The Tim Ferriss Show - #637: Stephen Wolfram — Personal Productivity Systems, Richard Feynman Stories, Computational Thinking as a Superpower, Perceiving a Branching Universe, and The Ruliad... The Biggest Object in Metascience

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Starting point is 00:04:58 The Tim Ferriss Show. Hello boys and girls, ladies and germs. This is Tim Ferriss, and welcome to another episode of The Tim Ferriss Show, where it is my job to attempt to deconstruct world-class performers from all different fields to dissect how they do what they do, how they think, the mental models they use, what it is that makes them tick, makes them work in the way that they do. My guest today is Stephen Wolfram. You can find him on Twitter at Stephen underscore Wolfram. That's S-T-E-P-H-E-N underscore W-O-L-F-R-A-M. He is 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,

Starting point is 00:05:42 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. You can find all things Stephen Wolfram at stephenwolfram.com. He published his first scientific paper at the age of 15 and had received his PhD in theoretical physics from Caltech by the age of 20. I'm just going to mention a few more things to give you some flavor before we dive into the conversation. In recognition of his early work in physics and

Starting point is 00:06:14 computing, Wolfram became in 1981 the youngest recipient of a MacArthur Fellowship that's also nicknamed the Genius Fellowship or Genius Grant. The release of Wolfram Alpha in May 2009 was widely regarded as a historic step that has defined a new dimension for computation and artificial intelligence and is now relied on by millions of people every day to compute answers both directly and through intelligent assistants such as Siri and Alexa. Stephen has incredible endurance, incredible energy, incredible breadth of interest and expertise. And without further ado, please enjoy this wide-ranging conversation with none other than Stephen Wolfram. Stephen, it's nice to see you. Thank you for making the time today. Pleased to be here. And I was impressed that just before we started recording, when I fumbled an attempt to recall

Starting point is 00:07:13 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? I keep an awful lot of stuff. So I've got all my email going back 30 years. I have a habit of writing kind of 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 a paper document, so to speak. But I have scans of, well, it's like a quarter million pages or so of 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 keystrokes I type and screen captures and all

Starting point is 00:08:01 kinds of things like that. How do you use the logging of the keystrokes? I don't usually. You do not? Usually I don't. 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.

Starting point is 00:08:18 That was the thing that caused me like 25 years ago to start recording those. There's 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. You know, do I type faster or slower on my new keyboard? You know, those kinds of things. And you can track that type of thing. Easy to answer that question if you have that data. So it's one thing to record or ingest information. It's quite another to structure your thinking. And 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

Starting point is 00:09:12 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? Yeah, what I mean by that is some kind of framework in which I'm doing something. 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. And I've been lucky

Starting point is 00:09:51 enough, my main life work is building our computational language, Wolfram language, which is this language that's supposed to represent sort of 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 found, you know, for example, writing my blog-like kind of writings thing. That's another kind of matrix into which I can put, you know, when I do kind of historical studies of things, I'll write a piece about that historical study, and that's a place to put it. When it's sort of too small a thing, I just don't have a place to put it, and it tends to die on the vine. You know, 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

Starting point is 00:10:39 years ago. 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 sort of randomness gets generated in the world and so on. And 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 kind of the scientific answer to that question. But then I thought I should write a piece kind of describing my 50-year journey of trying to answer this question.

Starting point is 00:11:19 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, this sort of very slow process, I build this intellectual framework, and then I get some other clue.

Starting point is 00:12:05 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 that when sort of slowly building up the kind of conceptual framework needed, then it's often very sudden to kind of take the next step. That was a kind of a striking thing to me. So I want to ask a few follow-up questions about conceptual frameworks and perhaps

Starting point is 00:12:37 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? That's a good question. I was wondering it myself. I thought it would be really easy, but it wasn't quite so easy. I have done quite a lot of historical

Starting point is 00:13:22 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 order of thermodynamics thing, the second order 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've collected the material for it. I

Starting point is 00:13:59 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 kind of 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 kind of history of science, but one actually has really precise detailed data on how some idea got developed. And so I thought kind of an interesting example

Starting point is 00:14:33 of essentially computational history. You know, one thinks about computational X for all X. This is computational history. And it's kind of like, what can you do in computational history? What kinds of things can you kind of 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 sort of lived through it. But then when I look back, I realize there were threads that could be

Starting point is 00:15:00 joined that I absolutely did not see at the time. So, you know, 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 kind of a violation of my intuition. It took me a couple of years to kind of come to terms with the fact that that was possible. Same time, several of actually sort of top mathematicians who were sort of 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

Starting point is 00:15:41 these things from 1984 and so on. And they did a bunch of very sophisticated math and they couldn't figure out anything. And 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 out anything else. So 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,

Starting point is 00:16:29 because if the rules define some computation, it's 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 kind of having to follow each step in the evolution of the system. And that's a really important fact about science. It's kind of a way in which science explains from within science that science has questions that it can't readily answer. And 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 intellectual

Starting point is 00:17:19 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 sort of 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 that kind of what really happened there, what was really important there, which I had not realized at the time. Makes me think a bit of the Sherlock Holmes, the case of the dog that didn't bark in the night. But the term that I'd love for you just to elaborate on a little bit, and I apologize,

Starting point is 00:17:59 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 is an example. We humans like to sort of 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're able to sort of 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 kind of organizing the things that we see, in that case, just using natural language. There have been in sort of history a variety of kinds of organizational

Starting point is 00:18:50 approaches. Logic, for example, was one from antiquity. Then mathematics is another kind of organizational approach to say, this is how you kind of 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 sort of 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 sort of 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

Starting point is 00:19:38 read it too. And then the computer can kind of help us to get further with that. So 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 things about the world and describe them in a sort of somewhat precise, abstract way. I see computational language as being kind of another level in that evolution, except that we get to kind of share the burden of seeing what happens, not just with other humans, but with computers. And for me, that's kind of 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 there are models of actual trees and dogs and trajectories on the earth and things like this.

Starting point is 00:20:27 And then there are, how do you break that down to something even more primitive? 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 sort of 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.

Starting point is 00:21:10 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 kind of, for me, this notion of computation is kind of having this way to kind of 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

Starting point is 00:21:56 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 sort of the computational description of the world comes from sort of 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 sometime around 500 years ago, people started inventing, you know, a plus sign, an equal 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

Starting point is 00:22:37 and then calculus. And then we have this whole sort of mathematical approach to science that was able to be done. And I guess my own sort of personal last 40 years of effort has been to sort of 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 kind of the mathematical way of talking about the world. I have a question about natural languages, and 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,

Starting point is 00:23:24 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 a tongue-in-cheek comment. That's from ancient times. But I went to fancy schools in England when I was growing up, and 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. And 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.

Starting point is 00:24:09 But 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. And I'm not really, you know, I've been very deeply involved in computational language. I've been interested in human language, but I'm not from the point of view of sort 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 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, it's not so important anymore. Just like I could have been a champion map reader, and I'm glad I didn't put huge effort

Starting point is 00:24:39 into that because I just use a GPS now. Yeah, 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 sort of 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 how much it has improved. And 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

Starting point is 00:25:33 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? You know, I think one of the things that it really kind of drills into is the whole question of, can you actually express the same thoughts in different human languages? And that's kind of a deep issue. And I think what we realize is that language is one kind of representation of organized human thoughts. And in a sense, it is a kind of societal construction of, you know, we all kind of know what a chair is.

Starting point is 00:26:14 So when we use the word chair, we kind of 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 they're really just quotes isn't a translation for that word because there just isn't the sort of 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 kind 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, you know, as you start generalizing that, it's kind of like, okay, how do we communicate with sort of the alien intelligences? You know, how do you communicate with cats and dogs? How do

Starting point is 00:26:58 you communicate with AIs? Things like this. These are all kind of 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. And so it's sort of interesting to see how this process of translation can work and sort of how far out can you translate things. And I guess for computers, you know, 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. Computers can compute all kinds

Starting point is 00:27:39 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 sort of an interesting question to understand kind of to what extent we can translate the things we think we care about into something which can be represented computationally that comes back again. And I suppose after one's spent one's life working on something, everything somehow relates to these questions like, how do you make this kind of computational language to represent kind of human thoughts in a computational way? Now, when you talk about natural language translation and so on,

Starting point is 00:28:31 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 kind of computational question, precise sort of symbolic representation that we can then compute the answer from. But whatever poetry there might have been in that question, like, can you tell me the population of 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 sort of appreciative way that somebody describes some country translated into some other human language,

Starting point is 00:29:31 that notion of appreciation would have been the most important part of that sort of 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. You can see that if you imagine you have two machine learning neural net 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 sort of a direct

Starting point is 00:30:15 translation. Okay, you know, 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 kind of direct internal translation, just as for humans, you know, even if we could do, you know, 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 learn things inside will be different. And that sort of thought experiment, so to speak, about thoughts, direct transfer of thoughts, that sort of also applies that the thing that is the robust transfer of

Starting point is 00:30:51 thoughts is basically language. The thoughts themselves are not directly transferable, but we package them into language, which is this kind of formal representation of thoughts that we can transfer from one mind to another, so to speak. That's my way of thinking about that, at least. This underscores, I think, part of the appeal for me in learning these languages, even when they really have very little utility. So I was just studying Romanian, which has very limited use. And part of the fascination is, as you were mentioning, these concepts or labels that take the form of language. Even if the translations, a literal translation, can the thinking of a target population, whether Japanese or Romanian, to be a lot of fun.

Starting point is 00:31:47 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, I'll stop my mini TED talk here in a second, but the structure of the language,

Starting point is 00:32:10 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 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

Starting point is 00:32:43 time. I have not read this book of yours, but Idea Makers, so a compilation of essays. How did you choose the players on the field for this? How did you choose the people you included? 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 sort of 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 are both people I knew to people like Ada Lovelace and Ramanujan, who are people who died long before I was born.

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Starting point is 00:35:09 So Dick Feynman, I'd love to, if you would indulge me, just to tell me a bit of your experience with Dick Feynman. So 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. And I do have 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.

Starting point is 00:35:45 So 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? I met him when I was 18 and he was 60. And he would always say, I was as quick as you were, but now I'm three times older than you were. He could be quite competitive in those kinds of things. But the thing that I liked about him, like 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.

Starting point is 00:36:26 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, you know, he'd have some question, he would do all this complicated math to work out the answer. Then he'd get this answer, and then he would think, well, how can I have figured out that answer by just some intuitive

Starting point is 00:36:59 argument without having to go through all the complicated math? I think he thought, 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. And so 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 don't know how on earth he figured this out. And people have been trying to reverse engineer the math for years. That was one of his little sort of tricks on the world, so to speak.

Starting point is 00:37:34 Oh, that's an incredible intellectual sleight of hand. Would Ramanujan, this is someone I know even less about, personify the intuition that Feynman was referring to? Well, Ramanujan is a different kind of story. So Ramanujan was a kind of slightly, you know, 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,

Starting point is 00:38:22 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 at 10 to the 10 to the 100 or something, some huge place, which he couldn't reach with calculation.

Starting point is 00:39:10 So in a sense, he was kind of, I think, a great experimental mathematician. Had he used computers, he would have had a whole different set of things he could have 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 kind of 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. I remember when

Starting point is 00:39:40 I was younger, I happened to start using computers to do physics sort of very early on when I was a teenager and so on. And for some reason, you know, the tools existed to be able to do some mathematical computations by computer, but people weren't using them. kind of a kid was I kind of discovered the fact that computers can be really powerful for doing science and so on when I was kind of 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.

Starting point is 00:40:35 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 understand the fact that that was a thing back in those days. So yeah, I've seen this kind of interesting phenomenon of when you use tools, where people don't necessarily even understand that those tools exist. It kind of has some interesting consequences. I was just going to say, the attribution I'm going to skip, but what was it?

Starting point is 00:41:02 Any sufficiently advanced form of technology is indistinguishable from magic? Something like that. Yeah, maybe an Arthur C. Clarke line. I think that sounds like Arthur C. Clarke. Right. Yeah, no, I think that the thing that I've spent a lot of my kind of scientific effort getting intuition from doing computer experiments. In another age, I would have been there with test tubes and other things doing physical experiments. Thank goodness I can get away with doing computer experiments

Starting point is 00:41:29 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 kind of inform your intuition and allow you to build things up. And it's kind of like this thing that comes from nowhere, because it's just coming from not the natural world, but kind of 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. 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.

Starting point is 00:42:14 So he asked each of us to present a heresy. And in this context, what that meant was something you believe to be true or to be the case that other people in this group 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,

Starting point is 00:43:03 that was actually, in fact, had some grounds to it. Does anything come to mind? I mean, there's several, but there's one example of a big one, but this one is kind of, the resistance is crumbling rapidly. So 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. So 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 in the late 19th century is actually water is made of something. People had guessed it much earlier. It's made of molecules. So the question now is, what is space

Starting point is 00:43:57 made of? This thing that we kind of 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'd been pretty sure for quite a while that space has kind of an inner structure. It's just made of these discrete sort of 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 kind of 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

Starting point is 00:44:42 of these molecules bouncing around. But let's say you just look at a little vortex, a little eddy on the surface of the fluid, a 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 kind of fashion. And it's my strong belief that sort of everything we know in the universe, all the electrons and photons and the things that get made up of those, they're all just sort of things like eddies in the structure of this giant network that is the sort of underlying data structure of the universe and the underlying thing that space is made of.

Starting point is 00:45:21 Where would dark matter fit into this, be vetoed by this, be compatible or incompatible? 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 kind of standard theory of the way sort of 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 that are associated

Starting point is 00:46:06 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 kind of the, in terms of sort of missing energy, that is a much bigger by a factor of a hundred100 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. These are all kind of 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. We don't know where the energy is coming from and so on. One feature of our model of physics

Starting point is 00:46:50 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 sort of quantum fluctuation like things, those are what make space. And so it's kind of 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.

Starting point is 00:47:30 That's an example of how something like that works out in our kind of model of physics. 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? Here's another one. It's a little bit more detailed. So what's happened is, as a result of the physics project, we have come to understand a kind of 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 sort of foundational changes in those fields.

Starting point is 00:48:06 I'll mention another thing, which 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. 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 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.

Starting point is 00:48:50 And in fact, one of the things that has just become clear to me now is that, okay, so statistical mechanics is about when you put lots of molecules together, what do they typically do? So 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 all together. 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 sort of have mechanical work that's doing things, eventually that's dissipated as heat. And once it's heat, that's kind of microscopic motion

Starting point is 00:49:35 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. So people have believed that it's sort of possible to, quote, derive the second-order orthodynamics 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 kind of 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.

Starting point is 00:50:11 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 sort of come from the same place, and they're all as derivable as each other. And they're all derivable in a really interesting way. They're all derivable ultimately from this strange thing we call the Rulliad, which is this limit of all possible computations, the kind of entangled limit of all possible computations. And what turns out to happen is all three of those theories are the result of observers like us sampling this Rulliad object. And what matters is that we have certain attributes as observers. For example,

Starting point is 00:50:53 we are computationally bounded. We can only fit a limited amount of sort 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 sort of 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. So in other words, even though at every moment, we are made from different atoms of space, the atoms other words, even though at every moment we are made from different atoms of space, the atoms of space that we were at one moment are being destroyed,

Starting point is 00:51:31 new ones are being created, and so on. Despite that, we believe that we are persistent in time. It's kind of 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 kind of how we sample this Rulliad thing, which is kind of 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. How do you spell Rulliad?

Starting point is 00:52:15 R-U-L-I-A-D. Oh, I got it. Look at that. Incredible. Now, is there a lay explanation or exploration of the Rulliad that isn't completely corrupted that one like myself might digest? The first thing to think about is 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 you just run that rule long enough and you'll get everything that happens in the universe. So in other words, I was talking before about how simple computational rules, you run them, they do really complicated things. So the most complicated thing we know about is the whole universe. So could we find a rule where we just write down this rule and we could run it long enough, it would just make the whole universe. So then you start thinking, well, let's imagine that we had that rule. Let me go actually one other place first, which is how quantum mechanics works.

Starting point is 00:53:15 So in classical mechanics, you have laws that describe sort of 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. And by classical, you mean Newtonian in this case? Newtonian physics, yes. Got it. Actually, relativistic physics works the same way. Got it, okay.

Starting point is 00:53:39 That's sort of the distinction between classical as in not quantum and quantum. So this was kind of the dividing line is kind of around 1920. It's about 100 years old. 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 kind of a Dick Feynman idea, this idea of path integrals and following many quantum paths. But kind of the notion is that in quantum mechanics, lots of different things happen. The ball goes on many different possible trajectories.

Starting point is 00:54:15 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. So that's kind of the traditional view of quantum mechanics. So 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

Starting point is 00:54:54 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.

Starting point is 00:55:31 So our minds are branching just like everything else in the universe is branching. So it turns out sort of 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 we, 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 sort of conflate lots of different paths that from the outside would look like the universe is

Starting point is 00:56:18 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 threat of experience. And so that process is kind of what drives the understanding of how quantum mechanics works. And actually, returning to Dick Feynman again, he always used to say, 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

Starting point is 00:56:54 quantum mechanics. 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 kind of a bizarre idea that turns out, I think, to sort of unlock how that works. But okay, so 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 kind of path. Well, let's say we've got this model and we say we find this rule, and this rule represents everything the universe does. We might imagine this day where we've got this rule that 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

Starting point is 00:57:38 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 7 trillion whatever? Why this one? And, you know, one of the big lessons of science over the last 500 years is kind of the Copernicus lesson. We're not very special. You know, 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 sort of space that makes up the universe. So even the idea that our rule is a simple rule as opposed to an incredibly complicated

Starting point is 00:58:16 rule seems very anti-Copernican. And so this really bothered me for a long time. And I realized, actually, it's something even more bizarre maybe going on, which is maybe the universe is not picking any particular rule. It's running all possible rules. And so what the rule you add 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 off from all possible starting points and you run them all. So you might say, how could that do anything interesting? The critical point is that sometimes those computers will end up making the same thing.

Starting point is 00:58:56 So in other words, two different computers might end up producing something which has the same structure. And so when 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 Rulliad. It's the entangled limit of all possible computations. And what's really kind of 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 kind of define your terms, you have the Rulliad. 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

Starting point is 00:59:47 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 sort of more coincidental. It's something that is a necessary feature from the kind of formal structure of what you've set up. So the Rulliad is this kind of necessary object. And now the thing which is kind of interesting is, okay, so you have this object that is this limit of all possible computations. So how do we experience that? Well, we are also part of that object.

Starting point is 01:00:29 So it's the same kind of a story as the branching mind perceiving the branching universe, except an even more abstracted version of that. It's how do we, as pieces, as elements inside this Rulliad, perceive the whole Rulliad? So one of the things one starts talking about is this notion of what we call ruleal 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.

Starting point is 01:01:00 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. What knits all of that together is a kind of a technical fact that's been known for about 100 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 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, 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

Starting point is 01:01:42 attribute different rules to the operation of the universe, but they're interconvertible in the same way as your computer can be made to run a spreadsheet rather than a word processor, so to speak. So one of the things that I kind of like about this is there's this notion of where are you in ruleal space? 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 ruleal 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 ruleal 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 the universe, we have the exploration of the universe by spacecraft

Starting point is 01:02:25 or whatever going out in physical space. We also have the exploration of the universe in ruleal space. And that's kind of the different minds and different ways of describing the universe represent kind of travel through ruleal space. You know, 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 rural space. And that's kind of a way to start representing those kinds of things. 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 have to say one more thing before, because you were asking about languages and you were asking about different human languages.

Starting point is 01:03:06 That's an example of being in different places in royal space. So you can imagine two languages where the way of thinking about the world is very similar. They are kind of correspond to nearby places in royal space, where it's pretty easy to translate, to travel from one to the other. Whereas things which are very different sort of views of the world are further away in ruleal space. And that's just a way of perhaps conceptualizing what this thing is about. When you said that, I was just thinking about gendered languages versus ungendered languages, where certain languages that don't conjugate, say, past tense like Chinese, Mandarin, and how that affects maybe where you stand in rural space. So how does a branching

Starting point is 01:03:48 mind perceive a branching universe or the branching mind 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 would have some resemblance. But there is a branching that I think for many folks listening will take place in linear time. 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?

Starting point is 01:04:48 So, I mean, first thing is, what is time? Exactly. 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.

Starting point is 01:05:16 And this phenomenon of computational irreducibility 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're 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 a space-time singularity,

Starting point is 01:06:01 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, it's 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. It's like, 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 the black hole. It's kind of bad news in a sense, if you want to have a future, so to speak, because time just

Starting point is 01:06:37 stopped. So time, as far as I'm concerned, is this inexorable progress of computation. And time is, 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's it the same time as somewhere else is complicated. You know, let's say we have a Mars colony one day and we define Earth's 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

Starting point is 01:07:27 we try and sort of back calculate that and say, well, it's the time when it would have reached if the clock had been 20 minutes early and so on. That whole question of sort of the way in which you 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. That's another kind of twist in this whole thing. But in quantum mechanics, the big issue is, is there just one 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.

Starting point is 01:08:09 But for most of us, most of the time, it's just there's a definite thread of experience that we have. What prompted that wonder or question about whether there are people... Because one of the features of kind 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

Starting point is 01:08:51 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. 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 computers 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.

Starting point is 01:09:35 He would do these calculations by hand that 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. It was an interesting challenge, so to speak. But actually, even at that time, we kind of concluded that the big question about sort of making use of quantum mechanics to compute

Starting point is 01:10:02 things is, how do you determine the answer? In the formal theory of quantum mechanics to compute things is, how do you determine the answer? In the formal theory of quantum mechanics, it's 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. 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.

Starting point is 01:10:42 I think that the formalism of quantum mechanics is super interesting. And this whole idea of these, what we call multi-way 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 advantage less convincing. It's also, as a practical matter, you know, 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,

Starting point is 01:11:26 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, like 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 sort of an external process of computation that's happening in the universe. I'm going to use a term that might be frustratingly undefined, overused to the point that it's often

Starting point is 01:12:10 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 subjective experience with certain underpinnings that can be currently explained. How do you think about, if at all, this may be a terrible question. I've always sort of avoided it because it's always seemed like a deeply slippery thing, but I was recently kind of confronted with, I need to apply the idea of consciousness, 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

Starting point is 01:12:51 are somewhat more filtered in what they do. And in particular, they have these features of computational boundedness, belief in persistence, and so on. And I think these are, 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

Starting point is 01:13:38 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's kind of the thing that consciousness, the sort of 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 our 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 very materialistic way? And 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,

Starting point is 01:14:26 we are this one point in ruleal 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 ruleal 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, you know, I was kind of doing an exercise recently, which I need to finish, which is to kind of describe what it's like to be a computer. And, you know, you imagine we humans, we live our lives, we remember a bunch of stuff through our lives. Eventually, it's all lost or we die.

Starting point is 01:15:08 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, quote, life experiences that we humans have? You know, there is a kind of a whole sort of inner thinking that's going on with 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?

Starting point is 01:15:44 Forget sort of the science fiction AI of the's like to be even a current computer? Forget sort of the science fiction AI of the future. Just talk about a current computer. What would it be like to be this sort of inside that experiencing things from the point of view of the machine, so to speak? 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, as 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? I started live streaming a bunch of working meetings that I do. And we started this about 2017, so a few years ago. And

Starting point is 01:16:49 it's really an interesting process because a large fraction of software design reviews that we do are live streamed. And that means, you know, 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. You know, we'll get, if we announce some particular topic we're going to work on, we'll get some world experts and 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 of our technology. And it's really a wonderful kind of immediate feedback. And I think it's, to me, it's a little bit kind of, it helps me feel that the time I'm spending doing very detailed grinding away, trying to figure out how software should be

Starting point is 01:17:46 designed and so on, it feels like the fact that I'm leaving some record of this feels like it makes it more meaningful. 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. I think I've been doing 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. And like last night, I probably recorded five hours of video work logs. I basically was working on this sort of

Starting point is 01:18:25 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 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. 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. You can find the minute where I figured this out and maybe I got it wrong. And you'll see,

Starting point is 01:19:11 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. 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. And part of why we can do it is that we have a pretty unique,

Starting point is 01:19:50 we're in a unique place and sort of 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, you know, you just spent 35 years building all this stuff. This is not one live stream is not going to let you rebuild that tower. So, you know So that's one thing. The other thing that I myself have become sort of confident enough that even though I know I'm going to say really stupid things on these live streams, I don't care. I'm more interested in 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. I don't have an internal sort of ego issue with either that process happening,

Starting point is 01:20:34 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. So another productivity hack, I suppose, related to live streaming is this. So I've also been doing at the beginning of the pandemic, I thought 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,

Starting point is 01:21:11 hour and a half on Fridays of science and technology Q&A for kids and others. Okay, so what has that done for me? Well, 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

Starting point is 01:22:02 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 kind of territory. 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 kind of 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 having to answer something, and that's been a very useful process. I know that in terms of explaining things like science kinds of things, I'm pretty sure I can feel that I've gotten better through hundreds of hours

Starting point is 01:22:44 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 I realized, oh my gosh, it relies on this thing and that thing and the other thing, which I learned when I was 12, 13 years old or something, but not everybody knows that. And I have to go explain that. And then I have to go explain this other thing that I thought was obvious. And pretty soon one's sort of descending into the swamp of complicated stuff.

Starting point is 01:23:25 Let's look at maybe some of the, 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? Like you're producing enough neurotransmitters and you store enough glycogen and so on that you are able to maintain a very high, seems like a very high rate of output. Do you think about energy management in that context? Or is that something that just comes so naturally out of the box that

Starting point is 01:24:02 you just have that advantage and you don't have to think about it? I think I'm lucky that I'm fairly energetic. It's always amusing to me that I'm an old guy now and I can sort of out-energize lots of young folk who are working with me and so on. Actually, I get a kick out of being able to do that. Yeah, why not? That helps add energy to the whole picture. But no, I think for me, one thing is that I do things that I like to do. And for me, that's a huge energizing force. I mean, 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.

Starting point is 01:24:40 Another strange thing I just did, I seem to have been reliving my life 50 years ago. And the last few weeks, I decided to organize, a couple of months ago, I initiated this, a reunion for my elementary school graduating class, which was 1972. So it's a small sort of subgroup of people. And so it was kind of the 50-year follow-up. It was actually, you know, I liked these people 50 years ago. I liked them 50 years later. That was nice. But I realized, you know, one of the things that's a little disappointing is some fraction of these people were like writing little blurbs about what we've been doing. And they're like, I retired, I retired, et cetera, et cetera, et cetera. They're British folk. So they had lots of witty things to say about what they were doing in their retirement.

Starting point is 01:25:26 So I was realizing, well, I'm not retired and I'm not even, 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. I've been lucky enough to be able to mostly do things that sort of add energy to me rather than taking it away. When I do creative kinds of things and I figure stuff out and even write things and so on, the process of finishing them and getting them done is energizing to me. Back in the day, before we realized this just didn't work, I would occasionally go shopping with my wife, okay? Long, long time, right? And I would try and take my wife to science museums. My wife is a mathematician, so the science museums are not so far away, but they're far enough away that she's like, oh, I'm getting so tired walking around the science museum. So for me, it would

Starting point is 01:26:21 have a physiological effect on me going around shopping, walking around shops. You know, I'm like, I feel really tired. You know, I have to, I have to kind of, you know, 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 kind of like 40 years old, hey, you should do some exercise. And that helped for me, I think, add a bunch of energy. I mean, I've walked more than 10,000 steps every single day for the last three years. And that's kind of just one of my constraints, even if I'm traveling or this or that or the other, the people who schedule stuff for me, I got to walk 10,000 steps. If that's around an airport on phone calls and things, so be it. But I'm just going to, that's a constraint on my life, so to speak.

Starting point is 01:27:08 And I think that has had a positive effect. For me, it's kind of been bizarre because, you know, 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. Yeah, your mitochondria are getting younger or rejuvenating. No, I mean, you know, another thing about that and about sort of the management of one's life and so on, I mean, 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 kind of it's all downhill from there. It's hard to get motivated. And I've been lucky enough that, well, for example, this physics project that just sort of arrived three years ago,

Starting point is 01:27:51 that was something I didn't really expect. It's a rejuvenating kind of thing. And it's just added a lot of energy. You know, it's just so many things to think about and so on. That's one thing. I think that 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. They may not be optimal, but they're habitual at least. I found that for me, I try and sort of 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 sort of simple and not having to think about them as possible so that I can spend my thinking effort on things

Starting point is 01:28:39 where I really want to think about these things and really want to spend my time on them. I've been fortunate enough that I've, for whatever reason, the mitochondria are still alive and kicking and providing good energy. I think it's mostly just doing things that I want to do. And also, you know, I tend to organize it so that, okay, there are things that I save up for. I'm going to do this if I'm feeling tired. And sometimes I have another set of things. I'm going to do this if I'm feeling tired. And sometimes I have another set of things,

Starting point is 01:29:05 I'm going to do this if I get sick. And I've had a whole bunch of those, and I've only been sick once in the last three years. So I've got this big pent-up supply of things to do when I'm sick. Now, are those just low-energy, low-interest tasks that nonetheless kind of have to get done, like talk to my accountant about X, Y, Z, whatever? Well, they're not usually talking to other people. They're usually organizing informational kinds of things. I see. I see. Or sometimes they're, watch this video that I've been meaning to watch and have never had time to

Starting point is 01:29:36 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. 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. And in a few cases, that was not a commercial plane. That was a private plane

Starting point is 01:30:27 without a lot of other people on it. And so it's kind of 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 like 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, 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 trips I've done. So far, I haven't gotten sick.

Starting point is 01:31:16 So far, so good. that you could not necessarily humidify, but maintain the moisture integrity of the sinal lining as well with a spray or something like that. 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. Try to keep the nose well hydrated. Right. It's kind of a strange thing. You're, you're on some flight going somewhere and you're like continually tapping things into your nose or whatever. You know, one of the things I found that for me, it's like, keep the list of things to do

Starting point is 01:31:54 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. I'm so, 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. So I do the same thing when I'm driving places. I always maintain a call while driving list.

Starting point is 01:32:16 You know, phone calls that are slightly more, you know, I've got to do this sometime. I don't need to be in front of a computer. You know, this is something I can do then. It's actually good for lots of kinds of interactions that I do where I'd never get around to it. It's just like, you know, 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, you know, oh, I'm coming to wherever for a day, and you end up seeing them. It's been kind of the same thing for me with call while driving. It's like, well, I'm going to call

Starting point is 01:32:43 somebody. So this is that process. Well, 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. 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. So 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

Starting point is 01:33:25 topics. Is there anything else that you would like to mention in this conversation or call my audience's attention to? Anything at all in terms of closing remarks, comments, any grievances you'd like to air publicly, anything at all? Makes me want to ask you for a bunch of personal productivity hacks and so on. And the, what am I missing type thing? Cause that's 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. And it's kind of like, keep trying them.

Starting point is 01:34:01 Now I think, no, that's, we've covered all kinds of things. 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. People can find you on Twitter.

Starting point is 01:34:25 Steven, that's a PH, Stephen underscore Wolfram, then Facebook slash Stephen Wolfram, 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? The stuff I write ends up in writings.stevenwolffham.com. 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

Starting point is 01:35:05 is we just launched our Wolfram Institute, which is an attempted productivity hack. My company, which I started now 36 years ago, is kind of my machine for turning ideas that I have into real things. You know, 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 we 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? I've sort of 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 kind of a new thing in the last just few weeks. So stay tuned for

Starting point is 01:35:52 interesting things that are happening there. And I guess there'll be more live streaming of science in action and so on there. So those are a few things. I have to plug my life work. 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. 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, you know, 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 this is sort

Starting point is 01:36:39 of an inexorable piece of the future. And it's a big advantage if you can kind of grab the magic from the future. You know, I've taken to 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. You get to do that and then you can do all kinds of magic things with it. Learn that superpower. 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. So my parting pitch would be, if you don't understand

Starting point is 01:37:25 computational language and multiple language and so on, try to understand it. Because it is, for me, you talk about productivity hacks, the biggest amplifier, hugest productivity hack is the whole computational language idea. That's what, you know, all the things I've done in science and in technology, they kind of are all based on that idea and the kind of tower of technology that we built around that. That's my kind of parting sort of ultimate productivity hack. Wonderful. And for everybody listening, Mathematica, Wolfram Alpha, Wolfram Language, we will link to all of these things in the show notes at tim.blogs.com. 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

Starting point is 01:38:13 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. So thank you. Thank you very much.

Starting point is 01:39:01 And to everybody listening, I'll plug it one more time. You can go to tim.blog.com slash 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. Hey guys, this is Tim again. Just one more thing before you take off and that is Five Bullet Friday. Would you enjoy getting a short email from me every Friday that provides a little fun before the weekend? Between one and a half and two million people subscribe to my free newsletter, my super short newsletter called Five Bullet Friday. Easy to sign up, easy to cancel. It is

Starting point is 01:39:49 basically a half page that I send out every Friday to share the coolest things I've found or discovered or have started exploring over that week. It's kind of like my diary of cool things. It often includes articles I'm reading, books I'm reading, albums perhaps, gadgets, gizmos, all sorts of tech tricks and so on that get sent to me by my friends, including a lot of podcast guests. And these strange esoteric things end up in my field, and then I test them, and then I share them with you. So if that sounds fun, again, it's very short, a little tiny bite of goodness before you head off for the weekend, something to think about. If you'd like to try it out, just go to tim.blog slash Friday,

Starting point is 01:40:34 type that into your browser, tim.blog slash Friday, drop in your email and you'll get the very next one. Thanks for listening. This episode is brought to you by Eight Sleep. My God, am I in love with Eight Sleep. Good sleep is the ultimate game changer. More than 30% of Americans struggle with sleep. And I'm a member of that sad group. Temperature is one of the main causes of poor sleep and heat has always been my nemesis. I've suffered for decades, tossing and turning, throwing blankets off, putting them back on and repeating ad nauseum.

Starting point is 01:41:00 But now I am falling asleep in record time, faster than ever. Why? Because I'm using a simple device called the Pod Pro Cover by 8sleep. It's the easiest and fastest way to sleep at the perfect temperature. It pairs dynamic cooling and heating with biometric tracking to offer the most advanced but most user-friendly solution on the market. I polled all of you guys on social media about the best tools for sleep, enhancing sleep, and Eight Sleep was by far and away the crowd favorite. I mean, people were just raving fans of this. So I used it and here we are. Add the Pod Pro Cover to your current mattress

Starting point is 01:41:39 and start sleeping as cool as 55 degrees Fahrenheit or as hot as 110 degrees Fahrenheit. It also splits your bed in half so your partner can choose a totally different temperature. My girlfriend runs hot all the time. She doesn't need cooling. She loves the heat. And we can have our own bespoke temperatures on either side, which is exactly what we're doing. Now for me, and for many people, the result, eight sleep users fall asleep up to 32% faster, reduce sleep interruptions by up to 40%, and get more restful sleep overall. I can personally attest to this because I track it in all sorts of ways. It's the total solution for enhanced recovery, so you can take on the next day feeling refreshed. And good news, Eight Sleep has launched the next generation of the pod.

Starting point is 01:42:22 The new pod 3 enables more accurate sleep and health tracking with twice the number of sensors. It's just a smoother, better experience that delivers you the best sleep on earth. At least that has been true for me. Simply add this to your existing mattress and you're all set. It is not magic, but sometimes it does feel like it. It just works. For a limited time, 8sleep is offering my listeners, that's you guys, up to $450 off of their Sleep Fit holiday bundle, which includes my personal favorite, the Pod 3 cover. Go to 8sleep.com slash Tim to get the exclusive holiday savings. That's all spelled out, E-I-G-H-T, 8sleep.com slash Tim. 8sleep

Starting point is 01:43:03 currently ships within the USA, Canada, the UK, select countries in the EU and Australia. That's eightsleep.com slash Tim. This episode is brought to you by ButcherBox. ButcherBox makes it easy for you to get high quality humanely raised meat that you can trust. They deliver delicious 100% grass fed, grass finished beef, free-range organic chicken, heritage breed pork, and wild-caught seafood directly to your door. For me, in the past few weeks, I've cooked a ton of their salmon, as well as two delicious barbecue rib racks in the oven. Super simple. They were the most delicious pork ribs I've ever prepared. My freezer is full of

Starting point is 01:43:42 ButcherBox. When you become a member, you're joining a community focused on doing what's better for all. That means caring about the lives of animals, the livelihoods of farmers, treating our planet with respect, and enjoying better meals together. ButcherBox partners with folks, small farmers included, who believe in going above and beyond when it comes to caring for animals, the environment, and sustainability. And none of their meat is ever given antibiotics or added hormones. So how does it work? It's pretty simple. You choose your box and your delivery frequency. They offer five boxes, four curated box options, as well as the popular custom box. So you get

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The Tim Ferriss Show - #637: Stephen Wolfram — Personal Productivity Systems, Richard Feynman Stories, Computational Thinking as a Superpower, Perceiving a Branching Universe, and The Ruliad... The Biggest Object in Metascience

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