Transcript
00:00:00 The following is a conversation with Leonard Susskind.
00:00:03 He’s a professor of theoretical physics
00:00:04 at Stanford University and founding director
00:00:07 of Stanford Institute of Theoretical Physics.
00:00:10 He is widely regarded as one of the fathers
00:00:13 of string theory and in general,
00:00:14 as one of the greatest physicists of our time,
00:00:17 both as a researcher and an educator.
00:00:20 This is the Artificial Intelligence Podcast.
00:00:23 Perhaps you noticed that the people I’ve been speaking with
00:00:26 are not just computer scientists,
00:00:27 but philosophers, mathematicians, writers,
00:00:30 psychologists, physicists, and soon other disciplines.
00:00:34 To me, AI is much bigger than deep learning,
00:00:37 bigger than computing.
00:00:39 It is our civilization’s journey
00:00:40 into understanding the human mind
00:00:42 and creating echoes of it in the machine.
00:00:45 If you enjoy the podcast, subscribe on YouTube,
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00:00:53 at Lex Friedman, spelled F R I D M A M.
00:00:57 And now, here’s my conversation with Leonard Susskind.
00:01:02 You worked and were friends with Richard Feynman.
00:01:05 How has he influenced you, changed you
00:01:07 as a physicist and thinker?
00:01:10 What I saw, I think what I saw was somebody
00:01:13 who could do physics in this deeply intuitive way.
00:01:18 His style was almost to close his eyes
00:01:21 and visualize the phenomena that he was thinking about.
00:01:24 And through visualization, outflank the mathematical,
00:01:30 the highly mathematical and very, very sophisticated
00:01:34 technical arguments that people would use.
00:01:37 I think that was also natural to me,
00:01:39 but I saw somebody who was actually successful at it,
00:01:43 who could do physics in a way that I regarded
00:01:47 as simpler, more direct, more intuitive.
00:01:55 And while I don’t think he changed my way of thinking,
00:01:59 I do think he validated it.
00:02:01 He made me look at it and say, yeah,
00:02:03 that’s something you can do and get away with.
00:02:06 Practically didn’t get away with it.
00:02:08 So do you find yourself, whether you’re thinking
00:02:12 about quantum mechanics or black holes
00:02:14 or string theory, using intuition as a first step
00:02:19 or step throughout using visualization?
00:02:22 Yeah, very much so, very much so.
00:02:24 I tend not to think about the equations.
00:02:27 I tend not to think about the symbols.
00:02:30 I tend to try to visualize the phenomena themselves.
00:02:34 And then when I get an insight that I think is valid,
00:02:38 I might try to convert it to mathematics,
00:02:40 but I’m not a natural mathematician.
00:02:44 I’m good enough at it.
00:02:46 I’m good enough at it, but I’m not a great mathematician.
00:02:49 So for me, the way of thinking about physics
00:02:52 is first intuitive, first visualization,
00:02:57 scribble a few equations maybe,
00:02:59 but then try to convert it to mathematics.
00:03:02 Experience is that other people are better
00:03:04 at converting it to mathematics than I am.
00:03:08 And yet you’ve worked with very counterintuitive ideas.
00:03:12 No, that’s true.
00:03:12 That’s true.
00:03:13 You can visualize something counterintuitive.
00:03:15 How do you dare?
00:03:16 By rewiring your brain in new ways.
00:03:19 Yeah, quantum mechanics is not intuitive.
00:03:22 Very little of modern physics is intuitive.
00:03:26 Intuitive, what does intuitive mean?
00:03:29 It means the ability to think about it
00:03:31 with basic classical physics,
00:03:33 the physics that we evolved with throwing stones,
00:03:38 or splashing water, whatever it happens to be.
00:03:44 Quantum physics, general relativity,
00:03:47 quantum field theory are deeply unintuitive in that way.
00:03:51 But after time and getting familiar with these things,
00:03:55 you develop new intuitions.
00:03:57 I always said you rewire.
00:03:59 And it’s to the point where me and many of my friends,
00:04:04 I and many of my friends,
00:04:05 can think more easily quantum mechanically
00:04:10 than we can classically.
00:04:11 We’ve gotten so used to it.
00:04:13 I mean, yes, our neural wiring in our brain
00:04:17 is such that we understand rocks and stones and water
00:04:20 and so on.
00:04:21 We sort of evolved for that.
00:04:22 Evolved for it.
00:04:23 Do you think it’s possible to create a wiring
00:04:26 of neuron like state devices that more naturally
00:04:31 understand quantum mechanics, understand wave function,
00:04:35 understand these weird things?
00:04:38 Well, I’m not sure.
00:04:39 I think many of us have evolved the ability
00:04:42 to think quantum mechanically to some extent.
00:04:46 But that doesn’t mean you can think like an electron.
00:04:50 That doesn’t mean another example.
00:04:53 Forget for a minute quantum mechanics.
00:04:55 Just visualizing four dimensional space
00:04:58 or five dimensional space or six dimensional space,
00:05:02 I think we’re fundamentally wired
00:05:05 to visualize three dimensions.
00:05:08 I can’t even visualize two dimensions or one dimension
00:05:11 without thinking about it as embedded in three dimensions.
00:05:16 If I wanna visualize a line,
00:05:18 I think of the line as being a line in three dimensions.
00:05:23 Or I think of the line as being a line on a piece of paper
00:05:25 with a piece of paper being in three dimensions.
00:05:28 I never seem to be able to, in some abstract and pure way,
00:05:33 visualize in my head the one dimension,
00:05:35 the two dimension, the four dimension, the five dimensions.
00:05:38 And I don’t think that’s ever gonna happen.
00:05:41 The reason is I think our neural wiring
00:05:43 is just set up for that.
00:05:47 On the other hand, we do learn ways
00:05:49 to think about five, six, seven dimensions.
00:05:52 We learn ways, we learn mathematical ways,
00:05:56 and we learn ways to visualize them, but they’re different.
00:06:00 And so yeah, I think we do rewire ourselves.
00:06:04 Whether we can ever completely rewire ourselves
00:06:07 to be completely comfortable with these concepts, I doubt.
00:06:11 So that it’s completely natural.
00:06:13 To where it’s completely natural.
00:06:15 So I’m sure there’s somewhat, you could argue,
00:06:18 creatures that live in a two dimensional space.
00:06:22 Yeah, maybe there are.
00:06:23 And while it’s romanticizing the notion of curse,
00:06:28 we’re all living, as far as we know,
00:06:30 in three dimensional space.
00:06:31 But how do those creatures imagine 3D space?
00:06:35 Well, probably the way we imagine 4D,
00:06:37 by using some mathematics and some equations
00:06:40 and some tricks.
00:06:44 Okay, so jumping back to Feynman just for a second.
00:06:48 He had a little bit of an ego.
00:06:52 Yes.
00:06:54 Why, do you think ego is powerful or dangerous in science?
00:07:00 I think both, both, both.
00:07:02 I think you have to have both arrogance and humility.
00:07:06 You have to have the arrogance to say, I can do this.
00:07:10 Nature is difficult, nature is very, very hard.
00:07:13 I’m smart enough, I can do it.
00:07:16 I can win the battle with nature.
00:07:19 On the other hand, I think you also have to have
00:07:21 the humility to know that you’re very likely
00:07:26 to be wrong on any given occasion.
00:07:29 Everything you’re thinking could suddenly change.
00:07:33 Young people can come along and say things
00:07:35 you won’t understand and you’ll be lost and flabbergasted.
00:07:39 So I think it’s a combination of both.
00:07:42 You better recognize that you’re very limited,
00:07:46 and you better be able to say to yourself,
00:07:49 I’m not so limited that I can’t win this battle with nature.
00:07:53 It takes a special kind of person
00:07:56 who can manage both of those, I would say.
00:07:59 And I would say there’s echoes of that in your own work,
00:08:03 a little bit of ego, a little bit of outside of the box,
00:08:05 humble thinking.
00:08:08 I hope so.
00:08:09 So was there a time where you felt,
00:08:16 you looked at yourself and asked,
00:08:18 am I completely wrong about this?
00:08:19 Oh yeah, about the whole thing or about specific things?
00:08:23 The whole thing.
00:08:24 What do you mean?
00:08:25 Wait, which whole thing?
00:08:27 Me and me and my ability to do this thing.
00:08:29 Oh, those kinds of doubts.
00:08:31 First of all, did you have those kinds of doubts?
00:08:33 No, I had different kind of doubts.
00:08:35 I came from a very working class background
00:08:37 and I was uncomfortable in academia for,
00:08:41 oh, for a long time.
00:08:43 But they weren’t doubts about my ability or my,
00:08:48 they were just the discomfort in being in an environment
00:08:52 that my family hadn’t participated in,
00:08:56 I knew nothing about as a young person.
00:08:58 I didn’t learn that there was such a thing called physics
00:09:00 until I was almost 20 years old.
00:09:02 Yeah, so I did have certain kind of doubts,
00:09:09 but not about my ability.
00:09:11 I don’t think I was too worried
00:09:14 about whether I would succeed or not.
00:09:18 I never felt this insecurity, am I ever gonna get a job?
00:09:23 That had never occurred to me that I wouldn’t.
00:09:27 Maybe you could speak a little bit to this sense
00:09:29 of what is academia.
00:09:31 Because I too feel a bit uncomfortable in it.
00:09:37 There’s something I can’t put quite into words
00:09:40 what you have that’s not, doesn’t, if we call it music,
00:09:45 you play a different kind of music than a lot of academia.
00:09:48 How have you joined this orchestra?
00:09:51 How do you think about it?
00:09:54 I don’t know that I thought about it
00:09:56 as much as I just felt it.
00:09:58 Thinking is one thing, feeling is another thing.
00:10:02 I felt like an outsider until a certain age
00:10:07 when I suddenly found myself the ultimate insider
00:10:10 in academic physics.
00:10:14 And that was a sharp transition, and I wasn’t a young man.
00:10:19 I was probably 50 years old.
00:10:22 So you were never quite, it was a phase transition,
00:10:24 you were never quite in the middle.
00:10:27 Yeah, that’s right, I wasn’t.
00:10:29 I always felt a little bit of an outsider.
00:10:32 In the beginning, a lot an outsider.
00:10:37 My way of thinking was different,
00:10:40 my approach to mathematics was different,
00:10:43 but also my social background
00:10:47 that I came from was different.
00:10:49 Now these days, half the young people I meet,
00:10:51 they’re parents or professors.
00:10:53 That was not my case.
00:10:59 But then all of a sudden, at some point,
00:11:02 I found myself at very much the center of,
00:11:06 maybe not the only one at the center,
00:11:07 but certainly one of the people in the center
00:11:09 of a certain kind of physics.
00:11:12 And all that went away, it went away in a flash.
00:11:17 So maybe a little bit with Feynman,
00:11:21 but in general, how do you develop ideas?
00:11:24 Do you work through ideas alone?
00:11:26 Do you brainstorm with others?
00:11:27 Oh, both, both, very definitely both.
00:11:31 The younger time, I spent more time with myself.
00:11:36 Now, because I’m at Stanford,
00:11:39 because I have a lot of ex students
00:11:44 and people who are interested in the same thing I am,
00:11:50 I spend a good deal of time, almost on a daily basis,
00:11:54 interacting, brainstorming, as you said.
00:11:57 It’s a very important part.
00:12:00 I spend less time probably completely self focused
00:12:03 than with a piece of paper
00:12:07 and just sitting there staring at it.
00:12:09 What are your hopes for quantum computers?
00:12:13 So machines that are based on,
00:12:16 that have some elements of leverage quantum mechanical ideas.
00:12:21 Yeah, it’s not just leveraging quantum mechanical ideas.
00:12:24 You can simulate quantum systems on a classical computer.
00:12:29 Simulate them means solve the Schrodinger equation for them
00:12:33 or solve the equations of quantum mechanics
00:12:36 or solve the equations of quantum mechanics
00:12:40 on a computer, on a classical computer.
00:12:43 But the classical computer is not doing,
00:12:47 is not a quantum mechanical system itself.
00:12:49 Of course it is.
00:12:50 Everything’s made of quantum mechanics,
00:12:52 but it’s not functioning.
00:12:53 It’s not functioning as a quantum system.
00:12:56 It’s just solving equations.
00:12:58 The quantum computer is truly a quantum system
00:13:01 which is actually doing the things
00:13:05 that you’re programming it to do.
00:13:07 You want to program a quantum field theory.
00:13:12 If you do it in classical physics,
00:13:13 that program is not actually functioning in the computer
00:13:17 as a quantum field theory.
00:13:18 It’s just solving some equations.
00:13:21 Physically, it’s not doing the things
00:13:23 that the quantum system would do.
00:13:27 The quantum computer is really a quantum mechanical system
00:13:30 which is actually carrying out the quantum operations.
00:13:34 You can measure it at the end.
00:13:36 It intrinsically satisfies the uncertainty principle.
00:13:40 It is limited in the same way that quantum systems
00:13:44 are limited by uncertainty and so forth.
00:13:47 And it really is a quantum system.
00:13:49 That means that what you’re doing
00:13:51 when you program something for a quantum system
00:13:53 is you’re actually building a real version of the system.
00:13:58 The limits of a classical computer,
00:14:00 classical computers are enormously limited
00:14:02 when it comes to the quantum systems.
00:14:07 They’re enormously limited
00:14:09 because you’ve probably heard this before,
00:14:12 but in order to store the amount of information
00:14:14 that’s in a quantum state of 400 spins,
00:14:19 that’s not very many, 400 I can put in my pocket,
00:14:23 I can put 400 pennies in my pocket.
00:14:27 To be able to simulate the quantum state
00:14:32 of 400 elementary quantum systems, qubits we call them,
00:14:37 to do that would take more information
00:14:39 than can possibly be stored in the entire universe
00:14:43 if it were packed so tightly
00:14:46 that you couldn’t pack any more in.
00:14:50 400 qubits.
00:14:52 On the other hand, if your quantum computer
00:14:54 is composed of 400 qubits,
00:14:56 it can do everything 400 qubits can do.
00:14:59 What kind of space, if you just intuitively think
00:15:02 about the space of algorithms that that unlocks for us,
00:15:06 so there’s a whole complexity theory
00:15:08 around classical computers,
00:15:10 measuring the running time of things,
00:15:12 and P, so on, what kind of algorithms
00:15:14 just intuitively do you think it unlocks for us?
00:15:18 Okay, so we know that there are a handful of algorithms
00:15:22 that can seriously beat classical computers
00:15:25 and which can have exponentially more power.
00:15:28 This is a mathematical statement.
00:15:29 Nobody’s exhibited this in the laboratory.
00:15:32 It’s a mathematical statement.
00:15:33 We know that’s true, but it also seems more and more
00:15:37 that the number of such things is very limited.
00:15:40 Only very, very special problems
00:15:45 exhibit that much advantage for a quantum computer,
00:15:49 of standard problems.
00:15:52 To my mind, as far as I can tell,
00:15:53 the great power of quantum computers
00:15:55 will actually be to simulate quantum systems.
00:15:59 If you’re interested in a certain quantum system
00:16:02 and it’s too hard to simulate classically,
00:16:07 you simply build a version of the same system.
00:16:09 You build a version of it.
00:16:11 You build a model of it
00:16:12 that’s actually functioning as the system.
00:16:14 You run it, and then you do the same thing
00:16:16 you would do to the quantum system.
00:16:18 You make measurements on it, quantum measurements on it.
00:16:21 The advantage is you can run it much slower.
00:16:26 You could say, why bother?
00:16:27 Why not just use the real system?
00:16:29 Why not just do experiments on the real system?
00:16:32 Well, real systems are kind of limited.
00:16:33 You can’t change them.
00:16:34 You can’t manipulate them.
00:16:36 You can’t slow them down so that you can poke into them.
00:16:40 You can’t modify them in arbitrary kinds of ways
00:16:43 to see what would happen if I change the system a little bit.
00:16:48 I think that quantum computers will be extremely valuable
00:16:55 in understanding quantum systems.
00:17:00 At the lowest level of the fundamental laws.
00:17:04 They’re actually satisfying the same laws
00:17:06 as the systems that they’re simulating.
00:17:09 Okay, so on the one hand, you have things like factoring.
00:17:13 Factoring is the great thing of quantum computers.
00:17:17 Factoring large numbers, that doesn’t seem that much
00:17:20 to do with quantum mechanics.
00:17:22 It seems to be almost a fluke that a quantum computer
00:17:28 can solve the factoring problem in a short time.
00:17:34 And those problems seem to be extremely special, rare,
00:17:38 and it’s not clear to me
00:17:40 that there’s gonna be a lot of them.
00:17:42 On the other hand, there are a lot of quantum systems.
00:17:45 Chemistry, there’s solid state physics,
00:17:47 there’s material science, there’s quantum gravity,
00:17:51 there’s all kinds of quantum field theory.
00:17:54 And some of these are actually turning out
00:17:56 to be applied sciences,
00:17:58 as well as very fundamental sciences.
00:18:01 So we probably will run out of the ability
00:18:05 to solve equations for these things.
00:18:07 Solve equations by the standard methods of pencil and paper.
00:18:11 Solve the equations by the method of classical computers.
00:18:16 And so what we’ll do is we’ll build versions
00:18:18 of these systems, run them,
00:18:22 and run them under controlled circumstances
00:18:24 where we can change them, manipulate them,
00:18:26 make measurements on them,
00:18:28 and find out all the things we wanna know.
00:18:30 So in finding out the things we wanna know
00:18:33 about very small systems, is there something
00:18:38 that we can also find out about the macro level,
00:18:42 about something about the function, forgive me,
00:18:45 of our brain, biological systems,
00:18:48 the stuff that’s about one meter in size
00:18:50 versus much, much smaller?
00:18:53 Well, what all the excitement is about
00:18:55 among the people that I interact with
00:18:56 is understanding black holes.
00:18:58 Black holes.
00:18:59 Black holes are big things.
00:19:02 They are many, many degrees of freedom.
00:19:04 There is another kind of quantum system that is big.
00:19:08 It’s a large quantum computer.
00:19:11 And one of the things we’ve learned
00:19:13 is that the physics of large quantum computers
00:19:15 is in some ways similar to the physics
00:19:17 of large quantum black holes.
00:19:19 And we’re using that relationship.
00:19:22 Now you asked, you didn’t ask about quantum computers
00:19:24 or systems, you didn’t ask about black holes,
00:19:28 you asked about brains.
00:19:29 Yeah, about stuff that’s in the middle of the two.
00:19:32 It’s different.
00:19:34 So black holes are,
00:19:36 there’s something fundamental about black holes
00:19:39 that feels to be very different than a brain.
00:19:42 Yes.
00:19:43 And they also function in a very quantum mechanical way.
00:19:45 Right.
00:19:46 Okay.
00:19:47 It is, first of all, unclear to me,
00:19:50 but of course it’s unclear to me.
00:19:52 I’m not a neuroscientist.
00:19:55 I have, I don’t even have very many friends
00:19:58 who are neuroscientists.
00:20:00 I would like to have more friends who are neuroscientists.
00:20:02 I just don’t run into them very often.
00:20:05 Among the few neuroscientists
00:20:07 I’ve ever talked about about this,
00:20:09 they are pretty convinced
00:20:12 that the brain functions classically,
00:20:16 that it is not intrinsically a quantum mechanical system
00:20:20 or it doesn’t make use of the special features,
00:20:23 entanglement, coherence, superposition.
00:20:26 Are they right?
00:20:27 I don’t know.
00:20:28 I sort of hope they’re wrong
00:20:30 just because I like the romantic idea
00:20:32 that the brain is a quantum system.
00:20:35 But I think probably not.
00:20:38 The other thing,
00:20:40 big systems can be composed of lots of little systems.
00:20:44 Materials, the materials that we work with and so forth
00:20:47 are, can be large systems, a large piece of material,
00:20:52 but they’re made out of quantum systems.
00:20:55 Now, one of the things that’s been happening
00:20:57 over the last good number of years
00:21:00 is we’re discovering materials and quantum systems,
00:21:04 which function much more quantum mechanically
00:21:08 than we imagined.
00:21:09 Topological insulators, this kind of thing,
00:21:12 that kind of thing.
00:21:13 Those are macroscopic systems,
00:21:15 but they’re just superconductors.
00:21:17 Superconductors have a lot of quantum mechanics in them.
00:21:22 You can have a large chunk of superconductor.
00:21:25 So it’s a big piece of material.
00:21:26 On the other hand, it’s functioning and its properties
00:21:29 depend very, very strongly on quantum mechanics.
00:21:32 And to analyze them, you need the tools of quantum mechanics.
00:21:37 If we can go on to black holes
00:21:41 and looking at the universe
00:21:42 as a information processing system,
00:21:45 as a computer, as a giant computer.
00:21:46 It’s a giant computer.
00:21:48 What’s the power of thinking of the universe
00:21:50 as an information processing system?
00:21:52 Or what is perhaps its use
00:21:55 besides the mathematical use of discussing black holes
00:21:59 and your famous debates and ideas around that
00:22:02 to human beings,
00:22:06 or life in general as information processing systems?
00:22:08 Well, all systems are information processing systems.
00:22:13 You poke them, they change a little bit, they evolve.
00:22:16 All systems are information processing systems.
00:22:18 So there’s no extra magic to us humans?
00:22:22 It certainly feels, consciousness intelligence
00:22:25 feels like magic.
00:22:26 It sure does.
00:22:26 Where does it emerge from?
00:22:29 If we look at information processing,
00:22:33 what are the emergent phenomena
00:22:35 that come from viewing the world
00:22:37 as an information processing system?
00:22:39 Here is what I think.
00:22:41 My thoughts are not worth much in this.
00:22:43 If you ask me about physics,
00:22:44 my thoughts may be worth something.
00:22:46 If you ask me about this,
00:22:48 I’m not sure my thoughts are worth anything.
00:22:50 But as I said earlier,
00:22:53 I think when we do introspection,
00:22:55 when we imagine doing introspection
00:22:57 and try to figure out what it is
00:22:58 when we do when we’re thinking,
00:23:00 I think we get it wrong.
00:23:03 I’m pretty sure we get it wrong.
00:23:04 Everything I’ve heard about the way the brain functions
00:23:07 is so counterintuitive.
00:23:09 For example, you have neurons which detect vertical lines.
00:23:14 You have different neurons
00:23:15 which detect lines at 45 degrees.
00:23:17 You have different neurons.
00:23:19 I never imagined that there were whole circuits
00:23:21 which were devoted to vertical lines in my brain.
00:23:25 Doesn’t seem to be the way my brain works.
00:23:28 My brain seems to work if I put my finger up vertically
00:23:31 or if I put it horizontally
00:23:32 or if I put it this way or that way.
00:23:33 It seems to me it’s the same circuits.
00:23:36 It’s not the way it works.
00:23:38 The way the brain is compartmentalized
00:23:41 seems to be very, very different
00:23:43 than what I would have imagined
00:23:45 if I were just doing psychological introspection
00:23:49 about how things work.
00:23:51 My conclusion is that we won’t get it right that way,
00:23:55 that how will we get it right?
00:23:59 I think maybe computer scientists will get it right eventually.
00:24:03 I don’t think there are any ways near it.
00:24:04 I don’t even think they’re thinking about it,
00:24:06 but eventually we will build machines perhaps
00:24:11 which are complicated enough
00:24:15 and partly engineered, partly evolved,
00:24:18 maybe evolved by machine learning and so forth.
00:24:21 This machine learning is very interesting.
00:24:23 By machine learning, we will evolve systems
00:24:26 and we may start to discover mechanisms
00:24:30 that have implications for how we think
00:24:35 and for what this consciousness thing is all about
00:24:39 and we’ll be able to do experiments on them
00:24:42 and perhaps answer questions
00:24:43 that we can’t possibly answer by introspection.
00:24:49 So that’s a really interesting point.
00:24:51 In many cases, if you look at even a string theory,
00:24:55 when you first think about a system,
00:24:56 it seems really complicated, like the human brain,
00:24:59 and through some basic reasoning
00:25:02 and trying to discover fundamental low level behavior
00:25:07 of the system, you find out that it’s actually much simpler.
00:25:10 Do you, one, have you, is that generally the process
00:25:13 and two, do you have that also hope
00:25:15 for biological systems as well,
00:25:17 for all the kinds of stuff we’re studying at the human level?
00:25:21 Of course, physics always begins
00:25:23 by trying to find the simplest version of something
00:25:25 and analyze it.
00:25:26 Yeah, I mean, there are lots of examples
00:25:28 where physics has taken very complicated systems,
00:25:33 analyzed them and found simplicity in them for sure.
00:25:36 I said superconductors before, it’s an obvious one.
00:25:39 A superconductor seems like a monstrously complicated thing
00:25:42 with all sorts of crazy electrical properties,
00:25:45 magnetic properties and so forth.
00:25:48 And when it finally is boiled down
00:25:50 to its simplest elements,
00:25:52 it’s a very simple quantum mechanical phenomenon
00:25:56 called spontaneous symmetry breaking,
00:25:59 and which we, in other contexts, we learned about
00:26:04 and we’re very familiar with.
00:26:06 So yeah, I mean, yes, we do take complicated things,
00:26:10 make them simple, but what we don’t want to do
00:26:13 is take things which are intrinsically complicated
00:26:16 and fool ourselves into thinking
00:26:18 that we can make them simple.
00:26:20 We don’t want to make, I don’t know who said this,
00:26:22 but we don’t want to make them simpler
00:26:23 than they really are, okay?
00:26:26 Is the brain a thing which ultimately functions
00:26:30 by some simple rules or is it just complicated?
00:26:35 In terms of artificial intelligence,
00:26:37 nobody really knows what are the limits
00:26:40 of our current approaches, you mentioned machine learning.
00:26:43 How do we create human level intelligence?
00:26:44 It seems that there’s a lot of very smart physicists
00:26:48 who perhaps oversimplify the nature of intelligence
00:26:51 and think of it as information processing,
00:26:53 and therefore there doesn’t seem to be
00:26:55 any theoretical reason why we can’t artificially create
00:27:00 human level or superhuman level intelligence.
00:27:02 In fact, the reasoning goes,
00:27:04 if you create human level intelligence,
00:27:07 the same approach you just used
00:27:08 to create human level intelligence
00:27:10 should allow you to create superhuman level intelligence
00:27:13 very easily, exponentially.
00:27:16 So what do you think that way of thinking
00:27:18 that comes from physicists is all about?
00:27:22 I wish I knew, but there’s a particular reason
00:27:24 why I wish I knew.
00:27:27 I have a second job.
00:27:30 I consult for Google, not for Google, for Google X.
00:27:34 I am the senior academic advisor
00:27:39 to a group of machine learning physicists.
00:27:43 Now that sounds crazy because I know nothing
00:27:45 about the subject.
00:27:47 I know very little about the subject.
00:27:49 On the other hand, I’m good at giving advice,
00:27:52 so I give them advice on things.
00:27:53 Anyway, I see these young physicists
00:27:56 who are approaching the machine learning problem.
00:27:58 There is a real machine learning problem.
00:28:00 Namely, why does it work as well as it does?
00:28:03 Nobody really seems to understand
00:28:06 why it is capable of doing the kind of generalizations
00:28:09 that it does and so forth.
00:28:11 And there are three groups of people
00:28:14 who have thought about this.
00:28:17 There are the engineers.
00:28:19 The engineers are incredibly smart,
00:28:21 but they tend not to think as hard
00:28:23 about why the thing is working
00:28:26 as much as they do how to use it.
00:28:28 Obviously, they provided a lot of data,
00:28:31 and it is they who demonstrated
00:28:34 that machine learning can work much better
00:28:35 than you have any right to expect.
00:28:37 The machine learning systems are systems.
00:28:40 The system’s not too different
00:28:41 than the kind of systems that physicists study.
00:28:44 There’s not all that much difference
00:28:46 between quantum, in the structure of mathematics,
00:28:51 physically, yes, but in the structure of mathematics,
00:28:54 between a tensor network designed
00:28:57 to describe a quantum system on the one hand
00:29:01 and the kind of networks that are used in machine learning.
00:29:05 So there are more and more, I think,
00:29:08 young physicists are being drawn
00:29:10 to this field of machine learning,
00:29:12 some very, very good ones.
00:29:15 I work with a number of very good ones,
00:29:16 not on machine learning, but on having lunch.
00:29:20 On having lunch?
00:29:21 Right.
00:29:22 Yeah.
00:29:23 And I can tell you they are super smart.
00:29:27 They don’t seem to be so arrogant
00:29:30 about their physics backgrounds
00:29:32 that they think they can do things that nobody else can do.
00:29:35 But the physics way of thinking, I think,
00:29:37 will add great value to,
00:29:41 or will bring value to the machine learning.
00:29:43 I believe it will.
00:29:45 And I think it already has.
00:29:47 At what time scale do you think
00:29:50 predicting the future becomes useless
00:29:53 in your long experience
00:29:55 and being surprised at new discoveries?
00:29:57 Well, sometimes a day, sometimes 20 years.
00:30:03 There are things which I thought
00:30:07 we were very far from understanding,
00:30:09 which practically in a snap of the fingers
00:30:12 or a blink of the eye suddenly became understood,
00:30:17 completely surprising to me.
00:30:21 There are other things which I looked at and I said,
00:30:24 we’re not gonna understand these things for 500 years,
00:30:27 in particular quantum gravity.
00:30:29 The scale for that was 20 years, 25 years.
00:30:32 And we understand a lot
00:30:33 and we don’t understand it completely now by any means,
00:30:35 but I thought it was 500 years to make any progress.
00:30:40 It turned out to be very, very far from that.
00:30:42 It turned out to be more like 20 or 25 years
00:30:45 from the time when I thought it was 500 years.
00:30:48 So if we may, can we jump around quantum gravity,
00:30:51 some basic ideas in physics?
00:30:53 What is the dream of string theory mathematically?
00:30:59 What is the hope?
00:31:00 Where does it come from?
00:31:01 What problem is it trying to solve?
00:31:03 I don’t think the dream of string theory
00:31:05 is any different than the dream
00:31:06 of fundamental theoretical physics altogether.
00:31:09 Understanding a unified theory of everything.
00:31:12 I don’t like thinking of string theory
00:31:15 as a subject unto itself
00:31:17 with people called string theorists
00:31:19 who are the practitioners
00:31:21 of this thing called string theory.
00:31:24 I much prefer to think of them as theoretical physicists
00:31:28 trying to answer deep fundamental questions about nature,
00:31:32 in particular gravity,
00:31:33 in particular gravity and its connection
00:31:35 with quantum mechanics,
00:31:38 and who at the present time find string theory
00:31:41 a useful tool rather than saying
00:31:44 there’s a subject called string theorists.
00:31:46 I don’t like being referred to as a string theorist.
00:31:48 Yes, but as a tool, is it useful to think about our nature
00:31:54 in multiple dimensions, the strings vibrating?
00:31:57 I believe it is useful.
00:31:59 I’ll tell you what the main use of it has been up till now.
00:32:02 Well, it has had a number of main uses.
00:32:03 Originally, string theory was invented,
00:32:06 and I know that I was there.
00:32:07 I was right at the spot
00:32:08 where it was being invented literally,
00:32:13 and it was being invented to understand hadrons.
00:32:16 Hadrons are subnuclear particles,
00:32:19 protons, neutrons, mesons,
00:32:21 and at that time, the late 60s, early 70s,
00:32:28 it was clear from experiment
00:32:30 that these particles called hadrons could vibrate,
00:32:33 could rotate, could do all the things
00:32:36 that a little closed string can do,
00:32:39 and it was and is a valid and correct theory of these hadrons.
00:32:47 It’s been experimentally tested, and that is a done deal.
00:32:53 It had a second life as a theory of gravity,
00:32:56 the same basic mathematics,
00:32:58 except on a very, very much smaller distance scale.
00:33:02 The objects of gravitation are 19 orders of magnitude
00:33:07 or orders of magnitude smaller than a proton,
00:33:10 but the same mathematics turned up.
00:33:12 The same mathematics turned up.
00:33:14 What has been its value?
00:33:15 Its value is that it’s mathematically rigorous in many ways
00:33:20 and enabled us to find mathematical structures
00:33:27 which have both quantum mechanics and gravity.
00:33:30 With rigor, we can test out ideas.
00:33:34 We can test out ideas.
00:33:35 We can’t test them in the laboratory.
00:33:37 They’re 19 orders of magnitude too small
00:33:39 are things that we’re interested in,
00:33:41 but we can test them out mathematically
00:33:43 and analyze their internal consistency.
00:33:47 By now, 40 years ago, 35 years ago, and so forth,
00:33:53 people very, very much questioned the consistency
00:33:57 between gravity and quantum mechanics.
00:33:59 Stephen Hawking was very famous for it, rightly so.
00:34:02 Now, nobody questions that consistency anymore.
00:34:05 They don’t because we have mathematically precise
00:34:09 string theories which contain both gravity
00:34:12 and quantum mechanics in a consistent way.
00:34:15 So it’s provided that certainty that quantum mechanics
00:34:21 and gravity can coexist.
00:34:22 That’s not a small thing.
00:34:24 It’s a very big thing.
00:34:25 It’s a huge thing.
00:34:25 Einstein would be proud.
00:34:27 Einstein, he might be appalled.
00:34:28 I don’t know.
00:34:29 He didn’t like it.
00:34:30 He didn’t like it.
00:34:31 He might not be appalled, I don’t know.
00:34:32 He didn’t like quantum mechanics very much,
00:34:34 but he would certainly be struck by it.
00:34:37 I think that may be, at this time,
00:34:40 its biggest contribution to physics
00:34:42 in illustrating almost definitively
00:34:45 that quantum mechanics and gravity
00:34:46 are very closely related
00:34:48 and not inconsistent with each other.
00:34:51 Is there a possibility of something deeper,
00:34:53 more profound that still is consistent with string theory
00:34:58 but is deeper, that is to be found?
00:35:03 Well, you could ask the same thing about quantum mechanics.
00:35:04 Is there something?
00:35:05 Exactly.
00:35:06 Yeah, yeah.
00:35:07 I think string theory is just an example
00:35:09 of a quantum mechanical system
00:35:11 that contains both gravitation and quantum mechanics.
00:35:16 So is there something underlying quantum mechanics?
00:35:19 Perhaps something deterministic.
00:35:21 Perhaps something deterministic.
00:35:23 My friend, Ferad Etouf, whose name you may know,
00:35:27 he’s a very famous physicist.
00:35:29 Dutch, not as famous as he should be, but…
00:35:33 Hard to spell his name.
00:35:35 It’s hard to say his name.
00:35:36 No, it’s easy to spell his name.
00:35:37 Apostrophe, he’s the only person I know
00:35:39 whose name begins with an apostrophe.
00:35:42 And he’s one of my heroes in physics.
00:35:44 He’s a little younger than me,
00:35:45 but he’s nevertheless one of my heroes.
00:35:47 Etouf believes that there is some substructure to the world
00:35:52 which is classical in character,
00:35:55 deterministic in character,
00:35:58 which somehow by some mechanism
00:36:00 that he has a hard time spelling out
00:36:03 emerges as quantum mechanics.
00:36:07 I don’t.
00:36:08 The wave function is somehow emergent.
00:36:10 The wave function, not just the wave function,
00:36:13 but the whole thing that goes with quantum mechanics,
00:36:16 uncertainty, entanglement, all these things,
00:36:19 are emergent. So you think quantum mechanics
00:36:22 is the bottom of the well?
00:36:23 Is the…
00:36:25 Here I think is where you have to be humble.
00:36:30 Here’s where humility comes.
00:36:31 I don’t think anybody should say anything
00:36:33 is the bottom of the well at this time.
00:36:36 I think we can reasonably say,
00:36:40 I can reasonably say when I look into the well,
00:36:44 I can’t see past quantum mechanics.
00:36:47 I can’t see past quantum mechanics.
00:36:50 I don’t see any reason for there to be anything
00:36:52 beyond quantum mechanics.
00:36:55 I think Etouf has asked very interesting
00:36:58 and deep questions.
00:36:59 I don’t like his answers.
00:37:01 Well, again, let me ask,
00:37:03 if we look at the deepest nature of reality
00:37:06 with whether it’s deterministic
00:37:09 or when observed as probabilistic,
00:37:13 what does that mean for our human level
00:37:16 of ideas of free will?
00:37:18 Is there any connection whatsoever
00:37:21 from this perception, perhaps illusion of free will
00:37:24 that we have and the fundamental nature of reality?
00:37:27 The only thing I can say is I am puzzled by that
00:37:31 as much as you are.
00:37:32 The illusion of it.
00:37:33 The illusion of consciousness,
00:37:36 the illusion of free will, the illusion of self.
00:37:39 Does that connect to?
00:37:43 How can a physical system do that?
00:37:45 And I am as puzzled as anybody.
00:37:48 There’s echoes of it in the observer effect.
00:37:51 So do you understand what it means to be an observer?
00:37:55 I understand it at a technical level.
00:37:57 An observer is a system with enough degrees of freedom
00:38:00 that it can record information
00:38:02 and which can become entangled
00:38:03 with the thing that it’s measuring.
00:38:05 Entanglement is the key.
00:38:07 When a system which we call an apparatus or an observer,
00:38:12 same thing, interacts with the system
00:38:15 that it’s observing, it doesn’t just look at it.
00:38:19 It becomes physically entangled with it.
00:38:21 And it’s that entanglement which we call an observation
00:38:24 or a measurement.
00:38:26 Now, does that satisfy me personally as an observer?
00:38:32 Yes and no.
00:38:33 I find it very satisfying
00:38:34 that we have a mathematical representation
00:38:36 of what it means to observe a system.
00:38:40 You are observing stuff right now, the conscious level.
00:38:44 Do you think there’s echoes of that kind of entanglement
00:38:48 in our macro scale?
00:38:49 Yes, absolutely, for sure.
00:38:52 We’re entangled with,
00:38:53 quantum mechanically entangled with everything in this room.
00:38:56 If we weren’t, then it would just,
00:38:59 well, we wouldn’t be observing it.
00:39:03 But on the other hand, you can ask,
00:39:05 do I really, am I really comfortable with it?
00:39:10 And I’m uncomfortable with it in the same way
00:39:12 that I can never get comfortable with five dimensions.
00:39:15 My brain isn’t wired for it.
00:39:18 Are you comfortable with four dimensions?
00:39:21 A little bit more,
00:39:22 because I can always imagine the fourth dimension is time.
00:39:26 So the arrow of time, are you comfortable with that arrow?
00:39:29 Do you think time is an emergent phenomena
00:39:31 or is it fundamental to nature?
00:39:33 That is a big question in physics right now.
00:39:37 All the physics that we do,
00:39:40 or at least that the people that I am comfortable
00:39:42 with talking to, my friends, my friends.
00:39:49 No, we all ask the same question that you just asked.
00:39:51 Space, we have a pretty good idea is emergent
00:39:55 and it emerges out of entanglement and other things.
00:40:00 Time always seems to be built into our equations
00:40:03 as just what Newton pretty much would have thought.
00:40:06 Newton, modified a little bit by Einstein,
00:40:09 would have called time.
00:40:12 And mostly in our equations, it is not emergent.
00:40:19 Time in physics is completely symmetric,
00:40:21 forward and backward.
00:40:22 Right, it’s symmetric.
00:40:23 So you don’t really need to think about the arrow of time
00:40:27 for most physical phenomena.
00:40:29 For most microscopic phenomena, no.
00:40:33 It’s only when the phenomena involve systems
00:40:35 which are big enough for thermodynamics to become important,
00:40:38 for entropy to become important.
00:40:41 For a small system, entropy is not a good concept.
00:40:47 Entropy is something which emerges out of large numbers.
00:40:52 It’s a probabilistic idea or it’s a statistical idea
00:40:56 and it’s a thermodynamic idea.
00:40:58 Thermodynamics requires lots and lots
00:41:00 and lots of little substructures, okay?
00:41:04 So it’s not until you emerge at the thermodynamic level
00:41:09 that there’s an arrow of time.
00:41:11 Do we understand it?
00:41:13 Yeah, I think we understand better
00:41:15 than most people think they have.
00:41:17 Most people say they think we understand it.
00:41:19 Yeah, I think we understand it.
00:41:21 It’s a statistical idea.
00:41:23 You mean like second law of thermodynamics,
00:41:26 entropy and so on?
00:41:27 Yeah, take a pack of cards and you fling it in the air
00:41:29 and you look what happens to it, it gets random.
00:41:32 We understand it.
00:41:33 It doesn’t go from random to simple.
00:41:36 It goes from simple to random.
00:41:38 But do you think it ever breaks down?
00:41:41 What I think you can do is in a laboratory setting,
00:41:46 you can take a system which is somewhere intermediate
00:41:49 between being small and being large
00:41:53 and make it go backward.
00:41:56 A thing which looks like it only wants to go forward
00:41:59 because of statistical mechanical reasons,
00:42:01 because of the second law,
00:42:03 you can very, very carefully manipulate it
00:42:07 to make it run backward.
00:42:09 I don’t think you can take an egg, a Humpty Dumpty
00:42:11 who fell on the floor and reverse that.
00:42:15 But you can, in a very controlled situation,
00:42:18 you can take systems which appear to be evolving
00:42:22 statistically toward randomness,
00:42:25 stop them, reverse them, and make them go back.
00:42:29 What’s the intuition behind that?
00:42:30 How do we do that?
00:42:31 How do we reverse it?
00:42:33 You’re saying a closed system.
00:42:35 Yeah, pretty much closed system, yes.
00:42:38 Did you just say that time travel is possible?
00:42:41 No, I didn’t say time travel is possible.
00:42:44 I said you can make a system go backward.
00:42:45 In time.
00:42:46 You can make it go back.
00:42:48 You can make it reverse its steps.
00:42:49 You can make it reverse its trajectory.
00:42:51 Yeah.
00:42:52 How do we do it?
00:42:53 What’s the intuition there?
00:42:54 Does it have, is it just a fluke thing
00:42:58 that we can do at a small scale in the lab
00:43:00 that doesn’t have?
00:43:01 Well, what I’m saying is you can do it
00:43:02 a little bit better than a small scale.
00:43:05 You can certainly do it with a simple, small system.
00:43:10 Small systems don’t have any sense of the arrow of time.
00:43:14 Atoms, atoms are no sense of an arrow of time.
00:43:20 They’re completely reversible.
00:43:22 It’s only when you have, you know,
00:43:24 the second law of thermodynamics
00:43:25 is the law of large numbers.
00:43:28 So you can break the law because it’s not
00:43:30 a deterministic law. You can break it,
00:43:31 you can break it, but it’s hard.
00:43:33 It requires great care.
00:43:36 The bigger the system is, the more care,
00:43:38 the more, the harder it is.
00:43:40 You have to overcome what’s called chaos.
00:43:43 And that’s hard.
00:43:45 And it requires more and more precision.
00:43:47 For 10 particles, you might be able to do it
00:43:50 with some effort.
00:43:54 For a hundred particles, it’s really hard.
00:43:56 For a thousand or a million particles, forget it,
00:43:59 but not for any fundamental reason,
00:44:01 just because it’s technologically too hard
00:44:03 to make the system go backward.
00:44:08 So, no time travel for engineering reasons.
00:44:13 Oh, no, no, no, no.
00:44:15 What is time travel?
00:44:16 Time travel to the future?
00:44:19 That’s easy.
00:44:20 You just close your eyes, go to sleep,
00:44:22 and you wake up in the future.
00:44:23 Yeah, yeah, a good nap gets you there, yeah.
00:44:25 A good nap gets you there, right.
00:44:27 But reversing the second law of thermodynamics,
00:44:32 going backward in time for anything that’s human scale
00:44:36 is a very difficult engineering effort.
00:44:40 I wouldn’t call that time travel
00:44:41 because it gets too mixed up
00:44:43 with what science fiction calls time travel.
00:44:46 This is just the ability to reverse a system.
00:44:51 You take the system and you reverse the direction
00:44:55 of motion of every molecule in it.
00:44:58 That, you can do it with one molecule.
00:45:00 If you find a particle moving in a certain direction,
00:45:03 let’s not say a particle, a baseball,
00:45:06 you stop it dead and then you simply reverse its motion.
00:45:10 In principle, that’s not too hard.
00:45:12 And it’ll go back along its trajectory
00:45:15 in the backward direction.
00:45:16 Just running the program backwards.
00:45:18 Running the program backward.
00:45:19 Yeah. Okay.
00:45:20 If you have two baseballs colliding,
00:45:22 well, you can do it,
00:45:23 but you have to be very, very careful to get it just right.
00:45:28 If you have 10 baseballs, really, really, better yet,
00:45:32 10 billiard balls on an idealized,
00:45:36 frictionless billiard table.
00:45:38 Okay, so you start the balls all on a triangle, right?
00:45:41 And you whack them.
00:45:43 Depending on the game you’re playing,
00:45:44 you either whack them or you’re really careful,
00:45:46 but you whack them.
00:45:48 And they go flying off in all possible directions.
00:45:51 Okay, try to reverse that.
00:45:54 Try to reverse that.
00:45:55 Imagine trying to take every billiard ball,
00:45:57 stopping it dead at some point,
00:46:00 and reversing its motion
00:46:01 so that it was going in the opposite direction.
00:46:04 If you did that with tremendous care,
00:46:07 it would reassemble itself back into the triangle.
00:46:11 Okay, that is a fact.
00:46:14 And you can probably do it with two billiard balls,
00:46:16 maybe with three billiard balls if you’re really lucky.
00:46:19 But what happens is as the system
00:46:21 gets more and more complicated,
00:46:23 you have to be more and more precise
00:46:26 not to make the tiniest error,
00:46:27 because the tiniest errors will get magnified
00:46:30 and you’ll simply not be able to do the reversal.
00:46:34 So yeah, but I wouldn’t call that time travel.
00:46:38 Yeah, that’s something else.
00:46:39 But if you think of it, it just made me think,
00:46:43 if you think the unrolling of state
00:46:46 that’s happening as a program,
00:46:49 if we look at the world,
00:46:52 silly idea of looking at the world as a simulation,
00:46:56 as a computer.
00:46:59 But it’s not a computer, it’s just a single program.
00:47:03 A question arises that might be useful.
00:47:06 How hard is it to have a computer that runs the universe?
00:47:11 Okay, so there are mathematical universes
00:47:18 that we know about.
00:47:20 One of them is called anti de Sitter space,
00:47:22 where we, and it’s quantum mechanics,
00:47:28 I think we could simulate it in a computer,
00:47:32 in a quantum computer.
00:47:34 Classical computer, all you can do is solve its equations.
00:47:36 You can’t make it work like the real system.
00:47:39 If we could build a quantum computer, a big enough one,
00:47:41 a robust enough one, we could probably simulate a universe,
00:47:49 a small version of an anti de Sitter universe.
00:47:52 Anti de Sitter is a kind of cosmology.
00:47:57 So I think we know how to do that.
00:48:00 The trouble is the universe that we live in
00:48:02 is not the anti de Sitter geometry,
00:48:04 it’s the de Sitter geometry.
00:48:07 And we don’t really understand its quantum mechanics at all.
00:48:11 So at the present time,
00:48:12 I would say we wouldn’t have the vaguest idea
00:48:14 how to simulate a universe similar to our own.
00:48:18 No, we can ask, could we build in the laboratory
00:48:21 a small version, a quantum mechanical version,
00:48:27 the collection of quantum computers
00:48:29 and tangled and coupled together,
00:48:32 which would reproduce the phenomena that go on in the universe,
00:48:38 even on a small scale.
00:48:40 Yes, if it were anti de Sitter space,
00:48:43 no, if it’s de Sitter space.
00:48:44 Can you slightly describe de Sitter space
00:48:47 and anti de Sitter space?
00:48:48 Yeah.
00:48:49 What are the geometric properties of?
00:48:51 They differ by the sign of a single constant
00:48:54 called the cosmological constant.
00:48:57 One of them is negatively curved,
00:49:01 the other is positively curved.
00:49:04 Anti de Sitter space, which is the negatively curved one,
00:49:08 you can think of as an isolated system
00:49:11 in a box with reflecting walls.
00:49:14 You could think of it as a system
00:49:16 of quantum mechanical system isolated
00:49:19 in an isolated environment.
00:49:21 De Sitter space is the one we really live in.
00:49:23 And that’s the one that’s exponentially expanding,
00:49:26 exponential expansion, dark energy,
00:49:30 whatever we wanna call it.
00:49:31 And we don’t understand that mathematically.
00:49:35 Do we understand?
00:49:36 Not everybody would agree with me,
00:49:38 but I don’t understand.
00:49:39 They would agree with me,
00:49:40 they definitely would agree with me
00:49:41 that I don’t understand it.
00:49:44 What about, is there an understanding of the birth,
00:49:48 the origin, the big bang?
00:49:50 So there’s one problem with the other.
00:49:51 No, no, there’s theories.
00:49:53 There are theories.
00:49:55 My favorite is the one called eternal inflation.
00:49:58 The infinity can be on both sides,
00:50:00 on one of the sides and none of the sides.
00:50:02 So what’s eternal infinity?
00:50:05 Okay.
00:50:09 Infinity on both sides.
00:50:13 Oh boy.
00:50:13 Yeah, yeah, that’s.
00:50:15 Why is that your favorite?
00:50:16 Because it’s the most just mind blowing?
00:50:21 No.
00:50:22 Because we want a beginning.
00:50:23 No, why do we want a beginning?
00:50:26 In practice there was a beginning, of course.
00:50:28 In practice there was a beginning.
00:50:31 But could it have been a random fluctuation
00:50:36 in an otherwise infinite time?
00:50:39 Maybe.
00:50:41 In any case, the eternal inflation theory,
00:50:45 I think if correctly understood,
00:50:47 would be infinite in both directions.
00:50:50 How do you think about infinity?
00:50:52 Oh God.
00:50:53 So, okay, of course you can think about it mathematically.
00:50:57 I just finished this discussion with my friend Sergei Brin.
00:51:01 How do you think about infinity?
00:51:02 I say, well, Sergei Brin is infinitely rich.
00:51:07 How do you test that hypothesis?
00:51:09 Okay.
00:51:12 Such a good line.
00:51:13 Right.
00:51:15 Yeah, so there’s really no way
00:51:17 to visualize some of these things.
00:51:20 Yeah, no, this is a very good question.
00:51:22 Does physics have any,
00:51:24 does infinity have any place in physics?
00:51:27 Right.
00:51:28 Right, and all I can say is very good question.
00:51:35 So what do you think of the recent first image
00:51:39 of a black hole visualized from the Horizon Telescope?
00:51:43 It’s an incredible triumph of science.
00:51:45 In itself, the fact that there are black holes
00:51:47 which collide is not a surprise.
00:51:50 And they seem to work exactly
00:51:52 the way they’re supposed to work.
00:51:54 Will we learn a great deal from it?
00:51:57 I don’t know, we might.
00:52:00 But the kind of things we’ll learn
00:52:01 won’t really be about black holes.
00:52:05 Why there are black holes in nature
00:52:09 of that particular mass scale and why they’re so common
00:52:12 may tell us something about the structure,
00:52:15 evolution of structure in the universe.
00:52:18 But I don’t think it’s gonna tell us
00:52:19 anything new about black holes.
00:52:22 But it’s a triumph in the sense
00:52:23 that you go back 100 years
00:52:25 and it was a continuous development,
00:52:28 general relativity, the discovery of black holes,
00:52:31 LIGO, the incredible technology that went into LIGO.
00:52:37 It is something that I never would have believed
00:52:43 was gonna happen 30, 40 years ago.
00:52:47 And I think it’s a magnificent structure,
00:52:51 magnificent thing, this evolution of general relativity,
00:52:59 LIGO, high precision, ability to measure things
00:53:03 on a scale of 10 to the minus 21.
00:53:07 So, astonishing.
00:53:09 So you’re just in awe that this path
00:53:12 took us to this picture.
00:53:14 Is it different?
00:53:17 You’ve thought a lot about black holes.
00:53:19 How did you visualize them in your mind?
00:53:23 And is the picture different than you’ve visualized it?
00:53:26 No, it’s simply confirmed.
00:53:30 It’s a magnificent triumph to have confirmed
00:53:32 a direct observation that Einstein’s theory of gravity
00:53:37 at the level of black hole collisions actually works
00:53:42 is awesome, it is really awesome.
00:53:45 I know some of the people who are involved in that.
00:53:48 They’re just ordinary people.
00:53:49 And the idea that they could carry this out,
00:53:54 I just, I’m shocked.
00:53:56 Yeah, just these little homo sapiens?
00:53:59 Yeah, just these little monkeys.
00:54:00 Yeah, got together and took a picture of…
00:54:04 Slightly advanced limer’s, I think.
00:54:08 What kind of questions can science not currently answer
00:54:11 but you hope might be able to soon?
00:54:13 Well, you’ve already addressed them.
00:54:15 What is consciousness, for example?
00:54:17 You think that’s within the reach of science?
00:54:19 I think it’s somewhat within the reach of science,
00:54:21 but I think that now I think it’s in the hands
00:54:23 of the computer scientists and the neuroscientists.
00:54:27 Not a physicist, with the help.
00:54:29 Perhaps at some point, but I think physicists
00:54:31 will try to simplify it down to something
00:54:34 that they can use their methods
00:54:36 and maybe they’re not appropriate.
00:54:38 Maybe we simply need to do more machine learning
00:54:43 on bigger scales, evolve machines.
00:54:47 Machines not only that learn
00:54:49 but evolve their own architecture.
00:54:51 As a process of learning, evolve in architecture.
00:54:54 Not under our control, only partially under our control,
00:54:56 but under the control of machine learning.
00:55:00 I’ll tell you another thing that I find awesome.
00:55:03 You know this Google thing that they taught
00:55:05 the computers how to play chess?
00:55:07 Yeah, yeah.
00:55:08 Okay, they taught the computers how to play chess,
00:55:10 not by teaching them how to play chess,
00:55:12 but just having them play against each other.
00:55:14 Against each other, self play.
00:55:15 Against each other, this is a form of evolution.
00:55:18 These machines evolved, they evolved in intelligence.
00:55:25 They evolved in intelligence
00:55:27 without anybody telling them how to do it.
00:55:30 They were not engineered, they just played
00:55:33 against each other and got better and better and better.
00:55:36 That makes me think that machines can evolve intelligence.
00:55:43 What exact kind of intelligence, I don’t know.
00:55:46 But in understanding that better and better,
00:55:49 maybe we’ll get better clues as to what goes on
00:55:52 in our own intelligence.
00:55:53 What life in intelligence is.
00:55:55 Last question, what kind of questions can science
00:55:58 not currently answer and may never be able to answer?
00:56:01 Yeah.
00:56:02 Yeah.
00:56:05 Is there an intelligence out there
00:56:07 that’s underlies the whole thing?
00:56:09 You can call them with a G word if you want.
00:56:11 I can say, are we a computer simulation with a purpose?
00:56:18 Is there an agent, an intelligent agent
00:56:22 that underlies or is responsible for the whole thing?
00:56:27 Does that intelligent agent satisfy the laws of physics?
00:56:30 Does it satisfy the laws of quantum mechanics?
00:56:32 Is it made of atoms and molecules?
00:56:34 Yeah, there’s a lot of questions.
00:56:36 And I don’t see, it seems to me a real question.
00:56:42 It’s an answerable question.
00:56:43 Well, I don’t know if it’s answerable.
00:56:44 The questions have to be answerable to be real.
00:56:49 Some philosophers would say that a question
00:56:52 is not a question unless it’s answerable.
00:56:55 This question doesn’t seem to me answerable
00:56:58 by any known method, but it seems to me real.
00:57:05 There’s no better place to end.
00:57:07 Leonard, thank you so much for talking today.
00:57:08 Okay, good.