The following is the conversation with Scott Aronson, his second time in the podcast.

He is a professor at UT Austin, director of the Quantum Information Center, and previously a

professor at MIT. Last time we talked about quantum computing. This time we talk about

computation complexity, consciousness, and theories of everything. I'm recording this intro,

as you may be able to tell, in a very strange room in the middle of the night. I'm not really sure

how I got here or how I'm going to get out, but Hunters Thompson saying, I think, applies

to today and the last few days and actually the last couple of weeks. Life should not be a journey

to the grave with the intention of arriving safely in a pretty and well preserved body,

but rather to skid in broadside in a cloud of smoke, thoroughly used up, totally worn out,

and loudly proclaiming, wow, what a ride. So I figured whatever I'm up to here,

and yes, lots of wine is involved. I'm going to have to improvise, hence this recording.

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I'm learning, always improving. In fact, as Scott says, if you always win, then you're

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at Lex Friedman. And now here's my conversation with Scott Aronson. Let's start with the most absurd

question, but I've read you write some fascinating stuff about it. So let's go there. Are we living

in a simulation? What difference does it make, Lex? I mean, I'm serious. What difference? Because

if we are living in a simulation, it raises the question, how real does something have to be

in simulation for it to be sufficiently immersive for us humans? But I mean, even in principle,

how could we ever know if we were in one? A perfect simulation by definition is something

that's indistinguishable from the real thing. Well, we didn't say anything about perfect.

No, no, that's right. Well, if it was an imperfect simulation, if we could hack it,

find a bug in it, then that would be one thing. If this was like the matrix and there was a way

for me to do flying kung fu moves or something by hacking the simulation, well, then we would

have to cross that bridge when we came to it, wouldn't we? I mean, at that point, it's hard

to see the difference between that and just what people would ordinarily refer to as a world with

miracles. What about from a different perspective, thinking about the universe as a computation,

like a program running on a computer? Is that kind of a neighboring concept? It is. It is an

interesting and reasonably well defined question to ask, is the world computable? Does the world

satisfy what we would call in CS, the church touring thesis, that is, could we take any physical

system and simulate it to any desired precision by a touring machine, given the appropriate input

data? And so far, I think the indications are pretty strong that our world does seem to satisfy

the church touring thesis. At least if it doesn't, then we haven't yet discovered why not. But now,

does that mean that our universe is a simulation? Well, that word seems to suggest that there is

some other larger universe in which it is running. And the problem there is that if the simulation is

perfect, then we're never going to be able to get any direct evidence about that other universe.

We will only be able to see the effects of the computation that is running in this universe.

Well, let's imagine an analogy. Let's imagine a PC, a personal computer, a computer.

Is it possible with the advent of artificial intelligence for the computer to look outside

of itself to understand its creator? Is that a ridiculous connect analogy?

Well, with the computers that we actually have, first of all, we all know that humans have done

an imperfect job of enforcing the abstraction boundaries of computers. You may try to confine

some program to a playpen, but as soon as there's one memory allocation error in the C program,

then the program has gotten out of that playpen and it can do whatever it wants. This is how most

hacks work, viruses and worms and exploits. And you would have to imagine that an AI would be able

to discover something like that. Now, of course, if we could actually discover some exploit of

reality itself, then in some sense, we wouldn't have to philosophize about this. This would no

longer be a metaphysical conversation. But the question is, what would that hack look like?

Yeah, well, I have no idea. I mean, Peter Shore, the very famous person in quantum computing,

of course, has joked that maybe the reason why we haven't yet integrated general relativity in

quantum mechanics is that the part of the universe that depends on both of them was actually left

unspecified. And if we ever tried to do an experiment involving the singularity of a black

hall or something like that, then the universe would just generate an overflow error or something.

A blue screen of death. Yeah, we would just crash the universe. Now, the universe seemed to hold

up pretty well for 14 billion years. So my Occam's razor kind of guess has to be that it will continue

to hold up that the fact that we don't know the laws of physics governing some phenomenon

is not a strong sign that probing that phenomenon is going to crash the universe.

But of course, I could be wrong. But do you think on the physics side of things,

there's been recently a few folks, Eric Weinstein and Stephen Wolfram, that came out with the

theory of everything. I think there's a history of physicists dreaming and working on the unification

of all the laws of physics. Do you think it's possible that once we understand more physics,

not necessarily the unification of the laws, but just understand physics more deeply at the

fundamental level, we'll be able to start part of this as humorous, but looking to see if there's

any bugs in the universe that could be exploited for traveling at not just speed of light, but

just traveling faster than our current spaceships can travel, all that kind of stuff.

Well, I mean, to travel faster than our current spaceships could travel, you wouldn't need to

find any bug in the universe. The known laws of physics let us go much faster up to the speed of

light. And when people want to go faster than the speed of light, well, we actually know something

about what that would entail, namely that according to relativity, that seems to entail

communication backwards in time. So then you have to worry about closed time like curves and all

of that stuff. So in some sense, we know the price that you have to pay for these things.

That's right. That's right. We can't say that they're impossible, but we know that a lot

else in physics breaks. So now regarding Eric Weinstein and Stephen Wolfram, I wouldn't say

that either of them has a theory of everything. I would say that they have ideas that they hope

could someday lead to a theory of everything. Is that a worthy pursuit?

Well, I mean, certainly, let's say by theory of everything, we don't literally mean a theory of

cats and of baseball, but we just mean it in the more limited sense of everything,

a fundamental theory of physics, of all of the fundamental interactions of physics.

Of course, such a theory, even after we had it, would leave the entire question of all

the emergent behavior to be explored. So it's only everything for a specific definition of

everything. But in that sense, I would say, of course, that's worth pursuing. I mean,

that is the entire program of fundamental physics. All of my friends who do quantum gravity,

who do string theory, who do anything like that, that is what's motivating them.

Yeah, it's funny, though, but Eric Weinstein talks about this. I don't know much about the

physics world, but I know about the AI world. It is a little bit taboo to talk about AGI,

for example, on the AI side. So really, to talk about the big dream of the community, I would say,

because it seems so far away, it's almost taboo to bring it up, because it's seen as the kind of

people that dream about creating a truly superhuman level of intelligence that's really far out there,

people because we're not even close to that. And it feels like the same thing is true for

the physics community. I mean, Stephen Hawking certainly talked constantly about theory of

everything. People use those terms who were some of the most respected people in the whole world

of physics. But I think that the distinction that I would make is that people might react badly if

you use the term in a way that suggests that you thinking about it for five minutes have come up

with this major new insight about it. It's difficult. Stephen Hawking is not a great example,

because I think you can do whatever the heck you want when you get to that level. And I certainly

see seeing your faculty. At that point, one of the nice things about getting older is you

stop giving a damn. But community as a whole, they tend to roll their eyes very quickly at

stuff that's outside the quote unquote mainstream. Well, let me put it this way. If you asked Ed

Whitten, let's say, who is, you might consider a leader of the string community and thus very,

very mainstream in a certain sense, but he would have no hesitation in saying, of course,

they're looking for a unified description of nature, of general relativity, of quantum mechanics,

of all the fundamental interactions of nature, right? Now, whether people would call that a

theory of everything, whether they would use that term, that might vary. Lenny Susskin would

definitely have no problem telling you that if that's what we want, right?

For me who loves human beings and psychology, it's kind of ridiculous to say a theory that

unifies the laws of physics gets you to understand everything. I would say you're not even close to

understanding everything. Yeah, right. Well, yeah, I mean, the word everything is a little

ambiguous here, right? Because, you know, and then people will get into debates about, you know,

reductionism versus emergentism and blah, blah, blah. And so in not wanting to say theory of

everything, people might just be trying to short circuit that debate and say, you know, look,

you know, yes, we want a fundamental theory of, you know, the particles and interactions of nature.

Let me bring up the next topic that people don't want to mention, although they're getting more

comfortable with it is consciousness. You mentioned that you have a talk on consciousness

that they watched five minutes of, but the internet connection is really bad. Was this

my talk about, you know, refuting the integrated information theory? Yes, it might have been.

Which was this particular account of consciousness that, yeah, I think one can just show it doesn't

work. So let me... Much harder to say what does work. What does work, yeah. Yeah. Let me ask,

maybe it'd be nice to comment on... You talk about also like the semi hard problem of consciousness,

or like almost hard problem or kind of hard... Pretty hard problem, I think I call it.

So maybe can you talk about that, their idea of the approach to modeling consciousness and why

you don't find it convincing? What is it first of all? Okay, well, so what I called the pretty hard

problem of consciousness, this is my term, although many other people have said something

equivalent to this, okay. But it's just, you know, the problem of, you know, giving an account of

just which physical systems are conscious and which are not. Or, you know, if there are degrees of

consciousness, then quantifying how conscious a given system is. Oh, awesome. So that's the

pretty hard... Yeah, that's what I mean. That's it. I'm adopting it. I love it. That's a good

ring to it. And so, you know, the infamous hard problem of consciousness is to explain how something

like consciousness could arise at all, you know, in a material universe, right? Or, you know,

why does it ever feel like anything to experience anything, right? And, you know, so I'm trying

to distinguish from that problem, right? And say, you know, okay, I would merely settle for an account

that could say, you know, is a fetus conscious, you know, if so, which trimester, you know, is a

dog conscious, you know, what about a frog, right? Or even as a precondition, you take that both

these things are conscious, tell me which is more conscious. Yeah, for example, yes. Yeah, yeah.

I mean, if consciousness is some multidimensional vector, well, just tell me in which respects

these things are conscious and in which respect they aren't, right? And, you know, and have some

principled way to do it where you're not, you know, carving out exceptions for things that you like

or don't like, but could somehow take a description of an arbitrary physical system, and then just

based on the physical properties of that system, or the informational properties or how it's connected

or something like that, just in principle, calculate, you know, its degree of consciousness,

right? I mean, this would be the kind of thing that we would need, you know, if we wanted to

address questions like, you know, what does it take for a machine to be conscious, right,

or when should we regard AIs as being conscious? So now this IIT, this integrated information

theory, which has been put forward by Giulio Tinoni and a bunch of his collaborators over the last

decade or two. This is noteworthy, I guess, as a direct attempt to answer that question,

to, you know, answer the, to address the pretty hard problem, right? And they give a criterion

that's just based on how a system is connected. So it's up to you to sort of abstract a system

like a brain or a microchip as a collection of components that are connected to each other by

some pattern of connections, you know, and to specify how the components can influence each

other, you know, like where the inputs go, you know, where they affect the outputs. But then

once you've specified that, then they give this quantity that they call phi, you know, the Greek

letter phi. And the definition of phi has actually changed over time. It changes from one paper to

another. But in all of the variations, it involves something about what we in computer science would

call graph expansion. So basically what this means is that they want, in order to get a large value

of phi, it should not be possible to take your system and partition it into two components

that are only weekly connected to each other. Okay. So whenever we take our system and sort of

try to split it up into two, then there should be lots and lots of connections going between the

two components. Okay, well, I understand what that means on a graph. Do they formalize what,

how to construct such a graph or data structure, whatever, or is this one

of the criticism I've heard you kind of say is that a lot of the very interesting specifics are

usually communicated through like natural language, like, like through words. So it's like the details

aren't always clear. Well, they, well, it's true. I mean, they, they, they have nothing even resembling

a derivation of this phi. Okay. So what they do is they state a whole bunch of postulates,

you know, axioms that they think that consciousness should satisfy. And then there's some verbal

discussion. And then at some point, phi appears, right? And this, this was the first thing that

really made the hair stand on my neck, to be honest, because they are acting as if there's a

derivation, they're acting as if, you know, you're supposed to think that this is a derivation. And

there's nothing even remotely resembling a derivative, they just pull the phi out of a hat

completely is one of the key criticisms to use that details are missing or is there something

more fun to mention? That's not, that's not even the key criticism. That's just, that's just a side

point. Okay. The, the core of it is that I think that the, you know, that they want to say that

a system is more conscious, the larger its value of phi. And I think that that is obvious nonsense.

Okay. As soon as you think about it for like a minute, as soon as you think about it in terms of

could I construct a system that had an enormous value of phi, like, you know, even larger than

the brain has, but that is just implementing an error correcting code, you know, doing nothing

that we would associate with, you know, intelligence or consciousness or any of it. The answer is,

yes, it is easy to do that. Right. And so I wrote blog posts just making this point that, yeah,

it's easy to do that. Now, you know, Tenoni's response to that was actually kind of incredible.

Right. I mean, I, I admired it in a way because instead of disputing any of it,

he just bit the bullet in the sense, you know, he was one of the, the, the most audacious bullet

bitings I've ever seen in my career. Okay. He said, okay, then fine, you know, this system that

just applies this error correcting code, it's conscious, you know, and if it has a much larger

value of phi than you or me, it's much more conscious than you or me. You know, we just have

to accept what the theory says because, you know, science is not about confirming our intuitions.

It's about challenging them. And, you know, this is what my theory predicts that this thing is

conscious and, you know, or super duper conscious and how are you going to prove me wrong?

So the way I would argue against your blog post is I would say, yes, sure, you're right in general,

but for naturally arising systems developed through the process of evolution on earth,

the, this rule of the larger fee being associated with more consciousness is correct.

Yeah. So that's not what he said at all. Right. Right. Because he wants this to be completely

general. Right. So we can apply it to even computers. Yeah. I mean, I mean, the whole

interest of the theory is the, you know, the hope that it could be completely general, apply to

aliens, to computers, to animals, coma patients, to any of it. Right. Yeah. And so, so, so he just

said, well, you know, Scott is relying on his intuition, but, you know, I'm relying on this theory.

And, you know, to me, it was almost like, you know, are we being serious here? Like, like, like,

you know, like, like, okay, yes, in science, we try to learn highly nonintuitive things. But

what we do is we first test the theory on cases where we already know the answer. Right. Like,

if we, if someone had a new theory of temperature, right, then, you know, maybe we could check that

it says that boiling water is hotter than ice. And then if it says that the sun is hotter than

anything, you know, you've ever experienced, then maybe we, we trust that extrapolation. Right.

But like this, this theory, like, if, if, you know, it's now saying that, you know, a, a gigantic

grit, like regular grid of exclusive orgates can be way more conscious than a, you know, a person

or than, than any animal can be, you know, even if it, you know, is, you know, is, is, is so uniform

that it might as well just be a blank wall. Right. And, and so now the point is, if this theory is

sort of getting wrong, the question is a blank wall, you know, more conscious than a person,

then I would say what is, what is there for it to get right? So your sense is a blank wall

is not more conscious than a human being. Yeah. I mean, I mean, I mean, you could say that I am

taking that as one of my axioms. I'm saying, I'm saying that if, if a theory of consciousness

is, is getting that wrong, then whatever it is talking about at that point, I, I, I'm not going

to call it consciousness. I'm going to use a different word. You have to use a different word.

I mean, it's also, it's possible, just like with intelligence, that us humans conveniently

define these very difficult to understand concepts in a very human centric way.

Just like the Turing test really seems to define intelligence as a thing that's human like.

Right. But I would say that with any concept, you know, there's,

you know, like we, we, we first need to define it, right. And a definition is only a good definition

if it matches what we thought we were talking about, you know, prior to having a definition,

right. And I would say that, you know, fee as a definition of consciousness fails that test.

That is my argument. So, okay. Then let's, so let's take a further step. So you mentioned that

the universe might be a Turing machine. So like it might be computations or simulatable by one

anyway. Simulatable by one. So do you, what's your sense about consciousness? Do you think

consciousness is computation that we don't need to go to any place outside of the computable universe

to, you know, to, to understand consciousness, to build consciousness, to measure consciousness,

all those kinds of things. I don't know. These are what, you know, have been called the, the

vertiginous questions, right? There's the questions like, like, you know, that you get a feeling of

vertigo and thinking about them, right? I mean, I certainly feel like I am conscious in a way that

is not reducible to computation. But why should you believe me? Right? I mean, and, and, and if you

said the same to me, then why should I believe you? But as computer scientists, I feel like a

computer could be intelligent, could achieve human level intelligence. But, and that's actually a

feeling and a hope. That's not a scientific belief. It's just we've built up enough intuition, the

same kind of intuition you use in your blog. You know, that's what scientists do. They, I mean,

some of it is a scientific method, but some of it is just damn good intuition. I don't have a good

intuition about consciousness. Yeah, I'm not sure that anyone does or has in the, you know,

2,500 years that these things have been discussed, Lex. But do you think we will? Like one of the,

I got a chance to attend, can't wait to hear your opinion on this, but attend the Neuralink event.

And one of the dreams there is to, you know, basically push neuroscience forward. And the

hope in neuroscience is that we can inspect the machinery from which all this fun stuff emerges

and see, are we going to notice something special, some special sauce from which something like

consciousness or cognition emerges? Yeah, well, it's clear that we've learned an enormous amount

about neuroscience. We've learned an enormous amount about computation, you know, about machine

learning, about AI, how to get it to work. We've learned an enormous amount about the

underpinnings of the physical world, you know, and, you know, from one point of view, that's like

an enormous distance that we've traveled along the road to understanding consciousness.

From another point of view, you know, the distance still to be traveled on the road,

you know, maybe seems no shorter than it was at the beginning, right? So it's very hard to say.

I mean, you know, these are questions like, like in sort of trying to have a theory of consciousness,

there's sort of a problem where it feels like it's not just that we don't know how to make

progress, it's that it's hard to specify what could even count as progress, right? Because no

matter what scientific theory someone proposed, someone else could come along and say, well,

you've just talked about the mechanism, you haven't said anything about what breathes fire into the

mechanism, what really makes there's something that it's like to be it, right? And that seems

like an objection that you could always raise. Yes. No matter, you know, how much someone elucidated

the details of how the brain works. Okay, let's go to the Turing Test and Lobner Prize. I have this

intuition, call me crazy, but we, that a machine to pass the Turing Test and it's full, whatever

the spirit of it is, we can talk about how to formulate the perfect Turing Test, that that

machine has to be conscious, or we at least have to, I have a very low bar of what consciousness is.

I tend to, I tend to think that the emulation of consciousness is as good as consciousness.

So like consciousness is just a dance, a social, a social shortcut, like a nice useful tool.

But I tend to connect intelligence and consciousness together. So by that, do you

maybe just to ask what role does consciousness play? Do you think it passed in the Turing Test?

Well, look, I mean, it's almost tautologically true that if we had a machine that passed the

Turing Test, then it would be emulating consciousness, right? So if your position is that,

you know, emulation of consciousness is consciousness, then, you know, by definition,

any machine that passed the Turing Test would be conscious. But it's, but I mean,

you know, that you could say that, you know, that that is just a way to rephrase the original

question, you know, is an emulation of consciousness, you know, necessarily conscious,

right? And you can, you know, here, I'm not saying anything new that hasn't been

debated ad nauseam in the literature. Okay, but, you know, you could imagine some very hard cases,

like imagine a machine that passed the Turing Test, but they did so just by an enormous

cosmological sized lookup table that just cached every possible conversation that could be had.

The old Chinese room. Well, yeah, yeah, but this is, I mean, I mean,

the Chinese room actually would be doing some computation, at least in Searle's version, right?

Here, I'm just talking about a table lookup. Okay, now, it's true that for conversations

of a reasonable length, this, you know, lookup table would be so enormous, it wouldn't even

fit in the observable universe. Okay, but supposing that you could build a big enough

lookup table and then just, you know, pass the Turing Test just by looking up what the person

said, right? Are you going to regard that as conscious? Okay, let me try to make this formal,

and then you can shout it down. I think that the emulation of something is that something,

if there exists in that system, a black box, that's full of mystery. So like,

full of mystery to whom? To human inspectors. So does that mean that consciousness is

relative to the observer? Like, could something be conscious for us, but not conscious for an

alien that understood better what was happening inside the black box? Yes, yes. So that if inside

the black box is just a lookup table, the alien that saw that would say this is not conscious,

to us, another way to phrase the black box is layers of abstraction,

which make it very difficult to see to actually underlying functionality of the system.

And then we observe just the abstraction. And so it looks like magic to us. But once we

understand the inner machinery, it stops being magic. And so like, that's a prerequisite is

that you can't know how it works, some part of it. Because then there has to be in our human mind,

an entry point for the magic. So that's a formal definition of the system.

Yeah, well, look, I mean, I explored a view in this essay I wrote called The Ghost and the

Quantum Turing Machine seven years ago, that is related to that, except that I did not want to

have consciousness be relative to the observer, right? Because I think that, you know, if

consciousness means anything, it is something that is experienced by the entity that is

conscious, right? You know, like, I don't need you to tell me that I'm conscious, right? Nor do you

need me to tell you that you are, right? So, but basically what I explored there is, you know,

are there aspects of a system like a brain that just could not be predicted, even with

arbitrarily advanced future technologies? It's because of chaos combined with quantum

mechanical uncertainty, you know, things like that. I mean, that actually could be a property of the

brain, you know, if true, that would distinguish it in a principled way, at least from any currently

existing computer, not from any possible computer, but from, yeah, yeah.

This is a thought experiment. So if I gave you information that you're in the entire history

of your life, basically explain away free will with a lookup table, say that this was all predetermined,

that everything you experienced has already been predetermined. Wouldn't that take away

your consciousness? Wouldn't you yourself, wouldn't experience of the world change for you in a way

that you can't take back? Well, let me put it this way, if you could do like in a Greek tragedy,

where, you know, you would just write down a prediction for what I'm going to do, and then

maybe you put the prediction in a sealed box, and maybe, you know, you open it later, and you

show that you knew everything I was going to do, or, you know, of course, the even creepier version

would be you tell me the prediction, and then I try to falsify it, my very effort to falsify it

makes it come true, right? Let's, you know, let's even forget that, you know, that version as convenient

as it is for fiction writers, right? Let's just let's just do the version where you put the prediction

into a sealed envelope, okay? But if you could reliably predict everything that I was going to do,

I'm not sure that that would destroy my sense of being conscious, but I think it really would

destroy my sense of having free will, you know, and much, much more than any philosophical conversation

could possibly do that, right? And so I think it becomes extremely interesting to ask, you know,

could such predictions be done, you know, even in principle, is it consistent with the laws of

physics to make such predictions, to get enough data about someone that you could actually generate

such predictions without having to kill them in the process to, you know, slice their brain up into

little slivers or something. I mean, theoretically possible, right? Well, I don't know. I mean,

it might be possible, but only at the cost of destroying the person, right? I mean, it depends

on how low you have to go in sort of the substrate. Like if there was a nice digital abstraction layer,

if you could think of each neuron as a kind of transistor computing a digital function,

then you could imagine some nanorobots that would go in and we just scan the state of each

transistor, you know, of each neuron, and then, you know, make a good enough copy, right? But if it

was actually important to get down to the molecular or the atomic level, then, you know, eventually

you would be up against quantum effects. You would be up against the unclonability of quantum

states. So I think it's a question of how good of a replica, how good does the replica have to be

before you're going to count it as actually a copy of you or as being able to predict your actions?

That's a totally open question. Yeah, yeah, yeah. And especially once we say that, well,

look, maybe there's no way to make a deterministic prediction because, you know, we know that there's

noise buffeting the brain around, presumably even quantum mechanical uncertainty, you know,

affecting the sodium ion channels, for example, whether they open or they close,

you know, there's no reason why over a certain time scale that shouldn't be amplified just like

we imagine happens with the weather or with any other, you know, chaotic system. So if that stuff

is important, right, then, then, you know, we would say, well, you know, you can't,

you know, you're never going to be able to make an accurate enough copy. But now the hard part is,

well, what if someone can make a copy that sort of no one else can tell apart from you, right?

It says the same kinds of things that you would have said, maybe not exactly the same things,

because we agree that there's noise, but it says the same kinds of things. And maybe you alone

would say, no, I know that that's not me, you know, it's, it doesn't share my, I haven't felt my

consciousness leap over to that other thing. I still feel it localized in this version,

right? Then why should anyone else believe you? What are your thoughts? I'd be curious,

you're a really good person to ask, which is Penrose's, Roger Penrose's work on consciousness,

saying that there, you know, there is some with axons and so on. There might be some

biological places where quantum mechanics can come into play and through that create

consciousness somehow. Yeah. Okay. Well, I'm familiar with this work. Of course. You know,

I read Penrose's books as a teenager. They had a huge impact on me. Five or six years ago,

I had the privilege to actually talk these things over with Penrose, you know, at some length at

a conference in Minnesota. And, you know, he is, you know, an amazing personality. I admire the

fact that he was even raising such audacious questions at all. But, you know, to answer your

question, I think the first thing we need to get clear on is that he is not merely saying that

quantum mechanics is relevant to consciousness, right? That would be like, you know, that would be

tame compared to what he is saying, right? He is saying that, you know, even quantum mechanics

is not good enough, right? Because if supposing, for example, that the brain were a quantum computer,

maybe that's still a computer, you know, in fact, a quantum computer can be simulated by an ordinary

computer. It might merely need exponentially more time in order to do so, right? So that's simply

not good enough for him. Okay, so what he wants is for the brain to be a quantum gravitational

computer. Or he wants the brain to be exploiting as yet unknown laws of quantum gravity, okay,

which would, which would be uncomputable. Uncomputable. That's the key point. Okay, yes, yes.

That would be literally uncomputable. And I've asked him, you know, to clarify this, but

uncomputable, even if you had an oracle for the halting problem, or, you know, and, or, you know,

as high up as you want to go in the sort of high, the usual hierarchy of uncomputability,

he wants to go beyond all of that. Okay, so, so, you know, just to be clear, like, you know,

if we're keeping count of how many speculations, you know, there's probably like at least five

or six of them, right? There's first of all, that there is some quantum gravity theory that

would involve this kind of uncomputability, right? Most people who study quantum gravity

would not agree with that. They would say that what we've learned, you know, what little we

know about quantum gravity from the, this ADSCFT correspondence, for example, has been very much

consistent with the broad idea of nature being computable, right? But, but all right, but,

but supposing that he's right about that, then, you know, what most physicists would say is that

whatever new phenomena there are in quantum gravity, you know, they might be relevant at the

singularities of black holes, they might be relevant at the Big Bang. They are plainly not

relevant to something like the brain, you know, that is operating at ordinary temperatures,

you know, with ordinary chemistry, and, you know, the, the, the physics underlying the brain,

they would say that we have, you know, the fundamental physics of the brain, they would

say that we've pretty much completely known for, for generations now, right? Because,

you know, quantum field theory lets us sort of parameterize our ignorance, right? I mean,

Sean Carroll has made this case and, you know, in great detail, right? That sort of whatever

new effects are coming from quantum gravity, you know, they are sort of screened off by

quantum field theory, right? And this is, this brings, you know, brings us to the whole idea of

effective theories, right? But that, like, we have, you know, in, like, in the standard model of

elementary particles, right? We have a quantum field theory that seems totally adequate for all

of the terrestrial phenomena, right? The only things that it doesn't, you know, explain are,

well, first of all, you know, the details of gravity, if you were to probe it, like, at,

at, you know, extremes of, you know, curvature or at, like, incredibly small distances,

it doesn't explain dark matter. It doesn't explain black hole singularities, right? But these are all

very exotic things, very, you know, far removed from our life on Earth, right? So for Penrose,

to be right, he needs, you know, these phenomena to somehow affect the brain. He needs the brain

to contain antenna that are sensitive to this to this as yet unknown physics, right? And then he

needs a modification of quantum mechanics. Okay, so he needs quantum mechanics to actually be wrong.

Okay, he needs what he wants is what he calls an objective reduction mechanism or an objective

collapse. So this is the idea that once quantum states get large enough, then they somehow

spontaneously collapse, right? That, you know, and this is an idea that lots of people have explored.

You know, there's something called the GRW proposal that tries to, you know, say something along

those lines, you know, and these are theories that actually make testable predictions, right,

which is a nice feature that they have. But, you know, the very fact that they're testable may mean

that in the, you know, in the coming decades, we may well be able to test these theories and show

that they're wrong, right? You know, we may be able to test some of Penrose's ideas. If not,

not his ideas about consciousness, but at least his ideas that about an objective collapse of

quantum states, right? And people have actually, like Dick Baumeister have actually been working

to try to do these experiments. They haven't been able to do it yet to test Penrose's proposal.

Okay, but Penrose would need more than just an objective collapse of quantum states,

which would already be the biggest development in physics for a century since quantum mechanics

itself. Okay, he would need for consciousness to somehow be able to influence the direction

of the collapse so that it wouldn't be completely random, but that, you know, your dispositions

would somehow influence the quantum state to collapse more likely this way or that way.

Okay, finally, Penrose, you know, says that all of this has to be true because of an argument

that he makes based on Gertl's incompleteness theorem. Okay, now, Blake, I would say the

overwhelming majority of computer scientists and mathematicians who have thought about this,

I don't think that Gertl's incompleteness theorem can do what he needs it to do here,

right? I don't think that that argument is sound. Okay, but that is, you know,

that is sort of the tower that you have to ascend to if you're going to go where Penrose goes.

And the intuition uses with the incompleteness theorem is that basically

that there's important stuff that's not computable? No, it's not just that because,

I mean, everyone agrees that there are problems that are uncomputable, right? That's a mathematical

theorem, right? But what Penrose wants to say is that, you know, for example, there are statements,

you know, for, you know, given any formal system, you know, for doing math, right,

there will be true statements of arithmetic that that formal system, you know, if it's

adequate for math at all, if it's consistent and so on, will not be able to prove a famous example

being the statement that that system itself is consistent, right? No, you know, good formal system

can actually prove its own consistency to that can only be done from a stronger formal system,

which then can't prove its own consistency and so on forever. Okay, that's Gertl's theorem.

But now, why is that relevant to consciousness, right? Well, you know, I mean, I mean, the idea

that it might have something to do with consciousness as an old one, Gertl himself,

apparently thought that it, you know, a Lucas thought so, I think, in the 60s. And Penrose

is really just, you know, sort of updating what they and others had said. I mean, you know, the

idea that Gertl's theorem could have something to do with consciousness was, you know, in 1950,

when Alan Turing wrote his article about the Turing test, he already, you know, was writing

about that as like an old and well known idea. And as one that he, as a wrong one that he wanted

to dispense with. Okay, but the basic problem with this idea is, you know, Penrose wants to say

that and all of his predecessors, you know, want to say that, you know, even though, you know,

this given formal system cannot prove its own consistency, we as humans sort of looking at

it from the outside can just somehow see its consistency. Right. And the, you know, the rejoinder

to that, you know, from the very beginning has been, well, can we really? I mean, maybe, maybe,

you know, maybe, maybe he Penrose can, but, you know, can the rest of us? Right. And, you know,

I noticed that, that, you know, I mean, it is perfectly plausible to imagine a computer that

could say, you know, it would not be limited to working within a single formal system. Right.

They could say, I am now going to adopt the hypothesis that this, that my formal system

is consistent. Right. And I'm now going to see what can be done from that stronger vantage point

and, and so on. And, you know, and I'm going to add new axioms to my system. Totally plausible.

There's absolutely, Gertl's theorem has nothing to say about against an AI that could repeatedly

add new axioms. All it says is that there is no absolute guarantee that when the AI adds new

axioms that it will always be right. Right. Okay. And, you know, and that's of course the point

that Penrose pounces on, but the reply is obvious. And, you know, it's one that Alan Turing made

70 years ago. Namely, we don't have an absolute guarantee that we're right when we add a new

axiom. Right. We never have. And plausibly, we never will. So on Alan Turing, you took part in

the Lubna Prize. Not really. No, I didn't. I mean, there was this kind of ridiculous claim that was

made some almost a decade ago about a chat bot called Eugene Goosman. I guess you didn't participate

as a judge in the Lubna Prize, but you participated as a judge in that. I guess it was an exhibition

event or something like that. Or with Eugene, Eugene Goosman, that was just me writing a blog post

because some journalists called me to ask about it. Did you ever chat with him? I did chat with

Eugene Goosman. I mean, it was available on the web. The chat. Oh, interesting. I didn't know.

So yeah. So all that happened was that a bunch of journalists started writing

breathless articles about a first chat bot that passes the Turing test. And it was this thing

called Eugene Goosman that was supposed to simulate a 13 year old boy. And apparently,

someone had done some test where people were less than perfect, let's say, distinguishing

it from a human. And they said, well, if you look at Turing's paper and you look at the percentages

that he talked about, then it seemed like we're past that threshold. And I had a different way

to look at it instead of the legalistic way. Let's just try the actual thing out and let's

see what it can do with questions like, is Mount Everest bigger than a shoebox? Or just the most

obvious questions, right? And then, and you know, and the answer is, well, it just kind of parries

you because it doesn't know what you're talking about, right? So just clarify exactly in which

way they're obvious. They're obvious in the sense that you convert the sentences into the meaning

of the objects they represent and then do some basic obvious. We mean your common sense reasoning

with the objects that the sentences represent. Right, right. It was not able to answer, you know,

or even intelligently respond to basic common sense questions. Well, let me say something

stronger than that. There was a famous chatbot in the 60s called Eliza, right, that, you know,

that managed to actually fool, you know, a lot of people, right? Or people would pour their

hearts out into this Eliza because it simulated a therapist, right? And most of what it would do

was it would just throw back at you whatever you said, right? And this turned out to be incredibly

effective, right? Maybe, you know, therapists know this, this is, you know, one of their tricks.

But it, you know, it really had some people convinced. But, you know, this thing was just

like, I think it was literally just a few hundred lines of Lisp code, right? It was not only was

it not intelligent, it wasn't especially sophisticated. It was like a, it was a simple little hobbyist

program. And Eugene Goestman from what I could see was not a significant advance compared to

Eliza, right? So, so, and that was, that was really the point I was making. And this was,

you know, you didn't, in some sense, you didn't need a, like a computer science professor to

sort of say this, like anyone who was looking at it and who just had, you know, an ounce of sense

could have said the same thing, right? But because, you know, these journalists were, you know, calling

me, you know, like the first thing I said was, well, you know, no, you know, I'm a quantum

computing person. I'm not an AI person, you know, you shouldn't ask me. Then they said, look, you

can go here and you can try it out. I said, all right, all right, so I'll try it out. But now,

you know, did this whole discussion, I mean, it got a whole lot more interesting in just the last

few months. Yeah, I'd love to hear your thoughts about GPT3. Yeah, in the last few months,

we've had, you know, we've, we've, the world has now seen a chat engine or a text engine,

I should say, called GPT3. That, you know, I think it's still, you know, it does not pass a

Turing test. You know, there are no real claims that it passes the Turing test, right? You know,

this is, comes out of the group at OpenAI and, you know, they're, you know, they've been relatively

careful in what they've claimed about the system. But I think this, this, this, as clearly as Eugene

Goestman was not in advance over Eliza, it is equally clear that this is a major advance over,

over Eliza or really over anything that the world has seen before. This is a text engine

that can come up with kind of on topic, you know, reasonable sounding completions to just about

anything that you ask. You can ask it to write a poem about topic X in the style of Poet Y,

and it will have a go at that. And it will do, you know, not a perfect, not a great job,

not an amazing job, but, you know, a passable job, you know, definitely, you know, as, as good as,

you know, you know, in many cases, I would say better than I would have done, right?

You know, you can ask it to write, you know, an essay, like a student essay about pretty much

any topic, and it will get something that I am pretty sure would get at least a B minus, you

know, in most, you know, high school or even college classes, right? And, you know, in some sense,

you know, the way that it did this, the way that it achieves this, you know, Scott Alexander of the,

you know, the much more in the blog Slate Star Codex had a wonderful way of putting it. He said

that they basically just ground up the entire internet into a slurry, okay? And, you know,

to tell you the truth, I had wondered for a while why nobody had tried that, right? Like,

why not write a chatbot by just doing deep learning over a corpus consisting of the entire web,

right? And, and so, so, so now they finally have done that, right? And, you know, the results are,

are very impressive. You know, it's not clear that, you know, people can argue about whether this is

truly a step toward general AI or not. But this is an amazing capability that, you know, we didn't

have a few years ago, that, you know, if a few years ago, if you had told me that we would have it

now, that would have surprised me. Yeah. And I think that anyone who denies that is just not

engaging with, with what's there. So their model, it takes a large part of the internet and compresses

it in a small number of parameters relative to the size of the internet, and is able to, without

fine tuning, do a basic kind of a querying mechanism, just like you described, where you

specify a kind of poet and then you want to write a poem. And it somehow is able to do basically a

lookup on the internet of relevant things. I mean, that's what it, I mean, I mean, how else do you

explain it? Well, okay. I mean, I mean, the training involved, you know, massive amounts of data from

the internet and actually took lots and lots of computer power, lots of electricity, right? You

know, there are some, some very prosaic reasons why this wasn't done earlier, right? But, you know,

it costs some tens of millions of dollars, I think, you know, that's just for, but approximate, like

a few million dollars. Oh, okay, okay. Oh, really? Okay. It's more like four or five. Oh, all right.

All right. Thank you. I mean, as they, as they scale it up, you know, it will cost, but then the

hope is cost comes down and all that kind of stuff. But basically, you know, it is a neural net, you

know, so I mean, I mean, or what's now called a deep net, but, you know, they're basically the

same thing, right? So it's a, it's a form of, you know, algorithm that people have known about for

decades, right? But it is constantly trying to solve the problem, predict the next word, right?

So it's just trying to predict what comes next. It's not trying to decide what, what it should

say, what ought to be true. It's trying to predict what someone who had said all of the words up to

the preceding one would say next. Although to push back on that, that's how it's trained.

That's right. No, of course. But it's arguable. Yeah.

That our very cognition could be a mechanism as that simple.

Oh, of course. Of course. I never said that it wasn't.

But yeah. I mean, I mean, in some sense, that, that is, you know, if there is a deep

philosophical question that's raised by GPT three, then that is it, right? Are we doing anything

other than, you know, this, this predictive processing, just trying to constantly trying

to fill in a blank of what would come next after what we just said up to this point?

Is that what I'm doing right now? It's impossible. So the, the intuition that

a lot of people have, well, look, this thing is not going to be able to reason the mountain Everest

question. Do you think it's possible that GPT five, six and seven would be able to,

with this exact same process, begin to look, do something that looks like is indistinguishable

to us humans from reasoning? I mean, the truth is that we don't really know what the limits are,

right? Because, because, you know, what we've seen so far is that, you know, GPT three was

basically the same thing as GPT two, but just with, you know, a much larger network, you know,

more training time, bigger training corpus, right? And it was, you know, very noticeably better,

right? Then it's immediate predecessor. So we, you know, we don't know where you hit the ceiling

here, right? I mean, that's the, that's the amazing part and maybe also the scary part,

right? That, you know, now my guess would be that, that, you know, at some point, like there has to

be diminishing returns, like it can't be that simple, can it? Right? Right? Well, I wish that I had

more to base that guess on. Right. Yeah. I mean, some people say that there would be a limitation

on the, we're going to hit a limit on the amount of data that's on the internet. Yes. Yeah. So,

so sure. So there's certainly that limit. I mean, there's also, you know, like if you are looking

for questions that will stump GPT three, right, you can come up with some without, you know, like,

you know, even getting it to learn how to balance parentheses, right? Like it can, you know, it

doesn't do such a great job, right? You know, like, like, you know, and, you know, and its failures are

ironic, right? Like, like basic arithmetic, right? And you think, and you think, you know,

isn't that what computers are supposed to be best at? Yeah. Isn't that where computers already had

us beat a century ago? Yeah. Right. And, you know, and yet that's where GPT three struggles, right?

But it's, it's amazing, you know, that it's almost like a young child in that way, right?

That, but, but somehow, you know, because it is just trying to predict what, what comes next,

it doesn't know when it should stop doing that and start doing something very different,

like some more exact logical reasoning, right? And so, so, you know, the, you know, one is naturally

led to guess that our brain sort of has some element of predictive processing, but that it's

coupled to other mechanisms, right? That it's coupled to, you know, first of all, visual reasoning,

which GPT three also doesn't have any of, right? Although there's some demonstration that there's

a lot of promise there. Oh yeah, it can complete images. That's right. And using exact same kind

of transformer mechanisms to like watch videos on YouTube. And so the same, the same self supervised

mechanism to be able to look, it'd be fascinating to think what kind of completions you could do.

Oh yeah, no, absolutely. Although like if we ask it to like, you know, a word problem that

involve reasoning about the locations of things in space, I don't think it does such a great job

on those, right? To take an example. And so, so the guess would be, well, you know, humans have a

lot of predictive processing, a lot of just filling in the blanks, but we also have these other

mechanisms that we can couple to, or that we can sort of call as subroutines when we need to.

And that maybe, maybe, you know, to go further that one would want to integrate other forms of

reasoning. Let me go on another topic that is amazing, which is complexity. What,

and then start with the most absurdly romantic question of what's the most beautiful idea in

computer science or theoretical computer science to you? Like what just early on in your life,

or in general, have captivated you and just grabbed you? I think I'm going to have to go with the

idea of universality. You know, if you're really asking for the most beautiful, I mean, so universality

is the idea that, you know, you put together a few simple operations, like in the case of

Boolean logic, that might be the AND gate, the OR gate, the NOT gate, right? And then your first

guess is, okay, this is a good start. But obviously, as I want to do more complicated things, I'm going

to need more complicated building blocks to express that, right? And that was actually my guess when

I first learned what programming was. I mean, when I was, you know, an adolescent and someone showed

me AppleBasic and, you know, GWBasic. If anyone listening remembers that. Okay, but, you know,

I thought, okay, well, now, you know, I mean, I thought I felt like this is a revelation, you

know, it's like finding out where babies come from. It's like that level of, you know, why didn't

anyone tell me this before, right? But I thought, okay, this is just the beginning. Now I know how

to write a basic program. But, you know, really write an interesting program, like, you know,

a video game, which had always been my dream as a kid to, you know, create my own Nintendo games,

right? But, you know, obviously, I'm going to need to learn some way more complicated form

of programming than that. Okay, but, you know, eventually I learned this incredible idea of

universality. And that says that, no, you throw in a few rules, and then you can, you already have

enough to express everything. Okay, so for example, the and, the or, and the not gate can all, or in

fact, even just the and and the not gate, or even just even just the NAND gate, for example, is

already enough to express any Boolean function on any number of bits, you just have to string

together enough of them. You can build the universe with NAND gates, you can build the

universe out of NAND gates. Yeah, you know, the the simple instructions of basic are already enough,

at least in principle, you know, if we ignore details like how much memory can be accessed

and stuff like that, that is enough to express what could be expressed by any programming language

whatsoever. And the way to prove that is very simple. We simply need to show that in basic,

or whatever, we could write a an interpreter or a compiler for whatever is other programming

language we care about, like C or Java or whatever. And as soon as we had done that,

then ipso facto, anything that's expressible in C or Java is also expressible in basic.

Okay, and so this idea of universality, you know, goes back at least to Alan Turing in the 1930s,

when, you know, he wrote down this incredibly simple paired down model of a computer,

the Turing machine, right, which, you know, he paired down the instruction set to just

read a symbol, you know, go right a symbol, move to the left, move to the right, halt,

change your internal state, right, that's it. Okay, and anybody proved that, you know,

this could simulate all kinds of other things, you know, and so, so in fact, today we would say,

well, we would call it a Turing universal model of computation, that is, you know, just as it

has just the same expressive power that basic or Java or C plus plus or any of those other languages

have, because anything in those other languages could be compiled down to Turing machine. Now,

Turing also proved a different related thing, which is that there is a single Turing machine

that can simulate any other Turing machine, if you just describe that other machine on its tape,

right, and likewise, there is a single Turing machine that will run any C program, you know,

if you just put it on its tape, that's a second meaning of universality. First of all,

that he couldn't visualize it, and that was in the 30s, I think. Yeah, the 30s, that's right.

That's before computers really, I mean, I don't know how, I wonder what that felt like,

you know, learning that there's no Santa Claus or something, because I don't know if that's

empowering or paralyzing, because it doesn't give you any, it's like, you can't write a

software engineering book and make that the first chapter and say we're done.

Well, I mean, I mean, right, I mean, in one sense, it was this enormous flattening of the

universe, right? I had imagined that there was going to be some infinite hierarchy of more and

more powerful programming languages, you know, and then I kicked myself for, you know, for having

such a stupid idea. But apparently, Girdle had had the same conjecture in the 30s. Oh, good.

You're in a good company. Well, you know, and then Girdle read Turing's paper,

and he kicked himself, and he said, yeah, I was completely wrong about that. Okay,

but you know, I had thought that maybe where I can contribute will be to invent a new,

more powerful programming language that lets you express things that could never be expressed in

basic, right? And, you know, and, you know, how would you do that? Obviously, you couldn't do it

itself in basic, right? But, but, you know, there is this incredible flattening that happens once

you learn what is universality. But then it's also like an opportunity, because it means once you

know these rules, then, you know, the sky is the limit, right? Then you have kind of the same weapons

at your disposal that the world's greatest programmer has. It's now all just a question of

how you wield them, right? Exactly. But so every problem is solvable, but some problems are harder

than others. And well, yeah, there's the question of how much time, you know, of how hard is it to

write a program. And then there's also the questions of what resources does the program need? You

know, how much time, how much memory, those are much more complicated questions, of course,

ones that we're still struggling with today. Exactly. So you've, I don't know if you created

complexity zoo or... I did create the complexity zoo. What is it? What's complexity? Oh, all right,

all right, all right. Complexity theory is the study of sort of the inherent resources needed

to solve computational problems. Okay, so it's easiest to give an example. Like, let's say we

want to add two numbers, right? If I want to add them, you know, if the numbers are twice as long,

then it only, it will take me twice as long to add them, but only twice as long, right? It's

no worse than that. Or a computer. For a computer or for a person, we're using pencil and paper for

that matter. If you have a good algorithm. Yeah, that's right. I mean, even if you just use the

elementary school algorithm of just carrying, you know, then it takes time that is linear

in the length of the numbers, right? Now, multiplication, if you use the elementary school

algorithm is harder because you have to multiply each digit of the first number by each digit of

the second one. Yeah, and then deal with all the carries. So that's what we call a quadratic time

algorithm, right? If the numbers become twice as long, now you need four times as much time.

Okay. So now as it turns out, we people discovered much faster ways to multiply numbers using

computers. And today we know how to multiply two numbers that are n digits long using a number

of steps that's nearly linear in it. These are questions you can ask. But now let's think about

a different thing that people, you know, even countered in elementary school of factoring a

number. Okay, take a number and find its prime factors, right? And here, you know, if I give

you a number with 10 digits, I ask you for its prime factors. Well, maybe it's even so you know

that two is a factor, you know, maybe it ends in zero. So you know that 10 is a factor, right?

But, you know, other than a few obvious things like that, you know, if the prime factors are all

very large, then it's not clear how you even get started, right? You know, you, it seems like you

have to do an exhaustive search among an enormous number of factors. Now, and as many people might

know, the for better or worse, the security, you know, of most of the encryption that we currently

use to protect the internet is based on the belief, and this is not a theorem, it's a belief

that factoring is an inherently hard problem for our computers. We do know algorithms that are

better than just trial division and just trying all the possible divisors, but they are still

basically exponential. And exponential is hard. Yeah, exactly. So the fastest algorithms that

anyone has discovered, at least publicly discovered, you know, I'm assuming that the NSA doesn't know

something better. Yeah. Okay, but they take time that basically grows exponentially with the cube

root of the size of the number that you're factoring, right? So that cube root, that's the part that

takes all the cleverness, okay, but there's still an exponential, there's still an exponentiality

there. But what that means is that like, when people use a thousand bit keys for their cryptography,

that can probably be broken using the resources of the NSA or the world's other intelligence

agencies, you know, people have done analyses that say, you know, with a few hundred million

dollars of computer power, they could totally do this. And if you look at the documents that Snowden

released, you know, it looks a lot like they are doing that or something like that, it would kind

of be surprising if they weren't. Okay, but, you know, if that's true, then in some ways,

that's reassuring, because if that's the best that they can do, then that would say that they

can't break 2000 bit numbers, right? Right, exactly. Right, then 2000 bit numbers would be beyond

what even they could do. They haven't found an efficient algorithm. That's where all the

worries and the concerns of quantum computing came in that there could be some kind of shortcut

around that. Right, so complexity theory is a, you know, is a huge part of, let's say, the

theoretical core of computer science. You know, it started in the 60s and 70s as, you know, sort of an,

you know, autonomous field. So it was, you know, already, you know, I mean, you know, it was well

developed even by the time that I was born. But I, in 2002, I made a website called the

complexity zoo to answer your question, where I just tried to catalog the different complexity

classes, which are classes of problems that are solvable with different kinds of resources.

Okay, so these are kind of, you know, you could think of complexity classes as like being almost

to theoretical computer science, like what the elements are to chemistry, right? They're sort

of, you know, there are our most basic objects in a certain way. I feel like the elements have

have a characteristic to them where you can't just add an infinite number. Well, you could,

but beyond a certain point, they become unstable. Right, right. So it's like, you know, in theory,

you can have atoms with, you know, and look, look, I mean, I mean, a neutron star, you know,

is a nucleus with, you know, uncalled billions of, of, of, of, of, of, of, of, of neutrons in it,

of, of hadrons in it. Okay, but, you know, for, for sort of normal atoms, right, probably you

can't get much above 100, you know, atomic weight, 150 or so, or sorry, sorry, I mean, I mean,

beyond 150 or so protons without it, you know, very quickly fissioning with complexity classes.

Well, yeah, you, you can have an infinity of complexity classes. But, you know, maybe there's

only a finite number of them that are particularly interesting, right? Just like with anything else,

you know, you, you care about some more than about others. So what kind of interesting classes are

there? You can have just maybe say, what are the, if you taking a kind of computer science class,

what are the classes you learn? Good. Let me, let me tell you sort of the, the, the biggest ones,

the ones that you would learn first. So, you know, first of all, there is P, that's what it's called.

Okay, it stands for polynomial time. And this is just the class of all of the problems that you

could solve with a conventional computer, like your iPhone or your laptop, you know, by a completely

deterministic algorithm, right? Using a number of steps that grows only like the size of the input

raised to some fixed power. Okay, so if your algorithm is linear time, like, you know, for

adding numbers, okay, that, that problem is in P. If you have an algorithm that's quadratic time,

like the elementary school algorithm for multiplying two numbers, that's also in P.

Even if it was the size of the input to the tenth power or to the fiftieth power, well,

that wouldn't be very good in practice. But, you know, formally, we would still count that,

that would still be in P. Okay, but if your algorithm takes exponential time, meaning,

like, if every time I add one more data point to your input, if the time needed by the algorithm

doubles, if you need time like two to the power of the amount of input data, then that is that we

call an exponential time algorithm. Okay, and that is not polynomial. Okay, so P is all of the problems

that have some polynomial time algorithm. Okay, so that includes most of what we do with our

computers on a day to day basis, you know, all the, you know, sorting basic arithmetic, you know,

whatever is going on in your email reader or in Angry Birds. Okay, it's all in P. Then the next

super important class is called NP. That stands for non deterministic polynomial. Okay, does not

stand for not polynomial, which is a common confusion. But NP was basically all of the problems

where if there is a solution, then it is easy to check the solution if someone shows it to you.

Okay, so actually a perfect example of a problem in NP is factoring, the one I told you about before.

Like, if I gave you a number with thousands of digits, and I told you that it, you know, I asked

you, does this, does this have at least three non trivial divisors? Right, that might be a super

hard problem to solve, right, might take you millions of years using any algorithm that's

known, at least running on our existing computers. Okay, but if I simply showed you the divisors,

I said, here are three divisors of this number, then it would be very easy for you to ask your

computer to just check each one and see if it works, just divide it in, see if there's any remainder.

Right, and if they all go in, then you've checked, well, I guess there were. Right, so any problem

where, you know, wherever there's a solution, there is a short witness that can be easily,

like a polynomial size witness that can be checked in polynomial time, that we call an NP

problem. Okay, and yeah, so every problem that's in P is also an NP, right, because, you know,

you could always just ignore the witness and just, you know, if a problem is in P, you can just solve

it yourself. Right. Okay, but now the, in terms of the central, you know, mystery of theoretical

computer science is every NP problem in P. So if you can easily check the answer to a computational

problem, does that mean that you can also easily find the answer? Even though there's all these

problems that appear to be very difficult to find the answer, it's still an open question whether

a good answer exists. So what's your... Because no one has proven that there's no way to do it.

It's arguably the most, I don't know, the most famous, the most maybe interesting,

maybe you disagree with that problem in theoretical computer science. So what's your...

The most famous, for sure. P equals NP. Yeah. If you were to bet all your money,

where do you put your money? That's an easy one. P is not equal to NP. Okay, so... I like to say

that if we were physicists, we would have just declared that to be a law of nature, you know,

just like thermodynamics. That's hilarious. Given ourselves Nobel prizes for its discovery. Yeah,

yeah, no one. Look, if later, if later it turned out that we were wrong, we just give ourselves

another Nobel Prize. More Nobel Prizes, yeah. I mean, you know, but yeah, because we're...

So harsh, but so true. I mean, no, I mean, I mean, it's really just because we are mathematicians

or descended from mathematicians, you know, we have to call things conjectures that other people

would just call empirical facts or discoveries, right? But one shouldn't read more into that

difference in language, you know, about the underlying truth. So, okay, so you're a good

investor and good spender of money. So then let me ask another way. Is it possible at all?

And what would that look like if P indeed equals NP? Well, I do think that it's possible. I mean,

in fact, you know, when people really pressed me on my blog for what odds would I put,

I put, you know, two or 3% odds that P equals NP. Yeah, just because, you know, when P... I mean,

I mean, you really have to think about like, if there were 50, you know, mysteries like P versus

NP, and if I made a guess about every single one of them, would I expect to be right 50 times?

Right. And the truthful answer is no. Okay. Yeah. So, you know, and that's what you really mean

in saying that, you know, you have, you know, better than 98% odds for something. Okay. But

so, so yeah, you know, I mean, there could certainly be surprises. And look, if P equals NP,

well, then there would be the further question of, you know, is the algorithm actually efficient

in practice? Right. I mean, Don Knuth, who I know that you've interviewed as well, right? He

likes to conjecture that P equals NP, but that the algorithm is so inefficient that it doesn't

matter anyway, right? No, I don't know. I've listened to him say that. I don't know whether he says

that just because he has an actual reason for thinking it's true or just because it sounds cool.

Yeah. Okay. But, you know, that's a logical possibility, right? That the algorithm could

be n to the 10,000 time, or it could even just be n squared time, but with a leading constant of

it could be a Google times n squared, or something like that. And in that case,

the fact that P equals NP, well, it would, you know, ravage the whole theory of complexity.

We would have to, you know, rebuild from the ground up. But in practical terms, it might

mean very little, right? If the algorithm was too inefficient to run. If the algorithm could

actually be run in practice, like if it had small enough constants, you know, or if you could improve

it to where it had small enough constants that was efficient in practice, then that would change

the world. Okay. You think it would have like, what kind of impact? Well, okay. I mean, here's

an example. I mean, you could, well, okay, just for starters, you could break basically all of

the encryption that people use to protect the Internet. You could break Bitcoin and every

other cryptocurrency, or, you know, mine as much Bitcoin as you wanted, right? You know, become a,

you know, become a super duper billionaire, right? And then, and then plot your next move.

Right. Okay. That's just for starters. That's a good point.

Now, your next move might be something like, you know, you now have like a theoretically

optimal way to train any neural network to find parameters for any neural network, right?

So you could now say, like, is there any small neural network that generates the entire content

of Wikipedia? Right? If, you know, and now the question is not, can you find it? The question

has been reduced to, does that exist or not? Yes. If it does exist, then the answer would be, yes,

you can find it. Okay. If you had this algorithm in your hands, okay? You could ask your computer,

you know, I mean, I mean, P versus NP is one of these seven problems that carries this million

dollar prize from the Clay Foundation. You know, if you solve it, you know, and others are the

Riemann hypothesis, the punk array conjecture, which was solved, although the solver turned down the

prize, right? And, and, and, and four others. But what I like to say, the way that we can see that

P versus NP is the biggest of all of these questions is that if you had this fast algorithm, then you

could solve all seven of them. Okay. You just ask your computer, you know, is there a short proof of

the Riemann hypothesis, right? You know, that, that a machine could, in a language where a machine

could verify it and provided that such a proof exists, then your computer finds it in a short

amount of time without having to do a brute force search. Okay. So I mean, I mean, those are the

stakes of what we're talking about. But I hope that also helps to give your listeners some intuition

of why I and most of my colleagues would put our money on P not equaling NP.

Is it possible? I apologize. This is a really dumb question, but is it possible to

that a proof will come out that P equals NP, but an algorithm that makes P equals NP

is impossible to find? Is that like crazy? Okay. Well, well, if P equals NP, it would mean that

there is such an algorithm that it exists. Yeah. But, um, um, you know, it would, it would mean

that it exists. Now, you know, in practice, normally the way that we would prove anything

like that would be by finding the algorithm by finding one algorithm. But there is such a thing

as a nonconstructive proof that an algorithm exists. You know, this is really only reared its head,

I think a few times in the history of our field, right? But, you know, it is, it is theoretically

possible that, that, that, that such a thing could happen. But, you know, there are some,

even here, there are some amusing observations that one could make. So there is this famous

observation of Leonid Levin, who is, you know, one of the original discoverers of NP completeness,

right? And he said, well, consider the following algorithm that like I guarantee

will solve the NP problems efficiently, just as provided that P equals NP. Okay. Here is what it

does. It just runs, you know, it enumerates every possible algorithm in a gigantic infinite list,

right? From like in like alphabetical order, right? You know, and many of them maybe won't

even compile. So we just ignore those. Okay. But now we just, you know, run the first algorithm,

then we run the second algorithm, we run the first one a little bit more, then we run the first three

algorithms for a while, we run the first four for a while. This is called dovetailing, by the way.

This is a known trick in theoretical computer science. Okay. But we do it in such a way that,

you know, whatever is the algorithm out there in our list that solves NP complete, you know,

the NP problems efficiently, we'll eventually hit that one, right? And now the key is that

whenever we hit that one, you know, it, you know, by assumption, it has to solve the problem,

has to find the solution. And once it claims to find the solution, then we can check that

ourselves, right? Because these are problems, then we can check it. Now this is utterly

impractical, right? You know, you'd have to do this enormous exhaustive search among all the

algorithms. But from a certain theoretical standpoint, that is merely a constant prefactor.

That's merely a multiplier of your running time. So there are tricks like that one can do to say

that in some sense, the algorithm would have to be constructive. But you know, in the human sense,

you know, it is possible that to, you know, it's conceivable that one could prove such a thing

via a non constructive method. Is that likely? I don't think so. Not personally.

So that's P and P, but the complexity is always full of wonderful creatures.

Well, it's got about 500 of them. 500. So how do you get, yeah, what? Yeah, how do you get more?

Yeah, well, okay, I mean, I mean, I mean, just for starters, there is everything that we could do

with a conventional computer with a polynomial amount of memory, okay, but possibly an exponential

amount of time, because we get to reuse the same memory over and over again. Okay, that is called

P space. Okay, and that's actually a, we think an even larger class than NP. Okay, well, P is

contained in NP, which is contained in P space. And we think that those containments are strict.

And the constraint there is on the memory, the memory has to grow with polynomially with the size

of the practice. That's right. That's right. But in P space, we now have interesting things that

we're not in NP, like as a famous example, you know, from a given position in chess, you know,

does white or black have the win? Let's say, assuming provided that the game lasts only for a

reasonable number of moves, okay, or likewise for go. Okay, and, you know, even for the generalizations

of these games to arbitrary size boards, because with an eight by eight board, you could say that's

just a constant size problem, you just, you know, in principle, you just solve it in O of one time,

right? But so we really mean the generalizations of, you know, games to arbitrary size boards here.

Or another thing in P space would be, like, I give you some really hard constraint satisfaction

problem, like, you know, you know, traveling salesperson or, you know, packing boxes into

the trunk of your car or something like that. And I ask, not just is there a solution, which

would be an NP problem, but I ask how many solutions are there? Okay, that, you know,

count the number of solution of valid solutions. That that that actually gives those problems

lie in a complexity class called sharp P, or like, it looks like hashtag, like hashtag P. Got it.

Okay, which sits between NP and P space. There's all the problems that you can do in exponential

time. Okay, that's called X. So, and by the way, it was proven in the 60s that X is larger than P.

Okay, so we know that much. We know that there are problems that are solvable in exponential

time that are not solvable in polynomial time. Okay. In fact, we even know more, we know that

there are problems that are solvable in n cube time that are not solvable in n squared time.

And that those don't help us with a controversy between P and NP.

Unfortunately, it seems not or certainly not yet. Right. The the the techniques that we use

to establish those things, they're very, very related to how touring proved the unsolvability

of the halting problem. But they seem to break down when we're comparing two different resources,

like time versus space, or like, you know, P versus NP. Okay, but, you know, I mean, there's

there's what you can do with a randomized algorithm, right, that can sometimes, you know,

with some has some probability of making a mistake. That's called BPP, bounded error

probabilistic polynomial time. And then of course, there's one that's very close to my own heart,

what you can efficiently do, do in polynomial time using a quantum computer. Okay, and that's

called BQP. Right. And so, you know, what's understood about that class. Okay, so P is

contained in BPP, which is contained in BQP, which is contained in P space. Okay. So anything you

can, in fact, in in like, in something very similar to sharp P, BQP is basically, you know,

well, it's contained in like P with the magic power to solve sharp P problems. Okay, so why is

BQP contained in P space? Oh, that's an excellent question. So there there is, well, I mean, one

has to prove that. Okay, but the proof you could you could think of it as using Richard Feynman's

picture of quantum mechanics, which is that you can always, you know, we haven't really talked about

quantum mechanics in this, in this conversation we, we did in our previous one. Yeah, we did last

time. But yeah, we did last time. Okay, but, but basically, you could always think of a quantum

computation as like a branching tree of possibilities, where each possible path that you could take

through, you know, your the space has a complex number attached to it called an amplitude. Okay,

and now the rule is, you know, when you make a measurement at the end, will you see a random

answer? Okay, but quantum mechanics is all about calculating the probability that you're going to

see one potential answer versus another one, right? And the rule for calculating the probability

that you'll see some answer is that you have to add up the amplitudes for all of the paths that

could have led to that answer. And then, you know, that's a complex number. So that, you know, how could

that be a probability, then you take the squared absolute value of the result that gives you a

number between zero and one. Okay, so I just, I just summarized quantum mechanics in like 30

seconds. Okay, but, but now, you know, what this already tells us is that anything I can do with

a quantum computer, I could simulate with a classical computer if I only have exponentially

more time. Okay, and why is that? Because if I have exponential time, I could just write down this

entire branching tree and just explicitly calculate each of these amplitudes, right? You know, that

will be very inefficient, but it will work, right? It's enough to show that quantum computers could

not solve the halting problem, or, you know, they could never do anything that is literally uncomputable

in Turing sense. Okay, but now, as I said, there's even a stronger result, which says that BQP is

contained in P space. The way that we prove that is that we say, if all I want is to calculate the

probability of some particular output happening, you know, which is all I need to simulate a

quantum computer, really, then I don't need to write down the entire quantum state, which is

an exponentially large object. All I need to do is just calculate what is the amplitude for that

final state. And to do that, I just have to sum up all the amplitudes that lead to that state.

Okay, so that's an exponentially large sum, but I can calculate it just reusing the same memory

over and over for each term in the sum. Hence the P in the P space. Hence the P space. Yeah.

So what, out of that whole complexity zoo, and it could be BQP, what do you find is the most,

the class that captured your heart the most? The most beautiful class, it's just, yeah.

I use, as my email address, bqpqpoly at gmail.com. Yes, because bqp slash qpoly, well, you know,

amazingly no one had taken it. Amazing. But, you know, but this is a class that I was involved in

sort of defining proving the first theorems about in 2003 or so. So it was kind of close to my heart.

But this is like, if we extended bqp, which is the class of everything we can do efficiently

with a quantum computer, to allow quantum advice, which means imagine that you had some special

initial state that could somehow help you do computation. And maybe such a state would be

exponentially hard to prepare. But, you know, maybe somehow these states were formed in the

Big Bang or something, and they've just been sitting around ever since, right? If you found one,

and if this state could be like ultra power, there are no limits on how powerful it could be,

except that this state doesn't know in advance which input you've got, right? It only knows the

size of your input, you know, and that that's bqp slash qpoly. So that's that's one that I just

personally happen to love, okay? But, you know, if you're asking like, what's the, you know,

there's a class that I think is way more beautiful than, you know, or fundamental

than a lot of people even within this field realize that it is. That class is called szk,

or statistical zero knowledge. And, you know, there's a very, very easy way to define this

class, which is to say, suppose that I have two algorithms that each sample from probability

distributions, right? So each one just outputs random samples according to, you know, possibly

different distributions. And now the question I ask is, you know, let's say distributions over

strings of n bits, you know, so over an exponentially large space. Now I ask, are these two distributions

close or far as probability distributions? Okay, any problem that can be reduced to that,

you know, that can be put into that form is an szk problem. And the way that this class was

originally discovered was completely different from that. And it was kind of more complicated.

It was discovered as the class of all of the problems that have a certain kind of what's

called zero knowledge proof. The zero knowledge proofs are one of the central ideas in cryptography.

You know, Shafi Goldwasser and Silvio McCauley won the Turing Award for, you know, inventing them.

And they're at the core of even some some cryptocurrencies that, you know, people people

use nowadays. But there are zero knowledge proofs or ways of proving to someone that something is

true, like, you know, that there is a solution to this, you know, optimization problem or that

these two graphs are isomorphic to each other or something. But without revealing why it's true,

without revealing anything about why it's true. Okay, szk is all of the problems for which there

is such a proof that doesn't rely on any cryptography. Okay. And if you wonder, like, how could such a

thing possibly exist, right? Well, I can imagine that I had two graphs, and I wanted to convince you

that these two graphs are not isomorphic, meaning, you know, I cannot permute one of them so that

it's the same as the other one, right? You know, that might be a very hard statement to prove,

right? I might need, you know, you might have to do a very exhaustive enumeration of, you know,

all the different permutations before you were convinced that it was true. But what if there

were some all knowing wizard that said to you, look, I'll tell you what, just pick one of the

graphs randomly, then randomly permute it, then send it to me, and I will tell you which graph

you started with. Okay, and I will do that every single time. Right? And let's load that in. Okay,

that's got it. I got it. And let's say that that wizard did that 100 times, and it was right every

time, right? Now, if the graphs were isomorphic, then, you know, it would have been flipping a coin

each time, right? It would have had only a one in two to the 100 power chance of, you know,

of guessing right each time. But, you know, so, so if it's right every time, then now you're

statistically convinced that these graphs are not isomorphic, even though you've learned nothing

new about why they aren't so fascinating. So yeah, so so SDK is all of the problems that have

protocols like that one. But it has this beautiful other characterization. It's shown up again and

again in my in my own work and you know, a lot of people's work. And I think that it really is one

of the most fundamental classes. It's just that people didn't realize that when it was first

discovered. So we're living in the middle of a pandemic currently. Yeah. How has your life

been changed? Or no, better to ask, like, how is your perspective of the world change with this

world changing event of a pandemic over taking the entire world? Yeah, well, I mean, I mean,

all of our lives have changed, you know, like, I guess, as with no other event since I was born,

you know, you would have to go back to World War Two for something I think of this magnitude,

you know, on, you know, the way that we live our lives. As for how it has changed my worldview,

I think that the the failure of institutions, you know, like, like, like the CDC, like, you know,

other institutions that we sort of thought were trustworthy, like a lot of the media

was staggering was was absolutely breathtaking. It is something that I would not have predicted.

Right. I think I wrote on my blog that, you know, you know, it's it's it's fascinating to, like,

rewatch the movie Contagion from a decade ago, right, that correctly foresaw so many aspects of,

you know, what was going on, you know, a an airborne, you know, virus originates in China,

spreads to, you know, much of the world, you know, shuts everything down until a vaccine can be

developed. You know, everyone has to stay at home, you know, you know, it gets, you know,

an enormous number of things right. Okay. But the one thing that they could not imagine,

you know, is that like in this movie, everyone from the government is like hyper competent,

hyper, you know, dedicated to the public good, right? And the best of the best, you know, yeah,

they're the best of the best, you know, they could, you know, and there are these conspiracy

theorists, right, who think, you know, you know, this is all fake news, there's no there's not

really a pandemic. And those are some random people on the internet, who are the hyper competent

government people have to, you know, oppose, right? They, you know, in trying to envision the

worst thing that could happen, like, you know, the the there was a failure of imagination,

the movie makers did not imagine that the conspiracy theorists and the, you know, and the

incompetence in the nutcases would have captured our institutions and be the ones actually running

things. So you had a certain I love competence in all walks of life. I love so much energy. I'm

so excited, but people do amazing job. And I like you. Well, maybe you can clarify, but I had maybe

not intuition, but I hope that government is best could be ultra competent. What, first of all,

two questions, like, how do you explain lack of confidence? And the other maybe on the positive

side, how can we build a more competent government? Well, there's an election in two months. I mean,

you know, you have a faith that the election, I, you know, it's not going to fix everything. But,

you know, it's like, I feel like there is a ship that is sinking, and you could at least stop the

sinking. But, you know, I think that there are there are much, much deeper problems. I mean,

I think that, you know, it is it is plausible to me that, you know, a lot of the the failures,

you know, with the CDC, with some of the other health agencies, even, you know, you know,

predate Trump, you know, predate the, you know, right wing populism that is sort of taken over

much of the world now. And, you know, I think that, you know, it was is, you know, it is very

I'm actually, you know, I've actually been strongly in favor of, you know, rushing vaccines of,

you know, I thought that we could have done, you know, human human challenge trials,

you know, which were not done, right, we could have, you know, like I had, you know, volunteers,

you know, to actually, you know, be, you know, get vaccines, get, you know, exposed to COVID.

So, you know, innovative ways of accelerating what we've done previously over a long

time. I thought that, you know, each, each month that you, that, that a vaccine is, is closer

is like trillions of dollars. Are you surprised? And of course, lives, you know, at least, you

know, hundreds of thousands of lives. Are you surprised that it's taking this long? We still

don't have a plan. They're still not a feeling like anyone is actually doing anything in terms of

alleviate like any kind of plan. So there's a bunch of stuff, this vaccine, but you could also do

a testing infrastructure where everybody's tested nonstop with contact tracing, all that kind of

stuff. Well, I mean, I'm as surprised as almost everyone else. I mean, this is a historic failure.

It is one of the biggest failures in the 240 year history of the United States, right? And we should

be, you know, crystal clear about that. And, you know, one thing that I think has been missing,

you know, even, even from the more competent side is like, you know, is sort of the, the World War II

mentality, right? The, you know, the mentality of, you know, let, let's just, you know, you know,

if, if, if we can, by breaking a whole bunch of rules, you know, get a vaccine and, you know,

and even half the amount of time as we thought, then let's just do that because, you know,

you, you know, like, like we have to, we have to weigh all of the moral qualms that we have about

doing that against the moral qualms of not doing. And one key little aspect of that, that's deeply

important to me and we'll go in that topic next is the World War II mentality wasn't just about,

you know, breaking all the rules to get the job done. There was a togetherness to it. There's,

so I would, if I were president right now, it seems quite elementary to unite the country

because we're facing a crisis. It's easy to make the virus the enemy. And it's very surprising to

me that the division, the division has increased as opposed to decreased. That's, that's hard

breaking. Yeah. Well, look, I mean, it's been said by others that this is the first time in the

country's history that we have a president who does not even pretend to, you know, want to unite

the country, right? Yeah. You know, I mean, I mean, Lincoln, who fought a civil war, you know,

you know, said he wanted to unite the country, right? You know, and, and I do, I do worry enormously

about what happens if the results of this election are contested, you know, and, you know, will there

be violence as a result of that? And will we have a clear path of succession? And, you know,

look, I mean, you know, this is all we're, we're going to find out the answers to this in two

months. And if none of that happens, maybe I'll look foolish, but I am willing to go on the record

and say, I am terrified about that. We have been reading the rise and fall, the third right.

So if I can, this, this is like one little voice just to put out there that I think November

will be a really critical month for people to breathe and put love out there. Do not,

you know, anger in those, in that context, no matter who wins, no matter what is said,

will destroy our country, may destroy our country, may destroy the world because of the power of

the country. So it's really important to be patient, loving, empathetic. Like one of the

things that it troubles me is that even people on the left are unable to have a love and respect

for people who voted for Trump. They can't imagine that there's good people that could vote for the

opposite side. And that's... Oh, I know there are, because I know some of them, right? I mean,

you know, it's still, you know, maybe it baffles me, but, you know, I know such people.

Let me ask you this. It's also heartbreaking to me on the topic of cancel culture. So in the

machine learning community, I've seen it a little bit, that there's aggressive attacking of people

who are trying to have a nuanced conversation about things. And it's troubling because it feels like

nuanced conversation is the only way to talk about difficult topics. And when there's a thought

police and speech police on any nuanced conversation that everybody has to, like in an animal farm,

chant that racism is bad and sexism is bad, which is things that everybody believes.

And they can't possibly say anything nuanced. It feels like it goes against any kind of progress

from my kind of shallow perspective. But you've written a little bit about cancel culture. Do

you have thoughts that are interesting to say about this? Well, I mean, to say that I am opposed to,

you know, this trend of cancellations or of, you know, shouting people down rather than engaging

them, that would be a massive understatement, right? And I feel like, you know, I have put my

money where my mouth is, you know, not as much as some people have, but, you know, I've tried to do

something. I mean, I have defended, you know, some unpopular people and unpopular, you know,

ideas on my blog. I've, you know, tried to defend, you know, norms of open discourse, of, you know,

reasoning with our opponents, even when I've been shouted down for that on social media,

you know, called a racist, called a sexist, all of those things. And which, by the way, I should

say, you know, I would be perfectly happy to, you know, say, you know, if we had time to say,

you know, you know, 10,000 times, you know, my hatred of racism, of sexism, of homophobia,

right? But what I don't want to do is to cede to some particular political faction the right to

define exactly what is meant by those terms to say, well, then you have to agree with all of these

other extremely contentious positions, or else you are a misogynist, or else you are a racist,

right? I say that, well, no, you know, you know, don't like, don't I or, you know, don't people

like me also get a say in the discussion about, you know, what is racism, about what is going to

be the most effective to combat racism, right? And, you know, this cancellation mentality,

I think, is spectacularly ineffective at its own professed goal of, you know, combating racism and

sexism. What's a positive way out? So I try to, I don't know if you see what I do on Twitter,

but on Twitter, I mostly, in my whole, in my life, I've actually, it's who I am to the core,

is like, I really focus on the positive, and I try to put love out there in the world. And still,

I get attacked. And I look at that and I wonder like, you too, I didn't know, like, I haven't

actually said anything difficult and nuanced. You talk about somebody like Steven Pinker,

who I actually don't know the full range of things that that he's attacked for, but he tries to say

difficult, he tries to be thoughtful about difficult topics. He does. And obviously he

just gets slaughtered by, well, I mean, I mean, I mean, I mean, yes, but it's also amazing how well

Steve has withstood it. I mean, he just survived an attempt to cancel him just a couple of months

ago, right? Psychologically, he survives it too, which worries me because I don't think I can.

Yeah, I've gotten to know Steve a bit. He is incredibly unperturbed by this stuff.

And I admire that and I envy it. I wish that I could be like that. I mean, my impulse when I'm

getting attacked is I just want to engage every single, like, anonymous person on Twitter and Reddit

who is saying mean stuff about me. And I want to say, well, look, can we just talk this over for

an hour? And then, you know, you'll see that I'm not that bad. And, you know, sometimes that even

works. The problem is then there's the, you know, the 20,000 other ones, right? And that's not, but

psychologically, does that wear on you? It does. It does. But yeah, I mean, in terms of what is the

solution, I mean, I wish I knew, right? And so, you know, in a certain way, these problems are

maybe harder than P versus NP, right? I mean, you know, but I think that part of it has to be for,

you know, that I think that there's a lot of sort of silent support for what I'll call the open

discourse side, the, you know, reasonable enlightenment side. And I think that that support

has to become less silent, right? I think that a lot of people, they sort of, you know, like agree

that a lot of these cancellations and attacks are ridiculous, but are just afraid to say so,

right? Or else they'll get shouted down as well, right? That's just the standard witch hunt dynamic,

which, you know, of course, this, you know, this faction understands and exploits to its great

advantage. But, you know, more people just, you know, said, you know, like, we're not going to

stand for this, right? You know, this is, you know, guess what? We're against racism, too. But,

you know, this, you know, what you're doing is ridiculous, right? You know, and the hard part is,

like, it takes a lot of mental energy. It takes a lot of time, you know, even if you feel like

you're not going to be canceled or, you know, you're staying on the safe side, like, it takes a lot

of time to, to, to phrase things in exactly the right way and to, you know, respond to everything

people say. So, but I think that, you know, the more people speak up than, you know, from, from,

from all political persuasions, you know, from like all, you know, walks of life, then, you know,

the easier it is to move forward. Since we've been talking about love, can you, last time I

talked to you about the meaning of life a little bit, but here has, it's a weird question to ask

a computer scientist, but has love for other human beings, for, for things, for the world around you

played an important role in your life? Have you, you know, it's easy for a world class

computer scientist, you could even call yourself like a physicist, everything to be lost in the

books. Is the connection to other humans, love for other humans played an important role?

Well, I love my kids. I love my wife. I love my parents. You know, I am probably not, not

different from most people in loving their families. And, and in that being very important

in my life. Now, I should remind you that, you know, I am a theoretical computer scientist.

If you're looking for deep insight about the nature of love, you're probably looking in the

wrong place to ask me, but, but sure, it's been important. But is it, is there something from

a computer science perspective to be said about love? Is there, or is that, is that even beyond

into the realm of, beyond the realm of consciousness and all that? There was, there was this great

cartoon. I think it was one of the classic XKCDs where it's, it shows like a heart and it's like,

you know, squaring the heart, taking the four year transform of the heart, you know, integrating the

heart, you know, you know, each, each thing. And then it says, you know, my normal approach is useless

here. I'm so glad I asked this question. I think there's no better way to, to end this. I hope we

get a chance to talk again. This is an amazing, cool experiment to do it outside. And I'm really

glad you made it out. Yeah. Well, I appreciate it a lot. It's been a pleasure. And I'm glad you

were able to come out to Austin. Thanks. Thanks for listening to this conversation with Scott

Aronson. And thank you to our sponsors, Aidsleep, SimplySafe, ExpressVPN, and BetterHelp. Please

check out these sponsors in the description to get a discount and to support this podcast.

If you enjoy this thing, subscribe on YouTube, review it with five stars and up a podcast,

follow on Spotify, support on Patreon, or connect with me on Twitter at Lex Freedman.

And now let me leave you with some words from Scott Aronson that I also gave to you in the

introduction, which is if you always win, then you're probably doing something wrong. Thank you

for listening and for putting up with the intro and outro in this strange room in the middle of

nowhere. And I very much hope to see you next time in many more ways than one.