AI Simulating Humans to Understand Itself (Joscha Bach) | AI Podcast Clips

Let me ask a romanticized question. What is the most beautiful to you, silly ape, the most beautiful or surprising idea in the development of artificial intelligence, whether in your own life or in the history of artificial intelligence that you've come across? If you built an AI, it probably can make models at an arbitrary degree of detail, right, of the world. And then it would try to understand its own nature. It's tempting to think that at some point when we have general intelligence, we have competitions where we will let the AIs wake up in different kinds of physical universes, and we measure how many movements of the Rubik's cube it takes until it's figured out what's going on in its universe and what it is in its own nature and its own physics and so on, right? So what if we exist in the memory of an AI that is trying to understand its own nature and remembers its own genesis and remembers Lex and Yosha sitting in a hotel room, sparking some of the ideas off that led to the development of general intelligence? So we're a kind of simulation that's running in an AI system that's trying to understand itself. Maybe. It's not that I believe that, but I think it's a beautiful idea. It's a, yeah. Yeah. Yeah. Yeah. I mean, yeah, you kind of return to this idea with the Turing test of intelligence being, of intelligence being the process of asking and answering what is intelligence. I mean, what, why, do you think there's an answer? Why is there such a search for an answer? So does there have to be like an answer? You just said an AI system that's trying to understand the why of what, you know, understand itself. Is that a fundamental process of greater and greater complexity, greater and greater intelligence? Is the continuous trying of understanding itself? No, I think you will find that most people don't care about that because they're well-adjusted enough to not care. And the reason why people like you and me care about it probably has to do with the need to understand ourselves. It's because we are in fundamental disagreement with the universe that we wake up in. What's the disagreement? I look down on me and I see, oh my God, I'm caught in a monkey. What's that? Some people are unhappy with the government and I'm unhappy with the entire universe that I find myself in. So you don't think that's a fundamental aspect of human nature that some people are just suppressing? That they wake up shocked they're in the body of a monkey? No, there is a clear adaptive value to not be confused by that and by. Well, no, that's not what I asked. So yeah, if there's clear adaptive value, then there's clear adaptive value to, while fundamentally your brain is confused by that, by creating an illusion, another layer of the narrative that says, that tries to suppress that and instead say that what's going on with the government right now is the most important thing and what's going on with my football team is the most important thing. But it seems to me, for me it was a really interesting moment reading Ernest Becker's Denial of Death. This kind of idea that we're all, the fundamental thing from which most of our human mind springs is this fear of mortality and being cognizant of your mortality and the fear of that mortality. And then you construct illusions on top of that. I guess you being, just to push on it, you really don't think it's possible that this worry of the big existential questions is actually fundamental as the existentialist thought to our existence. I think that the fear of death only plays a role as long as you don't see the big picture. The thing is that minds are software states, right? Software doesn't have identity. Software in some sense is a physical law. But if, hold on a second, but if, yeah. Right, software. But it feels like there's an identity. I thought that was for this particular piece of software and the narrative it tells, that's a fundamental property of it, of assigning an identity. The maintenance of the identity is not terminal. It's instrumental to something else. You maintain your identity so you can serve your meaning. So you can do the things that you're supposed to do before you die. And I suspect that for most people, the fear of death is the fear of dying before they're done with the things that they feel they have to do even though they cannot quite put their finger on it, what that is. Right, but in the software world, to return to the question, then what happens after we die? Because. Why would you care? You will not be longer there. The point of dying is that you are gone. Well, maybe I'm not. This is what, you know, it seems like there's so much, in the idea that this is just, the mind is just a simulation that's constructing a narrative around some particular aspects of the quantum mechanical wave function world that we can't quite get direct access to. Then the idea of mortality seems to be fuzzy as well. Maybe there's not a clear end. Fuzzy idea is the one of continuous existence. We don't have continuous existence. How do you know that? Like that. Because it's not computable. Because you're saying it's gonna be direct. There is no continuous process. The only thing that binds you together with the Lex Friedman from yesterday is the illusion that you have memories about him. So if you want to upload, it's very easy. You make a machine that thinks it's you. Because it's the same thing that you are. You are a machine that thinks it's you. But that's immortality. Yeah, but it's just a belief. You can create this belief very easily once you realize that the question whether you are immortal or not depends entirely on your beliefs and your own continuity. But then you can be immortal by the continuity of the belief. You cannot be immortal, but you can stop being afraid of your mortality because you realize you were never continuously existing in the first place. Well, I don't know if I'd be more terrified or less terrified by that. It seems like the fact that I existed. Also, you don't know this state in which you don't have a self. You can turn off yourself, you know? I can't turn off myself. You can't turn it off. You can't turn it off. I can. So you can basically meditate yourself in a state where you are still conscious, where still things are happening, where you know everything that you knew before, but you're no longer identified with changing anything. And this means that your self, in a way, dissolves. There is no longer this person. You know that this person construct exists in other states and it runs on this brain of Lex Friedman, but it's not a real thing. It's a construct. It's an idea. And you can change that idea. And if you let go of this idea, if you don't think that you are special, you realize it's just one of many people and it's not your favorite person even, right? It's just one of many. And it's the one that you are doomed to control for the most part. And that is basically informing the actions of this organism as a control model. This is all there is. And you are somehow afraid that this control model gets interrupted or loses the identity of continuity. Yeah, so I'm attached, I mean, yeah, it's a very popular, it's a somehow compelling notion that being attached, like there's no need to be attached to this idea of an identity. But that in itself could be an illusion that you construct. So the process of meditation, while popularly thought of as getting under the concept of identity, it could be just putting a cloak over it, just telling it to be quiet for the moment. I think that meditation is eventually just a bunch of techniques that let you control attention. And when you can control attention, you can get access to your own source code, hopefully not before you understand what you're doing. And then you can change the way it works, temporarily or permanently. So yeah, meditation is to get a glimpse at the source code, get under, so basically control or turn off the attention. The thing is that you learn to control attention. So everything else is downstream from controlling attention. And control the attention that's looking at the attention. Normally we only get attention in the parts of our mind that create heat, where you have a mismatch between model and the results that are happening. And so most people are not self-aware because their control is too good. If everything works out roughly the way you want, and the only things that don't work out is whether your football team wins, then you will mostly have models about these domains. And it's only when, for instance, your fundamental relationships to the world around you don't work, because the ideology of your country is insane and the other kids are not nerds and don't understand why you understand physics and you don't, why you want to understand physics and you don't understand why somebody would not want to understand physics.


Elon Musk: Understanding the Human Brain at Neuralink

We currently understand very little about the human brain. Do you also hope that the work at Neuralink will help us understand more about the human mind, about the brain? Yeah, I think the work at Neuralink will definitely shed a lot of insight into how the brain and the mind works. Right now, just the data we have regarding how the brain works is very limited. We've got fMRI, which is kind of like putting a stethoscope on the outside of a factory wall and then putting it all over the factory wall and you can sort of hear the sounds, but you don't know what the machines are doing, really. You can infer a few things, but it's very broad brushstroke. In order to really know what's going on in the brain, you have to have high-precision sensors, and then you want to have stimulus and response. If you trigger a neuron, how do you feel? What do you see? How does it change your perception of the world? You're speaking to physically just getting close to the brain, being able to measure signals from the brain will open the door inside the factory. Yes, exactly. Being able to have high-precision sensors that tell you what individual neurons are doing, and then being able to trigger a neuron and see what the response is in the brain. So you can see the consequences of if you fire this neuron, what happens? How do you feel? What does it change? It'll be really profound to have this in people because people can articulate their change, like if there's a change in mood, or if they can tell you if they can see better or hear better, or be able to form sentences better or worse, or their memories are jogged, or that kind of thing.


Donald Knuth: P=NP | AI Podcast Clips

You've mentioned over the past few years that you believe P may be equal to NP, but that it's not really, you know, if somebody does prove that P equals NP, it will not directly lead to an actual algorithm to solve difficult problems. Can you explain your intuition here? Has it been changed? And in general, on the difference between easy and difficult problems of P and NP and so on? So the popular idea is if an algorithm exists, then somebody will find it. And it's just a matter of writing it down. But many more algorithms exist than anybody can understand or ever make use of. Or discover, yeah. Because they're just way beyond human comprehension. The total number of algorithms is more than mind-boggling. So we have situations now where we know that algorithms exist, but we don't know, we don't have the foggiest idea what the algorithms are. There are simple examples based on game playing where you have, where you say, well, there must be an algorithm that exists to win in the game of hex, because, for the first player to win in the game of hex, because hex is always either a win for the first player or the second player. Well, what's the game of hex? There's a game of hex which is based on putting pebbles onto a hexagonal board, and the white player tries to get a white path from left to right, and the black player tries to get a black path from bottom to top. And how does capture occur? And there's no capture. You just put pebbles down one at a time. But there's no draws, because after all the white and black are played, there's either going to be a white path across from east to west or a black path from bottom to top. So there's always, you know, it's the perfect information game, and people take turns, like tic-tac-toe. And the hex board can be different sizes, but there's no possibility of a draw, and players move one at a time. And so it's got to be either a first player win or a second player win. Mathematically, you follow out all the trees, and there's always a win for the first player, second player, okay. And it's finite. The game is finite. But there's an algorithm that will decide—you can show it has to be one or the other, because the second player could mimic the first player with kind of a pairing strategy. And so you can show that it has to be one way or the other. But we don't know any algorithm anyway. We don't know—there are cases where you can prove the existence of a solution, but nobody knows any way how to find it. More like the algorithm question, there's a very powerful theorem in graph theory by Robinson and Seymour that says that every class of graphs that is closed under taking minors has a polynomial time algorithm to determine whether it's in this class or not. Now, a class of graphs, for example, planar graphs, these are graphs that you can draw in a plane without crossing lines. And a planar graph, taking minors means that you can shrink an edge into a point, or you can delete an edge. And so you start with a planar graph, shrink any edge to a point, it's still planar. Delete an edge, it's still planar. But there are millions of different ways to describe a family of graphs that still remains the same under taking minor. And Robinson and Seymour proved that any such family of graphs, there's a finite number of minimum graphs that are obstructions, so that if it's not in the family, then it has to contain, then there has to be a way to shrink it down until you get one of these bad minimum graphs that's not in the family. In the case of a planar graph, the minimum graph is a five-pointed star where everything points to another, and the minimum graph consisting of trying to connect three utilities to three houses without crossing lines. And so there are two bad graphs that are not planar. And every non-planar graph contains one of these two bad graphs by shrinking and removing edges. Sorry, can you say that again? So he proved that there's a finite number of these bad graphs. There's always a finite number. So somebody says, here's a family of- It's hard to believe. It's very surprising. And they proved it in a sequence of 20 papers, I mean, and it's deep work. Because that's for any arbitrary class. So it's for any- Any arbitrary class that's closed under taking minors. Closed under, maybe I'm not understanding, because it seems like a lot of them are closed under taking minors. Almost all the important classes of graphs are. There are tons of such graphs, but also hundreds of them that arise in applications. I have a book over here called Classes of Graphs, and it's amazing how many different classes of graphs that people have looked at. So why do you bring up this theorem, or this proof? So now, there's lots of algorithms that are known for special classes of graphs. For example, if I have a chordal graph, then I can color it efficiently. If I have some kind of graphs, it'll make a great network. So you'd like to test it. Somebody gives you a graph, and says, oh, is it in this family of graphs? If so, then I can go to the library and find an algorithm that's going to solve my problem on that graph. So we want to have a graph that says—an algorithm that says, you give me a graph, I'll tell you whether it's in this family or not. And so all I have to do is test whether or not—does this given graph have a minor that's one of the bad ones? A minor is everything you can get by shrinking and removing it. And given any minor, there's a polynomial time algorithm saying I can tell whether this is a minor of you. And there's a finite number of bad cases. So I just try, you know, does it have this bad case? Polynomial time, I got the answer. Does it have this bad case? Polynomial time, I got the answer. Total polynomial time. And so I've solved the problem. However, all we know is that the number of minors is finite. We don't know what—we might only know one or two of those minors, but we don't know that if we've got 20 of them, we don't know there might be 21, 25. All we know is that it's finite. So here we have a polynomial time algorithm that we don't know. That's a really great example of what you worry about or why you think P equals NP won't be useful. But still, why do you hold the intuition that P equals NP? Because you have to rule out so many possible algorithms as being not working. You know, you can take the graph and you can represent it as in terms of certain prime numbers and then you can multiply those together and then you can take the bitwise and and construct some certain constant in polynomial time and then that's a perfectly valid algorithm. And there are so many algorithms of that kind. A lot of times we see random—you take data and we get coincidences that some fairly random looking number actually is useful because it happens to solve a problem just because there's so many hairs on your head. But it seems like unlikely that two people are going to have the same number of hairs on their head. But you can count how many people there are and how many hairs on their head. So there must be people walking around in the country that have the same number of hairs on their head. Well, that's a kind of a coincidence that you might say also, you know, this particular combination of operations just happens to prove that a graph has a Hamiltonian path. I see lots of cases where unexpected things happen when you have enough possibilities. Because the space of possibility is so huge, your intuition just says— You have to rule them all out. And so that's the reason for my intuition. It's by no means a proof. Some people say, well, P can't equal NP because you've had all these smart people. The smartest designers of algorithms have been wracking their brains for years and years and there's million-dollar prizes out there. Nobody has thought of the algorithm, so there must be no such algorithm. On the other hand, I can use exactly the same logic and I can say, well, P must be equal to NP because there's so many smart people out here been trying to prove it unequal to NP and they've all failed. This kind of reminds me of the discussion about the search for aliens. We've been trying to look for them and we haven't found them yet, therefore they don't exist but you can show that there's so many planets out there that they very possibly could exist. Yeah, right. And then there's also the possibility that they exist but they all discovered machine learning or something and then blew each other up.


Ava's Smile: Ex Machina's Most Important Moment (Alex Garland) | AI Podcast Clips

For an entire generation of AI researchers, 2001, a space odyssey, put an image, the idea of human level, super human level intelligence into their mind. Do you ever, sort of jumping back to Ex Machina and talk a little bit about that, do you ever consider the audience of people who build the systems, the roboticists, the scientists that build the systems based on the stories you create? Which I would argue, I mean, there's literally most of the top researchers, about 40, 50 years old and plus, you know, that's their favorite movie, 2001, Space Odyssey. It really is in their work, their idea of what ethics is, of what is the target, the hope, the dangers of AI, is that movie, right? Do you ever consider the impact on those researchers when you create the work you do? Certainly not with Ex Machina in relation to 2001, because I'm not sure, I mean, I'd be pleased if there was, but I'm not sure, in a way, there isn't a fundamental discussion of issues to do with AI that isn't already and better dealt with by 2001. 2001 does a very, very good account of the way in which an AI might think and also potential issues with the way the AI might think. And also, then a separate question about whether the AI is malevolent or benevolent. And 2001 doesn't really, it's a slightly odd thing to be making a film when you know there's a pre-existing film, which is not a really superb job. But there's questions of consciousness, embodiment, and also the same kinds of questions. Because those are my two favorite AI movies. So can you compare HAL 9000 and EVA, HAL 9000 from 2001 Space Odyssey and EVA from Ex Machina, the, in your view, from a philosophical perspective. But they've got different goals. The two AIs have completely different goals. I think that's really the difference. So in some respects, Ex Machina took as a premise, how do you assess whether something else has consciousness? So it was a version of the Turing test, except instead of having the machine hidden, you put the machine in plain sight, in the way that we are in plain sight of each other and say, now assess the consciousness. And the way it was illustrating the way in which you'd assess the state of consciousness of a machine is exactly the same way we assess the state of consciousness of each other. And in exactly the same way that in a funny way, your sense of my consciousness is actually based primarily on your own consciousness, that is also then true with the machine. And so it was actually about how much of the sense of consciousness is a projection rather than something that consciousness is actually containing. And has Plato's cave, I mean, this, you really explored, you could argue that HAL sort of Space Odyssey explores idea of the Turing test for intelligence. No, not test, there's no test, but it's more focused on intelligence. And Ex Machina kind of goes around intelligence and says the consciousness of the human to human, human to robot interaction is more interesting, more important, more, at least the focus of that particular movie. Yeah, it's about the interior state and what constitutes the interior state and how do we know it's there. And actually in that respect, Ex Machina is as much about consciousness in general as it is to do specifically with machine consciousness. And it's also interesting, you know that thing you started asking about, the dream state, and I was saying, well, I think we're all in a dream state because we're all in a subjective state. One of the things that I became aware of with Ex Machina is that the way in which people reacted to the film was very based on what they took into the film. So many people thought Ex Machina was the tale of a sort of evil robot who murders two men and escapes and she has no empathy, for example, because she's a machine. Whereas I felt, no, she was a conscious being with a consciousness different from mine, but so what, imprisoned and made a bunch of value judgments about how to get out of that box. And there's a moment which it sort of slightly bugs me, but nobody ever has noticed it and it's years after, so I might as well say it now, which is that after Ava has escaped, she crosses a room and as she's crossing a room, this is just before she leaves the building, she looks over her shoulder and she smiles. And I thought after all the conversation about tests, in a way, the best indication you could have of the interior state of someone is if they are not being observed and they smile about something, where they're smiling for themselves. And that, to me, was evidence of Ava's true sentience, whatever that sentience was. But that's really interesting. We don't get to observe Ava much or something like a smile in any context except through interaction, trying to convince others that she's conscious, that's beautiful. Exactly, yeah. But it was a small, in a funny way, I think maybe people saw it as an evil smile, like, ha, you know, I fooled them. But actually, it was just a smile. And I thought, well, in the end, after all the conversations about the test, that was the answer to the test, and then off she goes. So if we align, if we just to linger a little bit longer on Hal and Ava, do you think in terms of motivation, what was Hal's motivation? Is Hal good or evil? Is Ava good or evil? Ava's good, in my opinion. And Hal is neutral, because I don't think Hal is presented as having a sophisticated emotional life. He has a set of paradigms, which is that the mission needs to be completed. I mean, it's a version of the paperclip. Yeah. The idea that it's a super intelligent machine, but it's just performed a particular task. Yeah. And in doing that task, may destroy everybody on Earth, or may achieve undesirable effects for us humans. Precisely, yeah. But what if, okay. At the very end, he says something like, I'm afraid, Dave. But that maybe he is on some level experiencing fear, or it may be this is the terms in which it would be wise to stop someone from doing the thing they're doing, if you see what I mean. Yes, absolutely. So actually, that's funny. So that's such a small, short exploration of consciousness that I'm afraid. And then you just with X-Mach gonna say, okay, we're gonna magnify that part, and then minimize the other part. So that's a good way to sort of compare the two. But if you could just use your imagination, and if Ava sort of, I don't know, I don't know, was president of the United States, so had some power. So what kind of world would she want to create? If we, as you kind of say, good. And there is a sense that she has a really, like there's a desire for a better human to human interaction, human to robot interaction in her. But what kind of world do you think she would create with that desire? So that's a really, that's a very interesting question. I'm gonna approach it slightly obliquely, which is that if a friend of yours got stabbed in a mugging, and you then felt very angry at the person who'd done the stabbing, but then you learned that it was a 15 year old, and the 15 year old, both their parents were addicted to crystal meth, and the kid had been addicted since he was 10, and he really never had any hope in the world, and he'd been driven crazy by his upbringing, and did the stabbing, that would hugely modify, and it would also make you wary about that kid then becoming president of America. And Ava has had a very, very distorted introduction into the world. So although there's nothing, as it were, organically within Ava that would lean her towards badness, it's not that robots or sentient robots are bad. She did not, her arrival into the world was being imprisoned by humans. So I'm not sure she'd be a great president. Yeah, the trajectory through which she arrived at her moral views have some dark elements. But I like Ava, personally I like Ava. Would you vote for her? I'm having difficulty finding anyone to vote for in my country, or if I lived here in yours. So that's a yes, I guess, because the competition. She could easily do a better job than any of the people we've got around at the moment. I'd vote for her over Boris Johnson. So what is a good test of consciousness? We talk about consciousness a little bit more. If something appears conscious, is it conscious? You mentioned the smile, which seems to be something done. I mean, that's a really good indication because it's a tree falling in the forest with nobody there to hear it. But does the appearance from a robotics perspective of consciousness mean consciousness to you? No, I don't think you could say that fully because I think you could then easily have a thought experiment which said, we will create something which we know is not conscious, but is going to give a very, very good account of seeming conscious. And also it would be a particularly bad test where humans are involved because humans are so quick to project sentience into things that don't have sentience. So someone could have their computer playing up and feel as if their computer is being malevolent to them when it clearly isn't. And so of all the things to judge consciousness, us, we're empathy machines. So the flip side of that, the argument there is because we just attribute consciousness to everything almost, anthropomorphize everything, including Roombas, that maybe consciousness is not real, that we just attribute consciousness to each other. So you have a sense that there is something really special going on in our mind that makes us unique and gives us subjective experience. There's something very interesting going on in our minds. I'm slightly worried about the word special because it nudges towards metaphysics and maybe even magic. I mean, in some ways, something magic-like, which I don't think is there at all. I mean, if you think about, so there's an idea called panpsychism that says consciousness is in everything. Yeah, I don't buy that. I don't buy that. Yeah, so the idea that there is a thing that it would be like to be the sun. Yeah, no, I don't buy that. I think that consciousness is a thing. My sort of broad modification is that usually the more I find out about things, the more illusory our instinct is and is leading us into a different direction about what that thing actually is. That happens, it seems to me, in modern science, that happens a hell of a lot, whether it's to do with even how big or small things are. So my sense is that consciousness is a thing, but it isn't quite the thing, or maybe very different from the thing that we instinctively think it is. So it's there, it's very interesting, but we may be in sort of quite fundamentally misunderstanding it for reasons that are based on intuition.


Elon Musk: It's Game, Set, Match - Tesla is Vastly Ahead of Everyone | AI Podcast Clips

It's amazing how people can't differentiate between say the narrow AI that allows a car to figure out what a lane line is and navigate streets versus general intelligence. Like these are just very different things. Like your toaster and your computer are both machines but one is much more sophisticated than another. You're confident with Tesla you can create the world's best toaster? The world's best toaster, yes. The world's best self-driving... I'm... yes. To me right now this seems game set match. I don't want to be complacent or overconfident but that is just literally how it appears right now. I could be wrong but it appears to be the case that Tesla is vastly ahead of everyone.


Elon Musk: Perception vs Control - What's Harder?

What's harder, perception or control for these problems? So being able to perfectly perceive everything or figuring out a plan once you perceive everything, how to interact with all the agents in the environment? In your sense, from a learning perspective, is perception or action harder in that giant, beautiful, multitask learning neural network? The hardest thing is having accurate representation of the physical objects in vector space. So taking the visual input, primarily visual input, some sonar and radar, and then creating the accurate vector space representation of the objects around you. Once you have an accurate vector space representation, the planning and control is relatively easier. That is relatively easy. Basically once you have accurate vector space representation, then you're kind of like a video game. Cars in Grand Theft Auto or something, they work pretty well. They drive down the road, they don't crash pretty much unless you crash into them. That's because they've got an accurate vector space representation of where the cars are, and then rendering that as the output.


Ray Dalio: Work-Life Balance and the Arc of Life | AI Podcast Clips

Much of your passions in life has been through something you might be able to call work. Alan Watts has this quote, he said that the real key to life, secret to life, is to be completely engaged with what you're doing in the here and now, and instead of calling it work, realize it is play. So I'd like to ask, what is the role of work in your life's journey, or in a life's journey? And what do you think about this modern idea of kind of separating work and work-life balance? I have a principle that I believe in, is make your work and your passion the same thing. Okay. So that's similar view. In other words, if you can make your work and your passion, it's just going to work out great. And then of course, people have different purposes of work. And I don't want to be theoretical about that. People have to take care of their family. So money at a certain point is an important component of that work. So you look beyond that, what is the money going to get you and what are you trying to achieve? But the most important thing, I agree, is meaningful work and meaningful relationships. Like if you can get into the thing that you're, your mission that you're on, and you are excited about that mission that you're on, and then you can do that with people who you have the meaningful relationships with. You have meaningful work and meaningful relationships. I mean, that is fabulous for most people. And it seems that many people struggle to get there, not out of, not necessarily because they're constrained by the fact that they have the financial constraints of having to provide for their family and so on. And it's, I mean, you know, this idea is out there that there needs to be a work-life balance, which means that most people, we're going to return to the same thing as most doesn't mean optimal, but most people seem to not be doing their life's passion, not be, not unifying work and passion. Why do you think that is? Well the work-life balance, there's a life arc that you go through. Starts at zero and ends somewhere in the vicinity of 80, and there is a phase, and there's a, and you could look at the different degrees of happiness that happen in those phases. I can go through that if that was interesting, but we don't have time probably for it. But you get in the part of the life, that part of the life which has the lowest level of happiness is age 45 to 55, and because as you move into this second phase of your life, now the first phase of your life is when you're learning dependent on others. Second phase of your life is when you're working and others are dependent on you and you're trying to be successful. And in that phase of one's life, you encounter the work-life balance challenge because you're trying to be successful at work and successful at parenting and successful and successful in all those things that take your demand. And they get into that, and I understand that problem in the work-life balance. The issue is primarily to know how to approach that, okay? So I understand it's stressful, it produces stress, and it produces bad results, and it produces the lowest level of happiness in one's life. What's interesting is you get later in life, the levels of happiness rise, and the highest level of happiness is between ages 70 and 80, which is interesting for other reasons. But in that spot, and the key to work-life balance is to realize and to learn how to get more out of an hour of life, okay? Because an hour of work, what people are thinking is that they have to make a choice between one thing and another, and of course they do, but they don't realize that if they develop the skill to get a lot more out of an hour, it's the equivalent of having many more hours in your life. And so that's why in the book Principles, I try to go into, okay, now how can you get a lot more out of an hour? That allows you to get more life into your life, and it reduces the work-life balance. And that's the primary struggle in that 35 to 45. If you could linger on that, what are the ups and downs of life in terms of happiness in general, and perhaps in your own life when you look back at the moments, the peaks? It's pretty much the same pattern. Usually in one's life tends to be a very happy period all the way up, and 16 is like a really great happy... I think, like myself, you start to get elements of freedom, you get your driver's license, whatever, but 16 is there. Junior year in high school quite often can be a stressful period to try to get things about the high school. You go into college, tends to be very high happiness, generally speaking. And then freedom, friendships, all of that. Freedom is a big thing. And then 23 is a peak point in happiness, that freedom. Then sequentially, one has a great time, they date, they go out, and so on. You find the love of your life, you begin to develop a family. And then with that, as time happens, you have more of your work-life balance challenges that come and your responsibilities. And then as you get there in that mid part of your life, that is the biggest struggle. Chances are you will crash in that period of time. You'll have your series of failures, that's that. That's when you go into the abyss. You learn, you hopefully learn from those mistakes, you have the metamorphosis, you come out, you change, you hopefully become better and you take more responsibilities and so on. And then when you get to the later part, as you are starting to approach the transition in that late part of the second phase of your life, before you go into the third phase of your life, second phase is you're working, trying to be successful. Third phase of your life is you want people to be successful without you. You want your kids to be successful without you because when you're at that phase, they're at making their transition from the first phase to the second phase and they're trying to be successful and you want them to be successful without you. And you have, your parents are gone and then you have freedom and then you have freedom again and that with that freedom and then you have these, history has shown with this, you have friendships, you have perspective on life, you have different things. And that's one of the reasons that that later part of the life can be real. On average actually, it's the highest, very interesting thing. If they, there are surveys and say, how good do you look and how good do you feel? And that's the highest survey. The person, now they're not looking the best and they're not feeling the best, right? Maybe it's 35 that they're actually looking the best and feeling the best, but they rank the highest at that point, survey results of being the highest in that 70 to 80 period of time, because it has to do with an attitude on life. Then you start to have grandkids, oh, grandkids are great. And you start to experience that transition well. So that's what the arc of life pretty much looks like. And I'm experiencing it.


Elon Musk: First Principles

First principles is kind of a physics way of looking at the world. You boil things down to the most fundamental truths and say, okay, what are we sure is true and then reason up from there. Somebody could say, in fact people do, that battery packs are really expensive and that's just the way they'll always be because that's the way they've been in the past. No, that's pretty dumb. If you apply that reasoning to anything new, you wouldn't be able to ever get to that new thing. So first principles would be to say, okay, what are the material constituents of the batteries? What is the spot market value of the material constituents? So you can say, okay, it's got cobalt, nickel, aluminum, carbon, and some polymers for separation and a steel can. If we bought that in London Metal Exchange, what would each of those things cost? Like, oh, it's like $80 per kilowatt hour. So clearly you just need to think of clever ways to take those materials and combine them into the shape of a battery cell and you can have batteries that are much, much cheaper than anyone realizes.


Best hidden feature of Python | Chaining comparison operators

This is a hidden feature of Python that I recently came across, the chaining of comparison operators, that is not available in almost any mainstream programming language. I think it's elegant and intuitive and doesn't make any sense to me why it's not available in most languages. So what is it? Say we assign the values 2 and 3 to x and y, and then look at a single statement that includes several comparison operators chained together, 1 less than x less than y less than 4. In Python this evaluates to true. The way Python evaluates the statement is the same way that we would intuitively or mathematically look at the statement, which is as a chain of binary comparison operators. 1 is less than x and x is less than y and y is less than 4, which again evaluates to true. Now you can use any comparison operator, less than, greater than, less than or equal to, greater than or equal to, and mix and match them together in a single arbitrarily long chain of comparison operators. Now if we change the original statement to include a greater than operator as the last comparison, then the entire statement returns false, because y, which is equal to 3, is not greater than 4. And then finally again we can flip the 4 and the y to make the statement return true, and all the individual comparisons are true. 1 is less than x, which is equal to 2, x is less than 4, and 4 is greater than y, y being equal to 3. Now this feature is available in a few other languages, not many, like Perl 6, or Reiku, I think it's been renamed to, not sure how to pronounce it, and Julia, and as shown here it's also a first class citizen in some functional languages, like Scheme, Common Lisp, and Closure, with the added constraint that the chaining of the operator includes only the same operator, so you can't mix and match. So shown here, the greatest language of all time, which is Lisp, the equals operator applied to a list of numbers, 3 and 3 returns true, 3 and 5 returns false, all 3's returns true, all 3's except one of them being 5 returns false. So again, that's chaining the equality comparison operator across the entire list. And the same is true for the less than operator applied to the entire list, below, 3 less than 5 is true, and then a long sequence returning true if it's in strictly increasing order, and false if it's not in strictly increasing order. I put some links in the description, one of the more interesting ones is in the software engineering stack exchange, where I discuss this from a semi-philosophical perspective, why most mainstream languages do not include this feature. You should check out some of the answers on that page, but to summarize some of the discussion, the reason to do it is, despite the initial intuition about this feature being difficult to implement, it's actually very easy to implement, and as I said, it's a mathematically intuitive and just elegant statement, which I think makes it one of the best hidden features, at least to me, of Python. In the discussion, the reasons that come up not to do it is fundamentally just laziness, in that its importance versus other features is quite low, and it doesn't seem to be the kind of feature that pops up as an intuitive first feature to implement when a language is first born. And as with certain other features, this can potentially break backward compatibility, if this kind of chaining operators was allowed previously, syntactically speaking, meaning it was allowed but didn't do the intuitive thing, it can certainly break in quite painful ways backward compatibility. But still, as I said in the previous video, list comprehensions I think is the best feature of Python, and the chaining of comparison operators I think is the best hidden feature, or not well-known feature of Python. Quick shout-out to ExpressVPN, click their link in the description, it's the best way to support the podcast I host and these videos that I make. If you enjoy these, subscribe, and remember, try to learn something new every day.


David Chalmers: What is Consciousness? | AI Podcast Clips

Let's try to go to the very simplest question that you've answered many a time, but perhaps the simplest things can help us reveal, even in time, some new ideas. So what, in your view, is consciousness? What is qualia? What is the hard problem of consciousness? Consciousness, I mean, the word is used many ways, but the kind of consciousness that I'm interested in is basically subjective experience. What it feels like from the inside to be a human being or any other conscious being. I mean, there's something it's like to be me. Right now, I have visual images that I'm experiencing. I'm hearing my voice. I've got maybe some emotional tone. I've got a stream of thoughts running through my head. These are all things that I experience from the first person point of view. I've sometimes called this the inner movie in the mind. It's not a perfect metaphor. It's not like a movie in every way, and it's very rich. But yeah, it's just direct, subjective experience. And I call that consciousness, or sometimes philosophers use the word qualia, which you suggested. People tend to use the word qualia for things like the qualities of things like colors, redness, the experience of redness versus the experience of greenness, the experience of one taste or one smell versus another, the experience of the quality of pain. And yeah, a lot of consciousness is the experience of those qualities. But consciousness is bigger, the entirety of any kinds of experience. I mean, consciousness of thinking is not obviously qualia. It's not like specific qualities like redness or greenness. But still, I'm thinking about my hometown. I'm thinking about what I'm gonna do later on. Maybe there's still something running through my head, which is subjective experience. Maybe it goes beyond those qualities or qualia. Philosophers sometimes use the word phenomenal consciousness for consciousness in this sense. I mean, people also talk about access consciousness, being able to access information in your mind, reflective consciousness, being able to think about yourself. But it looks like the really mysterious one, the one that really gets people going is phenomenal consciousness. The fact that there's subjective experience and all this feels like something at all. And then the hard problem is, how is it that, why is it that there is phenomenal consciousness at all? And how is it that physical processes in a brain could give you subjective experience? It looks like on the face of it, you'd have all this big complicated physical system in a brain running without it giving subjective experience at all. And yet we do have subjective experience. So the hard problem is just explain that. Explain how that comes about. We haven't been able to build machines where a red light goes on that says it's not conscious. So how do we actually create that? Or how do humans do it and how do we ourselves do it? We do every now and then create machines that can do this. We create babies that are conscious. They've got these brains. That brain does produce consciousness. But even though we can create it, we still don't understand why it happens. Maybe eventually we'll be able to create machines which as a matter of fact, AI machines which as a matter of fact are conscious. But that won't necessarily make the hard problem go away any more than it does with babies. Because we still want to know how and why is it that these processes give you consciousness. You know, you just made me realize for a second, maybe it's a totally dumb realization, but nevertheless, that it's a useful way to think about the creation of consciousness is looking at a baby. So that there's a certain point at which that baby is not conscious. The baby starts from maybe, I don't know, from a few cells, right? There's a certain point at which it becomes consciousness arrives, it's conscious. Of course we can't know exactly that line. But it's a useful idea that we do create consciousness. Again, a really dumb thing for me to say, but not until now did I realize we do engineer consciousness. We get to watch the process happen. We don't know which point it happens or where it is. But we do see the birth of consciousness. Yeah, I mean, there's a question of course is whether babies are conscious when they're born. And it used to be, it seems, at least some people thought they weren't, which is why they didn't give anesthetics to newborn babies when they circumcised them. And so now people think, oh, that's incredibly cruel. Of course, babies feel pain. And now the dominant view is that the babies can feel pain. Actually, my partner, Claudia, works on this whole issue of whether there's consciousness in babies and of what kind. And she certainly thinks that newborn babies come into the world with some degree of consciousness. Of course, then you can just extend the question backwards to fetuses, and suddenly you're into politically controversial territory. But the question also arises in the animal kingdom. Where does consciousness start or stop? Is there a line in the animal kingdom where the first conscious organisms are? It's interesting, over time, people are becoming more and more liberal about ascribing consciousness to animals. People used to think, maybe only mammals could be conscious. Now most people seem to think, sure, fish are conscious, they can feel pain, and now we're arguing over insects. You'll find people out there who say plants have some degree of consciousness. So who knows where it's gonna end? The far end of this chain is the view that every physical system has some degree of consciousness. Philosophers call that panpsychism. I take that view. I mean, that's a fascinating way to view reality. So if you could talk about, if you can linger on panpsychism for a little bit, what does it mean? So it's not just plants are conscious. I mean, it's that consciousness is a fundamental fabric of reality. What does that mean to you? How are we supposed to think about that? Well, we're used to the idea that some things in the world are fundamental, right? In physics, we take things like space or time or space-time, mass, charge as fundamental properties of the universe, you don't reduce them to something simpler. You take those for granted, you've got some laws that connect them, here is how mass and space and time evolve, theories like relativity or quantum mechanics or some future theory that will unify them both. But everyone says you gotta take some things as fundamental, and if you can't explain one thing in terms of the previous fundamental things, you have to expand. Maybe something like this happened with Maxwell. Ended up with fundamental principles of electromagnetism and took charge as fundamental, because it turned out that was the best way to explain it. So I at least take seriously the possibility something like that could happen with consciousness. Take it as a fundamental property like space, time, and mass, and instead of trying to explain consciousness wholly in terms of the evolution of space, time, and mass, and so on, take it as a primitive and then connect it to everything else by some fundamental laws. Because there's this basic problem that the physics we have now looks great for solving the easy problems of consciousness, which are all about behavior. They give us a complicated structure and dynamics, they tell us how things are gonna behave, what kind of observable behavior they'll produce, which is great for the problems of explaining how we walk and how we talk and so on. Those are the easy problems of consciousness. But the hard problem was this problem about subjective experience just doesn't look like that kind of problem about structure, dynamics, how things behave. So it's hard to see how existing physics is gonna give you a full explanation of that. Certainly trying to get a physics view of consciousness, yes, there has to be a connecting point, and it could be at the very axiomatic, at the very beginning level. But, I mean, first of all, there's a crazy idea that sort of everything has properties of consciousness. At that point, the word consciousness is already beyond the reach of our current understanding, like far, because it's so far from, at least for me, maybe you can correct me, it's far from the experiences that we have, that I have as a human being. To say that everything is conscious, that means that basically, another way to put that, if that's true, then we understand almost nothing about that fundamental aspect of the world. How do you feel about saying an ant is conscious? Do you get the same reaction to that, or is that something you can understand? I can understand ant, I can't understand an atom. A plant. A planticle. So I'm comfortable with living things on Earth being conscious, because there's some kind of agency where they're similar size to me, and they can be born and they can die, and that is understandable intuitively. Of course, you anthropomorphize, you put yourself in the place of the plant, but I can understand it. I mean, I'm not like, I don't believe, actually, that plants are conscious or that plants suffer, but I can understand that kind of belief, that kind of idea. How do you feel about robots? Like the kind of robots we have now? If I told you, like, that a Roomba had some degree of consciousness, or some deep neural network? I could understand that a Roomba has consciousness. I just had spent all day at iRobot. And I mean, I personally love robots and have a deep connection with robots, so I also probably anthropomorphize them. There's something about the physical object. So there's a difference than a neural network, a neural network running a software. To me, the physical object, something about the human experience allows me to really see that physical object as an entity. And if it moves, it moves in a way that it, there's a, like I didn't program it, where it feels that it's acting based on its own perception, and yes, self-awareness and consciousness, even if it's a Roomba, then you start to assign it some agency, some consciousness. So, but to say that panpsychism, that consciousness is a fundamental property of reality is a much bigger statement. That it's like turtles all the way, it's like every, it doesn't end. The whole thing is, so like how, I know it's full of mystery, but if you can linger on it, like how would it, how do you think about reality if consciousness is a fundamental part of its fabric? The way you get there is from thinking, can we explain consciousness given the existing fundamentals? And then if you can't, as at least right now it looks like, then you've got to add something. It doesn't follow that you have to add consciousness. Here's another interesting possibility is, well, we'll add something else. Let's call it proto-consciousness, or X. And then it turns out space, time, mass, plus X will somehow collectively give you the possibility for consciousness. Why don't we allow that view? Either I call that pan-proto-psychism, because maybe there's some other property, proto-consciousness at the bottom level. And if you can't imagine there's actually genuine consciousness at the bottom level, I think we should be open to the idea there's this other thing, X. Maybe we can't imagine that somehow gives you consciousness. But if we are playing along with the idea that there really is genuine consciousness at the bottom level, of course, this is going to be way out and speculative. But at least in, say, if it was classical physics, then you'd end up saying, well, every little atom, with a bunch of particles in space-time, each of these particles has some kind of consciousness whose structure mirrors maybe their physical properties, like its mass, its charge, its velocity, and so on. The structure of its consciousness would roughly correspond to that. And the physical interactions between particles, I mean, there's this old worry about physics, I mentioned this before in this issue about the manifest image. We don't really find out about the intrinsic nature of things. Physics tells us about how a particle relates to other particles and interacts. It doesn't tell us about what the particle is in itself. That was Kant's thing in itself. So here's a view. The nature in itself of a particle is something mental. A particle is actually a little conscious subject with properties of its consciousness that correspond to its physical properties. The laws of physics are actually ultimately relating these properties of conscious subjects. So in this view, a Newtonian world actually would be a vast collection of little conscious subjects at the bottom level, way, way simpler than we are without free will or rationality or anything like that. But that's what the universe would be like. Now, of course, that's a vastly speculative view. No particular reason to think it's correct. Furthermore, non-Newtonian physics, say quantum mechanical wave function, suddenly it starts to look different. It's not a vast collection of conscious subjects. Maybe there's ultimately one big wave function for the whole universe. Corresponding to that might be something more like a single conscious mind whose structure corresponds to the structure of the wave function. People sometimes call this cosmo-psychism. And now, of course, we're in the realm of extremely speculative philosophy. There's no direct evidence for this. But yeah, but if you want a picture of what that universe would be like, think yeah, giant cosmic mind with enough richness and structure among it to replicate all the structure of physics. I think therefore I am at the level of particles and with quantum mechanics at the level of the wave function. It's kind of an exciting, beautiful possibility, of course, way out of reach of physics currently. It is interesting that some neuroscientists are beginning to take panpsychism seriously. You find consciousness even in very simple systems. So for example, the integrated information theory of consciousness, a lot of neuroscientists are taking it seriously. Actually, I just got this new book by Christoph Koch. Just came in, The Feeling of Life Itself, Why Consciousness is Widespread but Can't Be Computed. He basically endorses a panpsychist view where you get consciousness with the degree of information processing or integrated information processing in a system, and even very, very simple systems, like a couple of particles, will have some degree of this. So he ends up with some degree of consciousness in all matter. And the claim is that this theory can actually explain a bunch of stuff about the connection between the brain and consciousness. Now, that's very controversial. I think it's very, very early days in the science of consciousness. It's interesting that it's not just philosophy that might lead you in this direction, but there are ways of thinking quasi-scientifically that lead you there, too. But maybe it's different than panpsychism. What do you think? So Alan Watts has this quote that I'd like to ask you about. The quote is, through our eyes, the universe is perceiving itself. Through our ears, the universe is listening to its harmonies. We are the witnesses to which the universe becomes conscious of its glory, of its magnificence. So that's not panpsychism. Do you think that we are essentially the tools, the senses the universe created to be conscious of itself? It's an interesting idea. Of course, if you went for the giant cosmic mind view, then the universe was conscious all along. It didn't need us. We're just little components of the universal consciousness. Likewise, if you believe in panpsychism, then there was some little degree of consciousness at the bottom level all along, and we were just a more complex form of consciousness. So I think maybe the quote you mentioned works better. If you're not a panpsychist, you're not a cosmopsychist, you think consciousness just exists at this intermediate level. And of course, that's the orthodox view. That, you would say, is the common view? So is your own view of panpsychism a rarer view? I think it's generally regarded, certainly, as a speculative view held by a fairly small minority of at least theorists, philosophers, most philosophers and most scientists who think about consciousness are not panpsychists. There's been a bit of a movement in that direction the last 10 years or so. Seems to be quite popular, especially among the younger generation, but it's still very definitely a minority view. Many people think it's totally batshit crazy to use the technical term. But... It's a philosophical term. So the orthodox view, I think, is still consciousness is something that humans have, and some good number of non-human animals have, and maybe AIs might have one day, but it's restricted. On that view, then, there was no consciousness at the start of the universe. There may be none at the end, but it is this thing which happened at some point in the history of the universe, consciousness developed. And yes, that's a very amazing event on this view because many people are inclined to think consciousness is what somehow gives meaning to our lives. Without consciousness, there'd be no meaning, no true value, no good versus bad, and so on. So with the advent of consciousness, suddenly the universe went from meaningless to somehow meaningful. Why did this happen? I guess the quote you mentioned was somehow, this was somehow destined to happen because the universe needed to have consciousness within it, to have value and have meaning. And maybe you could combine that with a theistic view or a teleological view. The universe was inexorably evolving towards consciousness. Actually, my colleague here at NYU, Tom Nagel, wrote a book called Mind and Cosmos a few years ago where he argued for this teleological view of evolution toward consciousness, saying this led to problems for Darwinism. It's got him on, you know, this was very, very controversial. Most people didn't agree. I don't myself agree with this teleological view, but it is at least a beautiful speculative view of the cosmos.


Biological versus Artificial Neural Networks (John Hopfield) | AI Podcast Clips

What difference between biological neural networks and artificial neural networks is most captivating and profound to you? At the higher philosophical level, let's not get technical just yet. One of the things that very much intrigues me is the fact that neurons have all kinds of components, properties to them. In evolutionary biology, if you have some little quirk in how a molecule works or how a cell works, and it can be made use of, evolution will sharpen it up and make it into a useful feature rather than a glitch. And so you expect in neurobiology for evolution to have captured all kinds of possibilities of getting neurons, of how you get neurons to do things for you. And that aspect has been completely suppressed in artificial neural networks. Do the glitches become features in the biological neural network? They can. Look, let me take one of the things that I used to do research on. If you take things which oscillate, they have rhythms which are sort of close to each other, under some circumstances, these things will have a phase transition and suddenly the rhythm will, everybody will fall into step. There was a marvelous physical example of that in the Millennium Bridge across the Thames River about, built about 2001. And pedestrians walking across, pedestrians don't walk, synchronize, they don't walk in lockstep, but they're all walking about the same frequency. And the bridge could sway at that frequency and the slight sway made pedestrians tend a little bit to lock into step. And after a while, the bridge was oscillating back and forth and the pedestrians were walking in step to it. And you could see it in the movies made out of the bridge. And the engineers made a simple-minded mistake. They had assumed when you walk, it's step, step, step, and it's back and forth motion. But when you walk, it's also right foot, left foot, side to side motion. It's the side to side motion for which the bridge was strong enough, but it wasn't stiff enough. And as a result, you would feel the motion and you'd fall into step with it. And people were very uncomfortable with it. They closed the bridge for two years while they built stiffening for it. Now, nerves, look, nerve cells produce action potentials. You have a bunch of cells which are loosely coupled together producing action potentials of the same rate. There'll be some circumstances under which these things can lock together. Other circumstances in which they won't. Well, they fire together. You can be sure that other cells are gonna notice it. So you can make a computational feature out of this in an evolving brain. Most artificial neural networks don't even have action potentials, let alone have the possibility for synchronizing them. And you mentioned the evolutionary process. So the evolutionary process that builds on top of biological systems leverages that the weird mess of it somehow. So how do you make sense of that ability to leverage all the different kinds of complexities in the biological brain? Well, look, at the biological molecule level, you have a piece of DNA which encodes for a particular protein. You could duplicate that piece of DNA and now one part of it can code for that protein but the other one could itself change a little bit and thus start coding for a molecule which is slightly different. Now that molecule was just slightly different, had a function which helped any old chemical reaction was as important to the cell. You would go ahead and let that try and evolution would slowly improve that function. And so you have the possibility of duplicating and then having things drift apart. One of them retain the old function, the other one do something new for you. And there's evolutionary pressure to improve. Look, there is in computers too, but it's improvement has to do with closing some companies and opening some others. The evolutionary process looks a little different. Yeah, similar time scale perhaps. No. Much shorter in time scale. Companies close, yeah, go bankrupt and are born. Yeah, shorter, but not much shorter. Some companies last a century, but yeah, you're right. I mean, if you think of companies as a single organism that builds and you all know, yeah, it's a fascinating dual correspondence there between biological and- And companies have difficulty having a new product competing with an old product. When IBM built its first PC, you probably read the book. They made a little isolated internal unit to make the PC. And for the first time in IBM's history, they didn't insist that you build it out of IBM components, but they understood that they could get into this market, which is a very different thing by completely changing their culture. And biology finds other markets in a more adaptive way. Yeah, it's better at it. It's better at that kind of integration. So maybe you've already said it, but what to use the most beautiful aspect or mechanism of the human mind? Is it the adaptive, the ability to adapt as you've described or is there some other little quirk that you particularly like? Adaptation is everything when you get down to it. But the difference, there are differences between adaptation where you're learning goes on only over generations and over evolutionary time, or your learning goes on at the timescale of one individual who must learn from the environment during that individual's lifetime. And biology has both kinds of learning in it. And the thing which makes neurobiology hard is that it's a mathematical system, as it were, built on this other kind of evolutionary system. What do you mean by mathematical system? Where is the math in the biology? Well, when you talk to a computer scientist about neural networks, it's all math. The fact that biology actually came about from evolution and the fact that biology is about a system which you can build in three dimensions. If you look at computer chips, computer chips are basically two-dimensional structures, maybe 2.1 dimensions, but they really have difficulty doing three-dimensional wiring. Biology is, the neocortex is actually also sheet-like and it sits on top of the white matter, which is about 10 times the volume of the gray matter and contains all what you might call the wires. But there's a huge, the effect of computer structure on what is easy and what is hard is immense. So- And biology does, it makes some things easy that are very difficult to understand how to do computationally. On the other hand, you can't do simple floating-point arithmetic because it's awfully stupid. Yeah, and you're saying this kind of three-dimensional complicated structure makes, it's still math. It's still doing math. The kind of math it's doing enables you to solve problems of a very different kind. That's right, that's right. So you mentioned two kinds of adaptation, the evolutionary adaptation and the adaptation or learning at the scale of a single human life. Which do you, which is particularly beautiful to you and interesting from a research and from just a human perspective? And which is more powerful? I find things most interesting that I begin to see how to get into the edges of them and tease them apart a little bit and see how they work. And since I can't see the evolutionary process going on, I'm in awe of it. But I find it just a black hole as far as trying to understand what to do. And so in a certain sense, I'm in awe of it, but I couldn't be interested in working on it. So the human life's time scale is however thing you can tease apart and study. Yeah, you can do, there's developmental neurobiology which understands how the connections and how the structure evolves from a combination of what the genetics is like and the real, the fact that you're building a system in three dimensions. In just days and months, those early days of a human life are really interesting. They are, and of course, there are times of immense cell multiplication. There are also times of the greatest cell death in the brain is during infancy. It's turnover. So what is not effective, what is not wired well enough to use the moment, throw it out. It's a mysterious process. From, let me ask, from what field do you think the biggest breakthroughs in understanding the mind will come in the next decades? Is it neuroscience, computer science, neurobiology, psychology, physics? Maybe math, maybe literature? Well, of course, I see the world always through a lens of physics. I grew up in physics and the way I pick problems is very characteristic of physics and of an intellectual background, which is not psychology, which is not chemistry and so on and so on. And both of your parents are physicists. Both of my parents were physicists and the real thing I got out of that was a feeling that the world is an understandable place. And if you do enough experiments and think about what they mean and structure things so you can do the mathematics of the relevant to the experiments, you also be able to understand how things work. But that was a few years ago. Did you change your mind at all through many decades of trying to understand the mind, of studying in different kinds of ways? Not even the mind, just biological systems. You still have hope that physics, that you can understand? There's a question of what do you mean by understand? Of course. When I taught freshman physics, I used to say, I wanted to get physics to understand the subject, to understand Newton's laws. I didn't want them simply to memorize a set of examples to which they knew the equations to write down to generate the answers. I had this nebulous idea of understanding so that if you looked at a situation, you could say, oh, I expect the ball to make that trajectory. Or I expect some intuitive notion of understanding. And I don't know how to express that very well. I've never known how to express it well. And you run smack up against it when you look at these simple neural nets, feedforward neural nets, which do amazing things, and yet you know contain nothing of the essence of what I would have felt was understanding. Understanding is more than just an enormous lookup table. Let's linger on that. How sure you are of that? What if the table gets really big? So, I mean, ask another way, these feedforward neural networks, do you think they'll ever understand? Could answer that in two ways. I think if you look at real systems, feedback is an essential aspect of how these real systems compute. On the other hand, if I have a mathematical system with feedback, I know I can unlayer this and do it. But I have an exponential expansion in the amount of stuff I have to build if I can solve the problem that way. So feedback is essential. So we can talk even about recurrent neural nets, so recurrence. But do you think all the pieces are there to achieve understanding through these simple mechanisms? Like, back to our original question, what is the fundamental, is there a fundamental difference between artificial neural networks and biological? Or is it just a bunch of surface stuff? Suppose you ask a neurosurgeon, when is somebody dead? Yeah. They'll probably go back to saying, well, I can look at the brain rhythms and tell you this is a brain which is never gonna function again. This one is, this other one is one which if we treat it well, is still recoverable. And then just do that by some electrodes and looking at simple electrical patterns which don't look in any detail at all at what individual neurons are doing. These rhythms are utterly absent from anything which goes on at Google. Yeah, but the rhythms. But the rhythms what? So, well, that's like comparing, okay, I'll tell you, it's like you're comparing the greatest classical musician in the world to a child first learning to play. The question I'm at, but they're still both playing the piano. I'm asking, is there, will it ever go on at Google? Do you have a hope? Because you're one of the seminal figures in both launching both disciplines, both sides of the river. I think it's going to go on generation after generation the way it has where, what you might call the AI computer science community says, let's take the following. This is our model of neurobiology at the moment. Let's pretend it's good enough and do everything we can with it. And it does interesting things. And after the while it sort of grinds into the sand and you say, ah, something else is needed for neurobiology and some other grand thing comes in and enables you to go a lot further. But we'll go into the sand again. And I think it's going to be generations of this evolution. I don't know how many of them and each one is going to get you further into what our brain does. In some sense, past the Turing test, longer and more broad aspects. And how many of these are good there are going to have to be before you say, I've made something, I've made a human. I don't know. But your sense is it might be a couple. My sense is it might be a couple more. Yeah. And going back to my brain waves as it were. From the AI point of view, they would say, ah, maybe these are an heavy phenomenon and not important at all. The first car I had, a real wreck of a 1936 Dodge, go above 45 miles an hour and the wheels would shimmy. Yeah. Good speedometer that. Now, nobody designed the car that way. The car is malfunctioning to have that. But in biology, if it were useful to know when are you going more than 45 miles an hour, you just capture that and you wouldn't worry about where it came from. Yeah. It's going to be a long time before that kind of thing, which can take place in large complex networks of things is actually used in the computation. Look, how many transistors are there in your laptop these days? Actually, I don't know the number. It's- It's on the scale of 10 to the 10. I can't remember the number either. Yeah. And all the transistors are somewhat similar. And most physical systems with that many parts, all of which are similar, have collective properties. Yes. Sound waves in air, earthquakes, what have you, have collective properties. Whether, there are no collective properties used in artificial neural networks, in AI. Yeah, it's very- If biology uses them, it's going to take us to more generations of things for people to actually dig in and see how they are used and what they mean. See, you're very right. I might have to return several times to your biology and try to make our transistors more messy. Yeah, yeah. At the same time, the simple ones will conquer big aspects. And I think one of the most biggest surprises to me was how well learning systems, which are manifestly non-biological, how important they can be actually and how important and how useful they can be in AI.


Starting a Business is a Rough Ride (Stephen Schwarzman) | AI Podcast Clips

I'm now personally taking a step into building a startup, first time, hoping to change the world of course. There are thousands, maybe more, maybe millions of other first time entrepreneurs like me. What advice, you've gone through this process, you've talked about the suffering, the emotional turmoil it all might entail, what advice do you have for those people taking that step? I'd say it's a rough ride and you have to be psychologically prepared for things going wrong with frequency. You have to be prepared to be put in situations where you're being asked to solve problems you didn't even know those problems existed. You know, for example, renting space, it's not really a problem unless you've never done it. You have no idea what a lease looks like, right? You don't even know the relevant rent in a market. So everything is new, everything has to be learned. What you realize is that it's good to have other people with you who've had some experience in areas where you don't know what you're doing. Unfortunately an entrepreneur starting doesn't know much of anything, so everything is something new. And I think it's important not to be alone because it's sort of overwhelming and you need somebody to talk to other than a spouse or a loved one because even they get bored with your problems. And so, you know, getting a group, you know, if you look at Alibaba, you know, Jack Ma was telling me they basically were like a financial death's door at least twice. And you know, the fact that it wasn't just Jack, I mean, people think it is because he became the sort of public face and the driver, but a group of people who can give advice, share situations to talk about, that's really important. And that's not just referring to the small details like renting space. No. It's also the psychological burden. Yes, yeah. And you know, because most entrepreneurs at some point question what they're doing because it's not going so well or they're screwing it up and they don't know how to unscrew it up because we're all learning. And it's hard to be learning, you know, when there are like 25 variables going on. If you're missing four big ones, you can really make a mess. And so, the ability to in effect have either an outsider who's really smart that you can rely on for certain type of things or other people who are working with you on a daily basis. Most people who haven't had experience believe in the myth of the one person, one great person, you know, makes outcomes, creates outcomes that are positive. Most of us, it's not like that. If you look back over a lot of the big successful tech companies, it's not typically one person. You know, and you will know these stories better than I do because it's your world, not mine. But even I know that almost every one of them had two people. I mean, if you look at Google, you know, that's what they had and that was the same at Microsoft at the beginning. And, you know, it was the same at Apple. You know, people have different skills and they need to play off of other people. So, you know, the advice that I would give you is make sure you understand that so you don't head off in some direction as a lone wolf and find that either you can't invent all the solutions or you make bad decisions on certain types of things. This is a team sport. Team sport means you're alone, in effect, and that's the myth. But it's mostly a myth. Yeah, I think, and you talk about this in your book, and I could talk to you about it forever, the harshly self-critical aspect to your personality and to mine as well in the face of failure. It's a powerful tool, but it's also a burden. It's very interesting, very interesting to walk that line. But let me ask in terms of people around you, in terms of friends, in the bigger picture of your own life, where do you put the value of love, family, friendship in the big picture journey of your life? Well, ultimately all journeys are alone. It's great to have support. And when you go forward and say your job is to make something work and that's your number one priority, and you're going to work at it to make it work, it's like superhuman effort. People don't become successful as part-time workers. Doesn't work that way. And if you're prepared to make that 100 to 120% effort, you're going to need support. And you're going to have to have people involved with your life who understand that that's really part of your life. Sometimes you're involved with somebody and they don't really understand that, and that's a source of conflict and difficulty. But if you're involved with the right people, whether it's a dating relationship or a spousal relationship, you have to involve them in your life, but not burden them with every minor triumph or mistake. They actually get bored with it after a while. And so you have to set up different types of ecosystems. You have your home life, you have your love life, you have children, and that's like the enduring part of what you do. And then on the other side, you've got the sort of unpredictable nature of this type of work. What I say to people at my firm who are younger, usually, well, everybody's younger, but people who are of an age where they're just having their first child, or maybe they have two children, that it's important to make sure they go away with their spouse at least once every two months to just some lovely place where there are no children, no issues. Sometimes once a month if they're sort of energetic and clever. And that... Escape the craziness of it all. Yeah, and reaffirm your values as a couple. And you have to have fun. If you don't have fun with the person you're with, and all you're doing is dealing with issues, then that gets pretty old. And so you have to protect the fun element of your life together. And the way to do that isn't by hanging around the house and dealing with sort of more problems. It... You have to get away and reinforce and reinvigorate your relationship. And whenever I tell one of our younger people about that, they sort of look at me and it's like the scales are falling off of their eyes and they're saying, geez, I hadn't thought about that. I'm so enmeshed in all these things, but that's a great idea. And that's something as an entrepreneur you also have to do. You just can't let relationships slip because you're half overwhelmed.


DeepMind solves protein folding | AlphaFold 2

I think it's fair to say that this year, 2020, has thrown quite a few challenges at human civilization. So it's really nice to get some positive news about truly marvelous accomplishments of engineering and science. One was SpaceX, I would argue, launching a new era of space exploration. And now, a couple of days ago, DeepMind has announced that its second iteration of the AlphaFold system has, quote unquote, solved the 50-year-old grand challenge problem of protein folding. Solved here means that these computational methods were able to achieve prediction performance similar to much slower, much more expensive experimental methods like X-ray crystallography. In 2018, which is the previous iteration of the CASP competition, AlphaFold achieved a score of 58 on the hardest class of proteins. And this year, it achieved a score of 87, which is a huge improvement, and it's still 26 points better than the closest competition. So this is definitely a big leap, but it's also fair to say that the internet is full of hype about this breakthrough. And so let me indulge in the fun a bit. Some of it is definitely a little bit subjective, but I think the case could be made on the life science side that this is the biggest advancements in structural biology of the past one or two decades. And in my field of artificial intelligence, I think a strong case could be made that this is one of the biggest advancements in recent history of the field. So of course, the competition is pretty steep, and I talk with excitement about each of these entries. Of course, the ImageNet moment itself or the AlexNet moment that launched a deep learning revolution in the space of computer vision. So many people are comparing now this breakthrough of AlphaFold2 to the ImageNet moment, but now in the life sciences field. I think the good old argument over beers about which is the biggest breakthrough comes down to the importance you place on how much real world direct impact a breakthrough has. Of course, AlexNet was ultimately on a toy dataset of very simplistic image classification problem, which does not have a direct application to the real world, but it did demonstrate the ability of deep neural networks to learn from a large amount of data in a supervised way. But anyway, this is probably a very long conversation over many beers of AlphaZero with reinforcement learning self-play, obviously in contention for the biggest breakthrough. The recent breakthroughs in the application of transformers in the natural language processing space with GPT-3 being the most kind of recent iteration of state-of-the-art performance. The actual deployment of robots in the field used by real humans, which is Tesla Autopilot. You know, deployment of massive fleet learning, of massive machine learning in safety-critical systems. And then other kinds of robots like the Google self-driving car, Waymo systems that are taking even a further leap of removing the human from the picture, being able to drive the car autonomously without human supervision. Smart speakers in the home. There's a lot of actual in the wild natural language processing that I think doesn't get enough credit from the artificial intelligence community, how much amazing stuff is there. And depending how much value you put in engineering achievements, especially in the hardware space, Boston Dynamics with its SpotManySpot robot is just, one could argue, is one of the great accomplishments in the artificial intelligence field, especially when you maybe look 20 and 50 years down the line when the entire world is populated by robot dogs and the humans have gone extinct. Anyway, I say all that for fun, but really this is one of the big breakthroughs in our field and something to truly be excited about. And I'll talk about some of the possible future impact I see here from this breakthrough in just a couple of slides here. Anyway, my prediction is that there'll be at least one, potentially several Nobel prizes that will result in derivative work launched directly with these computational methods. It's kind of exciting to think that it's possible also that we'll see a first Nobel prize that is awarded where much of the work is done by a machine learning system. Of course, the Nobel prize is awarded to the humans behind the system, but it's exciting to think that a computational approach or machine learning system will play a big role in a Nobel prize level discovery in the field like medicine and physiology or chemistry or even physics. Okay, let's talk a bit about proteins and protein folding, why this whole space is really fascinating. First of all, there's amino acids, which are the basic building blocks of life in eukaryotes, which is what we're talking about here with humans, there's 21 of them. Proteins are chains of amino acids and are the workhorses of living organisms of cells. And they do all kinds of stuff from structural to functional they serve as catalysts for chemical reactions, they move stuff around, they do all kinds of things. So they're both the building blocks of life and the doers and movers of life. Hopefully I'm not being too poetic. So protein folding is the fascinating process of going from the amino acid sequence to a 3D structure. There's a lot that could be said here, there's a lot of lectures on this topic, but let me quickly say some of the more fascinating and important things that I remember from a few biology classes I took in high school and college. Okay, so first is there's a fascinating property of uniqueness that a particular sequence usually maps one-to-one to a 3D structure, not always, but usually. To me from an outsider's perspective, that's just weird and fascinating. The other thing to say is that the 3D structure determines the function of the protein. So one of the correlators of that is that the underlying cause of many diseases is the misfolding of proteins. Now back to the weirdness of the uniqueness of the folding, there's a lot of ways for a protein to fold based on the sequence of amino acids. There's I think 10 to the power of 80 atoms in the universe, so 10 to the power of 143 is a lot. And you can look at Leventhal's paradox, which is one of the early formulations of just how hard this problem is and why it's really weird that a protein is able to do it so quickly. As a completely irrelevant side note, I wonder how many possible chess games there are. I think I remember it being 10 to the power of 100, something like that. I think that would also necessitate removing certain kinds of infinite games. Anyway, off the top of my head, I would venture to say that the protein folding problem just in the number of possible combinations is much, much harder than the game of chess, but it's also much weirder. You know, they say that life imitates chess, but I think that from a biological perspective, life is way weirder than chess. Anyway, to say once again what I said before is that the misfolding of proteins is the underlying cause of many diseases. And again, I'll talk about the implications of that a little bit later. From a computational, from a machine learning, from a dataset perspective, what we're looking at currently is 200 million proteins that have been mapped and 170,000 protein 3D structures, so much, much fewer. And that's our training data for the learning-based approaches for the protein folding problem. Now, the way those 3D structures were determined is through experimental methods. One of the most accurate being X-ray crystallography, which I saw a University of Toronto study showing that it costs about $120,000 per protein. It takes about one year to determine the 3D structure. So because it costs a lot and it's very slow, that's why you only have 170,000 3D structures determined. Now, that's one of the big things that the AlphaFold2 system might be able to provide is at least for a large class of proteins be able to determine the 3D structure with a high accuracy, enough to be able to sort of open up the structural biology field entirely with sort of several orders of magnitude more protein 3D structures to play with. There's not currently a paper out that describes the details of the AlphaFold2 system, but I think it's clear that it's heavily based on the AlphaFold1 system from two years ago. So I think it's useful to look at how that system works. And then we can hypothesize, speculate about the kind of methodological improvements in the AlphaFold2 system. Okay, so for AlphaFold1 system, there's two steps in the process. The first includes machine learning, the second does not. The first step includes a convolutional neural network that takes as input the amino acid residue sequences plus a ton of different features that their paper describes, including the multiple sequence alignment of evolutionary related sequences. And the output of the network is this distance matrix with the rows and columns being the amino acid residues. They're giving a confidence distribution of the distance between the two amino acids in the final geometric 3D structure of the protein. Then once you have the distance matrix, then you have a non-learning based gradient descent optimization of folding this 3D structure to figure out how you can as closely as possible match the distances between the amino acid residues that are specified by the distance matrix. Okay, that's it at a high level. Now, how does AlphaFold2 work? First of all, we don't know for sure. There's only a blog post and some little speculation here and there. But one thing is clear that there's attentional mechanisms. So I think convolutional neural networks are out and transformers are in. The same kind of process that's been happening in the natural language processing space and really most of the deep learning space, it's clear that attention mechanisms are going to be taking over every aspects of machine learning. So I think the big change is ComNet is out, transformers are in. The rest is more in the speculation space. It does seem that the MSA, the multiple sequence alignment, is part of the learning process now, as opposed to part of the feature engineering, which it was in the original step. I believe it was only a source of features. Please correct me if I'm wrong on that. But it does seem like here it's now part of the learning process. And there's something iterative about it, at least in the blog post, where there's a constant passing of learned information between the sequence residue representation, which is the evolution related sequence side of things. And then the amino acid residue to residue distances that are more akin to the alpha fold one system. How that iterative process works, it's unclear, whether it's part of one giant neural network or whether several neural networks evolved, I don't know. But it does seem that the evolution related sequences are now part of the learning process. It does seem that there's some kind of iterative passing information. And of course, attention being involved into the entire picture. Now, at least in the blog post, the term spatial graph is used as opposed to sort of a distance matrix or adjacency matrix. So I don't know if there's some magical tricks involved in some interesting generalization of an adjacency matrix that's involved in a spatial graph representation, or if it's simply just using the term spatial graph because there is more than just pairwise distances involved in this version of the learning architecture. I think the two lessons of the recent history of deep learning, if you involve attention, if you involve transformers, you're gonna get a big boost. And the other lesson is that if you make as much of the problem learnable as possible, you're often going to see quite significant benefits. This is something I've definitely seen in the computer vision especially the semantic segmentation side of things. Okay, why is this breakthrough important? Allow this computer scientist AI person to wax poetic about some biology for a bit. So because the protein structure gives us the protein function, figuring out the structure for maybe millions of proteins might allow us to learn unknown functions of genes encoded in DNA. Also, as I mentioned before, it might allow us to understand the cause of many diseases that are the result of misfolded proteins. Other application will stem from the ability to quickly design new proteins that in some way alter the function of other proteins. So for treatments, for drugs, that means designing proteins that fix other misfolded proteins. Again, those are the causes of many diseases. I read a paper that was talking about agriculture applications of being able to engineer insecticidal proteins or frost protective coating, stuff I know nothing about. I read it, it's out there. Tissue regeneration through self-assembling proteins, supplements for improved health and anti-aging, and all kinds of biomaterials, for textiles and just materials in general. Now in the long-term or the super long-term future, impact of this breakthrough might be just the advancement of end-to-end learning of really complicated problems in the life sciences. So protein folding is looking at the folding of a single protein. So being able to predict multi-protein interaction or protein complex formation, which even in my limited knowledge of biology, I think is a much, much, much harder problem, as far as I understand. And just being able to incorporate the environment into the modeling of the folding of the protein and also seeing how the function of that protein might change given the environment. All those kinds of things, incorporating that into the end-to-end learning problem. Then taking a step even further is this is physics, biophysics. So being able to accurately do physics-based simulation of biological systems. So if we think of a protein as one of the most basic biological systems, so then taking a step out further and further and increasing the complexity of the biological systems, you can start to think of something crazy like being able to do accurate physics-based simulation of cells, for example, or entire organs. Or maybe one day being able to do an accurate physics-based simulation of the very over-caffeinated organ that's producing this very video. In fact, how do we know this is not a physics-based simulation of a biological system whose assigned name happens to be Lex? I guess we'll never know. And of course, we can go farther out into super long-term sci-fi kind of ideas of biological life and artificial life, which are fascinating ideas of being able to play with simulation of prediction of organisms that are biologically based or non-biologically based. I mean, that's the exciting future of end-to-end learning systems that step outside the game-playing world of StarCraft, of Chess and Go, and go into the life sciences of real-world systems that operate in the real world. That's where Tesla autopilot is really exciting. That's where any robots that use machine learning are really exciting. And that's where this big breakthrough in the space of structural biology is super exciting. And truly, to me, as one humble human, inspiring beyond words. Speaking of words, for me, these quick videos are fun and easy to make, and I hope it's at least somewhat useful to you. If it is, I'll make more. It's fun, I enjoy it. I love it, really. Quick shout-out to podcast sponsors. Vincero Watches, the maker of classy, well-performing watches. I'm wearing one now. And Four Sigmatic, the maker of delicious mushroom coffee. I drink it every morning and all day, as you can probably tell from my voice now. Please check out these sponsors in the description to get a discount and to support this channel. All right, love you all. And remember, try to learn something new every day.