Beauty Quarks (Harry Cliff) | AI Podcast Clips

So I work on a detector called LHCb, which is one of these four big detectors that are spaced around the ring. We do slightly different stuff to the big guys. There's two big experiments called ATLAS and CMS, 3,000 physicists and scientists and computer scientists on them each. They are the ones that discovered the Higgs and they look for supersymmetry and dark matter and so on. What we look at are standard model particles called bquarks, which depending on your preferences, either bottom or beauty, we tend to say beauty because it sounds sexier. But these particles are interesting because we can make lots of them. We make billions or hundreds of billions of these things. You can therefore measure their properties very precisely. So you can make these really lovely precision measurements. And what we are doing really is a sort of complementary thing to the other big experiments, which is, if you think of the sort of analogy they often use is, if you imagine you're in the jungle and you're looking for an elephant, say, and you are a hunter and you're kind of like, let's say there's the elephant's very rare, you don't know where in the jungle, the jungle's big. So there's two ways you go about this. Either you can go wandering around the jungle and try and find the elephant. The problem is if there's only one elephant and the jungle's big, the chances of running into it are very small. Or you could look on the ground and see if you see footprints left by the elephant. And if the elephant's moving around, you've got a chance, your better chance maybe of seeing the elephant's footprints. If you see the footprints, you go, okay, there's an elephant. I maybe don't know what kind of elephant it is, but I got a sense there's something out there. So that's sort of what we do. We are the footprint people. We are, we're looking for the footprints, the impressions that quantum fields that we haven't managed to directly create the particle of, the effects these quantum fields have on the ordinary standard model fields that we already know about. So these, these B particles, the way they behave can be influenced by the presence of say super fields or dark matter fields or whatever you like. And then the way they decay and behave can be altered slightly from what our theory tells us they ought to behave. And it's easier to collect huge amounts of data on B quarks. We get billions and billions of these things. You can make very precise measurements. And the only place really at the LHC or in really in high energy physics at the moment where there's fairly compelling evidence that there might be something beyond the standard model is in these B, these beauty quarks decays. Just to clarify, which is the difference between the different, the four experiments, for example, that you mentioned, is it the kind of particles that are being collided? Is it the energies of which they're collided? What's the fundamental difference between the different experiments? The collisions are the same. What's different is the design of the detectors. So Atlas and CMS are called, they're called what are called general purpose detectors. And they are basically barrel shaped machines and the collisions happen in the middle of the barrel and the barrel captures all the particles that go flying out in every direction. So in a sphere effectively that can flying out and it can record all of those particles. And what's the, sorry to be interrupting, but what's the, what's the mechanism of the recording? So these detectors, if you've seen pictures of them, they're huge, like Atlas is 25 meters high and 45 meters long. They're vast machines, instruments, I guess you should call them really. They are, they're kind of like onions. So they have layers, concentric layers of detector detectors, different sorts of detectors. So close into the beam pipe, you have what are called usually made of silicon, they're tracking detectors. So they're little made of strips of silicon or pixels of silicon. And when a particle goes through the silicon, it gives a little electrical signal and you get these dots, you know, electrical dots through your detector, which allows you to reconstruct the trajectory of the particle. So that's the middle. And then the outsides of these detectors, you have things called calorimeters, which measure the energies of the particles. And then very edge, you have things called muon chambers, which basically met these muon particles, which are the heavy version of the electron. They are, they're like high velocity bullets and they can get right to the edge of the detectors. If you see something at the edge, that's a muon. So that's broadly how they work. And all of that is being recorded. That's all being fed out to, you know, computers. That data must be awesome. Okay. So LHCb is different. So we, because we're looking for these be quarks, be quarks tend to be produced along the beam line. So in a collision, the be quarks tend to fly sort of close to the beam pipe. So we built a detector that sort of pyramid cone shaped basically, that just looks in one direction. So we ignore, if you have your collision stuff goes everywhere, we ignore all the stuff over here and going off sideways. We're just looking in this little region close to the beam pipe where most of these be quarks are made. So is there a different aspect of the sensors involved in the collection of the be quark? Yeah, trajectories. There are some differences. So one of the differences is that one of the ways, you know, you've seen a be quark is that be quarks are actually quite long lived by particle standards. So they live for 1.5 trillionths of a second, which is if you're, if you're a fundamental particle is a very long time. Because you know, the Higgs boson, I think lives for about a trillionth of a trillionth of a second, or maybe even less than that. So these are quite long lived things, and they will actually fly a little distance before they decay. So they will fly, you know, a few centimetres, maybe if you're lucky, then they'll decay into other stuff. So what we need to do in the middle of the detector, you want to be able to see, you have your place where the protons crash into each other, and that produces loads of particles that come flying out. So you have loads of lines, loads of tracks that point back to that proton collision. And then you're looking for a couple of other tracks, maybe two or three that point back to a different place that's maybe a few centimetres away from the proton collision. And that's the sign that little be particle has flown a few centimetres and decayed somewhere else. So we need to be able to very accurately resolve the proton collision from the be particle decay. So we are the middle of our detector is very sensitive, and it gets very close to the collision. So you have this really beautiful delicate silicon detector that sits, I think it's seven millimetres from the beam. And the LHC beam has as much energy as a jumbo jet at takeoff. So it's enough to melt a tonne of copper. So you have this furiously powerful thing sitting next to this tiny delicate, you know, silicon sensor. So those aspects of our detector that are specialised to measure these particular be quarks that we're interested in. And is there, I mean, I remember seeing somewhere that there's some mention of matter and antimatter connected to the be, these beautiful quarks. Is that, what's the connection? Yeah, what's the connection there? Yeah, so there is a connection, which is that when you produce these be particles, these particles can, because you don't see the be quark, you see the thing that be quark is inside. So they're bound up inside what we call beauty particles, where the be quark is joined together with another quark or two, maybe two other quarks, depending on what it is. There are a particular set of these be particles that exhibit this property called oscillation. So if you make a, for the sake of argument, a matter version of one of these be particles, as it travels, because of the magic of quantum mechanics, it oscillates backwards and forwards between its matter and antimatter versions. So it does this weird flipping about backwards and forwards. And what we can use this for is a laboratory for testing the symmetry between matter and antimatter. So if the symmetry between antimatter and antimatter is precise, it's exact, then we should see these be particles decaying as often as matter as they do as antimatter, because this oscillation should be even. It should spend as much time in each state. But what we actually see is that one of the states it spends more time in, it's more likely to decay in one state than the other. So this gives us a way of testing this fundamental symmetry between matter and antimatter.


What is Deep Reinforcement Learning? (David Silver, DeepMind) | AI Podcast Clips

If it's okay, can we take a step back and kind of ask the basic question of what is, to you, reinforcement learning? So reinforcement learning is the study and the science and the problem of intelligence in the form of an agent that interacts with an environment. So the problem you're trying to solve is represented by some environment, like the world in which that agent is situated. And the goal of RL is clear, that the agent gets to take actions. Those actions have some effect on the environment, and the environment gives back an observation to the agent saying, you know, this is what you see or sense. And one special thing which it gives back is called the reward signal, how well it's doing in the environment. And the reinforcement learning problem is to simply take actions over time so as to maximize that reward signal. So a couple of basic questions. What types of RL approaches are there? So I don't know if there's a nice, brief, inwards way to paint a picture of sort of value-based, model-based, policy-based reinforcement learning. Yeah. So now if we think about, okay, so there's this ambitious problem definition of RL. It's really, you know, it's truly ambitious. It's trying to capture and encircle all of the things in which an agent interacts with an environment and say, well, how can we formalize and understand what it means to crack that? Now let's think about the solution method. Well, how do you solve a really hard problem like that? Well, one approach you can take is to decompose that very hard problem into pieces that work together to solve that hard problem. And so you can kind of look at the decomposition that's inside the agent's head, if you like, and ask, well, what form does that decomposition take? And some of the most common pieces that people use when they're kind of putting this system, the solution method together, some of the most common pieces that people use are whether or not that solution has a value function. That means, is it trying to predict, explicitly trying to predict how much reward it will get in the future? Does it have a representation of a policy? That means something which is deciding how to pick actions. Is that decision-making process explicitly represented? And is there a model in the system? Is there something which is explicitly trying to predict what will happen in the environment? And so those three pieces are to me, some of the most common building blocks. And I understand the different choices in RL as choices of whether or not to use those building blocks when you're trying to decompose the solution. Should I have a value function represented? Should I have a policy represented? Should I have a model represented? And there are combinations of those pieces. And of course, other things that you could add into the picture as well. But those three fundamental choices give rise to some of the branches of RL with which we're very familiar. And so those, as you mentioned, there is a choice of what's specified or modeled explicitly. And the idea is that all of these are somehow implicitly learned within the system. So it's almost a choice of how you approach a problem. Do you see those as fundamental differences or are these almost like small specifics, like the details of how you solve the problem, but they're not fundamentally different from each other? I think the fundamental idea is maybe at the higher level. The fundamental idea is the first step of the decomposition is really to say, well, how are we really going to solve any kind of problem where you're trying to figure out how to take actions and just from this stream of observations, you've got some agent situated in its sensory motor stream and getting all these observations in, getting to take these actions and what should it do? How can you even broach that problem? Maybe the complexity of the world is so great that you can't even imagine how to build a system that would understand how to deal with that. And so the first step of this decomposition is to say, well, you have to learn. The system has to learn for itself. And so note that the reinforcement learning problem doesn't actually stipulate that you have to learn. You could maximize your rewards without learning. It would just wouldn't do a very good job of it. So learning is required because it's the only way to achieve good performance in any sufficiently large and complex environment. So that's the first step. So that step gives commonality to all of the other pieces because now you might ask, well, what should you be learning? What does learning even mean? You know, in this sense, learning might mean, well, you're trying to update the parameters of some system, which is then the thing that actually picks the actions. And those parameters could be representing anything. They could be parameterizing a value function or a model or a policy. And so in that sense, there's a lot of commonality in that whatever is being represented there is the thing which is being learned and it's being learned with the ultimate goal of maximizing rewards. But the way in which you decompose the problem is really what gives the semantics to the whole system. Are you trying to learn something to predict well, like a value function or a model? Are you learning something to perform well, like a policy? And the form of that objective is kind of giving the semantics to the system. And so it really is at the next level down a fundamental choice. And we have to make those fundamental choices as system designers or enabler our algorithms to be able to learn how to make those choices for themselves. So then the next step you mentioned, the very first thing you have to deal with is, can you even take in this huge stream of observations and do anything with it? So the natural next basic question is, what is deep reinforcement learning? And what is this idea of using neural networks to deal with this huge incoming stream? So amongst all the approaches for reinforcement learning, deep reinforcement learning is one family of solution methods that tries to utilize powerful representations that are offered by neural networks to represent any of these different components of the solution, of the agent, like whether it's the value function or the model or the policy. The idea of deep learning is to say, well, here's a powerful toolkit that's so powerful that it's universal in the sense that it can represent any function and it can learn any function. And so if we can leverage that universality, that means that whatever we need to represent for our policy or for our value function or for our model, deep learning can do it. So that deep learning is one approach that offers us a toolkit that has no ceiling to its performance, that as we start to put more resources into the system, more memory and more computation and more data, more experience, more interactions with the environment, that these are systems that can just get better and better and better at doing whatever the job is they've asked them to do. Whatever we've asked that function to represent, it can learn a function that does a better and better job of representing that knowledge, whether that knowledge be estimating how well you're going to do in the world, the value function, whether it's going to be choosing what to do in the world, the policy, or whether it's understanding the world itself, what's going to happen next, the model. Nevertheless, the fact that neural networks are able to learn incredibly complex representations that allow you to do the policy, the model, or the value function is, at least to my mind, exceptionally beautiful and surprising. Was it surprising to you? Can you still believe it works as well as it does? Do you have good intuition about why it works at all and works as well as it does? I think, let me take two parts to that question. I think it's not surprising to me that the idea of reinforcement learning works because in some sense, I feel it's the only thing which can, ultimately. I feel we have to address it and there must be success is possible because we have examples of intelligence. It must at some level be possible to acquire experience and use that experience to do better in a way which is meaningful to environments of the complexity that humans can deal with. It must be. Am I surprised that our current systems can do as well as they can do? I think one of the big surprises for me and a lot of the community is really the fact that deep learning can continue to perform so well despite the fact that these neural networks that they're representing have these incredibly nonlinear kind of bumpy surfaces, which to our kind of low dimensional intuitions make it feel like surely you're just going to get stuck and learning will get stuck because you won't be able to make any further progress. And yet, the big surprise is that learning continues and these, what appear to be local optima turn out not to be because in high dimensions when we make really big neural nets, there's always a way out and there's a way to go even lower. And then you're still not in a local optima because there's some other pathway that will take you out and take you lower still. And so no matter where you are, learning can proceed and do better and better and better without bound. And so that is a surprising and beautiful property of neural nets, which I find elegant and beautiful and somewhat shocking that it turns out to be the case. As you said, which I really like, to our low dimensional intuitions, that's surprising. Yeah. We're very tuned to working within a three dimensional environment. And so to start to visualize what a billion dimensional neural network surface that you're trying to optimize over, what that even looks like, is very hard for us. And so I think that really, if you try to account for the essentially the AI winter, where people gave up on neural networks, I think it's really down to that lack of ability to generalize from low dimensions to high dimensions. Because back then we were in the low dimensional case. People could only build neural nets with 50 nodes in them or something. And to imagine that it might be possible to build a billion dimensional neural net, and it might have a completely different, qualitatively different property, was very hard to anticipate. And I think even now, we're starting to build the theory to support that. And it's incomplete at the moment, but all of the theory seems to be pointing in the direction that indeed, this is an approach which truly is universal, both in its representational capacity, which was known, but also in its learning ability, which is surprising. It makes one wonder what else we're missing due to our low dimensions intuitions that will seem obvious once it's discovered. I often wonder, when we one day do have AIs, which are superhuman in their abilities to understand the world, what will they think of the algorithms that we developed back now? Will it be looking back at these days and thinking that, will we look back and feel that these algorithms were naive first steps, or will they still be the fundamental ideas which are used even in a hundred thousand, 10,000 years? It's hard to know. They'll watch back to this conversation with a smile, maybe a little bit of a laugh. My sense is, I think just like when we used to think that the sun revolved around the earth, they'll see our systems of today, reinforcement learning as too complicated. That the answer was simple all along. There's something, just like you said in the game of Go, I mean, I love the systems of like cellular automata, that there's simple rules from which incredible complexity emerges. So it feels like there might be some very simple approaches, just like where Sutton says, right? These simple methods with compute over time seem to prove to be the most effective. I 100% agree. I think that if we try to anticipate what will generalize well into the future, I think it's likely to be the case that it's the simple, clear ideas which will have the longest legs and which will carry us furthest into the future. Nevertheless, we're in a situation where we need to make things work today. And sometimes that requires putting together more complex systems where we don't have the full answers yet as to what those minimal ingredients might be.


Simon Sinek: Leadership, Hard Work, Optimism and the Infinite Game | Lex Fridman Podcast #82

The following is a conversation with Simon Sinek, author of several books, including Start With Why, Leaders Eat Last, and his latest, The Infinite Game. He's one of the best communicators of what it takes to be a good leader, to inspire, to build businesses that solve big, difficult challenges. This is the Artificial Intelligence Podcast. If you enjoy it, subscribe on YouTube, review it with five stars on Apple Podcasts, support it on Patreon, or simply connect with me on Twitter at Lex Friedman, spelled F-R-I-D-M-A-N. As usual, I'll do one or two minutes of ads now, and never any ads in the middle that can break the flow of the conversation. I hope that works for you, and doesn't hurt the listening experience. Quick summary of the ads. Two sponsors, Cash App and Masterclass. Please consider supporting the podcast by downloading Cash App and using code LEXPODCAST, and signing up to Masterclass at masterclass.com slash LEX. This show is presented by Cash App, the number one finance app in the App Store. When you get it, use code LEXPODCAST. Cash App lets you send money to friends, buy Bitcoin, and invest in the stock market with as little as one dollar. Since Cash App allows you to buy Bitcoin, let me mention that cryptocurrency in the context of the history of money is fascinating. I recommend Ascent of Money as a great book on this history. Debits and credits on ledgers started around 30,000 years ago. The US dollar created over 200 years ago, and Bitcoin, the first decentralized cryptocurrency, released just over 10 years ago. So given that history, cryptocurrency's still very much in its early days of development, but it's still aiming to and just might redefine the nature of money. So again, if you get Cash App from the App Store or Google Play, and use the code LEXPODCAST, you get $10, and Cash App will also donate $10 to FIRST, an organization that is helping to advance robotics and STEM education for young people around the world. This show is sponsored by Masterclass. Sign up at masterclass.com slash LEX to get a discount and to support this podcast. When I first heard about Masterclass, I honestly thought it was too good to be true. For $180 a year, you get an all-access pass to watch courses from experts at the top of their field. To list some of my favorites, Chris Hatfield on space exploration, Neil deGrasse Tyson on scientific thinking and communication, Will Wright, the creator of SimCity, and Sims on game design. I love that game. Jane Goodall on conservation, Carlos Santana, one of my favorite guitarists on guitar, Garry Kasparov on chess. Obviously I'm Russian. I love Garry. Daniel Negrano on poker, one of my favorite poker players. Also Phil Ivey gives a course as well, and many, many more. Chris Hatfield explaining how rockets work and the experience of being launched into space alone is worth the money. By way of advice, for me, the key is not to be overwhelmed by the abundance of choice. Pick three courses you want to complete, watch each all the way through from start to finish. It's not that long, but it's an experience that will stick with you for a long time, I promise. It's easily worth the money. You can watch it on basically any device. Once again, sign up at masterclass.com slash Lex to get a discount and to support this podcast. And now here's my conversation with Simon Sinek. In the Infinite Game, your most recent book, you describe the finite game and the infinite game. So from my perspective of artificial intelligence and game theory in general, I'm a huge fan of finite games from the broad philosophical sense is something that in the robotics, artificial intelligence space, we know how to deal with. And then you describe the infinite game, which has no exact static rules, has no well-defined static objective, the players are known, unknown, they change, there's a dynamic element. So this is something that applies to business, politics, life itself. So can you try to articulate the objective function here of the infinite game or in the cliche, broad philosophical sense, what is the meaning of life? Go for the start with the softballs. Yeah, easy question first. So James Kars was the philosopher who originally articulated this concept of finite and infinite games. And when I learned about it, it really challenged my view of how the world works, right? Because I think we all think about winning and being the best and being number one. But if you think about it, only in a finite game can that exist, a game that has fixed rules, agreed upon objectives and known players like football or baseball. There's always a beginning, middle and end. And if there's a winner, there has to be a loser. Infinite games, as Kars describes them, as you said, have known and unknown players, which means anyone can join. It has a changeable rules, which means you can play however you want. And the objective is to perpetuate the game, to stay in the game as long as possible. In other words, there's no such thing as being number one or winning in a game that has no finish line. And what I learned is that when we try to win in a game that has no finish line, we try to be number, we try to be the best in a game that has no agreed upon objectives or agreed upon metrics or timeframes. There's a few consistent and predictable outcomes, the decline of trust, the decline of cooperation, the decline of innovation. And I find this fascinating because so many of the ways that we run most organizations is with a finite mindset. So trying to reduce the beautiful complex thing that is life or politics or business into something very narrow, and in that process, the reductionist process, you lose something fundamental that makes the whole thing work in the long term. So returning, I'm not gonna let you off the hook easy, what is the meaning of life? So what is the objective function that is worthwhile to pursue? Well, if you think about our tombstones, they have the date we were born and the date we died, but really it's what we do with the gap in between. There's a poem called the dash. It's the dash that matters. It's what we do between the time we're born and the time we die that gives our life meaning. And if we live our lives with a finite mindset, which means to accumulate more power or money than anybody else, to outdo everyone else, to be number one, to be the best, we don't take any of us with us. We don't take any of it with us. We just die. The people who get remembered, the way we wanna be remembered is what kind of people we were, right? Devoted mother, loving father, what kind of person we were to other people. Jack Welch just died recently. And the Washington Post, when it wrote the headline for his obit, it wrote, he pleased Wall Street and distressed employees. And that's his legacy. A finite player who is obsessed with winning, who leaves behind a legacy of short-term gains for a few and distress for many. That's his legacy. And every single one of us gets the choice of the kind of legacy we wanna have. Do we wanna be remembered for our contributions or our detractions? To live with a finite mindset, to live a career with a finite mindset, to be number one, be the best, be the most famous, you live a life like Jack Welch. You know, to live a life of service, to see those around us rise, to contribute to our communities, to our organizations, to leave them in better shape than we found them. That's the kind of legacy most of us would like to have. So day-to-day, when you think about what is the fundamental goals, dreams, motivations of an infinite game, of seeing your life, your career as an infinite game, what does that look like? I mean, I guess I'm sort of trying to stick on this personal ego, personal drive, the thing that, the fire, the reason we wanna wake up in the morning and the reason we can't go to bed because we're so excited. What is that? So for me, it's about having a just cause. It's about a vision that's bigger than me, that my work gets to contribute to something larger than myself. That's what drives me every day. I wake up every morning with a vision of a world that does not yet exist, a world in which the vast majority of people wake up every single morning inspired, feel safe at work, and return home fulfilled at the end of the day. It is not the world we live in. And so that we still have work to do is the thing that drives me. I know what my underlying values are. I wake up to inspire people to do the things that inspire them. And these are the things that, these are the things that I, these are my go-tos, my touch points that inspire me to keep working. I think of a career like an iceberg. If you have a vision for something, you're the only one who can see the iceberg underneath the ocean. But if you start working at it, a little bit shows up. And now a few other people can see what you imagine, be like, oh, right, yeah, no, I wanna help build that as well. And if you have a lot of success, then you have a lot of iceberg and people can see this huge iceberg and they say, you've accomplished so much. But what I see is all the work still yet to be done. Yet I still see the huge iceberg underneath the ocean. And so the growth, you talk about momentum. So the incremental revealing of the iceberg is what drives you. Well, it necessarily is incremental. What drives me is that, is the realization, is realizing the iceberg, bringing more of the iceberg from the unknown to the known, bringing more of the vision from the imagination to reality. And you have this fundamental vision of optimism. You call yourself an optimist. I mean, in this world, I have a sort of, I see myself a little bit as the main character from the Idiot by Dostoevsky, who is also kind of seen by society as a fool because he was optimistic. So one, can you maybe articulate where that sense of optimism comes from? And maybe also try to articulate your vision of the future where people are inspired, where optimism drives us. It's easy to forget that when you look at social media and so on, with the word toxicity and negativity can often get more likes, that optimism has a sort of a beauty to it. And I do hope it's out there. So can you try to articulate that vision? Yeah, so, I mean, for me, optimism and being an optimist is just seeing the silver lining in every cloud. Even in tragedy, it brings people together. And the question is, can we see that? Can you see the beauty that is in everything? I don't think optimism is foolishness. I don't think optimism is blindness, though it probably involves some naivete, the belief that things will get better, the belief that we tend towards the good, even in times of struggle or bad. You can't sustain war, but you can sustain peace. I think things that are stable are more sustainable, things that are optimistic are more sustainable than things that are chaotic. So you see people as fundamentally good. I mean, some people may disagree that you can't sustain peace, you can't sustain war. I mean, you don't have to, I think war is costly. It involves life and money, and peace does not involve those things. It requires work. I'm not saying it doesn't require work, but it doesn't drain resources, I think, the same way that war does. The people that would say that we will always have war, and I just talked to the historian of Stalin, is, would say that conflict and the desire for power and conflict is central to human nature. I concur. But something in your words also, perhaps it's the naive aspect that I also share, is that you have an optimism that people are fundamentally good. I'm an idealist, you know, and I think idealism is good. I'm not a fool to believe that the ideals that I imagine can come true. Of course, there'll never be world peace, but shouldn't we die trying? You know, I think that's the whole point. That's the whole point of vision. Vision should be idealistic, and it should be, for all practical purposes, impossible. But that doesn't mean we shouldn't try, and it's the milestones that we reach that take us closer to that ideal that make us feel that our life and our work have meaning, and we're contributing to something bigger than ourselves. You know, just because it's impossible doesn't mean we shouldn't try. As I said, we're still moving the ball down the field. We're still making progress. Things are still getting better, even if we never get to that ideal state. So I think idealism is a good thing. You know, in the word infinite game, one of the beautiful and tragic aspects of life, human life at least, at least from the biological perspective, is that it ends. So sadly, it's- To some people, yeah. Fine, it's tragic to some people, or is it ends, it ends- I think some people believe that it ends on the day you die, and some people think it continues on. There's, and there's a lot of different ways to think what continues on even looks like. But let me drag it back to the personal. Sure. Which is, how do you think about your own mortality? Are you afraid of death? How do you think about your own death? I definitely haven't accomplished everything I want to contribute to. I would like more time on this earth to keep working towards that vision. Do you think about the fact that it ends for you? Are you cognizant of- Of course I'm cognizant of it. I mean, aren't we all? I don't dwell on it. I'm aware of it. I know that my life is finite, and I know that I have a certain amount of time left on this planet, and I'd like to make that time be valuable. You know, some people would think that ideas kind of allow you to have a certain kind of immortality. Yeah. Maybe to linger on this kind of question, so first to push back on the, you said that everyone's cognizant of their mortality. There's a guy named Ernest Becker who would disagree that you basically say that most of human cognition is created by us trying to create an illusion and try to hide the fact from ourselves the fact that we're gonna die, to try to think that it's all gonna go on forever. But the fact that we know that it doesn't. Yes, but this mix of denial. I mean, I think the book's called Denial of Death. It's this constant denial that we're running away from. In fact, some would argue that the inspiration, the incredible ideas you've put out there, your TED Talk has been seen by millions and millions of people, right? It's just you trying to desperately fight the fact that you are biologically mortal. Your creative genius comes from the fact that you're trying to create ideas that live on long past you. Well, that's very nice of you. I mean, I would like my ideas to live on beyond me because I think that is a good test that those ideas have value in the lives of others. I think that's a good test that others would continue to talk about or share the ideas long after I'm gone, I think is perhaps the greatest compliment one can get for one's own work. But I don't think it's my awareness of my mortality that drives me to do it. It's my desire to contribute that drives me to do it. It's the optimist vision. It's the pleasure and the fulfillment you get from inspiring others. It's as pure as that. Let me ask, listen, I'm rushing. I'm trying to get you to- You're good, you're good. I'm enjoying it. Get you into these dark areas. You're good, I'm enjoying it. Is the ego tied up into it somehow? So your name is extremely well known. If your name wasn't attached to it, do you think you would act differently? I mean, for years I hated that my name was attached to it. I had a rule for years that I wouldn't have my face on the front page of the website. I had a fight with the publisher because I didn't want my name big on the book. I wanted it tiny on the book because I kept telling them it's not about me, it's about the ideas. They wanted to put my name on the top of my book, I refused. None of my books have my names on the top because I won't let them. They would like very much to put my name on the top of the book, but the idea has to be bigger than me. I'm not bigger than the idea. That's beautifully put. Do you think ego- But I also am aware that I've become, I've become recognized as the messenger. And even though I still think the message is bigger than me, I recognize that I have a responsibility as the messenger. And whether I like it or not is irrelevant. I accept the responsibility. I am happy to do it. I'm not sure how to phrase this, but there's a large part of the culture right now that emphasizes all the things that nobody disagrees with, which is health, sleep, diet, relaxation, meditation, vacation, are really important. And there's no, you know, it's like you can't really argue against that. In fact, people- Less sleep. Less- I'm just, I'm joking. Yes, well, that's the thing. I often speak to the fact that passion and love for what you're doing and the two words hard work, especially in the engineering fields, are more important than, are more important to prioritize than sleep. Even though sleep is really important, your mind should be obsessed with the hard work, with the passion, and so on. And then I get some pushback, of course, from people. What do you make sense of that? Is that just me, the crazy Russian engineer, really pushing hard work? Probably. I think that's a short-term strategy. I think if you sacrifice your health for the work, at some point it catches up with you. And at some point, it's like going, going, going, and you get sick. Your body will shut down for you if you refuse to take care of yourself. You know, you get sick. It's what happens. Sometimes, you know, more severe illness than something that just slows you down. So I think taking, like getting sleep, I mean, there've been studies on this that, you know, executives, for example, who get a full night's sleep and stop at a reasonable hour actually accomplish more, are more productive than people who work and burn the midnight oil because their brains are working better because they're well-rested. So, you know, working hard, yes, but when that works smart, I think that giving our minds and our bodies rest makes us more efficient. I think just driving, driving, driving, driving is a short-term strategy. So, but to push back on that a little bit, the annoying thing is you're like 100% right in terms of science, right? But the thing is, it's because you're 100% right, that weak part of your mind uses that fact to convince you, like what, so, you know, I get all kinds of, my mind comes up with all kinds of excuses to try to convince me that I shouldn't be doing what I'm doing. To rationalize. To rationalize. And so, what I have a sense, I think what you said about executives and leaders is absolutely right, but there's the early days. The early days of madness and passion. For sure. Then I feel like emphasizing sleep, thinking about sleep as giving yourself a way out from the fact that those early days, especially, can be suffering. As long, it's not sustainable. You know, it's not sustainable. Sure, if you're investing all that energy in something at the beginning to get it up and running, then at some point, you're gonna have to slow down. Or your body will slow you down for you. Like, you can choose or your body can choose. I mean. So, okay, so you don't think, from my perspective, it feels like people have gotten a little bit soft. But you're saying no. I think that there seems evidence that working harder and later have taken a back seat. I think we have to be careful with broad generalizations. But I think if you go into the workplace, there are people who would complain that more people now than before, you know, look at their watches and say, oh, it's five o'clock, go bye, right? Now, is that a problem with the people? You're saying it's the people giving themselves excuses and people don't work hard. Or is it the organizations aren't giving them something to believe in, something to be passionate about? We can't manufacture passion. You can't just tell someone be passionate. You know, that's not how it works. Passion's an output, not an input. Like if I believe in something and I wanna contribute all that energy to do it, we call that passion. You know, working hard for something we love is passion. Working hard for something we don't care about is called stress. But we're working hard either way. So I think the organizations bear some accountability and our leaders bear some accountability, which is if they're not offering a sense of purpose, if they're not offering us a sense of cause, if they're not telling us that our work is worth more than simply the money it makes, then yeah, I'm gonna come at five o'clock because I don't really care about making you money. Remember, we live in a world right now where a lot of people, rather a few people, are getting rich on the hard work of others. And so I think when people look up and say, well, why would I do that? If you're not gonna look after me and then you're gonna lay me off at the end of the year because you missed your arbitrary projections, you're gonna lay me off because you missed your arbitrary projections, then why would I offer my hard work and loyalty to you? So I don't think we can immediately blame people for going soft. I think we can blame leaders for their inability or failure to offer their people something bigger than making a product or making money. Yeah, so that's brilliant and start with why leaders last. Your books, you basically talk about what it takes to be a good leader. And so some of the blame should go on the leader, but how much of it is on finding your passion? How much is it on the individual? And allowing yourself to pursue that passion, pushing yourself to your limits to really take concrete steps along your path towards that passion. Yeah, there's mutual responsibility. There's mutual accountability. I mean, we're responsible as individuals to find the organizations and find the leaders that inspire us. And organizations are responsible for maintaining that flame and giving people who believe what they believed a chance to contribute. So to linger on it, have you by chance seen the movie Whiplash? Yes. Again, maybe I'm romanticizing suffering. Again. I'm not, it's the Russian. Yeah, the Russians love suffering. But people who haven't seen, it's the movie Whiplash as a drum instructor that pushes the drum musician to his limits to bring out the best in him. And there's a toxic nature to it. There's suffering in it. You've worked with a lot of great leaders, a lot of great individuals. Is that toxic relationship as toxic as it appears in the movie? Or is that fundamental? I've seen that relationship, especially in the past with Olympic athletes, especially in athletics, extreme performers seem to do wonders. It does wonders for me. There's some of my best relationships. Now, I'm not representative of everyone, certainly. But some of my best relationships for mentee and mentor have been toxic from an external perspective. What do you make of that movie? What do you make of that kind of relationship? That's not my favorite movie. Oh, okay. So you don't think that's a healthy, you don't think that kind of relationship is a great example of a great leader? I think it's a short-term strategy. I mean, look, being hard on someone is not the same as toxicity. If you go to the Marine Corps, a drill instructor will be very hard on their Marines. And then, but still even on the last day of bootcamp, they'll take their hat off and they'll become a human. But of all the drill instructors, the three or four main drill instructors assigned to a group of recruits, the one that they all want the respect of is the one that's the hardest on them. That's true. And you hear, there's plenty of stories of people who wanna earn the respect of a hard parent or a hard teacher. But fundamental, that parent, that teacher, that drill instructor has to believe in that person, has to see potential in them. It's not a formula, which is if I'm hard on people, they'll do well, which is there has to still be love. It has to be done with absolute love and it has to be done responsibly. I mean, some people can take a little more pressure than others, but it's not, I don't, I think it's irresponsible to think of it as a formula that if I'm just toxic at people, they will do well. It depends on their personalities. First of all, that works for some, but not all. And second of all, it can't be done willy-nilly. It has to still be done with care and love. And sometimes you can get equal or better results without all of the toxicity. So one of the, I guess toxicity on my part was a really bad word to use, but if we talk about what makes a good leader and just look at an example in particular, looking at Elon Musk, he's known to push people to their limits in a way that I think really challenges people in a way they've never been challenged before to do the impossible, but it can really break people. And jobs was hard and Amazon is hard. And, you know, but the thing that's important is none of them lie about it. You know, people ask me about Amazon all the time. Like Jeff Bezos never lied about it. You know, even the ones who like Amazon don't last more than a couple of years before they burn out. But when we're honest about the culture, then it gives people the opportunity who like to work in that kind of culture to choose to work in that kind of culture as opposed to pretending and saying, oh no, this is all, you know, it's all lovey-lovey here. And then you show up and it's the furthest thing from it. So, I mean, you know, I think the reputations of putting a lot of pressure on people to, you know, jobs was not an easy man to work for. He pushed people, but everyone who worked there was given the space to create and do things that they would not have been able to do anywhere else and work at a level that they didn't work anywhere else. And jobs didn't have all the answers. I mean, he pushed his people to come up with answers. He wasn't just looking for people to execute his ideas. And people did, people accomplished more than they thought they were capable of, which is wonderful. How do you, you're talking about the infinite game and not thinking about too short term, and yet you see some of the most brilliant people in the world being pushed by Ilan Musk to accomplish some of the most incredible things. When we're talking about autopilot, we're talking about some of the hardware engineering, they do some of the best work of their life and then leave. How do you balance that in terms of what it takes to be a good leader, what it takes to accomplish great things in your life? Yeah, so I think there's a difference between someone who can get a lot out of people in the short term and building an organization that can sustain beyond any individual. There's a difference. When you say beyond any individual, you mean beyond, beyond like if the leader dies. Correct, like could Tesla continue to do what it's doing without Ilan Musk? And you're perhaps implying, which is a very interesting question, that it cannot. I don't know. The argument you're making of this person who pushes everyone arguably is not a repeatable model. Is Apple the same without Steve Jobs or is it slowly moving in a different direction? Or has he established something that could be resurrected with the right leader? That was his dream, I think, is to build an organization that lives on beyond him. At least I remember reading that somewhere. I think that's what a lot of leaders desire, which is to create something that was bigger than them. Most businesses, most entrepreneurial ventures could not pass the school bus test, which is if the founder was hit by a school bus, would everyone continue the business without them or would they all just go find jobs? And the vast majority of companies would fail that test, especially in the entrepreneurial world, that if you take the inspired visionary leader away, the whole thing collapses. So is that a business or is that just a force of personality? And a lot of entrepreneurs face that reality, which is they have to be in every meeting, make every decision, come up with every idea, because if they don't, who will? And the question is, well, what have you done to build your bench? Sometimes it's ego, the belief that only I can. Sometimes it's just things did so well for so long that just forgot. And sometimes it's a failure to build the training programs or hire the right people that could replace you, who are maybe smarter and better. And browbeating people is only one strategy. I don't think it's necessarily the only strategy, nor is it always the best strategy. I think people get to choose the cultures they wanna work in. So this is why I think companies should be honest about the kind of culture that they've created. I heard a story about Apple where somebody came in from a big company. He had accomplished a lot and his ego was very large and he was going on about how he did this and he did that and he did this and he did that. And somebody from Apple said, we don't care what you've done. The question is, what are you gonna do? And that's, for somebody who wants to be pushed, that's the place you go because you choose to be pushed. Now, we all wanna be pushed to some degree. Anybody who wants to accomplish anything in this world wants to be pushed to some degree, whether it's through self-pressure or external pressure or public pressure, whatever it is. But I think this whole idea of one size fits all is a false narrative of how leadership works. But what all leadership requires is creating an environment in which people can work at their natural best. But you have a sense that it's possible to create a business where it lives on beyond you. So if we look at now, if we just look at this current moment, I just recently talked to Jack Dorsey, CEO of Twitter, and he's under a lot of pressure now. I don't know if you're aware of the news that he's being pushed out as a potential CEO of Twitter because he's the CEO already of an incredibly successful company. Plus he wants to go to Africa to live a few months in Africa to connect with the world that's outside of the Silicon Valley. And sort of there's this idea, well, can Twitter live without Jack? We'll find out. But you have a general, as a student of great leadership, you have a general sense that it's possible. Yeah, of course it's possible. I mean, what Bill Gates built with Microsoft may not have survived Steve Ballmer if the company weren't so rich, but Satya Nardala is putting it back on track again. It's become a visionary company again. It's attracting great talent again. It went through a period where they couldn't get the best talent and the best talent was leaving. Now people want to work for Microsoft again. Well, that's not because of pressure. Ballmer put more pressure on people, mainly to hit numbers than anything else. That didn't work. Yes. Right? And so the question is, what kind of pressure are we putting on people? We're putting on pressure people to hit numbers or hit arbitrary deadlines, or putting on pressure on people because we believe that they can do better work. And the work that we're trying to do is to advance a vision that's bigger than all of us. And if you're gonna put pressure on people, it better be for the right reason. Like if you're gonna put pressure on me, it better be for a worthwhile reason. If it's just to hit a goal, if it's just to hit some arbitrary data or some arbitrary number or make a stock price hit some target, you can keep it. I'm out of here. Yes. But if you want to put pressure on me because we are brothers and sisters in arms, working to advance a cause bigger than ourselves, that we believe whatever we're gonna build will significantly contribute to the greater good of society, then go ahead, I'll take the pressure. And if you look at the apples and if you look at the Elon Musk's, you know, the jobs in the Elon Musk, they fundamentally believe that what they were doing would improve society and it was for the good of humankind. And so the pressure, in other words, what they were doing was more important, more valuable than any individual on the team. And so the pressure they put on people served a greater good. And so we looked to the left and we looked to the right to each other and said, we're in this together. We accept this. We want this. But if it's just pressure to hit a number or make the widget move a little faster, that's soul sucking. That's not passion. That's stress. And I think a lot of leaders confuse that making people work hard is not what makes them passionate. Giving them something to believe in and work on is what drives passion. And when you have that, then turning up the pressure only brings people together, drives them further. If done the right way. Done the right way. Speaking of pressure, I'm gonna give you 90 seconds to answer the last question, which is if I told you that tomorrow was your last day to live, you talked about mortality, sunrise to sunset, can you tell me, can you take me through the day? What do you think that day would involve? You can't spend it with your family. I told you as well. I would probably want to fill all of my senses with things that excite my senses. I'd want to look at beautiful art. I'd want to listen to beautiful music. I'd want to taste incredible food. I'd want to smell amazing tastes. I'd want to touch, you know, something that's beautiful to touch. I'd want all of my senses to just be consumed with things that I find beautiful. And you talked about this idea of, we don't do it often these days, of just listening to music, turning off all the devices and actually taking in and listening to music. So as an addendum, if we were to talk about music, what song would you be blasting in this last day you're alive? Is it Led Zeppelin? What are we talking about? That I love. No, no. There's probably gonna be a Beatles song in there. There'll definitely be some Beethoven in there. The classics. The classics. Yeah, exactly. Thank you so much for talking today. Thank you for making time for it. Under pressure, we made it happen. It was great. Thanks for listening to this conversation with Simon Sinek. And thank you to our sponsors, Cash App and Masterclass. Please consider supporting the podcast by downloading Cash App and using code LEXPODCAST and signing up to Masterclass at masterclass.com slash LEX. If you enjoy this podcast, subscribe on YouTube, review it with five stars on Apple Podcast, support on Patreon, or simply connect with me on Twitter, at Lex Friedman. And now let me leave you with some words from Simon Sinek. There are only two ways to influence human behavior. You can manipulate it, or you can inspire it. Thank you for listening. I hope to see you next time.


Toward a Fundamental Theory of Physics (Stephen Wolfram) | AI Podcast Clips

What kind of computation do you think the fundamental laws of physics might emerge from? Just to clarify, so you've done a lot of fascinating work with kind of discrete kinds of computation that you know, you could sell your automata and we'll talk about it, have this very clean structure. It's a nice way to demonstrate that simple rules can create immense complexity. But what kind, you know, is that actually, are cellular automata sufficiently general to describe the kinds of computation that might create the laws of physics? Just to give, can you give a sense of what kind of computation do you think would create the laws of physics? So this is a slightly complicated issue because as soon as you have universal computation, you can in principle simulate anything with anything, but it is not a natural thing to do. And if you're asking, were you to try to find our physical universe by looking at possible programs in the computational universe of all possible programs, would the ones that correspond to our universe be small and simple enough that we might find them by searching that computational universe? We got to have the right basis, so to speak, we have got to have the right language in effect for describing computation for that to be feasible. So the thing that I've been interested in for a long time is what are the most structuralist structures that we can create with computation? So in other words, if you say a cellular automaton has a bunch of cells that are arrayed on a grid and it's very, you know, and every cell is updated in synchrony at a particular, you know, when there's a click of a clock, so to speak, and it goes a tick of a clock and every cell gets updated at the same time. That's a very specific, very rigid kind of thing. But my guess is that when we look at physics and we look at things like space and time, that what's underneath space and time is something as structureless as possible, that what we see, what emerges for us as physical space, for example, comes from something that is sort of arbitrarily unstructured underneath. And so I've been for a long time interested in kind of what are the most structuralist structures that we can set up? And actually what I had thought about for ages is using graphs, networks, where essentially so let's talk about space, for example. So what is space? It's a kind of a question I might ask. Back in the early days of quantum mechanics, for example, people said, oh, for sure, space is going to be discrete because all these other things we're finding are discrete, but that never worked out in physics. And so space and physics today is always treated as this continuous thing, just like you could have imagined it. I mean, the very first thing Euclid says in his sort of common notions is, you know, a point is something which has no part. In other words, there are points that are arbitrarily small and there's a continuum of possible positions of points. And the question is, is that true? And so, for example, if we look at, I don't know, a fluid like air or water, we might say, oh, it's a continuous fluid. We can pour it, we can do all kinds of things continuously. But actually we know, because we know the physics of it, that it consists of a bunch of discrete molecules bouncing around and only in the aggregate is it behaving like a continuum. And so the possibility exists that that's true of space too. People haven't managed to make that work with existing frameworks and physics. But I've been interested in whether one can imagine that underneath space and also underneath time is something more structureless. And the question is, is it computational? So there are a couple of possibilities. It could be computational, somehow fundamentally equivalent to a Turing machine. Or it could be fundamentally not. So how could it not be? It could not be, so a Turing machine essentially deals with integers, whole numbers, at some level. And, you know, it can do things like it can add one to a number. It can do things like this. It can also store whatever the heck it did. Yes. It has an infinite storage. But when one thinks about doing physics or sort of idealized physics or idealized mathematics, one can deal with real numbers, numbers with an infinite number of digits, numbers which are absolutely precise. And one can say, we can take this number and we can multiply it by itself. Are you comfortable with infinity in this context? Are you comfortable in the context of computation? Do you think infinity plays a part? I think that the role of infinity is complicated. Infinity is useful in conceptualizing things. It's not actualizable. Almost by definition, it's not actualizable. But do you think infinity is part of the thing that might underlie the laws of physics? I think that, no. I think there are many questions that you ask about, you might ask about physics, which inevitably involve infinity. Like when you say, you know, is faster than light travel possible? You could say, given the laws of physics, can you make something even arbitrarily large, even quote, infinitely large, that will make faster than light travel possible? Then you're thrown into dealing with infinity as a kind of theoretical question. But I mean, talking about, you know, sort of what's underneath space and time and how one can make a computational infrastructure. One possibility is that you can't make a computational infrastructure in a Turing machine sense, that you really have to be dealing with precise real numbers. You're dealing with partial differential equations, which have precise real numbers at arbitrarily closely separated points. You have a continuum for everything. Could be that that's what happens, that there's sort of a continuum for everything and precise real numbers for everything. And then the things I'm thinking about are wrong. You know, that's the risk you take if you're, you know, if you're trying to sort of do things about nature, is you might just be wrong. It's not, it's for me personally, it's kind of a strange thing. I've spent a lot of my life building technology where you can do something that nobody cares about, but you can't be sort of wrong in that sense, in the sense you build your technology and it does what it does. But I think, you know, this question of what, you know, what the sort of underlying computational infrastructure for the universe might be, it's, so it's sort of inevitable it's going to be fairly abstract, because if you're going to get all these things, like there are three dimensions of space, there are electrons, there are muons, there are quarks, there are this, you don't get to, if the model for the universe is simple, you don't get to have sort of a line of code for each of those things. You don't get to have sort of the muon case, the tau lepton case and so on. All of those things have to be emergent somehow. Right. So. Something deeper. Right. So that means it's sort of inevitable that it's a little hard to talk about what the sort of underlying structuralist structure actually is. Do you think human beings have the cognitive capacity to understand, if we're to discover it, to understand the kinds of simple structure from which these laws can emerge? Like do you think that's a hopeless pursuit? Well here's what I think. I think that, I mean I'm right in the middle of this right now. I'm telling you that I think this human, yeah, I mean this human has a hard time understanding, you know, a bunch of the things that are going on. But what happens in understanding is one builds waypoints. I mean if you said understand modern 21st century mathematics, starting from, you know, counting back in, you know, whenever counting was invented 50,000 years ago, whatever it was, right. That will be really difficult. But what happens is we build waypoints that allow us to get to higher levels of understanding. And we see the same thing happening in language. You know, when we invent a word for something, it provides kind of a cognitive anchor, a kind of a waypoint that lets us, you know, like a podcast or something. You could be explaining, well, it's a thing which works this way, that way, the other way. But as soon as you have the word podcast and people kind of societally understand it, you start to be able to build on top of that. And so I think, and that's kind of the story of science actually too. I mean, science is about building these kind of waypoints where we find this sort of cognitive mechanism for understanding something. Then we can build on top of it. You know, we have the idea of, I don't know, differential equations. We can build on top of that. We have this idea or that idea. So my hope is that if it is the case that we have to go all the way sort of from the sand to the computer and there's no waypoints in between, then we're toast. We won't be able to do that. Well eventually we might. So if we're, us clever apes are good enough at building those abstractions, eventually from sand we'll get to the computer, right? And it just might be a longer journey. The question is whether it is something that you asked, whether our human brains will quote understand what's going on. And that's a different question because for that it requires steps that are for, that are sort of from which we can construct a human understandable narrative. And that's something that I think I am somewhat hopeful that that will be possible. Although, you know, as of literally today, if you ask me, I'm confronted with things that I don't understand very well. And so this is a small pattern in a computation trying to understand the rules under which the computation functions. And it's an interesting possibility under which kinds of computations such a creature can understand itself. My guess is that within, so we didn't talk much about computational irreducibility, but it's a consequence of this principle of computational equivalence. And it's sort of a core idea that one has to understand, I think, which is, question is you're doing a computation, you can figure out what happens in the computation just by running every step in the computation and seeing what happens. Or you can say, let me jump ahead and figure out, you know, have something smarter that figures out what's going to happen before it actually happens. And a lot of traditional science has been about that act of computational reducibility. It's like, we've got these equations and we can just solve them and we can figure out what's going to happen. We don't have to trace all of those steps. We just jump ahead because we solved these equations. Okay, so one of the things that is a consequence of the principle of computational equivalence is you don't always get to do that. Many, many systems will be computationally irreducible in the sense that the only way to find out what they do is just follow each step and see what happens. Why is that? Well, if you have, if you're saying, well, we, with our brains, we're a lot smarter. We don't have to mess around like the little cellular automaton going through and updating all those cells. We can just, you know, use the power of our brains to jump ahead. But if the principle of computational equivalence is right, that's not going to be correct because it means that there's us doing our computation in our brains. There's a little cellular automaton doing its computation. And the principle of computational equivalence says these two computations are fundamentally equivalent. So that means we don't get to say we're a lot smarter than the cellular automaton and jump ahead because we're just doing computation that's of the same sophistication as the cellular automaton itself. That's computational reducibility. It's fascinating. But the, and that's a really powerful idea. I think that's both depressing and humbling and so on that while we in a cellular automaton are the same, but the question we're talking about, the fundamental laws of physics is kind of the reverse question. You're not predicting what's going to happen. You have to run the universe for that. But saying, can I understand what rules likely generated me? I understand, but the problem is to know whether you're right, you have to have some computational reducibility because we are embedded in the universe. If the only way to know whether we get the universe is just to run the universe, we don't get to do that because it just ran for 14.6 billion years or whatever. And we don't, you know, we can't rerun it, so to speak. So we have to hope that there are pockets of computational reducibility sufficient to be able to say, yes, I can recognize those are electrons there. And I think that it is a, it's a feature of computational irreducibility. It's sort of a mathematical feature that there are always an infinite collection of pockets of reducibility. The question of whether they land in the right place and whether we can sort of build a theory based on them is unclear. But to this point about, you know, whether we as observers in the universe built out of the same stuff as the universe can figure out the universe, so to speak, that relies on these pockets of reducibility. Without the pockets of reducibility, it won't work, can't work. But I think this question about how observers operate, it's one of the features of science over the last hundred years, particularly, has been that every time we get more realistic about observers, we learn a bit more about science. So for example, relativity was all about observers don't get to say when, you know, what's simultaneous with what. They just wait for the light signal to arrive to decide what's simultaneous. Or for example, in thermodynamics, observers don't get to say the position of every single molecule in a gas. They can only see the kind of large scale features. And that's why the second law of thermodynamics, law of entropy increase, and so on works. If you could see every individual molecule, you wouldn't conclude something about thermodynamics. You would conclude, oh, these molecules just all doing these particular things. You wouldn't be able to see this aggregate fact. So I strongly expect that, in fact, in the theories that I have, that one has to be more realistic about the computation and other aspects of observers in order to actually make a correspondence between what we experience. In fact, my little team and I have a little theory right now about how quantum mechanics may work, which is a very wonderfully bizarre idea about how the sort of thread of human consciousness relates to what we observe in the universe. But there's several steps to explain what that's about. What do you make of the mess of the observer at the lower level of quantum mechanics? Sort of the textbook definition with quantum mechanics kind of says that there's two worlds. One is the world that actually is, and the other is that's observed. What do you make sense of that kind of observing? Well, I think, actually, the ideas we've recently had might actually give away into this. And that's, I don't know yet. I mean, I think that's, it's a mess. I mean, the fact is there is a, one of the things that's interesting, and when people look at these models that I started talking about 30 years ago now, they say, oh, no, that can't possibly be right. You know, what about quantum mechanics? Right? You say, okay, tell me what is the essence of quantum mechanics? What do you want me to be able to reproduce to know that I've got quantum mechanics, so to speak? Well, and that question comes up, comes up very operationally, actually, because we've been doing a bunch of stuff with quantum computing. And there are all these companies that say, we have a quantum computer. We say, let's connect to your API and let's actually run it. And they're like, well, maybe you shouldn't do that yet. We're not quite ready yet. And one of the questions that I've been curious about is, if I have five minutes with a quantum computer, how can I tell if it's really a quantum computer or whether it's a simulator at the other end? Right? And turns out it's really hard. It turns out there isn't, it's like a lot of these questions about sort of what is intelligence, what's life. It's a boring test for quantum computing. That's right. That's right. It's like, are you really a quantum computer? And I think the simulation. Yes, exactly. Is it just a simulation or is it really a quantum computer? Same issue all over again. So this whole issue about the sort of mathematical structure of quantum mechanics and the completely separate thing that is our experience in which we think definite things happen, whereas quantum mechanics doesn't say definite things ever happen. Quantum mechanics is all about the amplitudes for different things to happen. But yet our thread of consciousness operates as if definite things are happening. To linger on the point, you've kind of mentioned the structure that could underlie everything and this idea that it could perhaps have something like a structure of a graph. Can you elaborate why your intuition is that there's a graph structure of nodes and edges and what it might represent? Right. Okay. So the question is, what is in a sense the most structuralist structure you can imagine? Right? And in fact, what I've recently realized in the last year or so, I have a new most structuralist structure. By the way, the question itself is a beautiful one and a powerful one in itself. So even without an answer, just the question is a really strong question. Right. But what's your new idea? Well it has to do with hypergraphs. Actually what is interesting about the sort of model I have now is, it's a little bit like what happened with computation. Everything that I think of as, oh, well maybe the model is this, I discover it's equivalent. And that's quite encouraging because it's like I could say, well, I'm going to look at trivalent graphs with three edges for each node and so on. Or I could look at this special kind of graph. Or I could look at this kind of algebraic structure. And it turns out that the things I'm now looking at, everything that I've imagined that is a plausible type of structuralist structure is equivalent to this. So what is it? Well, a typical way to think about it is, well, so you might have some collection of tuples, collection of, let's say numbers. So you might have 1, 3, 5, 2, 3, 4, little, just collections of numbers, triples of numbers, let's say, quadruples of numbers, pairs of numbers, whatever. And you have all these sort of floating little tuples. They're not in any particular order. And that sort of floating collection of tuples, and I told you this was abstract, represents the whole universe. The only thing that relates them is when a symbol is the same, it's the same, so to speak. So if you have two tuples and they contain the same symbol, let's say at the same position of the tuple, the first element of the tuple, then that represents a relation. Okay, so let me try and peel this back. Wow, okay. I told you it's abstract, but this is the... So the relationship is formed by some aspect of sameness. Right. So think about it in terms of a graph. So a graph, a bunch of nodes, let's say you number each node, okay? Then what is a graph? A graph is a set of pairs that say this node has an edge connecting it to this other node. So that's the... And a graph is just a collection of those pairs that say this node connects to this other node. So this is a generalization of that, in which instead of having pairs, you have arbitrary n-tuples. That's it. That's the whole story. And now the question is, okay, so that might represent the state of the universe. How does the universe evolve? What does the universe do? And so the answer is that what I'm looking at is transformation rules on these hypergraphs. In other words, you say this, whenever you see a piece of this hypergraph that looks like this, turn it into a piece of a hypergraph that looks like this. So on a graph, it might be when you see the subgraph, when you see this thing with a bunch of edges hanging out in this particular way, then rewrite it as this other graph. And so that's the whole story. So the question is, what... So now you say, I mean, as I say, this is quite abstract. And one of the questions is, where do you do those updating? So you've got this giant graph. What triggers the updating? What's the ripple effect of it? Is it... Yes. And I suspect everything's discrete, even in time. So... Okay, so the question is, where do you do the updates? Yes. And the answer is, the rule is, you do them wherever they apply. And you do them, the order in which the updates is done is not defined. That is, you can do them, so there may be many possible orderings for these updates. Now the point is, imagine you're an observer in this universe. So and you say, did something get updated? Well, you don't in any sense know until you yourself have been updated. So in fact, all that you can be sensitive to is essentially the causal network of how an event over there affects an event that's in you. That doesn't even feel like observation. That's like, that's something else. You're just part of the whole thing. Yes, you're part of it. But even to have... So the end result of that is all you're sensitive to is this causal network of what event affects what other event. I'm not making a big statement about sort of the structure of the observer. I'm simply saying, I'm simply making the argument that what happens, the microscopic order of these rewrites is not something that any observer, any conceivable observer in this universe can be affected by. Because the only thing the observer can be affected by is this causal network of how the events in the observer are affected by other events that happen in the universe. So the only thing you have to look at is the causal network. You don't really have to look at this microscopic rewriting that's happening. So these rewrites are happening wherever they... They happen wherever they feel like. Causal network, you said that there's not really... so the idea would be an undefined, like what gets updated, the sequence of things is undefined. That's what you mean by the causal network, but then the... No, the causal network is given that an update has happened, that's an event. Then the question is, is that event causally related to? Does that event... If that event didn't happen, then some future event couldn't happen yet. And so you build up this network of what affects what. And so what that does, so when you build up that network, that's kind of the observable aspect of the universe in some sense. And so then you can ask questions about how robust is that observable network of what's happening in the universe. Okay, so here's where it starts getting kind of interesting. So for certain kinds of microscopic rewriting rules, the order of rewrites does not matter to the causal network. And so this is, okay, mathematical logic moment, this is equivalent to the Church-Rossa property or the confluence property of rewrite rules. And it's the same reason that if you are simplifying an algebraic expression, for example, you can say, oh, let me expand those terms out, let me factor those pieces. Doesn't matter what order you do that in, you'll always get the same answer. And that's, it's the same fundamental phenomenon that causes for certain kinds of microscopic rewrite rules, that causes the causal network to be independent of the microscopic order of rewritings. Why is that property important? Because it implies special relativity. I mean, the reason it's important is that that property, special relativity says you can look at these sort of, you can look at different reference frames, you can have different, you can be looking at your notion of what space and what time can be different, depending on whether you're traveling at a certain speed, depending on whether you're doing this, that and the other. But nevertheless, the laws of physics are the same. That's what the principle of special relativity says, is laws of physics are the same independent of your reference frame. Well, it turns out this sort of change of the microscopic rewriting order is essentially equivalent to a change of reference frame, or at least there's a sub part of how that works that's equivalent to change of reference frame. So, somewhat surprisingly, and sort of for the first time in forever, it's possible for an underlying microscopic theory to imply special relativity, to be able to derive it. It's not something you put in as a, this is a, it's something where this other property, causal invariance, which is also the property that implies that there's a single thread of time in the universe. It might not be the case that that's, that is the, that's what would lead to the possibility of an observer thinking that definite stuff happens. Otherwise, you've got all these possible rewriting orders, and who's to say which one occurred. But with this causal invariance property, there's a notion of a definite thread of time. It sounds like that kind of idea of time, even space, would be emergent from the system. Oh yeah. No, I mean- So it's not a fundamental part of the system. No, no. At a fundamental level, all you've got is a bunch of nodes connected by hyper edges or whatever. So there's no time, there's no space. That's right. And, but the thing is that it's just like imagining, imagine you're just dealing with a graph, and imagine you have something like a, you know, like a honeycomb graph, where you have a bunch of hexagons. You know, that graph at a microscopic level, it's just a bunch of nodes connected to other nodes. But at a macroscopic level, you say that looks like a honeycomb, you know, lattice. It looks like a two-dimensional, you know, manifold of some kind. It looks like a two-dimensional thing. If you connect it differently, if you just connect all the nodes one to another, and kind of a sort of linked list type structure, then you'd say, well, that looks like a one-dimensional space. But at the microscopic level, all these are just networks with nodes. The macroscopic level, they look like something that's like one of our sort of familiar kinds of space. And it's the same thing with these hyper graphs. Now, if you ask me, have I found one that gives me three-dimensional space? The answer is not yet. So we don't know, you know, this is one of these things we're kind of betting against nature, so to speak. And I have no way to know. So there are many other properties of this, this kind of system that have a very beautiful actually and very suggestive. And it will be very elegant if this turns out to be right, because it's very clean. I mean, you start with nothing and everything gets built up. Everything about space, everything about time, everything about matter, it's all just emergent from the properties of this extremely low-level system. And that will be pretty cool if that's the way our universe works. Now, do I, on the other hand, the thing that I find very confusing is, let's say we succeed. Let's say we can say this particular sort of hyper graph rewriting rule gives the universe. Just run that hyper graph rewriting rule for enough times and you'll get everything. You'll get this conversation we're having. You'll get everything. It's that if we get to that point and we look at what is this thing, what is this rule that we just have that is giving us our whole universe? How do we think about that thing? Let's say, turns out the minimal version of this, and this is kind of cool thing for a language designer like me, the minimal version of this model is actually a single line of orphan language code. Which I wasn't sure was going to happen that way, but it's kind of, no, we don't know what, that's just the framework to know the actual particular hyper graph that might be a longer, the specification of the rules might be slightly longer. How does that help you accept marveling in the beauty and the elegance of the simplicity that creates the universe? Does that help us predict anything? Not really because of the irreducibility. That's correct. That's correct. But so the thing that is really strange to me, and I haven't wrapped my brain around this yet, is one keeps on realizing that we're not special in the sense that we don't live at the center of the universe. We don't blah, blah, blah. And yet if we produce a rule for the universe and it's quite simple and we can write it down in a couple of lines or something, that feels very special. How did we come to get a simple universe when many of the available universes, so to speak, are incredibly complicated? It might be a quintillion characters long. Why did we get one of the ones that's simple? And so I haven't wrapped my brain around that issue yet. If indeed we are in such a simple, the universe is such a simple rule, is it possible that there is something outside of this that we are in a kind of what people call the simulation, right? That we're just part of a computation that's being explored by a graduate student in an alternate universe? Well, you know, the problem is we don't get to say much about what's outside our universe because by definition our universe is what we exist within. Now can we make a sort of almost theological conclusion from being able to know how our particular universe works? Interesting question. I don't think that if you ask the question, could we, and it relates again to this question about extraterrestrial intelligence, you know, we've got the rule for the universe. Was it built in on purpose? Hard to say. That's the same thing as saying we see a signal from, you know, that we're, you know, receiving from some, you know, random star somewhere and it's a series of pulses and, you know, it's a periodic series of pulses, let's say. Was that done on purpose? Can we conclude something about the origin of that series of pulses? Just because it's elegant does not necessarily mean that somebody created it or that we can even comprehend what would create it. Yeah, I think it's the ultimate version of the sort of identification of the techno signature question. I think the ultimate version of that is was our universe a piece of technology, so to speak, and how on earth would we know? Because but I mean, it'll be, it's, I mean, you know, in the kind of crazy science fiction thing you could imagine, you could say, oh, somebody is going to have, you know, there's going to be a signature there, it's going to be, you know, made by so and so. But there's no way we could understand that, so to speak. And it's not clear what that would mean. Because the universe simply, you know, this, if we find a rule for the universe, we're not we're simply saying that rule represents what our universe does. We're not saying that that rule is something running on a big computer and making our universe. It's just saying that represents what our universe does in the same sense that, you know, laws of classical mechanics, differential equations, whatever they are, represent what mechanical systems do. It's not that the mechanical systems are somehow running solutions to those differential equations, those differential equations just representing the behavior of those systems. So what's the gap in your sense to linger on the fascinating, perhaps slightly sci-fi question? What's the gap between understanding the fundamental rules that create a universe and engineering a system, actually creating a simulation ourselves? You've talked about sort of, you've talked about, you know, nanoengineering kind of ideas that are kind of exciting, actually creating some ideas of computation in the physical space. How hard is it as an engineering problem to create the universe once you know the rules that create it? Well, that's an interesting question. I think the substrate on which the universe is operating is not a substrate that we have access to. I mean, the only substrate we have is that same substrate that the universe is operating in. So if the universe is a bunch of hypergraphs being rewritten, then we get to attach ourselves to those same hypergraphs being rewritten. We don't get to, and if you ask the question, you know, is the code clean? You know, can we write nice, elegant code with efficient algorithms and so on? Well, that's an interesting question. How, you know, that's this question of how much computational reducibility there is in the system. I mean, I've seen some beautiful cellular automata that basically create copies of itself within itself, right? That's the question, whether it's possible to create, like whether you need to understand the substrate or whether you can just- Yeah, well, right. I mean, so one of the things that is sort of one of my slightly sci-fi thoughts about the future, so to speak, is, you know, right now, if you poll typical people, you say, do you think it's important to find the fundamental theory of physics? You get, because I've done this poll informally at least, it's curious, actually, you get a decent fraction of people saying, oh yeah, that would be pretty interesting. I think that's becoming, surprisingly enough, more, I mean, a lot of people are interested in physics in a way that, like without understanding it, just kind of watching scientists, a very small number of them, struggle to understand the nature of our reality. Right. And I think that's somewhat true, and in fact, in this project that I'm launching into to try and find the fundamental theory of physics, I'm going to do it as a very public project. I mean, it's going to be live streamed and all this kind of stuff, and I don't know what will happen. It'll be kind of fun. I mean, I think that it's the interface to the world of this project. I mean, I figure one feature of this project is, you know, unlike technology projects that basically are what they are, this is a project that might simply fail, because it might be the case that it generates all kinds of elegant mathematics that has absolutely nothing to do with the physical universe that we happen to live in. Well, okay, so we're talking about kind of the quest to find the fundamental theory of physics. The first point is, you know, it's turned out it's kind of hard to find the fundamental theory of physics. People weren't sure that that would be the case. Back in the early days of applying mathematics to science, 1600s and so on, people were like, oh, in 100 years we'll know everything there is to know about how the universe works. It turned out to be harder than that. And people got kind of humble at some level, because every time we got to sort of a greater level of smallness and studying the universe, it seemed like the math got more complicated and everything got harder. Basically, when I was a kid, basically, I started doing particle physics, and when I was doing particle physics, I always thought finding the fundamental, fundamental theory of physics, that's a kooky business, we'll never be able to do that. But we can operate within these frameworks that we built for doing quantum field theory and general relativity and things like this, and it's all good and we can figure out a lot of stuff. Did you even at that time have a sense that there's something behind that too? Sure, sure. I just didn't expect that. I thought in some rather un... It's actually kind of crazy, thinking back on it, because it's kind of like there was this long period in civilization where people thought the ancients had it all figured out and we'll never figure out anything new. And to some extent, that's the way I felt about physics when I was in the middle of doing it, so to speak, was, you know, we've got quantum field theory, it's the foundation of what we're doing. And there's, you know, yes, there's probably something underneath this, but we'll sort of never figure it out. But then I started studying simple programs in the computational universe, things like cellular automata and so on. And I discovered that they do all kinds of things that were completely at odds with the intuition that I had had. And so after that, after you see this tiny little program that does all this amazingly complicated stuff, then you start feeling a bit more ambitious about physics and saying, maybe we could do this for physics too. And so that's, that got me started years ago now, in this kind of idea of could we actually find what's underneath all of these frameworks like quantum field theory, general relativity and so on. And people perhaps don't realize as clearly as they might, that, you know, the frameworks we're using for physics, which is basically these two things, quantum field theory, sort of the theory of small stuff and general relativity, theory of gravitation and large stuff. Those are the two basic theories and they're a hundred years old. I mean, general relativity was 1915, quantum field theory, well 1920s. So basically a hundred years old and they've, it's been a good run. There's a lot of stuff been figured out, but what's interesting is the foundations haven't changed in all that period of time. Even though the foundations had changed several times before that in the 200 years earlier than that. And I think the kinds of things that I'm thinking about, which are sort of really informed by thinking about computation and the computational universe, it's a different foundation. It's a different set of foundations and might be wrong, but it is at least, you know, we have a shot. And I think it's, you know, to me, it's, you know, my personal calculation for myself is, is you know, if it turns out that the finding the fundamental theory of physics, it's kind of low hanging fruit, so to speak, it'd be a shame if we just didn't think to do it. You know, if people just said, oh, you'll never figure that stuff out. Let's, you know, and it takes another 200 years before anybody gets around to doing it. You know, I think it's, I don't know how low hanging this fruit actually is. It may be, you know, it may be that it's kind of the wrong century to do this project. I mean, I think the, the, the cautionary tale for me, you know, I think about things that I've tried to do in technology where people thought about doing them a lot earlier. My favorite example is probably Leibniz who, who thought about making essentially encapsulating the world's knowledge in a computational form in the late 1600s and did a lot of things towards that and basically, you know, we finally managed to do this, but he was 300 years too early and that's the, that's kind of the, in terms of life planning, it's kind of like avoid things that can't be done in your, in your century, so to speak. Yeah. Timing, timing is everything. So you think if we kind of figure out the underlying rules that can create from which quantum field theory and general relativity can emerge, do you think that'll help us unify it at that level of abstraction? Oh, we'll know it completely. We'll know how that all fits together. Yes, without a question. And I mean, it's already, even the things I've already done, they're very, you know, it's very, very elegant actually, how, how things seem to be fitting together. Now, you know, is it right? I don't know yet. It's awfully suggestive. If it isn't right, it's then the designer of the universe should feel embarrassed, so to speak, because it's a really good way to do it. And your intuition in terms of designing universe, does God play dice? Is there, is there randomness in this thing or is it deterministic? So the kind of graph- That's a little bit of a complicated question because when you're dealing with these things that involve these rewrites that have, okay. Even randomness is an emergent phenomenon perhaps? Yes. I mean, it's a, yeah, well, randomness in many of these systems, pseudo-randomness and randomness are hard to distinguish. In this particular case, the current idea that we have about measurement and quantum mechanics is something very bizarre and very abstract. And I don't think I can yet explain it without kind of yakking about very technical things. Eventually I will be able to. But if that's right, it's kind of a, it's a weird thing because it slices between determinism and randomness in a weird way that hasn't been sliced before, so to speak. So like many of these questions that come up in science, where it's like, is it this or is it that? Turns out the real answer is it's neither of those things. It's something kind of different and sort of orthogonal to those categories. And so that's the current, this week's idea about how that might work. But we'll see how that unfolds. I mean, there's this question about a field like physics and sort of the quest for fundamental theory and so on. And there's both the science of what happens and there's the sort of the social aspect of what happens. Because in a field that is basically as old as physics, we're at, I don't know what it is, fourth generation, I don't know, fifth generation, I don't know what generation it is of physicists. And like, I was one of these, so to speak. And for me, the foundations were like the pyramids, so to speak. You know, it was that way and it was always that way. It is difficult in an old field to go back to the foundations and think about rewriting them. It's a lot easier in young fields where you're still dealing with the first generation of people who invented the field. And it tends to be the case that the nature of what happens in science tends to be, you know, you'll get, typically the pattern is some methodological advance occurs. And then there's a period of five years, 10 years, maybe a little bit longer than that, where there's lots of things that are now made possible by that methodological advance, whether it's, you know, I don't know, telescopes or whether that's some mathematical method or something. It's, you know, there's a something, something happens, a tool gets built, and then you can do a bunch of stuff. And there's a bunch of low hanging fruit to be picked. And that takes a certain amount of time. After that, all that low hanging fruit is picked, then it's a hard slog for the next however many decades or century or more to get to the next sort of level at which one can do something. And it tends to be the case that in fields that are in that kind of, I wouldn't say cruise mode because it's really hard work, but it's very hard work for very incremental progress.