The following is a conversation with Jeff Hawkins, a neuroscientist seeking to understand

the structure, function, and the origin of intelligence in the human brain.

He previously wrote a seminal book on the subject titled On Intelligence, and recently

a new book called A Thousand Brains, which presents a new theory of intelligence

that Richard Dawkins, for example, has been raving about, calling the book, quote,

brilliant and exhilarating. I can't read those two words and not think of him saying it in his

British accent. Quick mention of our sponsors, Code Academy, Biooptimizers, ExpressVPN,

ASleep, and Blinkist. Check them out in the description to support this podcast.

As a side note, let me say that one small but powerful idea that Jeff Hawkins mentions in his

new book is that if human civilization were to destroy itself, all of knowledge, all our creations

will go with us. He proposes that we should think about how to save that knowledge in a way that

long outlives us, whether that's on Earth, in orbit around Earth, or in deep space. And then

to send messages that advertise this backup of human knowledge to other intelligent alien

civilizations. The main message of this advertisement is not that we are here, but that we were once

here. This little difference somehow was deeply humbling to me, that we may with some nonzero

likelihood destroy ourselves, and that an alien civilization, thousands or millions of years

from now may come across this knowledge store, and they would only with some low probability even

notice it, not to mention be able to interpret it. And the deeper question here for me is what

information in all of human knowledge is even essential? Does Wikipedia capture it or not at

all? This thought experiment forces me to wonder what are the things we've accomplished and are

hoping to still accomplish that will outlive us? Is it things like complex buildings, bridges,

cars, rockets? Is it ideas like science, physics, and mathematics? Is it music and art? Is it

computers, computational systems, or even artificial intelligence systems? I personally

can't imagine that aliens wouldn't already have all of these things. In fact, much more and much

better. To me, the only unique thing we may have is consciousness itself, and the actual

subjective experience of suffering, of happiness, of hatred, of love. If we can record these experiences

in the highest resolution directly from the human brain, such that aliens will be able to replay

them, that is what we should store and send as a message. Not Wikipedia, but the extremes of

conscious experiences, the most important of which, of course, is love. This is the Lex

Friedman podcast, and here is my conversation with Jeff Hawkins. We previously talked over two

years ago. Do you think there's still neurons in your brain that remember that conversation,

that remember me and got excited? There's a Lex neuron in your brain that just finally has a purpose?

I do remember our conversation, or I have some memories of it, and I formed additional memories

of you in the meantime. I wouldn't say there's a neuron or a neuron in my brain that know you,

but there are synapses in my brain that have formed that reflect my knowledge of you and

the model I have of you in the world. Whether the exact same synapses were formed two years ago,

it's hard to say because these things come and go all the time. One thing to note about

brains is that when you think of things, you often erase the memory and rewrite it again.

So yes, but I have a memory of you, and that's instantiated in synapses. There's a simpler way

to think about it, Lex. We have a model of the world in your head, and that model is continually

being updated. I updated it this morning. You offered me this water, you said it was from the

refrigerator. I remember these things, and so the model includes where we live, the places we know,

the words, the objects in the world, but it's just monstrous model, and it's constantly being

updated, and people are just part of that model. So are animals, so are other physical objects,

so are events we've done. So it's no special in my mind, special place for the memories of humans.

I mean, obviously, I know a lot about my wife, but and friends, and so on, but it's not like a

special place for humans were over here, but we model everything, and we model other people's

behaviors, too. So if I said, there's a copy of your mind in my mind, it's just because I know

how humans, I've learned how humans behave, and I learned some things about you, and that's part

of my world model. Well, I just also mean the collective intelligence of the human species.

I wonder if there's something fundamental to the brain that enables that, so modeling other

humans with their ideas. You're actually jumping into a lot of big profits, like collective

intelligence is a separate topic that a lot of people like to talk about, we can talk about that.

But so that's interesting, like, you know, we're not just individuals, we live in society and so on.

But from our research point of view, and so again, let's just talk, we study the neocortex,

it's a sheet of neural tissue, it's about 75% of your brain, it runs on this very repetitive

algorithm. It's a very repetitive circuit. And so you can apply that algorithm to lots of different

problems, but it's all underneath, it's the same thing, we're just building this model.

So from our point of view, we wouldn't look for these special circuits someplace buried in your

brain that might be related to understanding other humans. It's more like, how do we build a

model of anything? How do we understand anything in the world? And humans are just another part

of the things we understand. So there's nothing, there's nothing to the brain that knows the

emergent phenomenon of collective intelligence? Well, I certainly know about that. I've heard

the terms I've read. No, but that's, right. Well, okay, right. As an idea. Well, I think we have

language, which is sort of built into our brains, and that's a key part of collective intelligence.

So there are some, you know, prior assumptions about the world we're going to live in, when we're

born, we're not just a blank slate. And so, you know, did we evolve to take advantage of those

situations? Yes. But again, we study only part of the brain, the neocortex. There's other parts

of the brain are very much involved in societal interactions and human emotions and how we interact

and even societal issues about, you know, how we interact with other people when we support them,

when we're greedy and things like that. I mean, certainly the brain is a great place

where to study intelligence. I wonder if it's the fundamental atom of intelligence? Well,

I would say it's absolutely an essential component, even if you believe in collective

intelligence as, hey, that's where it's all happening. That's what we need to study,

which I don't believe that, by the way. I think it's really important, but I don't think that is

the thing. But even if you do believe that, then you have to understand how the brain works in

doing that. It's, you know, it's more like we are intelligent, we are intelligent individuals,

and together we are much more magnified, our intelligence. We can do things that we couldn't

do individually. But even as individuals, we're pretty damn smart. And we can model things and

understand the world and interact with it. So to me, if you're going to start someplace,

you need to start with the brain, then you could say, well, how do brains interact with each other?

And what is the nature of language? And how do we share models that I've learned something about

the world? How do I share it with you? Which is really what, you know, sort of communal

intelligence is. I know something, you know something. We've had different experiences in

the world. I've learned something about brains. Maybe I can impart that to you. You've learned

something about, you know, whatever physics and you can part that to me. But it all comes down to

even just the epistemological question of, well, what is knowledge and how do you represent it in

the brain? Right? And it's not, that's where it's going to reside, right? Or in our writings.

It's obvious that human collaboration, human interaction is how we build societies. But

some of the things you talk about and work on, some of those elements of what makes up an intelligent

entity is there with a single person. Absolutely. I mean, we can't deny that the brain is the core

element here in, at least I think it's obvious, the brain is the core element in all theories of

intelligence. It's where knowledge is represented. It's where knowledge is created. We interact,

we share, we build upon each other's work. But without a brain, you'd have nothing. You know,

there would be no intelligence without brains. And so that's where we start. I got into this field

because I just was curious as to who I am. You know, how do I think? What's going on in my head

when I'm thinking? What does it mean to know something? I can ask what it means for me to

know something independent of how I learned it from you or from someone else or from society.

What does it mean for me to know that I have a model of you in my head? What does it mean to

know I know what this microphone does and how it works physically, even when I can't see it right

now? How do I know that? What does it mean? How do the neurons do that at the fundamental level

of neurons and synapses and so on? Those are really fascinating questions. And I'm happy to

just happy to understand those if I could. So in your new book, you talk about our brain,

our mind as being made up of many brains. So the book is called A Thousand Brains,

A Thousand Brain Theory of Intelligence. What is the key idea of this book?

The book has three sections and it has sort of maybe three big ideas. So the first section is

all about what we've learned about the neocortex and that's the thousand brains theory. Just to

complete the picture, the second section is all about AI and the third section is about the future

of humanity. So the thousand brains theory, the big idea there, if I had to summarize into one

one big idea, is that we think of the brain, the neocortex is learning this model of the world.

But what we learned is actually there's tens of thousands of independent modeling systems going

on. And so each, what we call a column in the cortex with about 150,000 of them is a complete

modeling system. So it's a collective intelligence in your head in some sense. So the thousand brains

theory says about where do I have knowledge about, you know, this coffee cup or where is the model

of this cell phone? It's not in one place. It's in thousands of separate models that are complementary

and they communicate with each other through voting. So this idea that we have, we feel like

we're one person, you know, that's our experience, we can explain that. But reality, there's lots of

these like, it's almost like little brains, like, but they're sophisticated modeling systems,

about 150,000 of them in each human brain. And that's a totally different way of thinking about

how the neocortex is structured than we or anyone else thought of even just five years ago.

So you mentioned you started this journey just looking in the mirror and trying to understand

who you are. So if you have many brains, who are you then?

So it's interesting, we have a singular perception, right? You know, we think, oh, I'm just here,

I'm looking at you. But it's, it's composed of all these things, like there's sounds and there's,

and there's this vision and there's touch and all kinds of inputs yet we have the singular

perception. And what the 1000 brain theory says, we have these models that are visual models,

we have audit models, auditory models, models of talk to models and so on, but they vote.

And so they send in the cortex, you can think about that these columns as about like little

grains of rice, 150,000 stacked next to each other. And each one is its own little modeling system.

But they have these long range connections that go between them. And we call those voting

connections or voting neurons. And so the different columns try to reach a consensus,

like, what am I looking at? Okay, you know, each one has some ambiguity, but they come to

a consensus. Oh, there's a water bottle, I'm looking at. We are only consciously able to

perceive the voting. We're not able to perceive anything that goes under the hood. So the voting

is what we're aware of. The results of the vote. Yeah, the voting. Well, it's, you can imagine

it this way. We were just talking about eye movements a moment ago. So as I'm looking at

something, my eyes are moving about three times a second. And with each movement,

a completely new input is coming into the brain. It's not repetitive. It's not shifting around.

It's completely new. I'm totally unaware of it. I can't perceive it. But yet if I looked at the

neurons in your brain, they're going on and off, on and off, on and off, on up. But the voting neurons

are not. The voting neurons are saying, you know, we all agree, even though I'm looking at different

parts of this is a water bottle right now. And that's not changing. And it's in some position

and pose relative to me. So I have this perception of the water bottle about two feet away from me

at a certain pose to me. That is not changing. That's the only part I'm aware of. I can't be

aware of the fact of the inputs from the eyes are moving and changing and all this other tapping.

So these long range connections are the part we can be conscious of. The individual activity in

each column is doesn't go anywhere else. It doesn't get shared anywhere else. It doesn't,

there's no way to extract it and talk about it or extract it and even remember it to say, oh,

yes, I can recall that. So, but these long range connections are the things that are accessible

to language. And to our, you know, it's like the hippocampus or our memories, you know, our

short term memory systems and so on. So we're not aware of 95% or maybe it's even 98% of what's

going on in your brain. We're only aware of this sort of stable, somewhat stable voting outcome

of all these things that are going on underneath the hood.

So what would you say is the basic element in the 1000 brains theory of intelligence,

of intelligence? Like, what's the atom of intelligence when you think about it?

Is it the individual brains and then what is a brain?

Well, let's, let's, can we just talk about what intelligence is first and then,

and then we can talk about the elements are. So in my, in my book, intelligence is the ability

to learn a model of the world, to build the internal to your head, a model that represents

the structure of everything, you know, to know what this is a table and that's a coffee cup and

this is a goose neck lamp and all these things. To know these things, I have to have a model

in my head. I just don't look at them and go, what is that? I already have internal representations

of these things in my head and I had to learn them. I wasn't born of any of that knowledge.

You were, you know, we have some lights in the room here. I, you know, that's not part of my

evolutionary heritage, right? It's not in my genes. So we have this incredible model and the model

includes not only what things look like and feel like, but where they are relative to each other

and how they behave. I've never picked up this water bottle before, but I know that if I took

my hand on that blue thing and I turn it, it'll probably make a funny little sound as the little

plastic things detach and then it'll rotate and it'll rotate a certain way and it'll come off.

How do I know that? Because I have this model in my head. So the essence of intelligence

is our ability to learn a model and the more sophisticated our model is, the smarter we are.

Not that there is a single intelligence because you can know about, you know a lot about things

that I don't know and I know about things you don't know and we can both be very smart,

but we both learned a model of the world through interacting with it. So that is the

essence of intelligence. Then we can ask ourselves, what are the mechanisms in the brain that allow

us to do that? And what are the mechanisms of learning, not just the neural mechanisms,

what are the general process by how we learn a model? So that was a big insight for us.

It's like, what are the, what are the actual things that, how do you learn this stuff?

It turns out you have to learn it through movement. You can't learn it just by,

that's how we learn, we learn through movement, we learn. So you build up this model by observing

things and touching them and moving them and walking around the world and so on.

So either you move or the thing moves. Somehow. Yeah, obviously you can learn

things just by reading a book, something like that, but think about if I were to say, oh,

here's a new house. I want you to learn, you know, what do you do? You have to walk,

you have to walk from room to room. You have to open the doors, look around,

see what's on the left, what's on the right. As you do this, you're building a model in your head.

It's just, that's what you're doing. You can't just sit there and say, I'm going to grok the

house. No, you know, or you don't even want to just sit down and read some description of it,

right? You literally physically interact with them. The same with like a smartphone. If I'm

going to learn a new app, I touch it and I move things around. I see what happens when I,

when I do things with it. So that's the basic way we learn in the world.

And by the way, when you say model, you mean something that can be used for prediction

in the future. It's, it's used for prediction and for behavior and planning.

Right. And does a pretty good job at doing so.

Yeah. Here's the way to think about the model. A lot of people get hung up on this. So

you can imagine an architect making a model of a house, right? So there's a physical model

that's small. And why don't they do that? Well, we do that because you can imagine what it would

look like from different angles. Okay, look from here, look from there. And you can also say,

well, how far to get from the garage to the, to the swimming pool or something like that,

right? You can imagine looking at this. And so what would be the view from this location?

So we build these physical models to let you imagine the future and imagine behaviors.

Now we can take that same model and put it in a computer. So we now, today, they all build

models of houses in a computer and they, and they do that using a set of,

we'll come back to this term in a moment, reference frames, but eventually you assign a

reference frame to the house and you assign different things for the house in different

locations. And then the computer can generate an image and say, okay, this is what it looks

like in this direction. The brain is doing something remarkably similar to this.

Surprising. It's using reference frames. It's building these, it's similar to a model in a

computer, which has the same benefits of building a physical model. It allows me to say, what would

this thing look like if it was in this orientation? What would likely happen if I pushed this button?

I've never pushed this button before. Or how would I accomplish something? I want to convey

a new idea of learned. How would I do that? I can imagine in my head, well, I could talk about it.

I could write a book. I could do some podcasts. I could, you know, maybe tell my neighbor,

you know, and I can imagine the outcomes of all these things before I do any of them.

That's the model that you do. Let's just plan the future and imagine the consequences or actions.

Prediction, you asked about prediction. Prediction is not the goal of the model. Prediction is an

inherent property of it. And it's how the model corrects itself.

So prediction is fundamental to intelligence. It's fundamental to building a model and the

model's intelligent. And let me go back and be very precise about this. Prediction,

you can think of prediction two ways. One is like, hey, what would happen if I did this?

That's the type of prediction. That's a key part of intelligence. But it isn't prediction. It's like,

oh, what's this water bottle going to feel like when I pick it up? And that doesn't seem very

intelligent. But the way to think, one way to think about prediction is it's a way for us to learn

where our model is wrong. So if I picked up this water bottle and it felt hot, I'd be very surprised.

Or if I picked up it was very light, it would be very, I'd be surprised. Or if I turned this top

and it didn't, I had to turn it the other way, I'd be surprised. And so all those might have

a prediction like, okay, I'm going to do it. I'm going to drink some water. Okay, I do this.

There it is. I feel opening, right? What if I had to turn it the other way? Or what if it's

split in two? Then I say, oh my gosh, I misunderstood this. I didn't have the right model. This thing,

my attention would be drawn to, I'll be looking at it going, well, how did that happen? Why did it

open up that way? And I would update my model by doing it. Just by looking at it and playing around

that update and saying, this is a new type of water bottle. So you're talking about sort of

complicated things like a water bottle. But this also applies for just basic vision, just like

seeing things. That's almost like a precondition of just perceiving the world as predicting.

Everything that you see is first passed through your prediction.

Everything you see and feel, in fact, this is the insight I had back in the late 80s,

and excuse me, early 80s. And another people have reached the same idea is that every sensory input

you get, not just vision, but touch and hearing, you have an expectation about it and a prediction.

Sometimes you can predict very accurately. Sometimes you can't. I can't predict what next

word is going to come out of your mouth. But as you start talking, I was better and better

predictions. And if you talked about some topics, I'd be very surprised. So I have this sort of

background prediction that's going on all the time for all of my senses. Again, the way I think

about that is this is how we learn. It's more about how we learn. It's a test of our understanding,

our predictions, our test. Is this really a water bottle? If it is, I shouldn't see

a little finger sticking out the side. And if I saw a little finger sticking out, I was like,

what the hell's going on? That's not normal.

I mean, that's fascinating. Let me linger on this for a second. It really honestly feels

that prediction is fundamental to everything, to the way our mind operates, to intelligence.

So it's just a different way to see intelligence, which is like everything starts a prediction.

And prediction requires a model. You can't predict something unless you have a model of it.

Right. But the action is prediction. So the thing the model does is prediction.

But you can then extend it to things like, what would happen if I took this today? I went and

did this. What would be likely? Or you can extend prediction to like, oh, I want to get a promotion

at work. What action should I take? And you can say, if I did this, I could predict what might

happen. If I spoke to someone, I predict what would happen. So it's not just low level predictions.

Yeah, it's all predictions. It's all predictions. It's like this black box, so you can ask basically

any question, low level or high level. So we started off with that observation. It's all,

it's this nonstop prediction. And I write about this in the book about, and then we asked,

how do neurons actually make predictions? And physically, like, what does the neuron do when

it makes a prediction? And, or the neural tissue does when it makes prediction. And then we asked,

what are the mechanisms by how we build a model that allows you to make prediction? So we started

with prediction as sort of the fundamental research agenda, if in some sense, like, and say, well,

we understand how the brain makes predictions. We will understand how it builds these models and how

it learns. And that's the core of intelligence. So it was like, it was the key that got us in the door

to say, that is our research agenda. Understand predictions. So in this whole process,

this, where does intelligence originate, would you say? So if we look at things that are much

less intelligence to humans, and you start to build up a human, the process of evolution,

where's this magic thing that has a prediction model or a model that's able to predict that

starts to look a lot more like intelligence? Is there a place where Richard Dawkins wrote an

introduction to your, to your book, an excellent introduction? I mean, it puts a lot of things

into context. And it's funny, just looking at parallels for your book and Darwin's origin of

species. So Darwin wrote about the origin of species. So what is the origin of intelligence?

Yeah, well, we have a theory about it. And it's just that is the theory. Theory goes as follows.

As soon as living things started to move, they're not just floating in sea, they're not just

a plant, you know, grounded someplace. As soon as they started to move, there was an advantage to

moving intelligently, to moving in certain ways. And there's some very simple things you can do,

you know, bacteria or single cell organisms can move towards a source of gradient of food or

something like that. But an animal that might know where it is and know where it's been and how to

get back to that place, or an animal that might say, oh, there was a source of food someplace,

how do I get to it? Or there was a danger, how do I get to it? There was a mate, how do I get to them?

There was a big evolutionary advantage to that. So early on, there was a pressure to start

understanding your environment, like, where am I? And where have I been? And what happened in those

different places? So we still have this neural mechanism in our brains. It's in the mammals,

it's in the hippocampus and enterinocortex, these are older parts of the brain. And these are very

well studied. We build a map of our environment. So these neurons in these parts of the brain know

where I am in this room and where the door was and things like that. So a lot of other mammals

have this kind of animal? All mammals have this, right? And almost any animal that knows where it

is and get around must have some mapping system, must have some way of saying, I've learned a map

of my environment. I have hummingbirds in my backyard. And they go to the same places all the

time. They must know where they are. They just know where they are. They're not just randomly

flying around. They know particular flowers they come back to. So we all have this. And it turns

out it's very tricky to get neurons to do this, to build a map of an environment. And so we now

know there's these famous studies that's still very active about place cells and grid cells and

these other types of cells in the older parts of the brain and how they build these maps of the

world. It's really clever. It's obviously been under a lot of evolutionary pressure over a long

period of time to get good at this. So animals know where they are. What we think has happened,

and there's a lot of evidence that suggests this, is that mechanism we learned to map

a space is was repackaged. The same type of neurons was repackaged into a more compact form.

And that became the cortical column. And it was in some sense,

genericized, if that's a word. It was turned into a very specific thing about learning maps

of environments to learning maps of anything, learning a model of anything, not just your

space, but coffee cups and so on. And it got sort of repackaged into a more compact version,

a more universal version, and then replicated. So the reason we're so flexible is we have a very

generic version of this mapping algorithm, and we have 150,000 copies of it. Sounds a lot like

the progress of deep learning. How so? To take neural networks that seem to work well for a

specific task, compress them, and multiply it by a lot. And then you just stack them on top of it.

It's like the story of transformers in natural language processing.

Deep learning networks, they end up, you're replicating an element, but you still need

the entire network to do anything. Here, what's going on, each individual element is a complete

learning system. This is why I can take a human brain, cut it in half, and it still works. It's

pretty amazing. It's fundamentally distributed. It's fundamentally distributed, complete modeling

systems. But that's our story we like to tell. I would guess it's likely largely right,

but there's a lot of evidence supporting that story, this evolutionary story.

The thing which brought me to this idea is that the human brain got big very quickly,

so that led to the proposal a long time ago that, well, there's this common element just

instead of creating new things, it just replicated something. We also are extremely flexible. We

can learn things that we had no history about. So that tells it that the learning algorithm is

very generic. It's very universal because it doesn't assume any prior knowledge about what it's

learning. So you combine those things together and you say, okay, well, how did that come about?

Where did that universal algorithm come from? It had to come from something that wasn't universal.

It came from something that was more specific. Anyway, this led to our hypothesis that you

would find grid cells and play cell equivalents in the New York Cortex. When we first published our

first papers on this theory, we didn't know of evidence for that. It turns out there was some,

but we didn't know about it. And since then, so then we became aware of evidence for grid cells

in certain parts of the New York Cortex. And then now there's been new evidence coming out. There's

some interesting papers that came out just January of this year. So one of our predictions was if

this evolutionary hypothesis is correct, we would see grid cell play cell equivalents, cells that

work like them through every column in the New York Cortex, and that's starting to be seen.

And what does it mean that why is it important that they're present?

Because it tells us, well, we're asking about the evolutionary origin of intelligence, right?

So our theory is that these columns in the Cortex are working on the same principles,

the modeling systems. And it's hard to imagine how neurons do this. And so we said, hey,

it's really hard to imagine how neurons could learn these models of things. We can talk about

the details of that if you want. But let's, but there's this other part of the brain, we know

the learned models of environments. So could that mechanism to learn to model this room be

used to learn to model the water bottle? Is it the same mechanism? So we said it's much more

likely the brain's using the same mechanism, which case it would have these equivalent cell types.

So it's basically the whole theory is built on the idea that these columns have reference frames

and they're learning these models and these grid cells create these reference frames. So it's

basically the major, in some sense, the major predictive part of this theory is that we will

find these equivalent mechanisms in each column in the near Cortex, which tells us that that's

what they're doing. They're learning these sensory model models of the world. So just

we're pretty confident that would happen. But now we're seeing the evidence.

So the evolutionary process nature does a lot of copy and paste and see what happens.

Yeah. Yeah, there's no direction to it. But, but it just found out like, Hey, if I took this,

these elements and made more of them, what happens? And let's hook them up to the eyes and

let's hook them to ears. And that seems to work pretty well for us. Again, just to take a quick

step back to our conversation of collective intelligence. Do you sometimes see that as just

another copy and paste aspect is copying pasting these brains and humans and making a lot of them

and then creating social structures that then almost operates as a single brain?

I wouldn't have said it, but you said it sounded pretty good.

So to you, the brain is fundamental is like, is it something?

Yeah. I mean, our goal is to understand how the neocortex works. We can argue how essential that

is to understanding human brain because it's not the entire human brain. You can argue how

essential that is to understanding human intelligence. You can argue how essential this

to, to, you know, sort of communal intelligence. I'm not, I didn't, our goal was to understand

the neocortex. Yeah. So what is the neocortex and where does it fit in the various aspect of

what the brain does? Like, how important is it to you? Well, obviously, again, we, I mentioned

again, in the beginning, it's, it's about 70 to 75% of the volume of a human brain. So it, you know,

it dominates our brain in terms of size, not in terms of number of neurons, but in terms of size.

Size isn't everything, Jeff. I know. But it's, it's nothing, it's nothing to be, it's not that.

We know that all high level vision, hearing and touch happens in neocortex. We know that

all language occurs and is understood in the neocortex, whether that's spoken language, written

language, sign language, whether language of mathematics, language of physics, music, math,

you know, we know that all high level planning and thinking occurs in the neocortex. If I were to

say, you know, what part of your brain designed a computer and understands programming and, and

creates music, it's all the neocortex. So then that's a kind of undeniable fact.

If, but then there's other parts of our brain are important too, right? Our emotional states,

our body regulating our body. So the way I like to look at it is, you know, could you, can you

understand the neocortex without the rest of the brain? And some people say you can't,

and I think absolutely you can. It's not that they're not interacting, but you can understand it.

Can you understand the neocortex without understanding the emotions of fear? Yes,

you can. You can understand how the system works. It's just a modeling system. I make the,

the analogy in the book that it's, it's like a map of the world and how that map is used

depends on who's using it. So how our map of our world in our neocortex, how we, how we

manifest as a human depends on the rest of our brain. What are our motivations? You know,

what are my desires? Am I a nice guy or not a nice guy? Am I a cheater or am I, you know,

not a cheater? You know, how important different things are in my life. So, so, but the neocortex

can be understood on its own. And, and I say that as a neuroscientist, I know there's all these

interactions and I want to say I don't know them and we don't think about them. But a layperson's

point of view, you can say it's a modeling system. I don't generally think too much about the communal

aspect of intelligence, which you brought up a number of times already. So that's not really

been my concern. I just wonder if there's a continuum from the origin of the universe, like

this pockets of complexities that form living organisms. I wonder if we're just, if you look

at humans, we feel like we're at the top. And I wonder if there's like just where everybody

probably, every living type pocket of complexity is probably thinks they're the part in the French,

they're the shit. They're at the top of the pyramid. Well, if they're thinking.

Well, and then what is thinking? Well, that in a sense, the whole point is in their sense of the

world, they, their sense is that they're at the top of it. I think what is the turtle?

But you're, you're, you're bringing up, you know, the problems of complexity and complexity theory

are, you know, it's a huge, interesting problem in science. And, you know, I think we've made

surprisingly little progress in understanding complex systems in general. And so, you know,

the Santa Fe Institute was founded to, to study this and even the scientists there will say it's

really hard. We haven't really been able to figure out exactly, you know, that science

hasn't really congealed yet. We're still trying to figure out the basic elements of that science.

What, you know, where does complexity come from and what is it and how you define it,

whether it's DNA, creating bodies or phenotypes or individuals creating societies or ants and,

you know, markets and so on. It's, it's a very complex thing. I'm not a complexity theorist

person, right? I, I think you need to ask, well, the brain itself is a complex system. So,

can we understand that? I think we've made a lot of progress understanding how the brain works.

So, but I haven't brought it out to like, oh, well, where are we on the complexity spectrum?

You know, it's like, it's a great question. I prefer for that answer to be, we're not special.

It seems like if we're honest, most likely we're not special. So, if there is a spectrum,

we're probably not in some kind of significant place. I think there's one thing we could say

that we are special. And again, only here on earth, I'm not saying I'm bad, is that if we

think about knowledge, what we know, we clearly, human brains have, the only brains have a certain

types of knowledge. We're the only brains on, on this earth to understand what the earth is,

how old it is, that the universe is a picture as a whole with the only organisms understand DNA and

the origins of, you know, of species. No other species on, on this planet has that knowledge.

So, if we think about, I like to think about, you know, one of the endeavors of humanity is to

understand the universe as much as we can. I think our species is further along in that,

undeniably, whether our theories are right or wrong, we can debate, but at least we have theories.

You know, we, we know that what the sun is and how fusion is and how what black holes are and,

you know, we know general theory and relativity and no other animal has any of this knowledge.

So, from that sense, we're special. Are we special in terms of the, the hierarchy of complexity in,

in the universe? Probably not.

Can we look at a neuron? Yeah, you say that prediction happens in the neuron. What does

that mean? So, the neuron tradition is seen as the basic element of the, the brain.

So, we, I mentioned this earlier, that prediction was our research agenda.

Yeah. We said, okay, how does the brain make a prediction? Like, I'm about to grab this water

bottle and my brain is predicting what I'm going to feel on, on all my parts of my fingers. If I

felt something really odd on any part here, I'd notice it. So, my brain is predicting what it's

going to feel as I grab this thing. So, what is that? How does that manifest itself in neural

tissue, right? We got brains made of neurons and there's chemicals and there's neurons and there's

spikes and the connect, you know, where is, where is the prediction going on? And one argument could

be that, well, when I'm predicting something, a neuron must be firing in advance. It's like, okay,

this neuron represents what you're going to feel and it's firing. It's sending a spike. And certainly,

that happens to some extent. But our predictions are so ubiquitous that we're making so many of them,

which we're totally unaware of. Just the vast majority of them have no idea that you're doing

this. That it, there wasn't really, we were trying to figure how could this be? Where are these,

where are these happening, right? And I won't walk you through the whole story unless you insist on

it, but we came to the realization that most of your predictions are occurring inside individual

neurons, especially these, the most common neuron, the pyramidal cells. And there are, there's a

property of neurons. I mean, everyone knows, or most people know that a neuron is a cell and it

has this spike called an action potential and it sends information. But we now note that there's

these spikes internal to the neuron. They're called dendritic spikes. They travel along the

branches of the neuron and they don't leave the neuron. They're just internal only. They're far

more dendritic spikes than there are action potentials, far more. They're happening all the

time. And what we came to understand that those dendritic spikes, the ones that are occurring,

are actually a form of prediction. They're telling the neuron, the neuron is saying, I expect that

I might become active shortly. And that internal, so the internal spike is a way of saying,

you're going to, you might be generating external spikes soon. I predicted you're going to become

active. And we've, we've, we wrote a paper in 2016, which explained how this manifests itself

in neural tissue and how it is that this all works together. But the vast, we think it's,

there's a lot of evidence supporting it. So we, that's where we think that most of these predictions

are internal. That's why you can't be, the internal neuron, you can't perceive them.

From understanding the, the prediction mechanism of a single neuron, do you think there's deep

insights to be gained about the prediction capabilities of the mini brains within the

bigger brain and the brain? Oh yeah. Yeah. Yeah. So having a prediction

side of their individual neuron is not that useful. You know, what, so what?

The way it manifests itself in neural tissue is that when a neuron, a neuron emits these spikes

are a very singular type of vent. If a neuron is predicting that it's going to be active,

it emits its spike very a little bit sooner, just a few milliseconds sooner than it would

have otherwise. It's like, I give the analogy to the book, it's like a sprinter on a starting

block in a race. And if someone says, get ready, set, you get up and you're ready to go. And then

when your race starts, you get a little bit earlier start. So that, it's that, that ready set is like

the prediction and the neurons like ready to go quicker. And what happens is when you have a whole

bunch of neurons together and they're all getting these inputs, the ones that are in the predictive

state, the ones that are anticipating to become active, if they do become active, they, they

happen sooner, they disable everything else and it leads to different representations in the brain.

So you have to, it's not isolated just to the neuron, the prediction occurs with the neuron,

but the network behavior changes. So what happens under different predictions, different inputs

have different representations. So how I, what I predict is going to be different under different

contexts, you know, what my input will be is different under different contexts. So this is,

this is a key to the whole theory, how this works. So the theory of the 1000 brains, if you were to

count the number of brains, how would you do it? The 1000 main theory says that basically every

cortical column in the, in your neocortex is a complete modeling system. And that when I ask

where do I have a model of something like a coffee cup, it's not in one of those models,

it's in thousands of those models. There's thousands of models of coffee cups. That's what

the 1000 brains, there's a voting mechanism, then there's a voting mechanism, which you

leads, which you're, which is the thing you're, which you're conscious of, which leads to your

singular perception. That's why you, you perceive something. So that's the 1000 brain theory.

The details of how we got to that theory are complicated. It wasn't, we just thought of it

one day. And one of those details that we had to ask, how does a model make predictions? And we

talked about just these predictive neurons. That's part of this theory. That's like saying, oh,

it's a detail, but it was like a crack in the door. It's like, how are we going to figure out how

these neurons build, do this? You know, what is going on here? So we just looked at prediction

as like, well, we know that's ubiquitous. We know that every part of the cortex is making

predictions. Therefore, whatever the predictive system is, it's going to be everywhere. We know

there's a gazillion predictions happening at once. So this is, we can start teasing apart,

you know, ask questions about, you know, how can neurons be making these predictions? And that

sort of built up to now what we have this 1000 brain theory, which is complex, you know, which

is, I can state it simply, but we just didn't think of it. We had to get there step by step,

very, it took years to get there. And where does reference frames fit in? So yeah.

Okay. So again, a reference frame, I mentioned earlier about the, you know, model of a house.

And I said, if you're going to build a model of a house in a computer, they have a reference

frame. And you can think of referencing like Cartesian coordinates, like X, Y and Z axes.

So I can say, oh, I'm going to design a house. I can say, well, the front door is at this location,

XYZ and the roof is at this location, XYZ and so on. That's the type of reference frame.

So it turns out for you to make a prediction and then I walk you through the thought experiment

in the book where I was predicting what my finger was going to feel when I touched a coffee cup,

was a ceramic coffee cup, but this one will do. And what I realized is that to make a prediction

when my finger is going to feel like it's going to feel different than this, which it feel different

if I touch the hole or the thing on the bottom, make that prediction. The cortex needs to know

where the finger is, the tip of the finger relative to the coffee cup and exactly relative to the

coffee cup. And to do that, I have to have a reference frame for the coffee cup. There has

to have a way of representing the location of my finger to the coffee cup. And then we realized,

of course, every part of your skin has to have a reference frame relative to things that touch.

And then we did the same thing with vision. But so the idea that a reference frame is necessary

to make a prediction when you're touching something or when you're seeing something

and you're moving your eyes or you're moving your fingers, it's just a requirement to know what to

predict. If I have a structure, I'm going to make a prediction. I have to know where it is.

I'm looking or touching it. So then we say, well, how do neurons make reference frames?

It's not obvious. You know, XYZ coordinates don't exist in the brain. It's just not the way it works.

So that's when we looked at the older part of the brain, the hippocampus and the enteronocortex,

where we knew that in that part of the brain, there's a reference frame for a room or a reference

frame for environment. Remember, I talked earlier about how you could make a map of this room.

So we said, oh, they are implementing reference frames there. So we knew that a reference

frames needed to exist in every quarter of a column. And so that was a deductive thing. We

just deduced it. So you take the old mammalian ability to know where you are in a particular

space and you start applying that to higher and higher levels. Yeah. You first you apply it to

like where your finger is. So here's what I think about it. The old part of the brain says,

where's my body in this room? Yeah. The new part of the brain says, where's my finger relative to

this object? Yeah. Where is a section of my retina relative to this object? I'm looking at one

little corner. Where is that relative to this patch of my retina? Yeah. And then we take the same

thing and apply the concepts, mathematics, physics, you know, humanity, whatever you want to think

of. And eventually you're pondering your own mortality. Well, whatever. But the point is,

when we think about the world, when we have knowledge about the world, how is that knowledge

organized, Lex? Where is it in your head? The answer is, it's in reference frames. So the way I

learned the structure of this water bottle, where the features are relative to each other,

when I think about history or democracy or mathematics, there's same basic underlying

structures happening. There's reference frames for where the knowledge that you're signing

things to. So in the book, I go through examples like mathematics and language and politics.

But the evidence is very clear in the neuroscience. The same mechanism that we used to model this

coffee cup, we're going to use to model high level thoughts. You're the demise of the humanity,

whatever you want to think about. It's interesting to think about how different are the representations

of those higher dimensional concepts, higher level concepts, how different the representation

there is in terms of reference frames versus spatial. But interesting thing, it's a different

application, but it's the exact same mechanism. But isn't there some aspect to higher level

concepts that they seem to be hierarchical? They just seem to integrate a lot of information

into them. So is our physical objects. So take this water bottle. I'm not particular to this

brand, but this is a Fiji water bottle, and it has a logo on it. I use this example in my book,

in my book, our company's coffee cup has a logo on it. But this object is hierarchical.

It's got like a cylinder and a cap, but then it has this logo on it, and the logo has a word.

The word has letters, the letters have different features. And so I don't have to remember,

I don't have to think about this. So I say, oh, there's a Fiji logo on this water bottle. I don't

have to go through and say, oh, what is the Fiji logo? It's the F and I and the J and I, and there's

a hibiscus flower. And oh, it has a, you know, the stamen on it. I don't have to do that. I just

incorporate all of that in some sort of hierarchical representation. I say, you know, put this logo on

this water bottle. And, and, and then the logo has a word and the word has letters, all hierarchical.

It's all that stuff is big. It's amazing that the brain instantly just does all that. The idea

that there's, there's water, it's liquid, and the idea that you can drink it when you're thirsty,

the idea that there's brands. And then there's like, all of that information is instantly

like built into the whole thing once you proceed. So I wanted to get back to your point about

hierarchical representation. The world itself is hierarchical, right? And I can take this

microphone in front of me. I know inside there's going to be some electronics. I know there's

going to be some wires and I know there's going to be a little dive from them was back and forth.

I don't see that, but I know it. So everything in the world is hierarchical. Just go into room.

It's composed of other components of kitchen has a refrigerator, you know, the refrigerator has a

door, the door has a hinge, the hinge has screws and pin. So anyway, the modeling system that

exists in every cortical column learns the hierarchical structure of objects. So it's a

very sophisticated modeling system in this grain of rice. It's hard to imagine, but this

grain of rice can do really sophisticated things. It's got a hundred thousand neurons in it.

It's very sophisticated. So that same mechanism that can model a water bottle or a coffee cup

can model conceptual objects as well. That's the beauty of this discovery that this guy,

Vernon Mountcastle, made many, many years ago, which is that there's a single cortical algorithm

underlying everything we're doing. So common sense concepts and higher level concepts are

all represented in the same way. They're set in the same mechanisms. It's a little bit like

computers, right? All computers are universal Turing machines. Even the little teeny one

that's in my toaster and the big one that's running some cloud servers someplace.

They're all running on the same principle. They can apply different things. So the brain is all

built on the same principle. It's all about learning these models, structured models using

movement and reference frames. And it can be applied to something as simple as a water bottle

and a coffee cup. And it can be like just thinking like, what's the future of humanity? And, you

know, why do you have a hedgehog on your desk? I don't know. Nobody knows. Well, I think it's

a hedgehog. That's right. It's a hedgehog in the fog. It's a Russian reference. Does it give you any

inclination or hope about how difficult that is to engineer common sense reasoning?

So how complicated is this whole process? So looking at the brain, is this a marvel of

engineering? Or is it pretty dumb stuff stacked on top of each other over a pretty extensive

copy? Can it be both? Can it be both, right? I don't know if it can be both, because if it's

an incredible engineering job, that means it's so evolution did a lot of work.

Yeah, but then it just copied that, right? So as I said earlier, the figuring out how to model

something like a space is really hard. And evolution had to go through a lot of trick

and these cells I was talking about, these grid cells and place cells, they're really complicated.

This is not simple stuff. This neural tissue works on these really unexpected weird mechanisms.

But it did it. It figured it out. But now you could just make lots of copies of it.

But then finding, yeah, so it's a very interesting idea that it's a lot of copies

of a basic mini brain. But the question is, how difficult it is to find that mini brain that

you can copy and paste effectively? Well, today, we know enough to build this. I'm sitting here with,

you know, I know the steps we have to go. There's still some engineering problems to solve,

but we know enough. And it's not like, Oh, this is an interesting idea, we have to go think about

it in other few decades. No, we actually understand it in pretty well details. So not all the details,

but most of them. So it's complicated, but it is an engineering problem. So in my company,

we are working on that. We are basically the roadmap, how we do this. It's not going to take

decades. It's better a few years, optimistically, but I think that's possible. It's, you know,

complex things. If you understand them, you can build them. So in which domain do you think it's

best to build them? Are we talking about robotics, like entities that operate in the physical world

that are able to interact with that world? Are we talking about entities that operate in the

digital world? Are we talking about something more like, more specific, like is done in the

machine learning community, where you look at natural language or computer vision?

Where do you think is easiest? It's the first, it's the first two more than the third one,

I would say. Again, let's just use computers as an analogy. The pioneers are computing people

like John Van Norman on Turing, they created this thing, you know, we now call the universal

Turing machine, which is a computer, right? Did they know how it was going to be applied,

where it was going to be used, you know, could they envision any of the future? No,

they just said, this is like a really interesting computational idea about algorithms and how you

can implement them in a machine. And we're doing something similar to that today, like we are,

we are building this sort of universal learning principle that can be applied to many, many

different things. But the robotics piece of that, the interactive.

Okay, all right. Let us be specific. You can think of this cortical column as what we call a

sensory motor learning system. It has the idea that there's a sensor, and then it's moving.

That sensor can be physical. It could be like my finger, and it's moving in the world. It could

be like my eye, and it's physically moving. It can also be virtual. So it could be, an example

would be, I could have a system that lives in the internet that that actually samples information

on the internet and moves by following links. That's, that's a sensory motor system. So

it's just something that echoes the process of a finger moving along.

But in a very, very loose sense, it's, it's like, again, learning is inherently about

the subring, the structure in the world and discover the structure in the world,

you have to move through the world, even if it's a virtual world, even if it's a conceptual world,

you have to move through it. You don't, it doesn't exist in one, it has some structure to it.

So here's, here's a couple of predictions that getting what you're talking about.

So in humans, the same algorithm does robotics, right? It moves my arms, my eyes, my body, right?

And so in my, in the future, to me, robotics and AI will merge. They're not going to be

separate fields because they're going to, the, the, the algorithms to really controlling robots

are going to be the same algorithms we have in our brain, that the brain, that these

sensory motor algorithms, today we're not there, but I think that's going to happen.

And, and then, so, but not all AI systems will have to be robotics. You can have systems that

have very different types of embodiments. Some will have physical movements, some will have

not have physical movements. It's a very generic learning system. Again, it's like computers,

the Turing machine is, it's like, it doesn't say how it's supposed to be implemented. It doesn't

tell you how big it is. It doesn't tell you what you can apply it to, but it's an interesting,

it's a computational principle. Cortical column equivalent is a computational principle about

learning. It's about how you learn and it can be applied to a gazillion things. This is, I think

this is, I think this impact of AI is going to be as large, if not larger than computing has been

in the last century, by far, because it's, it's getting at a fundamental thing. It's not a vision

system or a learning system. It's a, it's not a vision system or a hearing system. It is a learning

system. It's a fundamental principle, how you learn the structure in the world, how you gain

knowledge and be intelligent. And that's what the thousand brain says was going on. And we have a

particular implementation in our head, but it doesn't have to be like that at all. Do you think

there's going to be some kind of impact? Okay, let me ask it another way. What do increasingly

intelligent AI systems do with us humans in the following way? Like, how hard is the human in a

loop problem? How hard is it to interact the finger on the coffee cup equivalent of having a

conversation with a human being? So how hard is it to fit into our little human world?

I don't, I think it's a lot of engineering problems. I don't think it's a fundamental problem.

I could ask you the same question. How hard is it for computers to fit into a human world?

Right. That, I mean, that's essentially what I'm asking. Like, how much are we,

elitist are we as humans? Like, we tried to keep out systems?

I don't know. I'm not sure. I think I'm not sure that's the right question. Let's look at

computers as an analogy. Computers are million times faster than us. They do things we can't

understand. Most people have no idea what's going on when they use computers, right? How

we integrate them in our society? Well, we don't think of them as their own entity. They're not

living things. We don't afford them rights. We, we rely on them. Our survival as seven billion

people or something like that is relying on computers now.

Don't you think that's a fundamental problem that we see them as something we can't,

we don't give rights to? Computers?

So yeah, computers. So robots, computers, intelligent systems, it feels like for them to

operate successfully, they would need to have a lot of the elements that we would start having

to think about, like, should this entity have rights?

I don't think so. I think it's tempting to think that way. First of all, I don't think anyone,

hardly anyone thinks that's for computers today. No one says, oh, this thing needs a right. I

shouldn't be able to turn it off. Or, you know, if I throw it in the trash can, you know, and hit

it with a sledgehammer, my, my, for my criminal act, no, no one thinks that. And now we think

about intelligent machines, which is where you're going. And, and all of a sudden we're like, well,

now we can't do that. I think the basic problem we have here is that people think intelligent

machines will be like us. They're going to have the same emotions as we do, the same feelings as

we do. What if I can build an intelligent machine that have absolutely could care less about whether

it was on or off or destroyed or not? It just doesn't care. It's just like a map. It's just

a modeling system. It has no desires to live, nothing. Is it possible to create a system that

can model the world deeply and not care about whether it lives or dies? Absolutely. No question

about it. To me, that's not 100% obvious. It's obvious to me. So we can, we can debate if you

want. Where does your, where does your desire to live come from? It's an old evolutionary design.

I mean, we can argue, does it really matter if we live or not? Objectively no, right? We're all

going to die eventually. But evolution makes us want to live. Evolution makes us want to fight

to live. Evolutionists want to care and love one another and to care for our children and our,

and our relatives and our family and, and so on. And those are all good things. But they come about

not because we're smart, because we're animals that grew up. You know, the hummingbird in my

backyard cares about its offspring. You know, they, every living thing in some sense cares about,

you know, surviving. But when we talk about creating intelligent machines, we're not creating

life. We're not creating evolving creatures. We're not creating living things. We're just

creating a machine that can learn really sophisticated stuff. And that machine, it may even be able to

talk to us, but it doesn't, it's not going to have a desire to live unless somehow we put it

into that system. Well, there's learning, right? The thing is, but you don't learn to like want to

live. It's built into you. It's, well, people like Ernest Becker argue. So, okay, there's the fact

of finiteness of life. The way we think about it is something we learned, perhaps. So, okay.

Yeah. And some people decide they don't want to live. And some people decide, you know,

you can, but the desire to live is built in DNA, right? But I think what I'm trying to get to is,

in order to accomplish goals, it's useful to have the urgency of mortality. So what the Stoics

talked about is meditating in your mortality. It might be a very useful thing to do, to die

and have the urgency of death. And to realize the, to conceive yourself as an entity that operates

in this world that eventually will no longer be a part of this world. And actually conceive of

yourself as a conscious entity might be very useful for you to be a system that makes sense of the

world. Otherwise, you might get lazy. Well, okay. We're going to build these machines, right?

So, are we talking about building AI? But we're building the equivalent of the

cortical columns. The neocortex. The neocortex. And the question is, where do they arrive at?

Because we're not hard coding everything in. Well, in terms of, if you build the neocortex

equivalent, it will not have any of these desires or emotional states. Now, you could argue that

that neocortex won't be useful unless I give it some agency, unless I give it some desire,

unless I give it some motivation. Otherwise, you'll be just lazy to do nothing, right? You

could argue that. But on its own, it's not going to do those things. It's just not, it's just not

going to sit there and say, I understand the world. Therefore, I care to live. No, it's not going to

do that. It's just going to say, I understand the world. Why is that obvious to you? Do you think

it's possible? Okay, let me ask it this way. Do you think it's possible it will at least assign to

itself agency and perceive itself in this world as being a conscious entity as a useful way to

operate in the world and to make sense of the world? I think intelligent machine can be conscious,

but that does not, again, imply any of these desires and goals that you're worried about.

We can talk about what it means for a machine to be conscious.

By the way, not worry about, but get excited about. It's not necessarily that we should worry

about it. I think there's a legitimate problem or not a problem. A question asks, if you build

this modeling system, what's it going to model? What's its desire? What's its goal? What are we

applying it to? That's an interesting question. One thing, and it depends on the application.

It's not something that inherent to the modeling system. It's something we apply to the modeling

system in a particular way. If I wanted to make a really smart car, it would have to know about

driving in cars and what's important in driving in cars. It's not going to figure that out on its

own. It's not going to sit there and say, you know, I've understood the world and I've decided,

you know, no, no, no, we're going to have to tell it. We're going to have to say like,

so I imagine I make this car really smart. It learns about your driving habits. It learns

about the world. It's just, you know, is it one day going to wake up and say, you know what,

I'm tired of driving and doing what you want. I think I have better ideas about how to spend my

time. Okay. No, it's not going to do that. Well, part of me is playing a little bit of devil's

advocate, but part of me is also trying to think through this because I've studied cars quite a

bit and I've studied pedestrians and cyclists quite a bit. And as part of me that thinks

that there needs to be more intelligence than we realize in order to drive successfully,

that game theory of human interaction seems to require some deep understanding of human nature.

Okay. When a pedestrian crosses the street, there's some sense. They look at a car usually

and then they look away. There's some sense in which they say, I believe that you're not going

to murder me. You don't have the guts to murder me. This is the little dance of pedestrian car

interaction is saying, I'm going to look away and I'm going to put my life in your hands

because I think you're human. You're not going to kill me. And then the car in order to successfully

operate in like Manhattan streets has to say, no, no, no, no, I am going to kill you like a

little bit. There's a little bit of this weird inkling of mutual murder and that's a dance

and then somehow successfully operate through that. Do you think you were born of that?

Did you learn that social interaction? I think it might have a lot of the same elements that

you're talking about, which is we're leveraging things we were born with and applying them in the

context that I would have said that that kind of interaction is learned because people in

different cultures have different interactions like that. If you cross the street in different

cities and different parts of the world, they have different ways of interacting. I would say

that's learned and I would say an intelligent system can learn that too, but that does not

lead and the intelligent system can understand humans. It could understand that just like I can

study an animal and learn something about that animal. I could study apes and learn something

about their culture and so on. I don't have to be an ape to know that. I may not be completely,

but I can understand something. So intelligent machine can model that. That's just part of

the world. It's just part of the interactions. The question we're trying to get at, will the

intelligent machine have its own personal agency that's beyond what we assigned to it or its own

personal goals or will evolve and create these things? My confidence comes from understanding

the mechanisms I'm talking about creating. This is not hand wavy stuff. It's down in the details.

I'm going to build it and I know what it's going to look like and I know what's it going to behave.

I know what the kind of things it could do and the kind of things it can't do. Just like when I

build a computer, I know it's not going to on its own decide to put another register inside

of it. It can't do that. It's no way. No matter what your software does, it can't add a register

to the computer. So in this way, when we build AI systems, we have to make choices about how we

embed them. So I talk about this in the book. I said, intelligent system is not just a neocortex

equivalent. You have to have that, but it has to have some kind of embodiment, physical, virtual.

It has to have some sort of goals. It has to have some sort of ideas about dangers, about

things it shouldn't do like we build in safeguards in the systems. We have them in our bodies. We

have put them in the cars. My car follows my directions until the day it sees I'm about to

hit something and it ignores my directions and puts the brakes on. So we can build those things in.

So that's a very interesting problem, how to build those in. I think my differing opinion

about the risks of AI for most people is that people assume that somehow those things will

just appear automatically and it will evolve. And intelligence itself begets that stuff or

requires it. But it's not. Intelligence of the neocortex equivalent doesn't require this. The

neocortex equivalent just says, I'm a learning system. Tell me what you want me to learn. And

I'll ask me questions and I'll tell you the answers. But in that, again, it's like a map.

It doesn't, a map has no intent about things, but you can use it to solve problems.

Okay. So the building, engineering, the neocortex in itself is just creating an intelligent

prediction system. Modeling system. Sorry, modeling system. You can use it to then make

predictions. But you can also put it inside a thing that's actually acting in this world.

You have to put it inside something. Again, think of the map analogy. A map on its own

doesn't do anything. It's just inert. It can learn, but it's inert. So we have to embed

it somehow in something to do something. So what's your intuition here? You had a conversation

with Sam Harris recently that was sort of, you've had a bit of a disagreement and you're

sticking on this point. Elon Musk, Stuart Russell kind of have us worry existential

threats of AI. What's your intuition? Why, if we engineer an increasingly intelligent

neocortex type of system in the computer, why that shouldn't be a thing that we...

It was interesting to use the word intuition and Sam Harris used the word intuition too.

And when he used that intuition, that word immediately stopped and said,

that's the cut to the problem. He's using intuition. I'm not speaking about my intuition.

I'm speaking about something I understand, something I'm going to build, something I am

building, something I understand completely or at least well enough to know what it's all

I'm guessing. I know what this thing's going to do. And I think most people who are worried,

they have trouble separating out. They don't have the unknowledge or the understanding about

like, what is intelligence? How's it manifest in the brain? How's it separate from these other

functions in the brain? And so they imagine it's going to be human like or animal like.

It's going to have the same sort of drives and emotions we have, but there's no reason for that.

That's just because there's an unknown. If the unknown is like, oh my God,

I don't know what this is going to do. We have to be careful. It could be like us,

but really smarter. I'm saying, no, it won't be like us. It'll be really smarter,

but it won't be like us at all. But I'm coming from that not because I just

guessing I'm not using intuition. I'm basically like, okay, I understand this thing works.

This is what it does. It makes money to you. Okay. But to push back, so I also disagree with

the intuitions that Sam has, but so disagree with what you just said, which, you know,

what's a good analogy. So if you look at the Twitter algorithm in the early days,

just recommender systems, you can understand how recommender systems work. What you can't

understand in the early days is when you apply that recommender system at scale to thousands

and millions of people, how that can change societies. So the question is, yes, you're just

saying this is how an engineer in your cortex works, but when you have a very useful

TikTok type of service that goes viral, when your neural cortex goes viral, and then millions of

people start using it, can that destroy the world? No. Well, first of all, this is back,

one thing I want to say is that AI is a dangerous technology. I'm not denying that.

All technologies dangerous. Well, and AI, maybe particularly so. Okay. So

am I worried about it? Yeah, I'm totally worried about it. The thing where the narrow component

we're talking about now is the existential risk of AI. So I want to make that distinction because

I think AI can be applied poorly. It can be applied in ways that people are going to understand

the consequences of it. These are all potentially very bad things, but they're not the AI system

creating this existential risk on its own. And that's the only place that I disagree with other

people. Right. So I think the existential risk thing is humans are really damn good at surviving.

So to kill off the human race would be very, very different. Yes, but I'll go further. I don't think

AI systems are ever going to try to. I don't think AI systems are ever going to like say,

I'm going to ignore you. I'm going to do what I think is best. I don't think that's going to

happen, at least not in the way I'm talking about it. So the Twitter recommendation algorithm

is an interesting example. Let's use computers as an analogy again. I build a computer. It's a

universal computing machine. I can't predict what people are going to use it for. They can build

all kinds of things. They can even create computer viruses. It's all kinds of stuff.

So there's some unknown about its utility and about where it's going to go. But in the other

hand, I pointed out that once I build a computer, it's not going to fundamentally change how it

computes. It's like, I use the example of a register, which is a part internal part of a

computer. You know, I say it can't just say it because computers don't evolve. They don't replicate.

They don't evolve. They don't, you know, the physical manifestation of the computer itself

is not going to, there's certain things that can't do. Right. So we can break into things like

things that are possible to happen we can't predict and things that are just impossible to

happen. Unless we go out of our way to make them happen, they're not going to happen unless somebody

makes them happen. Yeah. So there's, there's a bunch of things to say. One is the physical

aspect, which you're absolutely right. We have to build a thing for it to operate in the physical

world and you can just stop building them. You know, the moment they're not doing the thing you

want them to do or just change the design or change the design. The question is, I mean,

there's, it's possible in the physical world, this is probably longer term is you automate the

building. It makes, it makes a lot of sense to automate the building. There's a lot of factories

that are doing more and more and more automation to go from raw resources to the final product.

It's possible to imagine that obviously much more efficient to keep, to create a factory

that's creating robots that do something, you know, they do something extremely useful for society.

It could be personal assistance. It could be, it could be, it could be your toaster, but a toaster

that's much has a deeper knowledge of your culinary preferences. And that could. Well,

I think now you've hit on the right thing. The real thing we need to be worried about next is

self replication. Right. That is the thing that we're in the physical world or even the virtual

world. Self replication, because self replication is dangerous. It's probably more likely to be

killed by a virus, you know, or a human engineered virus. Anybody can create a, you know, this

technology is getting so almost anybody, well, not anybody, but a lot of people could create

a human engineered virus that could wipe out humanity. That is really dangerous. No intelligence

required. Just self replication. So, so we need to be careful about that. So when I think about,

you know, AI, I'm not thinking about robots building robots. Don't do that. Don't build a,

you know, just. Well, that's because you're interested in creating intelligence. It seems

like self replication is a good way to make a lot of money. Well, fine. But so is, you know,

maybe editing viruses is a good way too. I don't know. The point is, if as a society,

when we want to look at existential risks, the existential risks we face that we can control

almost all evolve around self replication. Yes. The question is, I don't see a good way to make

a lot of money by engineering viruses and deploying them on the world. There could be,

there could be applications that are useful. But let's separate out. Let's separate out. I mean,

you don't need to. You only need some, you know, terrorists who want to do it because it doesn't

take a lot of money to make viruses. Let's just separate out what's risky and what's not risky.

I'm arguing that the intelligence side of this equation is not risky. It's not risky at all.

It's the self replication side of the equation that's risky. And I'm not dismissing that. I'm

scared as hell. It's like the paperclip maximizer thing. Those are often like talked about in the

same conversation. I think you're right. Like creating ultra intelligence, super intelligent

systems is not necessarily coupled with a self replicating, arbitrarily self replicating

systems. Yeah. And you don't get evolution unless you're self replicating. Yeah. And so I think

that's just this argument that people have trouble separating those two out. They just think, oh,

yeah, intelligence looks like us. And look how, look at the damage we've done to this planet.

Like how we've, you know, destroyed all these other species. Yeah. Well, we replicate,

which 8 billion of us or 7 billion of us now. I think the idea is that the more intelligent

we're able to build systems, the more tempting it becomes from a capitalist perspective of creating

products, the more tempting it becomes to create self reproducing systems.

All right. So let's say that's true. So does that mean we don't build intelligent systems? No,

that means we regulate, we understand the risks, we regulate them. You know, look, there's a lot

of things we could do as society, which have some sort of financial benefit to someone,

which could do a lot of harm. And we have to learn how to regulate those things.

We have to learn how to deal with those things. I will argue this. I would say the opposite.

Like I would say having intelligent machines at our disposal will actually help us in the end more

because it'll help us understand these risks better, help us mitigate these risks better.

There might be ways of saying, oh, well, how do we solve climate change problems? You know,

how do we do this or how do we do that? That just like computers are dangerous in the hands of the

wrong people, but they've been so great for so many other things, we live with those dangers.

And I think we have to do the same with intelligent machines. But we have to be

constantly vigilant about this idea of A, bad actors doing bad things with them and B,

don't ever, ever create a self replicating system. And by the way, I don't even know

if you could create a self replicating system that uses a factory that's really dangerous.

You know, nature's way of self replicating is so amazing.

You know, it doesn't require anything. It just, you know, the thing and resources and it goes,

right? Yeah. If I said to you, you know what, we have to build, our goal is to build a factory

that can make, that builds new factories. And it has to end the end supply chain. It has to

bind the resources, get the energy. I mean, that's really hard. You know, no one's doing that in the

next, you know, 100 years. I've been extremely impressed by the efforts of Elon Musk and Tesla

to try to do exactly that, not from raw resource. Well, he actually, I think states the goal is

to go from raw resource to the final car in one factory. That's the main goal. Of course,

it's not currently possible, but they're taking huge leaps. Well, he's not the only one to do that.

This has been a goal for many industries for a long, long time.

It's difficult to do. Well, a lot of people, what they do is instead they have like

a million suppliers and then they, like there's everybody's management.

They all co locate them and they kind of systems together.

It's a fundamental distributed system. I think that's, that also is not getting at the issue

I was just talking about, which is self replication. I mean, self replication means

there's no entity involved other than the entity that's replicating.

Right. And so if there are humans in this, in the loop, that's not really self replicating, right?

It's, unless somehow we're duped into it. But it's also don't necessarily

agree with you because you've kind of mentioned that AI will not say no to us.

I just think they will. Yeah. Yeah. So like, I think it's a useful feature to build in.

I'm just trying to like put myself in the mind of engineers to sometimes say no,

you know, if you, I get an example earlier, right? I get an example of my car, right?

My car turns the wheel and applies the accelerator and the brake as I say,

until it decides there's something dangerous. Yes. And then it doesn't do that.

Yeah. Now, that was something it didn't decide to do. It's something we programmed into the car.

And so good. It's a good idea, right? The question again, isn't like,

if we create an intelligent system or ever ignore our commands, of course we will sometimes.

Is it going to do it because it came up with its own goals that serve its purposes and it

doesn't care about our purposes? No, I don't think that's going to happen.

Okay. So let me ask you about these super intelligent cortical systems that we engineer

and us humans. Do you think with these entities operating out there in the world,

what is the future, most promising future look like? Is it us merging with them?

Or is it us? Like, how do we keep us humans around when you have increasingly intelligent

beings? Is it one of the dreams is to upload our minds in the digital space? So can we just

give our minds to these systems so they can operate on them? Is there some kind of more

interesting merger or is there more? In the third part of my book, I talked about all these scenarios

and let me just walk through them. Sure. The uploading the mind one. Yes.

Extremely, really difficult to do. Like, we have no idea how to do this even remotely right now.

So it would be a very long way away, but I make the argument you wouldn't like the result.

And you wouldn't be pleased with the result. It's really not what you think it's going to be.

Imagine I could upload your brain into a computer right now and now the computer's

sitting there going, Hey, I'm over here. Great. Get rid of that old bio person. I don't need

them. You're still sitting here. Yeah. What are you going to do? No, no, that's not me. I'm here.

Right. Yeah. Are you going to feel satisfied? Then you, but people imagine, look, I'm on my

deathbed and I'm about to, you know, expire and I pushed the button and now I'm uploaded. But

think about it a little differently. And so I don't think it's going to be a thing because

people by the time we're able to do this, if ever, because you have to replicate the entire body,

not just the brain, it's, it's really, it's, I walk through the issues. It's really substantial.

Do you have a sense of what makes us us? Is there, is there a shortcut to it can only save a certain

part that makes us truly arts? No, but I think that machine would feel like it's you too.

Right. Right. If you people just like, I have a child, right? I have two daughters.

They're independent people. I created them. Well, partly. Yeah. And

I don't, just because they're somewhat like me, I don't feel on them and they don't feel

like I'm me. So if you split the part, you have two people. So we can tell them to come back to

what makes, what consciousness we want. We can talk about that, but we don't have a remote

consciousness. I'm not sitting there going, oh, I'm conscious of that. I'm in that system over there.

So let's, let's stay on our topic. So one was uploading a brain. Yeah.

It ain't going to happen in a hundred years, maybe a thousand, but I don't think people are going to

want to do it. The merging your mind with, uh, you know, the neural link thing, right? Like,

again, really, really difficult. It's, it's one thing to make progress to control a prosthetic

arm. It's another to have like a billion or several billion, you know, things and understanding what

those signals mean. Like it's the one thing to like, okay, I can learn to think some patterns

to make something happen. It's quite another thing to have a system, a computer, which actually

knows exactly what cells it's talking to and how it's talking to them and interacting in a way

like that. Very, very difficult. We're not getting anywhere closer to that.

Interesting. Can I, can I ask a question here? So for me, what makes that merger very difficult

practically in the next 10, 20, 50 years is like literally the biology side of it, which is like,

it's just hard to do that kind of surgery in a safe way. But your intuition is even the machine

learning part of it, where the machine has to learn what the heck it's talking to. That's even

hard. I think it's even harder. And it's not, it's, it's easy to do when you're talking about

hundreds of signals. It's, it's a totally different thing to say talking about billions of signals.

So you don't think it's the raw, it's a machine learning problem. You don't think it could be

learned? Well, I'm just saying, no, I think you'd have to have detailed knowledge. You'd have to

know exactly what the types of neurons you're connecting to. I mean, in the brain, there's these

neurons that do all different types of things. It's not like a neural network. It's a very

complex organism system up here. We talked about the grid cells or the place cells, you know,

you have to know what kind of cells you're talking to and what they're doing and how their

timing works and all, all this stuff, which you can't today is no way of doing that, right?

But I think it's, I think it's a, I think the problem, you're right that the biological aspect

of like who wants to have a surgery and have this stuff inserted in your brain, that's a problem.

But this is when we solve that problem. I think the, the information coding aspect is much worse.

I think that's much worse. It's not like what they're doing today. Today, it's simple machine

learning stuff because you're doing simple things. But if you want to merge your brain,

like I'm thinking on the internet, I'm merged my brain with the machine and we're both doing,

that's a totally different issue. That's interesting. I tend to think if the, okay,

yeah, if you have a super clean signal from a bunch of neurons at the start, you don't know

what those neurons are. I think that's much easier than the getting of the clean signal.

I think if you think about today's machine learning, that's what you would conclude.

I'm thinking about what's going on in the brain and I don't reach that conclusion. So we'll have

to see. Sure. But I don't think even, even then, I think there's kind of a sad future.

Like, you know, do I, do I have to like plug my brain into a computer? I'm still a biologic

organism. I assume I'm still going to die. So what, what have I achieved? Right? You know,

what have I achieved to doing some sort of? Oh, I disagree. We don't know what those are, but it

seems like there could be a lot of different applications. It's like virtual reality is

to expand your brain's capability to like read Wikipedia. Yeah, but fine. But you're still

a biologic organism. Yes. Yes. You're still, you're still mortal. You're still all right. So,

so what are you accomplishing? You're making your life in this short period of time better,

right? Just like having the internet made our life better. Yeah. Yeah. Okay. So I think that's

of, of, if I think about all the possible gains we can have here, that's a marginal one. It's

an individual, hey, I'm better, you know, I'm smarter. But you'll find I'm not against it.

I just don't think it's earth changing. I, but, but so this is the true of the internet.

When each of us individuals are smarter, we get a chance to then share our smartness.

We get smarter and smarter together as like, as a collective. This is kind of like the

same colony. Why don't I just create an intelligent machine that doesn't have any of this biological

nonsense? This is all the same. It's, it's everything except don't burden it with my brain.

Yeah. Right. It has a brain. It is smart. It's like my child, but it's much, much smarter than

me. So I have a choice between doing some implant, doing some hybrid weird, you know,

biological thing that's bleeding and all these problems and limited by my brain or creating

a system which is super smart that I can talk to that helps me understand the world that can read

the internet, you know, read Wikipedia and talk to me. I guess my, the open questions there are

what does the manifestation of superintelligence look like? So like, what are we going to,

you talked about, why do I want to merge with AI? Like, what, what's the actual marginal benefit

here? If I, if we have a super intelligent system, yeah, how will it make our life better?

So let's, let's, that's a great question, but let's break it onto little pieces. All right.

On the one hand, it can make our life better in lots of simple ways. You mentioned like a care robot

or something that helps me do things, a cook side, I don't know what it does, right? Little things

like that. We have super better, smarter cars. We can have, you know, better agents, aids helping

us in our work environment and things like that. To me, that's like the easy stuff, the simple stuff

in the beginning. And so in the same way that computers made our lives better in ways, many,

many ways, I will have those kind of things. To me, the really exciting thing about AI is

sort of its transcendent, transcendent quality in terms of humanity. We're still biological

organisms. We're still stuck here on earth. It's going to be hard for us to live anywhere else.

I don't think you and I are going to want to live on Mars anytime soon. And, and we're flawed,

you know, we may end up destroying ourselves. It's totally possible. We, if not completely,

we could destroy our civilizations. You know, it's just face the fact that we have issues here,

but we can create intelligent machines that can help us in various ways. For example,

one example I gave, and that sounds a little sci fi, but I believe this, if we really want to

live on Mars, we'd have to have intelligent systems that go there and build the habitat for us,

not humans. Humans are never going to do this. It's just too hard. But could we have a thousand or

10,000, you know, engineer workers up there doing this stuff, building things, terraforming Mars?

Sure. Maybe we can move Mars. But then if we want to, if we want to go around the universe,

should I send my children around the universe? Or should I send some intelligent machine,

which is like a child that represents me and understands our needs here on earth that could

travel through space? So it sort of, in some sense, intelligence allows us to transcend the

limitations of our biology. And don't think of it as a negative thing. It's in some sense,

my children transcend my biology too, because they live beyond me. And they represent me,

and they also have their own knowledge, and I can impart knowledge to them. So intelligent

machines would be like that too, but not limited like us. But the question is, there's so many

ways that transcendence can happen. And the merger with AI and humans is one of those ways. So you

said intelligent, basically beings or systems propagating throughout the universe representing

us humans. They represent us humans in the sense they represent our knowledge and our history,

not us individually. Right, right. But I mean, the question is, is it just a database

with the really damn good model of the world? No, no, they're conscious, just like us.

Okay. But just different. They're different. Just like my children are different. They're like me,

but they're different. These are more different. I guess maybe I've already, I kind of, I take

a very broad view of our life here on Earth. I say, you know, why are we living here? Are we

just living because we live? Are we surviving because we can survive? Are we fighting just

because we want to just keep going? What's the point of it? Right? So to me, the point,

if I ask myself, what's the point of life is, what transcends that ephemeral sort of biological

experience is to me, this is my answer, is the acquisition of knowledge to understand more

about the universe and to explore. And that's partly to learn more, right? I don't view it as

a terrible thing if the ultimate outcome of humanity is we create systems that are intelligent,

that are offspring, but they're not like us at all. And we stay here and live on Earth as long

as we can, which won't be forever, but as long as we can. And, but that would be a great thing

to do. It's not, it's not like a negative thing. Well, would you be okay then if the human

species vanishes, but our knowledge is preserved and keeps being expanded by intelligent systems?

I want our knowledge to be preserved and expanded. Yeah. Am I okay with humans dying? No, I don't

want that to happen. But if it does happen, what if we were sitting here and this is the

last two people on Earth who were saying, Lex, we blew it, it's all over, right? Wouldn't I feel

better if I knew that our knowledge was preserved and that we had agents that knew about that,

that were trans, you know, that left Earth? I would want that. It's better than not having that.

You know, I make the analogy of like, you know, the dinosaurs, the poor dinosaurs, they live for,

you know, tens of millions of years. They raised their kids. They, you know, they,

they fought to survive. They were hungry. They, they did everything we do. And then they're all

gone. Yeah. Like, you know, and, and if we didn't discover their bones, nobody would ever know that

they ever existed, right? Do we want to be like that? I don't want to be like that. There's a sad

aspect to it. And it kind of is jarring to think about that it's possible that a human like intelligent

civilization has previously existed on Earth. The reason I say this is like, it is jarring to think

that we would not, if they weren't extinct, we wouldn't be able to find evidence of them.

After a sufficient amount of time. After a sufficient amount of time. Of course, there's like,

like basically humans, like if we destroy ourselves now, human civilization destroy ourselves now,

after a sufficient amount of time, we would not be, we'd find the evidence of the dinosaurs.

We would not find evidence of us humans. Yeah. That's kind of an odd thing to think about. Although

I'm not sure if we have enough knowledge about species going back through billions of years,

but we could, we could, we might be able to eliminate that possibility. But it's an interesting

question. Of course, this is a similar question to, you know, there were lots of intelligent

species about our galaxy that have all disappeared. Yeah. That's super sad that they're exactly that

there may have been much more intelligent alien civilizations in our galaxy. There are no longer

there. Yeah. You actually talked about this, that humans might destroy ourselves. Yeah. And how we

might preserve our knowledge and advertise that knowledge to other. Advertise is a funny word

to use. From a PR perspective. There's no financial gain in this.

You know, like make it like from a tourism perspective, make it interesting. Can you

describe how? Well, there's a couple things. I broke it down into two parts, actually three

parts. One is, you know, there's a lot of things we know that what if we were, what if we ended,

what if our civilization collapsed? Yeah, I'm not talking tomorrow. Yeah, we could be a thousand

years from that. Like, you know, we don't really know. But historically, it would be likely at

some point. Time flies when you're having fun. Yeah. You know, could we, and then intelligent

life evolved again on this planet? Wouldn't they want to know a lot about us and what we knew?

Wouldn't they wouldn't be able to ask us questions? So one very simple thing I said,

how would we archive what we know? That was a very simple idea. I said, you know what,

it wouldn't be that hard to put a few satellites, you know, going around the sun and we upload

Wikipedia every day and that kind of thing. So, you know, if we end up killing ourselves,

well, it's up there and the next intelligence piece will find it and learn something. They

would like that. They would appreciate that. So that's one thing. The next thing I said, well,

what if, you know, how we're outside of our solar system? We have the SETI program. We're

looking for these intelligent signals from everybody. And if you do a little bit of math,

which I did in the book, and you say, well, what if intelligent species only live for 10,000 years

before, you know, technologically intelligent species? Like, ones are really able to do the

task we're just starting to be able to do. Well, the chances are we wouldn't be able to see any of

them because they would have all been disappeared by now. They've lived for 10,000 years and now

they're gone. And so we're not going to find these signals being sent from these people because

if I say, what kind of signal could you create that would last a million years or a billion years?

That someone would say, damn it, someone smart lived there. We know that. That would be a life

changing event for us to figure that out. Well, what we're looking for today in the SETI program

isn't that. We're looking for very coded signals in some sense. And so I asked myself, what would

be a different type of signal one could create? I've always thought about this throughout my life

and in the book I gave one possible suggestion, which was we now detect planets going around

other suns, other stars, excuse me. And we do that by seeing this slight dimming of the light as

the planets move in front of them. That's how we detect planets elsewhere in our galaxy.

What if we created something like that that just rotated around the sun and it blocked out a little

bit of light in a particular pattern that someone said, hey, that's not a planet. That is a sign

that someone was once there. You can say, what if it's beating up pi, three point, whatever.

So I did it from a distance, broadly broadcast, takes no continue activation on our part. This

is the key, right? No one has to be seeing a running computer and supplying it with power.

It just goes on. So we go, it's continuous. And I argued that part of the SETI program

should be looking for signals like that. And to look for signals like that, you ought to figure

out how would we create a signal? Like, what would we create that would be like that, that would

persist for millions of years, that would be broadcast broadly that you could see from a

distance that was unequivocal, came from an intelligent species. And so I gave that one

example because they don't know what to know of actually. And then finally, right,

if, if our, ultimately our solar system will die at some point in time, you know, how do we go

beyond that? And I think it's possible, if at all possible, we'll have to create intelligent machines

that travel throughout the solar system or throughout the galaxy. And I don't think that's

going to be humans. I don't think it's going to be biological organisms. So these are just

things to think about, you know, like, what's the, you know, I don't want to be like the dinosaur.

I don't want to just live in, okay, that was it. We're done, you know. Well, there is a kind of

presumption that we're going to live forever, which I think it is a bit sad to imagine that the

message we send as, as he talked about is that we were once here instead of we are here. Well,

it could be we are still here. But it's more of a, it's more of an insurance policy in case we're

not here, you know? Well, I don't know, but there's something I think about, we humans don't often

think about this, but it's like, like, whenever I record a video, I've done this a couple of times

in my life, I've recorded a video for my future self, just for personal, just for fun. And it's

always just fascinating to think about that, preserving yourself for future civilizations. For

me, it was preserving myself for a future me, but that's a little, that's a little fun example

of archival. Well, these podcasts are preserving you and I in a way, for future, hopefully well

after we're gone. But you don't often, we're sitting here talking about this. You are not

thinking about the fact that you and I are going to die, and there'll be like 10 years after somebody

watching this, and we're still alive. You know, in some sense, I do. I'm here because I want to

talk about ideas. And these ideas transcend me, and they transcend this time and on our planet.

We're talking here about ideas that could be around a thousand years from now or a million years

from now. When I wrote my book, I had an audience in mind, and one of the clearest audiences was

aliens. No, were people reading this a hundred years from now? Yes. I said to myself, how do I

make this book relevant to somebody reading this a hundred years from now? What would they want to

know that we were thinking back then? What would make it like that was an interesting, it's still

an interesting book. I'm not sure I can achieve that, but that was how I thought about it because

these ideas, especially in the third part of the book, the ones we were just talking about,

you know, these crazy, it sounds like crazy ideas about, you know, storing our knowledge and,

and, you know, merging our brains or computers and sending, you know, our machines out into space,

is not going to happen in my lifetime. And they may not, and they may not happen in the next

hundred years. They may not happen for a thousand years. Who knows? But we have the unique opportunity

right now, we, you, me, and other people like this, to sort of at least propose the agenda

that might impact the future like that. It's a fascinating way to think, both like writing or

creating, try to make, try to create ideas, try to create things that hold up in time. Yeah. You

know, understanding how the brain works, we're going to figure that out once. That's it. It's

going to be figured out once. And after that, that's the answer. And people will, people will study

that thousands of years now. We still, we still, you know, venerate Newton and, and Einstein and,

and, you know, because, because ideas are exciting even well into the future. Well, the interesting

thing is like big ideas, even if they're wrong, are still useful. Like, yeah, especially if they're

not completely wrong. Like you're right, right, right. Right. Noons laws are not wrong. They're

just Einstein's are better. So, um, let's see. Yeah. I mean, but we're talking with Newton and

Einstein. We're talking about physics. I wonder if we'll ever achieve that kind of clarity about

understanding, um, like complex systems and the, this particular manifestation of complex systems,

which is the human brain. I'm, I'm totally optimistic we can do that. I mean, we're making

progress at it. I don't see any reason why we can't completely, I mean, completely understand in the

sense, um, you know, we don't really completely understand what all the molecules in this water

bottle are doing, but, you know, we have laws that sort of capture it pretty good. Um, and, uh,

so we'll have that kind of understanding. I mean, it's not like you're going to have to know what

every neuron in your brain is doing. Um, but enough to, um, first of all, to build it and second of

all, to do, you know, do what physics does, which is like have concrete experiments where we can

validate. We're, we're, we're, this is happening right now. Like it's not, this is not some future

thing. Um, you know, I'm very optimistic about, I'm, I know about our, our work and what we're

doing. We'll have to prove it to people. Um, but, um, I consider myself a rational person and, um,

you know, until fairly recently, I wouldn't have said that, but right now I'm, where I'm sitting

right now, I'm saying, you know, we, this is going to happen. There's, there's no big obstacles to

it. Um, we finally have a framework for understanding what's going on in the cortex and, um, and

that's liberating. It's, it's like, oh, it's happening. So I can't see why we wouldn't be able

to understand it. I just can't. Okay. Oh, so, I mean, on that topic, let me ask you to play devil's

advocate. Is it possible for you to imagine luck, look a hundred years from now and looking at your

book, uh, in which ways might your ideas be wrong? Oh, I worry about this all the time. Um,

yeah, it's still useful. Yeah. Yeah.

Um, I think there's, you know, um, well, I can, I can best relate it to like things I'm worried

about right now. So we talk about this voting idea, right? It's happening. There's, there's no

question it's happening, but it could be far more, um, uh, there's, there's enough things I

don't know about it that it might be working in different ways differently than I'm thinking about

the kind of what's voting, who's voting, you know, where are representations. I talked about,

like you have a thousand models of a coffee cup like that. That could turn out to be wrong, um,

because it may be, maybe there are a thousand models that are sub models, but not really a

single model of the coffee cup. Um, I mean, there's things that these are all sort of on the edges,

things that I present as like, oh, it's so simple and clean. Well, that's not that. It's always going

to be more complex. And, um, and there's parts of the theory, which I don't understand the

complexity well. So I think, I think the idea that the brain is a distributed modeling system is

not controversial at all, right? That's not, that's well understood by many people. The question then

is, are each quarterly column an independent modeling system? Right. Um, I could be wrong about

that. Um, I don't think so, but I worry about it. My intuition, not even thinking why you could be

wrong is the same intuition I have about any sort of physicist, uh, like strength theory, that we,

as humans, desire for a clean explanation. And, uh, a hundred years from now, uh, intelligent systems

might look back at us and laugh at how we try to get rid of the whole mess by having simple

explanation. When the reality is, it's, it's way messier. And in fact, it's impossible to understand

you can only build it. It's like this idea of complex systems and cellular automata,

you can only launch the thing, you cannot understand it. Yeah. I think that, you know,

the history of science suggests that's not likely to occur. Um, the history of science suggests that

like as a theorist and we're theorists, you look for simple explanations, right? Fully knowing

that whatever simple explanation you're going to come up with is not going to be completely correct.

I mean, it can't be. I mean, it's just, it's just more complexity. But that's the role of theorists

play. They, they sort of, they give you a framework on which you now can talk about a problem and

figure out, okay, now we can start digging more details. The best frameworks stick around while

the details change. You know, again, you know, the classic example is Newton and Einstein, right?

You know, um, Newton's theories are still used. They're still valuable. They're still practical.

They're not like wrong. Just they've been refined. Yeah. But that's in physics. It's not obvious,

by the way, it's not obvious for physics either that the universe should be such that's amenable

to these simple, but it's so far it appears to be as far as we can tell. Um, yeah. I mean, but

as far as we could tell, and, but it's also an open question whether the brain is amenable to

such clean theories. That's the brain, but intelligence. Well, I, I, I don't know. I would

take intelligence out of it. Just say, you know, um, well, okay. Um, the evidence we have suggested

that the human brain is, is, is a, at the one time extremely messy and complex, but there's

some parts that are very regular and structured. That's why we started the neocortex. It's

extremely regular in its structure. Yeah. And unbelievably so. And then I mentioned earlier,

the other thing is it's, it's universal abilities. It is so flexible to learn so many things. We

don't, we haven't figured out what it can't learn yet. We don't know, but we haven't figured out

yet, but he learns things that never was evolved to learn. So those give us hope. Um, that's why

I went into this field because I said, you know, this regular structure, it's doing this amazing

number of things. There's got to be some underlying principles that are, that are common

and other, other scientists have come up with the same conclusions. Um, and so it's promising.

It's promising. And, um, and that's, and whether the theories play out exactly this way or not,

that is the role that theorists play. And so far it's worked out well, even though, you know,

maybe, you know, we don't understand all the laws of physics, but so far it's been pretty damn

useful. The ones we have are, our theories are pretty useful. You mentioned that, uh, we should

not necessarily be at least to the degree that we are worried about the existential risks of

artificial intelligence relative to, uh, human risks from human nature being existential risk.

What aspect of human nature worries you the most in terms of the survival of the human species?

I mean, I'm disappointed in humanity as humans. I mean, all of us, I'm, I'm one, so I'm disappointed

myself too. Um, it's kind of a sad state. There's, there's two things that disappoint me. One is

how it's difficult for us to separate our rational component of ourselves from our evolutionary

heritage, which is, you know, not always pretty. You know, rape is a, is an evolutionary good

strategy for reproduction. Murder can be at times too. You know, making other people miserable

at times is a good strategy for reproduction. It's just, and it's just, and, and so now that we know

that, and yet we have this sort of, you know, we, you and I can have this very rational discussion

talking about, you know, intelligence and brains and life and so on. Some, it seems like it's so hard.

It's just a big transition to get humans, all humans to, to make the transition from like,

let's pay no attention to all that ugly stuff over here. Let's just focus on the

incident. What's unique about humanity is our knowledge and our intellect.

But the fact that we're striving isn't itself amazing, right? The fact that we're able to

overcome that part and it seems like we are more and more becoming successful at overcoming that

part. That is the optimistic view and I agree with you. Yeah. But I worry about it. I'm not saying,

I'm worrying about it. I think that was your question. I still worry about it. Yes. You know,

we could be, and tomorrow because some terrorists could get nuclear bombs and, you know,

blow us all up. Who knows, right? The other thing I think I'm disappointed is, and it's just,

I understand it. It's, I guess you can't really be disappointed. It's just a fact,

is that we're so prone to false beliefs. We, you know, we have a model in our head,

the things we can interact with directly, physical objects, people, that model is pretty good.

And we can test it all the time, right? I touch something, I look at it, talk to you, see

my model is correct. But so much of what we know is stuff I can't directly interact with. I can't,

I don't even know because someone told me about it. And so, so we're prone, inherently prone to

having false beliefs because if I'm told something, how am I going to know it's right or wrong, right?

And so then we have the scientific process, which says we are inherently flawed. So the only way we

can get closer to the truth is by looking for contrary evidence. Yeah. Like this conspiracy

theory, this, this theory that scientists keep telling me about that the earth is round.

As far as I can tell, when I look out, it looks pretty flat. Yeah. So yeah, there's, there's

attention, but it's also, I tend to believe that we haven't figured out most of this thing, right?

Most of nature around us is a mystery. And so it, but that doesn't work. Does that worry you?

I mean, it's like, oh, that's, that's like a pleasure more to figure out, right? Yeah,

that's exciting. But I'm saying like, there's going to be a lot of quote unquote, wrong ideas.

I mean, I've been thinking a lot about engineering systems like social networks and so on. And I've

been worried about censorship and thinking through all that kind of stuff because there's a lot of

wrong ideas. There's a lot of dangerous ideas, but then I also read a history, read history and see

when you censor ideas that are wrong. Now, this could be a small scale censorship, like a young

grad student who comes up, who like raises their hand and says some crazy idea. It's a form of

censorship could be, I shouldn't use the word censorship, but like de incentivize them from,

no, no, no, no, this is the way it's been done. Yeah, you're foolish kid, don't think so. Yeah,

you're foolish. So in some sense, those wrong ideas most of the time end up being wrong,

but sometimes end up being. I agree with you. So I don't like the word censorship.

At the very end of the book, I ended up with a sort of a plea or a recommended force of action.

And the best way I could, I know how to deal with this issue that you bring up

is if everybody understood, as part of your upbringing life, something about how your brain

works, that it builds a model of the world, how it worked, how basic it builds that model of the

world, and that the model is not the real world. It's just a model. And it's never going to reflect

the entire world, and it can be wrong, and it's easy to be wrong. And here's all the ways you

can get the wrong model in your head, right? It's not to prescribe what's right or wrong,

it's just to understand that process. If we all understood the process, and then I got together

and you said, I disagree with you, Jeff, and I said, Lex, I disagree with you, that at least we

understand that we're both trying to model something. We both have different information

which leads to our different models. And therefore, I shouldn't hold it against you,

and you shouldn't hold it against me. And we can at least agree that, well, what can we look for

in its common ground to test our beliefs, as opposed to so much as we raise our kids on dogma,

which is this is a fact, and this is a fact, and these people are bad. And if everyone knew just

to be skeptical of every belief and why and how their brains do that, I think we might have a

better world. Do you think the human mind is able to comprehend reality? So you talk about

this creating models that are better and better. How close do you think we get to reality? So

the wildest ideas is like Donald Hoffman saying, we're very far away from reality. Do you think

we're getting close to reality? Well, I guess it depends on what you define reality. We have a

model of the world that's very useful for basic goals of survival. Well, for our survival and

our pleasure, right? So that's useful. That means really useful. Oh, we can build planes,

we can build computers, we can do these things. I don't think, I don't know the answer to that

question. I think that's part of the question we're trying to figure out. Obviously, if we

end up with a theory of everything that really is a theory of everything, and all of a sudden,

everything comes into play and there's no room for something else, then you might feel like we

have a good model of the world. Yeah, but if we have a theory of everything and somehow, first of

all, you'll never be able to really conclusively say it's a theory of everything, but say somehow

we are very damn sure it's a theory of everything. We understand what happened at the Big Bang and how

just the entirety of the physical process. I'm still not sure that gives us an understanding of

the next many layers of the hierarchy of abstractions that form. Well, also, what if string

theory turns out to be true, and then you say, well, we have no reality, no modeling, what's

going on in those other dimensions that are wrapped into it on each other, right? Or the multiverse,

you know? I honestly don't know how for us, for human interaction, for ideas of intelligence,

how it helps us to understand that we're made up of vibrating strings that are

like 10 to the whatever times smaller than us. You could probably build better

weapons of better rockets, but you're not going to be able to understand intelligence.

I guess maybe better computers. No, you won't be able to. I think it's just more purely knowledge.

You might lead to a better understanding of the beginning of the universe,

right? It might lead to a better understanding of, I don't know. I think the acquisition of

knowledge has always been one where you pursue it for its own pleasure and you don't always know

what is going to make a difference. You're pleasantly surprised by the weird things you find.

Do you think for the for the New York Cortex in general, do you think there's a lot of innovation

to be done on the machine side? You use the computer as a metaphor quite a bit. Is there

different types of computer that would help us build intelligence? What are the physical

manifestations of intelligent machines? Oh, no, it's going to be totally crazy. We have no idea

how this is going to look out yet. You can already see this. Today, of course, we modeled these things

on traditional computers, and now GPUs are really popular with neural networks and so on.

But there are companies coming up with fundamentally new physical substrates

that are just really cool. I don't know if they're going to work or not,

but I think there'll be decades of innovation here, totally.

Do you think the final thing will be messy, like our biology is messy? Or do you think

it's the old bird versus airplane question? Or do you think we could just

build airplanes that fly way better than birds in the same way we can build

electrical in New York Cortex? Can I riff on the bird thing a bit? Because I think it's

interesting. People really misunderstand this. The Wright brothers, the problem they were trying

to solve was controlled flight, how to turn an airplane, not how to propel an airplane.

They weren't worried about that. They already had, at that time, there was already wing shapes,

which they had from studying birds. There was already gliders that carried people.

The problem was, if you put a rudder on the back of a glider and you turn it, the plane falls out

of the sky. The problem was, how do you control flight? They studied birds. They actually had

birds in captivity. They watched birds in wind tunnels. They observed them in the wild. They

discovered the secret was the birds twist their wings when they turn. That's what they did on

the Wright brothers fly. They had these sticks that you would twist the wing. That was their

innovation, not their propeller. Today, airplanes still twist their wings. We don't twist the entire

wing. We just the tail end of it. The flaps, which is the same thing. Today's airplanes

fly on the same principles as birds, which we observe. Everyone get that analogy wrong.

Let's step back from that. Once you understand the principles of flight,

you can choose how to implement them. No one's going to use bones and feathers and muscles,

but they do have wings. We don't flap them. We have propellers. When we have the principles

of computation that goes on to modeling the world in a brain, we understand those principles

very clearly. We have choices on how to implement them. Some of them be biological like and some

won't. I do think there's going to be a huge amount of innovation here. Do you think about

the innovation when in the computer? They had invented the transistor. They invented the silicon

chip. They had invented software. It's the things they had to do, memory systems.

It's going to be similar. It's interesting that the effectiveness of deep learning for

specific tasks is driving a lot of innovation in the hardware, which may have effects

for actually allowing us to discover intelligent systems that operate very differently or

much bigger than deep learning. Ultimately, it's good to have an application that's making our

life better now because the capitalist process, if you can make money, that works. The other way,

Neil deGrasse Tyson writes about this, is the other way we fund science, of course, is through

military conquests. It's an interesting thing that we're doing on this regard. We used to have

a series of biological principles. We can see how to build these intelligent machines,

but we've decided to apply some of these principles to today's machine learning techniques. One

that we didn't talk about this principle, one is sparsity in the brain. Most of the neurons

are inactive at any point in time. It's sparse and the connectivity is sparse. That's different

than deep learning networks. We've already shown that we can speed up existing deep learning

networks anywhere from 10 to a factor of 100, literally 100, and make them more robust at the

same time. This is commercially very, very valuable. If we can prove this actually in the

largest systems that are commercially applied today, there's a big commercial desire to do this.

Well, sparsity is something that doesn't run really well on existing hardware. It doesn't

really run really well on GPUs and on CPUs. That would be a way of bringing more brain

principles into the existing system on a commercially valuable basis. Another thing we

can think we can do is we're going to use these dendrites. I talked earlier about the prediction

occurring from inside and around. That basic property can be applied to existing neural networks

and allow them to learn continuously with something they don't do today.

The dendritic spikes that you were talking about.

Yeah. Well, we wouldn't model the spikes, but the idea that today's neural networks have to

go to point neurons is a very simple model of a neuron. By adding dendrites to them,

at just one more level of complexity that's in biological systems, you can solve problems

in continuous learning and rapid learning. We're trying to bring the existing

field. We'll see if we can do it. We're trying to bring the existing field of machine learning

commercially along with us. You brought up this idea of keeping paying for it commercially along

with us as we move towards the ultimate goal of a true AI system. Even small innovations on

neural networks are really, really exciting. It seems like such a trivial model of the brain

and applying different insights that just even, like you said, continuous learning or making it

more asynchronous or maybe making more dynamic or incentivizing. Or more robust. Even just

one more robust. And making it somehow much better, incentivizing sparsity somehow.

Yeah. Well, if you can make things 100 times faster, then there's plenty of incentive.

People spending millions of dollars just training some of these networks now,

these transforming networks.

Let me ask you a big question. For young people listening to this today in high school and college,

what advice would you give them in terms of which career path to take and maybe just about life in

general? Well, in my case, I didn't start life with any kind of goals. I was, when I was going to

college, I was like, oh, what do I study? Well, maybe I'll do intellectual engineering stuff.

I wasn't like, today you see some of these young kids are so motivated, change the world. I was like,

hey, whatever. But then I did fall in love with something besides my wife. But I fell in love

with this like, oh my God, it would be so cool to understand how the brain works.

And then I said to myself, that's the most important thing I could work on. I can't

imagine anything more important because if you understand how the brain's working,

build tells machines and they could figure out all the other big questions in the world.

So, and then I said, I want to understand how I work. So I fell in love with this idea and I

became passionate about it. And this is a trope people say this, but it's true.

Because I was passionate about it, I was able to put up with almost so much crap.

You know, I was in that, you know, I was like,

person said, you can't do this. I was a graduate student at Berkeley when they said,

you can't study this problem. You know, no one's can solve this or you can't get funded for it.

You know, then I went into do, you know, mobile computing and there's like people say,

you can't do that. You can't build a cell phone. You know, so, but all along I kept being motivated

because I wanted to work on this problem. I said, I want to understand the brain works.

I got myself, you know, I got one lifetime, I'm going to figure it out, do as best I can.

So by having that, because you know, these things, it's really, as you point out, Lex,

it's really hard to do these things. People, it's just, there's so many downers along the way.

So many way obstacles are getting your way. Yeah, I'm sitting here happy all the time,

but trust me, it's not always like that.

That's, I guess, the happiness that the passion is a prerequisite for surviving the whole thing.

Yeah, I think so. I think that's right. And so I don't want to sit to someone say, you know,

you need to find a passion and do it. No, maybe you don't. But if you do find something you're

passionate about, then, then you can follow it as far as your passion will let you put up with it.

Do you remember how you found it? This is how the spark happened.

Why, specifically for me?

Yeah, like, because you said, it's such an interesting, so like almost like later in life,

by later, I mean, like not when you were five, you, you didn't really know. And then all of a

sudden you fell in love with it. Yeah, yeah. There was there was two separate events that

compounded one another. One, when I was probably a teenager, it might have been 17 or 18,

I made a list of the most interesting problems I could think of. First was, why does the universe

exist? Seems like not existing is more likely. Yeah. The second one was, well, given exists,

why does it behave the way it does? You know, laws of physics, why is it equal to MC squared,

not MC cubed? You know, that's interesting question. I don't know. Third one was like,

what's the origin of life? And the fourth one was what's intelligence? And I stopped there.

I said, well, that's probably the most interesting one. And I put that aside

as a teenager. But then when I was 22, and I was reading the, no, I was, excuse me, I was 70,

it was 1979, excuse me, 1979. I was reading, so I was at that time was 22. I was reading

the September issue of Scientific American, which is all about the brain. And then the

final essay was by Francis Crick, who of DNA fame, and he had taken his interest

to studying the brain now. And he said, you know, there's something wrong here. He says,

we got all this data, all this fact, this is 1979, all these facts about the brain,

tons and tons of facts about the brain. Do we need more facts? Or do we just need to think

about a way of rearranging the facts we have? Maybe we're just not thinking about the problem

correctly. You know, he says, this shouldn't be, this shouldn't be like this, you know?

So I read that and I said, wow. I said, I don't have to become like an experimental

neuroscientist. I could just look at all those facts and try to become a theoretician and try

to figure it out. And I said, that, I felt like it was something I would be good at. I said,

I wouldn't be a good experimentalist. I don't have the patience for it. But I'm a good thinker

and I love puzzles. And this is like the biggest puzzle in the world. This is the biggest puzzle

of all time. And I got all the puzzle pieces in front of me. Damn, that was exciting.

And there's something obviously you can't convert into words. They just kind of

sparked this passion. And I have that a few times in my life, just something

just, just like you, it grabs you. Yeah. I thought it was something that was both important

and that I could make a contribution to. And so all of a sudden it felt like, oh, it gave me purpose

in life, you know? I honestly don't think it has to be as big as one of those four questions.

You can find those things in the smallest. Oh, absolutely. David Foster Wallace said,

like, the key to life is to be unboreable. I think it's very possible to find that

intensity of joy in the smallest thing. Absolutely. I'm just, you asked me my story.

Yeah. No, but I'm actually speaking to the audience. It doesn't have to be those four.

You happen to get excited by one of the bigger questions in the universe. But

even the smallest things and watching the Olympics now, just giving yourself life,

giving your life over to the study and the mastery of a particular sport is fascinating.

And if it sparks joy and passion, you're able to, in the case of the Olympics,

basically suffer for like a couple of decades to achieve. I mean, you can find joy and passion

just being a parent. I mean, yeah, the parenting one is funny. So I was not always, but for a long

time, wanted kids and get married and stuff. And especially it has to do with the fact that

I've seen a lot of people that I respect get a whole other level of joy from kids. And at,

you know, at first is like, you're thinking is, well, like, I don't have enough time in the day,

right? If I have this passion to solve intelligence. Which is true.

But like, if I want to solve intelligence, how's this kid situation going to help me?

But then you realize that, you know, like you said, the things that sparks joy and it's very

possible that kids can provide even a greater or deeper, more meaningful joy than those bigger

questions when they enrich each other. And that seemed like a, obviously, when I was younger,

it's probably a counterintuitive notion because there's only so many hours in the day. But then

life is finite and you have to pick the things that give you joy.

But you also understand you can be patient too. I mean, it's finite, but we do have, you know,

whatever, 50 years or something. It's us alone. Yeah. So in my case, you know, in my case,

I had to give up on my dream of the neuroscience because I was a graduate student at Berkeley

and they told me I couldn't do this and I couldn't get funded. And, you know, and,

and so I went back in, and went back into the computing industry for a number of years. I

thought it would be four, but it turned out to be more. But I said, but I said, I'll come back.

You know, I definitely, I'm definitely going to come back. I know I'm going to do this computer

stuff for a while, but I'm definitely coming back. Everyone knows that. And it's the same

as raising kids. Well, yeah, you still, you have to spend a lot of time with your kids. It's fun,

enjoyable. But that doesn't mean you have to give up on other dreams. It just means that you have

to wait a week or two to work on that next idea. You talk about the darker side of me,

disappointing sides of human nature that we're hoping to overcome so that we don't destroy

ourselves. I tend to put a lot of value in the broad general concept of love, of the human capacity

to, of compassion towards each other, of just kindness, whatever that longing of like just the

human, human to human connection, it connects back to our initial discussion. I tend to see a lot

of value in this collective intelligence aspect. I think some of the magic of human civilization

happens when there's a party is not as fun when you're alone. I totally agree with you on these

issues. Do you think from a New York Cortex perspective, what role does love play in the

human condition? Well, those are two separate things from the New York Cortex. I don't think it

doesn't impact our thinking about the New York Cortex. From a human condition point of view,

I think it's core. I mean, we get so much pleasure out of loving people and helping people.

I'll rack it up to old brain stuff, and maybe we can throw it under the bus of evolution,

if you want. That's fine. It doesn't impact how we think about how we model the world,

but from a humanity point of view, I think it's essential.

Well, I tend to give it to the new brain, and also I tend to think the sum of aspects of that

need to be engineered into AI systems, both in their ability to have compassion for other humans

and their ability to maximize love in the world between humans. I'm more thinking about the social

network. Whenever there's a deep integration between AI systems and humans, there's specific

applications where it's AI and humans. I think that's something that's often not talked about in

terms of metrics over which you try to maximize, which metric to maximize in a system. It seems

like one of the most powerful things in societies is the capacity to love.

It's a great way of thinking about it. I have been thinking more of these fundamental

mechanisms in the brain as opposed to the social interaction between humans and AI systems in

the future. If you think about that, you're absolutely right, but that's a complex system.

I can have intelligent systems that don't have that component, but they're not interacting

with people. They're just running something or building a building someplace or something,

I don't know. If you think about interacting with humans, it has to be engineered in there.

I don't think it's going to appear on its own. That's a good question.

In terms of, from a reinforcement learning perspective, whether the darker

size of human nature or the better angels of our nature win out, statistically speaking,

I don't know. I tend to be optimistic and hope that love wins out in the end.

You've done a lot of incredible stuff. Your book is driving towards this fourth question

that you started with on the nature of intelligence. What do you hope your legacy

for people reading 100 years from now? How do you hope they remember your work?

How do you hope they remember this book? Well, I think as an entrepreneur or a scientist or

any human who's trying to accomplish some things, I have a view that really all you can do is

accelerate the inevitable. It's like, if we didn't figure out, if we didn't study the brain,

someone else would study the brain. If Elon just didn't make electric cars, someone else would do

it eventually. If Thomas Edison didn't invent a light bulb, we wouldn't be using candles today.

What you can do as an individual is you can accelerate something that's beneficial and make

it happen sooner than whatever. That's really it. That's all you can do. You can't create a new

reality that it wasn't going to happen. From that perspective, I would hope that our work,

not just me, but our work in general, people would look back and said, hey, they really helped make

this better future happen sooner. They helped us understand the nature of false beliefs sooner

than what I made up. Now, we're so happy that we have these intelligent machines doing these things,

helping us that maybe that solved the climate change problem, and they made it happen sooner.

I think that's the best I would hope for. Some would say, those guys just moved the needle forward

a little bit in time. Well, it feels like the progress of human civilization is not,

there's a lot of trajectories. If you have individuals that accelerate towards one direction

that helps steer human civilization. I think in those long stretch of time, all trajectories will

be traveled, but I think it's nice for this particular civilization on earth to travel down

one that's not. Yeah. Well, I think you're right. We have to take the whole period of World War II

and Nazism or something like that. Well, that was a bad side step. We've been on with that for a

while, but there is the optimistic view about life that ultimately it does converge in a positive

way. It progresses ultimately, even if we have years of darkness. I think you could perhaps,

that's accelerating the positive. It could also mean eliminating some bad missteps along the way,

too. But I'm an optimistic in that way. Despite we talked about the end of civilization,

I think we're going to live for a long time. I hope we are. I think our society in the future

is going to be better. We're going to have less discord. We're going to have less people killing

each other. We'll make them live in some way that's compatible with the carrying capacity of the

earth. I'm optimistic these things will happen. All we can do is try to get there sooner.

At the very least, if we do destroy ourselves, we'll have a few satellites that will tell alien

civilization that we were once here. Or maybe our future inhabitants of earth. Imagine the

planet of the ape scenario. We kill ourselves a million years from now or a billion years from

now. There's another species on the planet. There's curious creatures who are once here.

Jeff, thank you so much for your work and thank you so much for talking to me once again.

Well, it's great. I love what you do. I love your podcast. You have those interesting people

me aside. It's a real service I think you do for a very broader sense for humanity, I think.

Thanks, Jeff. All right. It's a pleasure. Thanks for listening to this conversation with Jeff

Hawkins. And thank you to Code Academy, Bio Optimizers, ExpressVPN, A Sleep, and Blinkist.

Check them out in the description to support this podcast. And now let me leave you with some words

from Albert Camus. An intellectual is someone whose mind watches itself. I like this because I'm

happy to be both halves, the watcher and the watched. Can they be brought together? This

is a practical question we must try to answer. Thank you for listening. I hope to see you next time.