The following is a conversation with Jeff Hawkins, a neuroscientist seeking to understand
the structure, function, and 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, Codecademy, 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
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? Like there’s a Lex neuron
in your brain that just like finally has a purpose? I do remember our conversation. 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 neurons in my brain that know you. 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 of the things to know about brains is that
when you think of things, you often erase the memory and rewrite it again. Yes, but I have
a memory of you, and that’s instantiated in synapses. There’s a simpler way to think about
it. We have a model of the world in your head, and that model is continually being updated.
I updated this morning. You offered me this water. You said it was from the refrigerator.
I remember these things. The model includes where we live, the places we know, the words,
the objects in the world. It’s a monstrous model, and it’s constantly being updated.
People are just part of that model. Our animals, our other physical objects, our events we’ve
done. In my mind, it’s no special place for the memories of humans. Obviously, I know a lot about
my wife and friends and so on, but it’s not like a special place for humans or over here.
We model everything, and we model other people’s behaviors too. If I said there’s a copy of your
mind in my mind, it’s just because I’ve learned how humans behave, and I’ve 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
topics. Collective intelligence is a separate topic that a lot of people like to talk about.
We could talk about that. That’s interesting. We’re not just individuals. We live in society
and so on. From our research point of view, again, let’s just talk. We studied 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. You can apply that algorithm to lots of different
problems, but underneath, it’s the same thing. We’re just building this model. 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? Humans are just another part of
the things we understand. There’s nothing to the brain that knows the
emergent phenomena of collective intelligence. Well, I certainly know about that. I’ve heard
the terms, I’ve read. No, but that’s as an idea.
Well, I think we have language, which is built into our brains. That’s a key part of collective
intelligence. There are some prior assumptions about the world we’re going to live in. When
we’re born, we’re not just a blank slate. Did we evolve to take advantage of those situations?
Yes. Again, we study only part of the brain, the neocortex. There’s other parts of the
brain that are very much involved in societal interactions and human emotions and how we
interact and even societal issues about how we interact with other people, when we support
them, when we’re greedy and things like that. 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 in a central 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. Even if you do believe that, then you have to understand how the brain works in
doing that. It’s more like 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. 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? What is the nature of language? How do we share
models that I’ve learned something about the world, how do I share it with you? Which is really
what 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 physics and you can impart that to me. Even just the epistemological
question of, well, what is knowledge and how do you represent it in the brain? That’s where it’s
going to reside for 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. 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. 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.
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 right now? How do I know that? What does it mean? How 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 be 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 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
big idea, is that we think of the brain, the neocortex as 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, we call the column in the cortex is 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, well, where do I have knowledge about this coffee cup or where’s the model of this cell phone?
It’s not in one place. It’s in thousands of separate models that are complimentary and
they communicate with each other through voting. So this idea that we feel like we’re one person,
that’s our experience. We can explain that. But reality, there’s lots of these, it’s almost like
little brains, but they’re sophisticated modeling systems, about 150,000 of them in each human
brain. And that’s a total 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? We think, oh, I’m just here. I’m looking at you. But it’s composed of all these things,
like there’s sounds and there’s vision and there’s touch and all kinds of inputs. Yeah,
we have the singular perception. And what the thousand brain theory says, we have these models
that are visual models. We have a lot of models that are auditory models, models that talk to
models and so on, but they vote. And so these things in the cortex, you can think about these
columns as 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. 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 on under the hood. So the
voting is what we’re aware of. The results of the vote.
Yeah. Well, 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 it around.
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 and off. But the voting neurons are not.
The voting neurons are saying, we all agree, even though I’m looking at different parts of this,
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 that 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 doesn’t go anywhere else. It doesn’t get shared anywhere else. 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.
But these long range connections are the things that are accessible to language and to our,
like the hippocampus, our memories, 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 thousand 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 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 gooseneck lamp and all this to know these things.
I have to have a model of it 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, um, 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 as our ability to learn a model and the more sophisticated our
model is, the smarter we are. Uh, 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 is the actual things that, how do you learn this stuff? It turns out
you have to learn it through movement. Um, you can’t learn it just by that’s how we learn. We
learn through movement. We learn. Um, 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. You obviously 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 the 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 can do it. You don’t even want to just sit down and read some
description of it, right? Yeah. You literally physically interact. 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 used for prediction and for behavior and planning. Right. And does a pretty good job
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 do 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, 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 you can say, 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’ll 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 basically you assign a reference frame for the palace 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 on 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 push this button? I’ve never pushed this button before, or how would I accomplish
something? I want to, I want to convey a new idea I’ve 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 what the model lets you do. It lets us plan the future and
imagine the consequences of our 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 a
type of prediction. That’s a key part of intelligence. But using prediction is like, oh,
what’s this water bottle going to feel like when I pick it up, you know? And that doesn’t seem very
intelligent. But 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
it up and it was very light, I’d be surprised. Or if I turned this top and 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’ll
drink some water. I’m okay. 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 of this thing. My attention would be drawn to it. I’d be looking at
it going, well, how the hell 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 with that update and say, 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. It’s almost like a
precondition of just perceiving the world is predicting it. So just everything that you see
is first passed through your prediction. Everything you see and feel. In fact,
this was the insight I had back in the early 80s. And I know that 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’ll get better and better predictions. And if you talk 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 are a 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, oh, what the hell’s going on? That’s not normal. I mean, that’s fascinating
that… 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 it also… Yeah. But you can
then extend it to things like, oh, what would happen if I took this today? I went and did this.
What would be likely? Or how… 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 predict what might happen.
If I spoke to someone, I predict what might 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
this nonstop prediction. And I write about this in the book. And then we asked, how do neurons
actually make predictions physically? Like what does the neuron do when it makes a prediction?
Or the neural tissue does when it makes a prediction. And then we asked, what are the
mechanisms by how we build a model that allows you to make predictions? So we started with prediction
as sort of the fundamental research agenda, if in some sense. And say, well, we understand how
the brain makes predictions. We’ll understand how it builds these models and how it learns.
And that’s the core of intelligence. So it was the key that got us in the door
to say, that is our research agenda. Understand predictions.
So in this whole process, 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 through 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 book, an excellent
introduction? I mean, it’s, 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?
Well, we have a theory about it and it’s just that, it’s a theory. The 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 the 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. In the
mammals, it’s in the hippocampus and entorhinal cortex, these are older parts of the brain.
And these are very well studied. We build a map of the 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?
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 when they’re not just randomly flying around. They
know. 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 are 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 now know where they are. What we think has happened,
and there’s a lot of evidence to suggest this, is that that mechanism we learned to map,
like a space, 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? So 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. Yeah. But in deep learning networks,
they end up, you’re replicating an element, but you still need the entire network to do anything.
Right. 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 the same thing.
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. And that tells it that the learning algorithm is very generic. It’s very
kind of universal because it doesn’t assume any prior knowledge about what it’s learning.
And 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. So anyway, this led to our hypothesis that
you would find grid cells and place cell equivalents in the neocortex. And 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. So then we became aware of evidence for grid
cells in parts of the neocortex. 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 place cell equivalents, cells that work
like them through every column in the neocortex. And that’s starting to be seen. 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, they’re 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 there’s this other part
of the brain, we know that learns 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 neocortex, which tells us that
that’s what they’re doing. They’re learning these sensory motor models of the world. So 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 it
just found out like, hey, if I took 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 and pasting
these brains and humans and making a lot of them and then creating social structures that then
almost operate as a single brain? I wouldn’t have said that, but you said it sounded pretty good.
So to you, the brain is its own thing.
I mean, our goal is to understand how the neocortex works. We can argue how essential
that is to understand the 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
is to sort of communal intelligence. Our goal was to understand the neocortex.
Yeah. So what is the neocortex and where does it fit
in the various aspects of what the brain does? Like how important is it to you?
Well, obviously, again, I mentioned again in the beginning, it’s about 70 to 75% of the volume of
the human brain. So 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 not that. We know that all high level vision,
hearing, and touch happens in the neocortex. We know that all language occurs and is understood
in the neocortex, whether that’s spoken language, written language, sign language,
whether it’s language of mathematics, language of physics, music. We know that all high level
planning and thinking occurs in the neocortex. If I were to say, what part of your brain designed
a computer and understands programming and creates music? It’s all the neocortex.
So then that’s an undeniable fact. 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,
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.
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 analogy
in the book that 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 manifest as a human depends on the rest of our
brain. What are our motivations? What are my desires? Am I a nice guy or not a nice guy?
Am I a cheater or not a cheater? How important different things are in my life?
But the neocortex can be understood on its own. And I say that as a neuroscientist,
I know there’s all these interactions, and I don’t want to say I don’t know them and we
don’t think about them. But from 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,
I wonder if there’s like just where everybody probably every living type pocket of complexity
probably thinks they’re the, pardon the French, they’re the shit. They’re at the top of the
pyramid. Well, if they’re thinking. Well, then what is thinking? In this sense,
the whole point is in their sense of the world, their sense is that they’re at the top of it.
I think what is a 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. Um, and you know, I think we’ve made surprisingly little progress
and understanding complex systems in general. Um, and so, you know, the Santa Fe Institute was
founded 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. Uh, 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 it’s 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? Um, and I, I think you
should ask, well, the brain itself is a complex system. So can we understand that? Um, I think
we’ve made a lot of progress understanding how the brain works. So, uh, but I haven’t
brought it out to like, oh, well, where are we on the complexity spectrum? You know, it’s like,
um, it’s a great question. I’d 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 or probably not in some
kind of significant place, there’s one thing we could say that we are special. And again,
only here on earth, I’m not saying is that if we think about knowledge, what we know,
um, we clearly human brains have, um, the only brains that have a certain types of knowledge.
We’re the only brains on this earth to understand, uh, 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. Uh, 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. Um, I think our species is further along in that
undeniably, um, 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 its fusion is and how what black holes
are and, you know, we know general theory of relativity and no other animal has any of this
knowledge. So in that sense that we’re special, uh, are we special in terms of the hierarchy of
complexity 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 traditionally is seen as the
basic element of the brain. So we, I mentioned this earlier that prediction was our research agenda.
Yeah. We said, okay, um, how does the brain make a prediction? Like I 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 does 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, where is the prediction going on? And one argument could be
that, well, when I’m predicting something, um, 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 me
have no idea that you’re doing this. Um, that it, there wasn’t really, we were trying to figure,
how could this be? Where, where are these, where are these happening? Right. And I won’t walk you
through the whole story unless you insist upon it. But we came to the realization that most of your
predictions are occurring inside individual neurons, especially these, the most common
are in the parameter cells. And there are, there’s a property of neurons. We, 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 know 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. There’s 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, 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 majority, 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, they’re internal to the neuron, you can’t perceive them.
Well, from understanding 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 of the neural
brain? Of the mini brains and then the bigger brain and the brain?
Oh yeah. Yeah. Yeah. So having a prediction side of their individual neuron is not that useful.
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 event. 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
been. It’s like, I give the analogy of the book is like a sprinter on a, on a starting blocks in a,
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
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 thousand brains,
if you were to count the number of brains, how would you do it? The thousand brain theory says
that basically every cortical column in the, in your, in your cortex 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 thousand brains, then there’s a voting mechanism, which you lead, 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 thousand brains theory. The details, 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’ve talked about just these predictive neurons.
That’s part of this theory. It’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 built through 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 where we can start teasing apart, you know, ask questions about, you know, how could
neurons be making these predictions? And that sort of built up to now what we have this thousand
brains theory, which is complex. You know, it’s just, 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 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 reference frame like Cartesian coordinates, like X, Y, and Z axes. So I could
say, oh, I’m going to design a house. I can say, well, the front door is at this location, X, Y,
Z, and the roof is at this location, X, Y, Z, and so on. That’s a type of reference frame.
So it turns out for you to make a prediction, and 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.
It was a ceramic coffee cup, but this one will do. And what I realized is that to make a prediction
of what my finger’s going to feel, like it’s going to feel different than this, what’s it feel
different if I touch the hole or this 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. It 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. 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 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 said, well, how do neurons make reference frames? It’s not obvious.
X, Y, Z 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 anterior cortex, where we knew
that in that part of the brain, there’s a reference frame for a room or a reference frame for an
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 reference frames needed to exist
in every quarter of a column. And so that was a deductive thing. We just deduced it. It has to
exist. 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. 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? The new part of the brain says,
where’s my finger relative to this object? 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?
And then we take the same thing and apply it to concepts, mathematics, physics, humanity,
whatever you want to think about. 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,
the same basic underlying structure is happening. There’s reference frames for where the knowledge
that you’re assigning 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 use to model this coffee cup, we’re going to use to model high level thoughts.
Your demise of 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 the 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? Like 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, 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 the
statement on it. I don’t have to do that. I just incorporate all of that in some sort of hierarchical
representation. I say, put this logo on this water bottle. And then the logo has a word
and the word has letters, all hierarchical. All that stuff is big. It’s amazing that the
brain instantly just does all that. The idea that 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
diaphragm that moves back and forth. I don’t see that, but I know it. So everything in the world
is hierarchical. You just go into a room. It’s composed of other components. The kitchen has a
refrigerator. 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 100,000 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 Malmkastel, 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, yeah. It’s a little bit like computers. 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 server 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 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 applied to thinking
what’s the future of humanity and 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
it 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
stuck on top of each other over? 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.
Yeah. Right. So as I said earlier, 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’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. Today, we know enough to build this.
I’m sitting here with, I know the steps we have to go. There’s still some engineering problems
to solve, but we know enough. And this is not like, oh, this is an interesting idea. We have
to go think about it for another few decades. No, we actually understand it pretty well in 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 a roadmap of how we do this.
It’s not going to take decades. It’s a matter of 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 it’s 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 in computing, people like John
Van Norman and Alan 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? 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 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’s be just 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 actually samples information on the internet and moves by
following links. That’s a sensory motor system. Something that echoes the process of a finger
moving along a cortical… But in a very, very loose sense. It’s like,
again, learning is inherently about discovering the structure of the world and discover the
structure of 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. It doesn’t exist in one… It has some structure
to it. So, here’s a couple of predictions at getting what you’re talking about.
In humans, the same algorithm does robotics. It moves my arms, my eyes, my body.
And so, in the future, to me, robotics and AI will merge. They’re not going to be separate fields
because the algorithms for really controlling robots are going to be the same algorithms we
have in our brain, these sensory motor algorithms. Today, we’re not there, but I think that’s going
to happen. 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 not have physical
movements. It’s a very generic learning system. Again, it’s like computers. The Turing machine,
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 a computational principle.
The cortical column equivalent is a computational principle about learning. It’s about how you
learn and it can be applied to a gazillion things. 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 getting at
a fundamental thing. It’s not a vision system or a learning system. 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 can gain knowledge and be intelligent. That’s what the thousand brains says
was going on. 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? How hard is
the human in the loop problem? How hard is it to interact? The finger on the coffee cup equivalent
of having a conversation with a human being. How hard is it to fit into our little human world?
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’s essentially what I’m asking. How elitist are we as humans? We try to keep out
systems. I don’t know. I’m not sure that’s the right question. Let’s look at computers as an
analogy. Computers are a 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. How do 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 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 don’t give rights to?
Computers? Yeah, computers. Robots,
computers, intelligence 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.
Should this entity have rights? I don’t think so. I think
it’s tempting to think that way. First of all, hardly anyone thinks that for computers today.
No one says, oh, this thing needs a right. I shouldn’t be able to turn it off. If I throw it
in the trash can and hit it with a sledgehammer, it might form a criminal act. No one thinks that.
Now we think about intelligent machines, which is where you’re going.
All of a sudden, you’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 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. There’s 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. We can debate it if we want.
Where does your desire to live come from? It’s an old evolutionary design. We could argue,
does it really matter if we live or not? Objectively, no. We’re all going to die eventually.
Evolution makes us want to live. Evolution makes us want to fight to live. Evolution makes us want
to care and love one another and to care for our children and our relatives and our family and so
on. Those are all good things. They come about not because we’re smart, because we’re animals
that grew up. The hummingbird in my backyard cares about its offspring. Every living thing
in some sense cares about surviving. 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. That machine,
it may even be able to talk to us. 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 want to
live. It’s built into you. It’s part of your DNA. People like Ernest Becker argue,
there’s the fact of finiteness of life. The way we think about it is something we learned,
perhaps. Okay. Yeah. Some people decide they don’t want to live. Some people decide 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. It’s 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 that 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 we’re talking
about building AIs. 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 can 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 and do nothing, right?
You could argue that. But on its own, it’s not going to do those things. 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 an 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 necessary that we should worry
about it. I think there’s a legitimate problem or not problem, a question asked,
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 and cars and what’s important in driving and cars.
It’s not going to figure that on its own. It’s not going to sit there and say, I’ve understood
the world and I’ve decided, no, no, no, no, we’re going to have to tell it. We’re going to have to
say, so I imagine I make this car really smart. It learns about your driving habits. It learns
about the world. 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 studied pedestrians and cyclists quite a bit. And there’s 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
that, 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 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.
All right. I would have said that that kind of interaction is learned because people in different
cultures to 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 assign to it or its own personal goals or will it 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 it’s 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. 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 the neocortex
equivalent. You have to have that. But it has to have some kind of embodiment, physical or 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. We build in safeguards into systems. We have them in our
bodies. We put them into 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 disappear
automatically and evolve. And intelligence itself begets that stuff or requires it.
But it’s not. Intelligence of the neocortex equipment doesn’t require this. The neocortex
equipment just says, I’m a learning system. Tell me what you want me to learn and ask me questions
and I’ll tell you the answers. And that, again, it’s again like a map. 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, right? A map on its own doesn’t
do anything. It’s just inert. It can learn, but it’s just 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 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 we didn’t use that intuition, that word. I immediately stopped and said,
oh, that’s the crux of 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… 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 knowledge or the understanding about 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’m just guessing,
I’m not using intuition. I’m basing it on 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 I also 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.
Yeah. So the question is, yes, you’re just saying this is how an engineer in your cortex works,
but the, like when you have a very useful, uh, 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. Uh, well, first of all, this is back. One thing I want to say is that, um, AI is a dangerous
technology. I don’t, I’m not denying that. All technology is dangerous. Well, and AI,
maybe particularly so. Okay. So, um, 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, right?
Yeah. So I want to make that distinction because I think AI can be applied poorly. It can be applied
in ways that, you know, people are going to understand the consequences of it. Um, 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, I think the
existential risk thing is, um, humans are really damn good at surviving. So to kill off the human
race, it’d be very, very difficult. Yes, but you can even, 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. Um, I don’t think that’s going to happen, at least not
in the way I’m talking about it. So you, the Twitter recommendation algorithm is an interesting
example. Let’s, let’s use computers as an analogy again, right? 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, they can even create computer viruses. It’s, you know, all kinds of stuff. So
there’s some unknown about its utility and about where it’s going to go. But on 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. Um, you
know, I say it can’t just sit there 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. Uh, 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,
uh, 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, uh, you know, that do something extremely useful for society.
It could be a personal assistance. It could be, uh, it could, it could be your toaster, but a
toaster as much as deeper knowledge of your culinary preferences. Yeah. And that could,
uh, I think now you’ve hit on the right thing. The real thing we need to be worried about 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 hand veneered virus. Anybody can create a, you know,
there’s the technology is getting so almost anybody, but 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, um, 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 interesting 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, uh, 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 wants to do it. Cause
it doesn’t take a lot of money to make viruses. Um, 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 arguing that
it’s not risky. And I’m not dismissing that. I’m scared as hell. It’s like the paperclip
maximizer thing. Yeah. Those are often like talked about in the same conversation.
Um, I think you’re right. Like creating ultra intelligent, 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 the gist
of 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 the 8 billion of us are 7 billion of us now. So, um, I think the idea is that the,
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, uh, 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, we understand the risks. Uh, we regulate them. Uh, 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. It’ll help us mitigate these risks
better. It 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? Um, 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. We just, but we have to be
constantly vigilant about this idea of a bad actors doing bad things with them and be,
um, don’t ever, ever create a self replicating system. Um, uh, and, 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. Um, you know, it doesn’t require
anything. It just, you know, the thing and resources and it goes right. Um, if I said to
you, you know what we have to build, uh, our goal is to build a factory that can make that builds
new factories and it has to end to end supply chain. It has to bind the resources, get the
energy. I mean, that’s really hard. It’s, you know, no one’s doing that in the next, you know,
a hundred years. I’ve been extremely impressed by the efforts of Elon Musk and Tesla to try to do
exactly that. Not, not from raw resource. Well, he actually, I think states the goal is to go from
raw resource to the, uh, the final car in one factory. Yeah. 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. Um, 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, they all co locate them and they, and they tie the systems together.
It’s a fundamental, I think that’s, that also is not getting at the issue I was just talking about,
um, which is self replication. It’s, um, I mean, self replication means there’s no
entity involved other than the entity that’s replicating. Um, 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 doing it. But it’s also, I 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, uh, I think it’s a useful feature to build in. I’m
just trying to like, uh, put myself in the mind of engineers to sometimes say no, you know, if you,
I gave the example earlier, right? I gave the example of my car, right? My car turns the wheel
and, 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. Now that was something it didn’t decide to do. It’s something
we programmed into the car. And so good. It was a good idea, right? The question again, isn’t like
if we create an intelligent system, will it ever ignore our commands? Of course it will. And
sometimes is it going to do it because it came up, 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, uh, super intelligent cortical systems that we engineer
and us humans, do you think, uh, 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, uh,
one of the dreams is to upload our minds in the digital space. So can we just
give our minds to these, uh, systems so they can operate on them? Is there some kind of more
interesting merger or is there more, more communication? I talked about all these
scenarios and let me just walk through them. Sure. Um, the uploading the mind one. Yes. Extremely,
really difficult to do. Like, like, we have no idea how to do this even remotely right now. Um,
so it would be a very long way away, but I make the argument you wouldn’t like the result.
Um, and you wouldn’t be pleased with the result. It’s really not what you think it’s going to be.
Um, imagine I could upload your brain into a, into a computer right now. And now the computer
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. Are you going to feel satisfied then? 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, 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 walked through the issues. It’s really substantial.
Um, do you have a sense of what makes us us? Is there, is there a shortcut to what can only save
a certain part that makes us truly ours? No, but I think that machine would feel like it’s you too.
Right. Right. You have two people, just like I have a child, I have a child, right? I have two
daughters. They’re independent people. I created them. Well, partly. Yeah. And, um, uh, 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 apart, you have two people. So we can tell them, come back to what, what makes,
what consciousness do you want? We can talk about that, but we don’t have like remote consciousness.
I’m not sitting there going, Oh, I’m conscious of that. You know, I mean, that system of,
so let’s say, let’s, let’s stay on our topic. One was uploading a brand. Yep. It ain’t gonna 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 that 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. Um, interesting. Can I, can I, uh, can
I ask a question here? What, 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 years. It’s, it’s a totally
different thing to say, talking about billions of signals. So you don’t think it’s the raw,
the 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, there are 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. There’s 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. 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. Right. 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 this 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 biological organism. I assume I’m still going to die.
So what have I achieved? Right. You know, what have I achieved? Oh, I disagree that 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, to like, to read Wikipedia.
Yeah. But, but fine. But, but you’re still a biological organism.
Yes. Yes. You know, you’re still, you’re still mortal. 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 know, fine. I’m not against it.
I just don’t think it’s earth changing. I, but, but it w 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 this
ant colony. Why don’t I just create an intelligent machine that doesn’t have any of this biological
nonsense that has all the same. 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 bleeding and all these problems and limited by my brain or creating a system,
which is super smart that I can talk to. Um, 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 men manifestation of super intelligence look like? So like, what are we
going to, you, 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, how will it make our life
better? So let’s, let’s, that’s a great question, but let’s break it down to 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. It cooks. 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. Um, um, and so in the same way that computers made our lives better
in ways, many, many ways, I will have those kinds of things. To me, the really exciting thing about AI
is the sort of it’s 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. Uh, I don’t think you and I are going to want to live on Mars anytime soon. Um,
um, and, um, and we’re flawed, you know, we may end up destroying ourselves. It’s totally possible.
Uh, we, if not completely, we could destroy our civilizations. You know, it’s this face the fact
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
wanted 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. Um, 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. Um, so it’s sort of, it, in some sense, intelligence allows us to transcend
our, the limitations of our biology, uh, with, and, and don’t think of it as a negative thing.
It’s in some sense, my children transcend my, the, my biology too, cause they, they live beyond me.
Yeah. Um, and we impart, they represent me and they also have their own knowledge and I can
impart knowledge to them. So intelligent machines will be like that too, but not limited like us.
I mean, but the question is, um, 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, uh, with the really damn good, uh, model of the world?
It’s conscious, it’s conscious just like us. Okay. But just different?
They’re different. Uh, just like my children are different. They’re like me, but they’re
different. Um, 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? Is it, 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 asked myself, what’s the point of life is what’s 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, uh, and to explore. And that’s partly to learn more. Right. Um, 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, 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 a, it’s not like a negative thing. Well, would, uh, you be okay then if, uh,
the human species vanishes, but our knowledge is preserved and keeps being expanded by intelligence
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, if it does happen, what if we were sitting here and this is
all the real, the last two people on earth and we’re 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, there were that left earth. I wouldn’t want that.
Mm. 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’s kind of, it’s jarring to
think about that. It’s possible that a human like intelligence civilization has previously existed
on earth. The reason I say this is like, it is jarring to think that we would not, if they went
extinct, we wouldn’t be able to find evidence of them after a sufficient amount of time. Of course,
there’s like, like basically humans, like if we destroy ourselves now, the human civilization
destroyed ourselves. Now, after a sufficient amount of time, we would not be, we’d find evidence of
the dinosaurs would not find evidence of 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 for 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 throughout our galaxy that have all disappeared. That’s super sad that they’re,
exactly that there may have been much more intelligent alien civilizations in our galaxy
that are no longer there. Yeah. You actually talked about this, that humans might destroy
ourselves 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 you think about this problem? 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, 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 now, like, so, you know, we don’t
really know, but, but historically it would be likely at some point. Time flies when you’re
having fun. Yeah. That’s a good way to put it. 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, that wouldn’t be that hard to put
a few satellites, you know, going around the sun and we’d 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 intelligent
species will find it and learn something. They would like that. They would appreciate that.
Um, uh, so that’s one thing. The next thing I said, well, what if, you know, how outside,
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, uh, 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 stuff we’re just starting
to be able to do. Um, well, the chances are we wouldn’t be able to see any of them because they
would have all been disappeared by now. Um, they would, 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, um,
but I said, what kind of signal could you create that would last a million years or a billion years
that someone would say, dammit, someone smart lived there that 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 study
program, isn’t that we’re looking for very coded signals in some sense. Um, and so I asked myself,
what would be a different type of signal one could create? Um, I’ve always thought about
this throughout my life. And in the book, I gave one, one possible suggestion, which was, um, uh,
we now detect planets going around other, other suns, uh, other stars, uh, excuse me. And we do
that by seeing this, the, the slight dimming of the light as the planets move in front of them.
That’s how, uh, we detect, uh, planets elsewhere in our galaxy. Um, what if we created something
like that, that just rotated around our, our, our, 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 pie, you know, three point,
whatever. Um, 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 senior running a computer and supplying it with
power. It just goes on. So we go, it’s continuous. And, and I argued that part of the study program
should be looking for signals like that. And to look for signals like that, you ought to figure
out what the, 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. You could see from
a distance that was unequivocal, came from an intelligent species. And so I gave that one
example. Um, cause they don’t know what I know of actually. And then, 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 it all possible, we’ll have to create intelligent machines
that travel throughout the, throughout the solar system or 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 old, 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, uh, I think it is a bit sad to imagine
that the message we send as, as you talk about is that we were once here instead of we are here.
Well, it could be, we are still here. Uh, 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 is something I think about,
we as humans don’t often think about this, but it’s like, like whenever I, um,
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, are, are preserving you and I in a way. Yeah. 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
cause I want to talk about ideas and these ideas transcend me and they transcend this time and, and
on our planet. Um, we’re talking here about ideas that could be around a thousand years from now.
Or a million years from now. I, 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 someone 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, like especially in the third part of the book, the ones
we were just talking about, you know, these crazy, sounds like crazy ideas about, you know,
storing our knowledge and, and, you know, merging our brains with computers and, and sending, you
know, our machines out into space. It’s not going to happen in my lifetime. Um, and they may not
have been happening in the next hundred years. They may not happen for a thousand years. Who knows?
Uh, but we have the unique opportunity right now. We, you, me, and other people in the world,
right now, we, you, me, and other people like this, um, to sort of at least propose the agenda,
um, that might impact the future like that. That’s a fascinating way to think, uh, both like
writing or creating, try to make, try to create ideas, try to create things that, uh, hold up
in time. Yeah. You know, when 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, um, 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, right? Right.
Newton’s laws are not wrong. They’re just Einstein’s they’re better. Um, so 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, but understanding, um, like complex systems and the, this particular
manifestation of complex systems, which is the human brain. I’m totally optimistic. We can do
that. I mean, we’re making progress at it. I don’t see any reasons 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
gonna 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, uh,
concrete experiments where we can validate this is happening right now. Like it’s not,
this is not some future thing. Um, you know, I’m very optimistic about it because I know about our,
our work and what we’re doing. We’ll have to prove it to people. Um, but, um,
I, 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, we could, this is going to happen. 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. So, I mean, on that topic, let me ask you to play devil’s advocate.
Is it possible for you to imagine, look, 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.
Yeah. 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 talked about this voting idea, right? It’s happening. There’s no question.
It’s happening, but it could be far more, um, um, there’s, there’s enough things I don’t know about
it that it might be working into 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, these are all sort of on the edges, things that I present as like,
Oh, it’s so simple and clean. Well, it’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 this brain is a distributed modeling system is not controversial at all. Right.
It’s not, that’s well understood by many people. The question then is,
are each quarter of a column an independent modeling system? 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 string 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 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
is 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
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. It’s 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, uh, not 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 suggests
that the human brain 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 it out yet, but it
can learn things that it 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 gotta 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 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, humans. I mean, all of us, I’m one. So I’m disappointed
myself too. Um, it’s kind of a sad state. 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, um, uh, 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. So many, it seems
like it’s so hard. It’s just a big, big transition to get humans, all humans to, to make the
transition from be like, let’s pay no attention to all that ugly stuff over here. Let’s just focus
on the interesting. What’s unique about humanity is our knowledge and our intellect. But the fact
that we’re striving is in 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, 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 in 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 that 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 if my model is correct. But so much of what we know is stuff I can’t directly
interact with. I only know because someone told me about it. And 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 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 is a tension, 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, does that worry you? I mean, it’s like, oh, 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. 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. Yeah. You’re a foolish kid. Don’t
think that’s it. 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. Um, at the very end of the book, I,
I ended up with a sort of a, um, a plea or a recommended force of action. Um, 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 in life, something about how your brain works, that it builds a model of the world,
uh, how it works, you know, how basically 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 a wrong model in your
head. Right? It’s not prescribed what’s right or wrong. Just understand that process. If we all
understood the processes and I got together and you say, 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 that’s common ground to test our, our beliefs, as opposed to so much,
uh, as we raise our kids on dogma, which is this is a fact, this is a fact, and these people are
bad. And, and, and, you know, where every, if everyone knew just to, 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
how close do you think we get to, uh, to reality? There’s 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, it depends on what you define reality. Uh, we are getting, we have a model of the world
that’s very useful, right? For, for basic goals. Well, for our survival and our pleasure right
now. Right. Um, so that’s useful. Um, I mean, it’s really useful. Oh, we can build planes. We can build computers. We can do these things. Right.
Uh, I don’t think, I don’t know the answer to that question. Um, I think that’s part of the
question we’re trying to figure out, right? Like, you know, obviously if you 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, uh, 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, 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. I don’t, you know, you could probably build better
weapons, a better rockets, but you’re not going to be able to understand intelligence. I guess,
I guess maybe better computers. No, you won’t be. I think it’s just more purely knowledge.
You might lead to a better understanding of the, of the beginning of the universe,
right? It might lead to a better understanding of, uh, I don’t know. I guess I think the acquisition
of knowledge has always been one where you, you pursue it for its own pleasure. Um, and you don’t
always know what is going to make a difference. Yeah. Uh, you’re pleasantly surprised by the,
the weird things you find. Do you think, uh, for the, for the neocortex in general, do you,
do you think there’s a lot of innovation to be done on the machine side? You know,
you use the computer as a metaphor quite a bit. Is there different types of computer that would
help us build intelligence manifestations of intelligent machines? Yeah. Or is it, oh no,
it’s going to be totally crazy. Uh, we have no idea how this is going to look out yet.
You can already see this. Um, today we’ve, of course, we model these things on traditional
computers and now, now GPUs are really popular with, with, uh, you know, neural networks and so
on. Um, but there are companies coming up with fundamentally new physical substrates, um, that
are just really cool. I don’t know if they’re going to work or not. Um, but I think there’ll
be decades of innovation here. Yeah. Totally. Do you think the final thing will be messy,
like our biology is messy? Or do you think, uh, it’s, it’s the, it’s the old bird versus
airplane question, or do you think we could just, um, build airplanes that, that fly way better
than birds in the same way we could build, uh, uh, electrical neocortex? Yeah. You know,
can I, can I, can I riff on the bird thing a bit? Because I think that’s interesting.
People really misunderstand this. The Wright brothers, um, 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. Interesting. Yeah. They already had, at that time,
there was already wing shapes, which they had from studying birds. There was already gliders
that carry 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. So the problem was how do you control flight? And they studied
birds and they actually had birds in captivity. They watched birds in wind tunnels. They observed
them in the wild and they discovered the secret was the birds twist their wings when they turn.
And so that’s what they did on the Wright brothers flyer. They had these sticks that
you would twist the wing. And that was the, that was their innovation, not the propeller.
And today airplanes still twist their wings. We don’t twist the entire wing. We just twist
the tail end of it, the flaps, which is the same thing. So today’s airplanes fly on the
same principles as birds would observe. So everyone get that analogy wrong, but 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
and we don’t flap them. We have propellers. So 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. And some of them will be biological like and some won’t.
And, but I do think there’s going to be a huge amount of innovation here.
Just think about the innovation when in the computer, they had to invent the transistor,
they invented the Silicon chip. They had to invent, you know, then this software. I mean,
it’s millions of things they had to do, memory systems. We’re going to do, it’s going to be
similar. Well, it’s interesting that the deep learning, 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 intelligence systems that operate very differently or at least much
bigger than deep learning. Yeah. Interesting. So ultimately it’s good to have an application
that’s making our life better now because the capitalist process, if you can make money.
Yeah. Yeah. That works. I mean, the other way, I mean, Neil deGrasse Tyson writes about this
is the other way we fund science, of course, is through military. So like, yeah. Conquests.
So here’s an interesting thing we’re doing on this regard. So we’ve decided, we used to have
a series of these biological principles and 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.
So one of the, we didn’t talk about this principle. One is a sparsity in the brain,
um, most of the neurons are active at any point in time. It’s sparse and the connectivity is sparse
and that’s different than deep learning networks. Um, so we’ve already shown that we can speed up
existing deep learning networks, uh, anywhere from 10 to a factor of a hundred. I mean,
literally a hundred, um, and make a more robust at the same time. So this is commercially very,
very valuable. Um, and so, you know, 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, um, on, um, GPUs, um, and on CPUs. And so that would be a way of sort of bringing more,
more brain principles into the existing system on a, on a commercially valuable basis.
Another thing we can think we can do is we’re going to use these dendrites,
um, models that we, uh, I talked earlier about the prediction occurring inside a neuron
that that basic property can be applied to existing neural networks and allow them to
learn continuously, which is something they don’t do today. And so the dendritic spikes that you
were talking about. Yeah. Well, we wouldn’t model the spikes, but the idea that you have
that neuron today’s neural networks have this company called the point neurons is a very simple
model of a neuron. And, uh, by adding dendrites to them at just one more level of complexity,
uh, that’s in biological systems, you can solve problems in continuous learning, um,
and rapid learning. So we’re trying to take, we’re trying to bring the existing field,
and we’ll see if we can do it. We’re trying to bring the existing field of machine learning,
um, commercially along with us, you brought up this idea of keeping, you know,
paying for it commercially along with us as we move towards the ultimate goal of a true AI system.
Even small innovations on your own networks are really, really exciting.
Is it seems like such a trivial model of the brain and applying different insights
that just even, like you said, continuous, uh, learning or, uh, making it more asynchronous
or maybe making more dynamic or like, uh, incentivizing, making it robust and making it
somehow much better incentivizing sparsity, uh, somehow. Yeah. Well, if you can make things a
hundred times faster, then there’s plenty of incentive. That’s true. People, people are
spending millions of dollars, you know, just training some of these networks. Now these, uh,
these transforming networks, let me ask you the big question for young people listening to this
today in high school and college, what advice would you give them in terms of, uh, which career
path to take and, um, maybe just about life in general? Well, in my case, um, I didn’t start
life with any kind of goals. I was, when I was going to college, it’s like, Oh, what do I study?
Well, maybe I’ll do this electrical engineering stuff, you know? Um, it wasn’t like, you know,
today you see some of these young kids are so motivated, like I’m changing the world. I was
like, you know, whatever. And, um, 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, I said to myself, that’s the most important thing I could work on. I can’t imagine
anything more important because if we understand how the brains work, you build tells the machines
and they could figure out all the other big questions of the world. Right. So, and then I
said, but 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 was, it’s true because I was passionate
about it. I was able to put up almost so much crap, you know, you know, I was, I was in that,
you know, I was like person said, you can’t do this. I was, 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 mobile computing and it was 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. And I got
myself, you know, I got one lifetime. I’m going to figure it out, do the best I can. So by having
that, cause you know, it’s really, as you pointed out, Lex, it’s really hard to do these things.
People, it just, there’s so many downers along the way. So many ways, obstacles to get in your
way. Yeah. I’m sitting here happy all the time, but trust me, it’s not always like that.
Well, that’s, I guess the happiness, 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 and 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 you can follow it as far as your passion will let you put up with it.
Do you remember how you found it? How the spark happened?
Why specifically for me?
Yeah. Cause 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 didn’t really know. And then all of a sudden you fell
in love with that idea. Yeah, yeah. There was two separate events that compounded one another.
One, when I was probably a teenager, it might’ve been 17 or 18, I made a list of the most
interesting problems I could think of. First was why does the universe exist? It seems like
not existing is more likely. The second one was, well, given it exists, why does it behave the way
it does? Laws of physics, why is it equal MC squared, not MC cubed? That’s an interesting
question. The 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, excuse me, it was
1979, excuse me, 1979, I was reading, so I was, at that time I 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. Cause he says, this shouldn’t be like this. So I read that and I said,
wow. I said, I don’t have to become like an experimental neuroscientist. I could just
take, look at all those facts and try and 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. It’s 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 that just kind of sparked this passion. And I have that a few times in my life,
just something just like you, it grabs you. Yeah. I felt 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. I honestly don’t think it has to be as big as one of those four
questions. I think 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 of 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 that has to do with the
fact that I’ve seen a lot of people that I respect get a whole nother level of joy from kids. And
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, 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, 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.
Yeah. 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. So 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 so I went back in the computing industry for a number of years. I thought it
would be four, but it turned out to be more. But I said, I’ll come back. 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 like raising kids. Well, yeah, 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 may have to wait a week or two to work on that next idea. Well, 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 of compassion towards each other, of just kindness, whatever that longing of
like just the 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 neocortex perspective, what role does love play in the human
condition? Well, those are two separate things from a neocortex point of view. It doesn’t impact
our thinking about the neocortex. 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 I 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 give it to
the old brain. Also, I tend to think that some 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 social networks. Whenever there’s a deep
AI systems in humans, specific applications where it’s AI and humans, I think that’s something that
often not talked about in terms of metrics over which you try to maximize,
like 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 fascinating. I think 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. 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 some place or something. I don’t know.
But if you think about interacting with humans, yeah, but it has to be engineered in there. I
don’t think it’s going to appear on its own. That’s a good question.
Yeah. Well, we could, we’ll leave that open. In terms of, from a reinforcement learning
perspective, whether the darker sides 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 and 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
a hundred 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. Yeah. It’s like,
you know, if we didn’t figure out, if we didn’t study the brain, someone else will study the
brain. If, you know, if Elon didn’t make electric cars, someone else would do it eventually.
And if, you know, if Thomas Edison didn’t invent a light bulb, we wouldn’t be using candles today.
So, what you can do as an individual is you can accelerate something that’s beneficial
and make it happen sooner than it would have. That’s really it. That’s all you can do.
You can’t create a new reality that it wasn’t going to happen. So, 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, you know, they helped us
understand the nature of false beliefs sooner than they might have. 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. So, 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, I do. It feels like the progress of human civilization is not, is there’s a lot
of trajectories. And if you have individuals that accelerate towards one direction that helps steer
human civilization. So, 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. Well, I think you’re right. We have to take the whole period of, you know, World War II,
Nazism or something like that. Well, that was a bad sidestep, right? We’ve been over there for a
while. But, you know, 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. So, yeah. So,
I think you can perhaps that’s accelerating the positive 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, you know, 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. You know, we’ll make them live in some sort of way that’s compatible
with the carrying capacity of the earth. I’m optimistic these things will happen. And all we
can do is try to get there sooner. And at the very least, if we do destroy ourselves,
we’ll have a few satellites orbiting that will tell alien civilization that we were once here.
Or maybe our future, you know, future inhabitants of earth. You know, imagine we,
you know, the planet of the apes in here. You know, we kill ourselves, you know,
a million years from now or a billion years from now. There’s another species on the planet.
Curious creatures were once here. Jeff, thank you so much for your work. And thank you so much for
talking to me once again. Well, actually, it’s great. I love what you do. I love your podcast.
You have the most interesting people, me aside. So it’s a real service, I think you do for,
in 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
Codecademy, BioOptimizers, ExpressVPN, Asleep, 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 the
practical question we must try to answer. Thank you for listening. I hope to see you next time.