize about that, which is a lot of these things, where it’s like, I’ve understood that since I was 12 years old. So it’s easy, and then I realize, oh, my gosh, it relies on this thing and that thing and the other thing, which, yeah, I learnt when I was 12, 13 years old or something. But not everybody knows that, and I have to go explain that. And I have to go explain this other thing that I thought was obvious, and pretty soon, one’s descending into the swamp of complicated stuff.
Tim Ferriss: So let’s look at maybe, if there is a there, there, we’ll find out, some of the physiological underpinnings of productivity. So, we have these physical bodies, and you seem to, this could just be my perception, but have energy reserves. I’m not using energy in a very precise, physics way but more in a metabolic way. Right? You’re producing enough neurotransmitters, and you store enough glycogen and so on that you are able to maintain, it seems like, a very high rate of output. Do you think about energy management in that context?
Stephen Wolfram: You know what —
Tim Ferriss: Or is that something that just comes so naturally out of the box that you just have that advantage, and you don’t have to think about it?
Stephen Wolfram: I think I’m lucky that I’m fairly energetic. And it’s always amusing to me that I’m an old guy, now, and I can out energize lots of young folk who are working with me and so on. And that always — Actually, I get a kick out of being able to do that. So that —
Tim Ferriss: Yeah, why not?
Stephen Wolfram: — that helps add energy to the whole picture. But no, I think for me, look, one thing is that I do things that I like to do. And for me, that’s a huge, energizing force. If I was like, “Oh, gosh, I have to do this, and I don’t really want to do it, and I’m not very interested,” and it’s like I’m jumping into things, where I really want to do this. It’s like I was — okay, another strange thing I just did, I seem to have been reliving my life 50 years ago, in the last few weeks, because I decided to organize, a couple months ago, I initiated a reunion for my elementary school graduating class, which was 1972. And so, it’s a small subgroup of people, and so it was the 50-year follow up.
And actually, it was, I liked these people 50 years ago. I liked them 50 years later. That was nice. But I realized one of the things that’s a little disappointing is some fraction of these people were — we were writing little blurbs about what we’ve been doing, and they’re like, “I retired.” “I retired,” etc, etc, etc. And they’re British folk, so they had lots of witty things to say about what they were doing in their retirement. But so, I was realizing, I’m not retired, and I’m not even — and I realized I’d been working more than 12 hours a day, every day, for basically all of the last 50 years. And I’m having a good time, and I’ve been lucky enough to be able to mostly do things that add energy to me rather than taking it away.
When I do creative things, and I figure stuff out and even write things and so on, that the process of finishing them and getting them done is energizing to me. And I think, if I was doing things where I’m like, oh, I don’t know, back in the day, before we realized this just didn’t work, I would occasionally go shopping with my wife. Okay. Wrong [inaudible 01:36:00].
Tim Ferriss: Uh-huh.
Stephen Wolfram: Right? And I would try and take my wife to science museums. Okay. So, my wife is a mathematician, so science museums are not so far away, but they’re far enough away that she was like, “Oh, I’m getting so tired walking around this science museum.” So, for me, it would have a physiological effect on me, going around —
Tim Ferriss: Shopping.
Stephen Wolfram: — looking around shops. I’m like, I feel really tired. I have to —
Tim Ferriss: Getting so sleepy.
Stephen Wolfram: — lay down, etc, etc, etc. So, I think the effect that I’m doing things I really want to be doing is an important effect. Now, having said that, I did discover, when I was 40 years old, hey, you should do some exercise, and that helped for me, I think, add a bunch of energy. For the last three years, I’ve walked more than 10,000 steps every single day, for the last three years, and that’s just one of my constraints. Even if I’m traveling or this or that or the other, the people who schedule stuff for me, it’s like I’ve got to walk 10,000 steps. If that’s around an airport on phone calls and things, so be it, but that’s a constraint on my life, so to speak. And I think that has had a positive effect. For me, it’s been bizarre because, when I was younger, I wasn’t in terribly good shape. I’m in better shape now. And so, for me, I don’t yet notice that I’m aging because I’m actually better off, in many ways, than I was when I was younger.
Tim Ferriss: Your mitochondria are getting younger, are rejuvenating.
Stephen Wolfram: Yeah, yeah. Right. No, another thing about that and about the management of one’s life and so on, I’ve been fortunate enough that, often, you’ll do something, and maybe you do something really cool when you’re 25 years old. And then, it’s all downhill from there, and it’s hard to get motivated. And I’ve been lucky enough that, for example, this Physics Project that just arrived, three years ago, that was something I didn’t really expect. It’s a rejuvenating thing, and it’s just added a lot of energy to my — it’s just there’s so many things to think about and so on. That’s one thing.
I sleep as close to eight hours as I can. I don’t try and game it of saying I’m going to try and shave off extra time and so on. I’m very habitual, in terms of when I go to sleep, when I wake up. Things I eat tend to be habitual, and they may not be optimal, but they’re habitual at least. I’ve found that, for me, I try and optimize away all those aspects of my life that I really don’t care about, so to speak, that I don’t keep those as simple and not having to think about them as possible so that I can spend my thinking effort on things where I really want to think about these things and really want to spend my time on them.
But, yeah. No, I’ve been fortunate enough that, for whatever reason, the mitochondria are still alive and kicking and providing good energy. But as I say, I think it’s mostly that just doing things that I want to do, and also, I tend to organize it so that there are things that I save up for. I’m going to do this if I’m feeling tired, or I have another set of things. I’m going to do this if I get sick. Okay. And I’ve had a whole bunch of those. And I’ve only been sick once in the last three years, so I didn’t have a chance to — I’ve got this big, pent-up supply of things to do when I’m sick.
Tim Ferriss: Now, are those just low-energy, low-interest tasks that nonetheless have to get done, like talk to my accountant about X, Y, Z, whatever, something like that?
Stephen Wolfram: Yeah. Yeah. Well, I’m not usually talking to other people, but usually organizing informational kinds of things.
Tim Ferriss: I see. I see.
Stephen Wolfram: Or sometimes, they’re… watch this video that I’ve been meaning to watch and have never had time to do. But I did learn, okay, so this is one not yet very scientific hack fact, which is, I was curious what has caused me to get sick when I’ve gotten sick? Why have I gotten sick? And so I have the data for, I think, 27 years maybe of, I think, all the times I’ve gotten sick. It’s always upper respiratory things. Right? And so I think I’ve gotten sick 25 times in 27 years. And the question is, what was I doing when I got sick? And was it, oh, I went out, and I met a bunch of people, or was it whatever? And the one correlation, and I haven’t been completely scientific about this, the one correlation was it was often two days after I was on a flight, on a plane. Okay.
And in a few cases, that was not a commercial plane. That was a private plane, so without a lot of other people on it, and so it’s interesting. And so then I asked my medical research friends and so on, “Hey, what’s going on here?” And here’s the theory. The theory would be a big part of upper respiratory defense, so to speak, is the innate immune system operating in one’s nose and so on. And if you get your nose dried out and so on, from being in the dry air and on planes and things like that, your little, innate immune system doesn’t stand a chance. So my hack has been take things like wheat germ and so on that stimulate, just before I go on a plane, take that and a couple of other things, and so far, we only have an N of about eight or something of trip side 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 maintain the sort of moisture integrity of the sinal lining, as well, with a spray or something like that.
Stephen Wolfram: Yeah, I thought about that. My most relevant medical research friend claimed it’s easier to just take choline than it is to try and do that.
Tim Ferriss: Try to keep the nose well hydrated.
Stephen Wolfram: Right. It’s a strange thing. You’re on some flight, going somewhere, and you’re continually stuffing things into your nose or whatever. No, that’s —
Tim Ferriss: So, Stephen —
Stephen Wolfram: One of the things I found that, for me, it’s keep the list of things to do when I’m tired because, for me, in terms of motivation and so on, it’s always nice. If I’m sick, I might be like, “Oh, my gosh, I’m sick. That’s so terrible.” But in a sense, I’m like, “Great, now I have a chance to do these things that I knew I had to do somehow.”
I do the same thing when I’m driving places. I always maintain a call-while-driving list of phone calls that are slightly more, “I’ve got 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 lots of interactions that I do, where I’d never get around to it. It’s just like, if there’s a person who lives in the same city that you do, you never see them. But if they live somewhere completely different, “Oh, I’m coming to wherever for a day,” and you end up seeing them. And it’s been the same thing for me with call-while-driving. It’s like, “I’m going to call somebody,” and so this is that process.
Tim Ferriss: Stephen, I’m so continually impressed with not just the breadth of your thinking, but how you log and track and interpret so much data. I think that I take a lot of notes, but you mentioned, at the top of this conversation, a quarter million pages, something along those lines. It’s just incredible.
Stephen Wolfram: That’s the stuff on paper actually. I have three million emails as well, so yeah, a lot of stuff, over a long period of time.
Tim Ferriss: So, you have a lot of stuff, over a long period of time, and I would love to, at some point, do a round two. I’m sure we could do themed conversations on probably several dozen different topics. Is there anything else that you would like to mention, in this conversation, or call my audience’s attention to —
Stephen Wolfram: Oh, my.
Tim Ferriss: — anything at all, in terms of closing remarks, comments, any grievances you’d like to air publicly, really anything at all that you’d like to mention before we —
Stephen Wolfram: Makes me want to ask you for a bunch of personal productivity hacks and so on and the what am I missing type thing —
Tim Ferriss: Oh, boy. Yeah.
Stephen Wolfram: — because one slowly accumulates these things. And I find I’ll try things and probably two-thirds of the things I try work, and one third don’t. But it’s keep trying them, but anyway.
Tim Ferriss: Yeah.
Stephen Wolfram: So, I think, no, we’ve covered all kinds of things.
Tim Ferriss: I look forward to hopefully seeing you again in person, at some point, but this has been delightful and very fun for me. I’ve taken copious notes, so I will be doing lots of follow up on my own. And you seem to be doing pretty well on the productivity side. If I think of anything that is a gross omission, I will be sure to send it to you.
Stephen Wolfram: Okay. Yeah.
Tim Ferriss: People can find you on Twitter, Stephen, that’s a ph, Stephen_Wolfram, then Facebook/stephenwolfram, linkedin.com, also, your name and then the website stephenwolfram.com. And we’ll link to everything that you’ve mentioned. Is there anything that you would like to point people to that is top of mind for you, at the moment, or any resources that people may not find on their own?
Stephen Wolfram: Well, let’s see. The stuff I write ends up in writings.stephenwolfram.com. And I put lots of effort into writing these things, so hopefully some people find them fun to read. Although even the process of writing them, as I was explaining, is a useful process in its own right. There’s also a recent thing, for me, is we just launched our Wolfram Institute, that’s, I suppose, an attempted productivity hack. My company, which I started now 36 years ago, is my machine for turning ideas that I have into real things. And it’s 800 people who are really, really good at doing that and coming up with their own ideas as well. But that’s been a thing, where mostly, we make products, but one of the problems I’ve been trying to solve is, if you’re making basic science, what’s the machine that does that?
And I’ve carved off a bit of resources from the company and so on, to do it, but we just recently launched Wolfram Institute, which is a thing whose goal is to do basic science. And that’s a new thing in the last just few weeks, and so stay tuned for interesting things that are happening there. And I guess there’ll be yet more live streaming of science in action and so on there. So, those are a few things. And I suppose the other, I have to plug my life work, which my life work is building Wolfram Language and Wolfram|Alpha and Mathematica and so on, which are all part of the same idea of make the world computational. And I suppose the one pitch I would make is, what we’ve built, I can inexorably see is an artifact from the future.
And in other words, the direction that things are going in, is going in this direction of representing the world computationally and being able to really make use of that. But there are a few million people who actually do make use of it in our technology stack, but there are a lot of millions of people who don’t and that this is an inexorable piece of the future. And it’s a big advantage if you can grab the magic from the future. I’ve done a lot of work with kids, who are learning our stuff and doing projects and so on, and I’ve taken to referring to learning computational language is a superpower. It’s something that you get to do that, and then you can do all kinds of magic things with it. Anyway, so learn that superpower, and more people should do it.
And it’s one of these things where you can kind of see, in the world, when things involve big ideas, there’s a certain, inexorable slowness to the way that they get adopted. And there are always some number of early adopters, who are the ones that run out in front, and so my parting pitch would be, if you don’t understand computational language and Wolfram Language and so on, try to understand it because it is, for me, the — you talk about productivity hacks. The biggest amplifier, hugest productivity hack is the whole computational language idea. That’s what all the things I’ve done in science and in technology, they are all based on that idea and the tower of technology that we built around that. So that’s my parting, ultimate productivity hack.
Tim Ferriss: Wonderful. And for everybody listening, Mathematica, Wolfram|Alpha, Wolfram Language, we will link to 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 that will pop right up. Stephen, I really enjoy learning from you because you’re not only an incredible thinker, technologist, I’m sure there are many multi-hyphenate labels I could apply, but you’re a very gifted communicator and teacher. So, the practical impact of what you do is not just manifested through the products used by millions of people and that will be used, in some form or another, by many, many millions more, but also in the principled and systematic thinking that you can share and do share with people, including kids, including with non-technical muggles, who are nonetheless very curious, like myself and no doubt with many, many millions of listeners on this podcast. So thank you. I really, really appreciate the time you take to do what you do and the time you’ve also taken to have this conversation.
Stephen Wolfram: Thank you.
Tim Ferriss: So, thank you very much. And to everybody listening, I’ll plug it one more time. You can go to tim.blog/podcast, for the show notes for all things we’ve mentioned in this episode and in all episodes. And until next time, be a little bit kinder than is necessary. Be very curious. Definitely paddle early for the superpowers that you can get ahead of, in terms of early adoption, like those that Stephen was mentioning. And thank you for tuning in.
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