Dr. Lex Fridman: Machines, Creativity & Love

[bright music]

- Welcome to the "Huberman Lab Podcast,"

where we discuss science and science-based tools

for everyday life.

I'm Andrew Huberman,

and I'm a Professor of Neurobiology and Ophthalmology

at Stanford School of Medicine.

Today I have the pleasure of introducing Dr. Lex Fridman

as our guest on the "Huberman Lab Podcast."

Dr. Fridman is a researcher at MIT specializing

in machine learning,

artificial intelligence and human robot interactions.

I must say that the conversation with Lex

was without question,

one of the most fascinating conversations

that I've ever had,

not just in my career, but in my lifetime.

I knew that Lex worked on these topics.

And I think many of you are probably familiar with Lex

and his interest in these topics

from his incredible podcast, the "Lex Fridman Podcast."

If you're not already watching that podcast,

please subscribe to it.

It is absolutely fantastic.

But in holding this conversation with Lex,

I realized something far more important.

He revealed to us a bit of his dream.

His dream about humans and robots,

about humans and machines,

and about how those interactions

can change the way that we perceive ourselves

and that we interact with the world.

We discuss relationships of all kinds,

relationships with animals,

relationships with friends,

relationships with family and romantic relationships.

And we discuss relationships with the machines.

Machines that move and machines that don't move,

and machines that come to understand us in ways

that we could never understand for ourselves,

and how those machines can educate us about ourselves.

Before this conversation,

I had no concept of the ways

in which machines could inform me

or anyone about themselves.

By the end,

I was absolutely taken with the idea,

and I'm still taken with the idea

that interactions with machines

have a very particular kind,

a kind that Lex understands and wants to bring to the world,

can not only transform the self,

but may very well transform humanity.

So whether or not you're familiar

with Dr. Lex Fridman or not,

I'm certain you're going to learn

a tremendous amount from him

during the course of our discussion,

and that it will transform

the way that you think about yourself and about the world.

Before we begin,

I want to mention that this podcast

is separate from my teaching and research roles at Stanford.

It is however part of my desire and effort

to bring zero cost to consumer information about science

and science-related tools to the general public.

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And now, my conversation with Dr. Lex Fridman.

- We meet again.

- We meet again.

Thanks so much for sitting down with me.

I have a question that I think is on a lot of people's minds

or ought to be on a lot of people's minds,

because we hear these terms a lot these days,

but I think most people,

including most scientists and including me

don't know really what is artificial intelligence,

and how is it different

from things like machine learning and robotics?

So, if you would be so kind as to explain to us,

what is artificial intelligence,

and what is machine learning?

- Well, I think that question is as complicated

and as fascinating as the question of, what is intelligence?

So, I think of artificial intelligence,

first, as a big philosophical thing.

Pamela McCormick said AI was the ancient wish

to forge the gods,

or was born as an ancient wish to forge the gods.

So I think at the big philosophical level,

it's our longing to create other intelligence systems.

Perhaps systems more powerful than us.

At the more narrow level,

I think it's also set of tools

that are computational mathematical tools

to automate different tasks.

And then also it's our attempt to understand our own mind.

So, build systems that exhibit some intelligent behavior

in order to understand what is intelligence

in our own selves.

So all of those things are true.

Of course, what AI really means as a community,

as a set of researchers and engineers,

it's a set of tools,

a set of computational techniques

that allow you to solve various problems.

There's a long history that approaches the problem

from different perspectives.

What's always been throughout one of the threads,

one of the communities goes under the flag

of machine learning,

which is emphasizing in the AI space,

the task of learning.

How do you make a machine

that knows very little in the beginning,

follow some kind of process

and learns to become better and better at a particular task?

What's been most very effective in the recent about 15 years

is a set of techniques that fall under the flag

of deep learning that utilize neural networks.

When your networks are these fascinating things inspired

by the structure of the human brain,

very loosely, but they have a,

it's a network of these little basic computational units

called neurons, artificial neurons.

And they have,

these architectures have an input and output.

They know nothing in the beginning,

and their task with learning something interesting.

What that's something interesting is,

usually involves a particular task.

There's a lot of ways to talk about this

and break this down.

Like one of them is how much human supervision

is required to teach this thing.

So supervised learning is broad category,

is the neural network knows nothing in the beginning

and then it's given a bunch of examples in computer vision

that will be examples of cats, dogs, cars, traffic signs,

and then you're given the image

and you're given the ground truth of what's in that image.

And when you get a large database of such image examples

where you know the truth,

the neural network is able to learn by example,

that's called supervised learning.

There's a lot of fascinating questions within that,

which is, how do you provide the truth?

When you given an image of a cat,

how do you provide to the computer

that this image contains a cat?

Do you just say the entire image is a picture of a cat?

Do you do what's very commonly been done,

which is a bounding box,

you have a very crude box around the cat's face saying,

this is a cat?

Do you do semantic segmentation?

Mind you, this is a 2D image of a cat.

So it's not,

the computer knows nothing

about our three-dimensional world,

is just looking at a set of pixels.

So, semantic segmentation is drawing a nice,

very crisp outline around the cat and saying, that's a cat.

That's really difficult to provide that truth.

And one of the fundamental open questions

in computer vision is,

is that even a good representation of the truth?

Now, there's another contrasting set of ideas

that our attention they're overlapping is,

well, it's used to be called unsupervised learning.

What's commonly now called self-supervised learning.

Which is trying to get less and less

and less human supervision into the task.

So self-supervised learning is more,

it's been very successful in the domain of language model,

natural English processing,

and now more and more as being successful

in computer vision task.

And the idea there is,

let the machine without any ground-truth annotation

just look at pictures on the internet,

or look at texts on the internet

and try to learn something generalizable

about the ideas that are at the core of language

or at the core of vision.

And based on that,

we humans at its best like to call that common sense.

So with this,

we have this giant base of knowledge

on top of which we build more sophisticated knowledge.

We have this kind of commonsense knowledge.

And so the idea with self-supervised learning

is to build this commonsense knowledge about,

what are the fundamental visual ideas

that make up a cat and a dog

and all those kinds of things

without ever having human supervision?

The dream there is the,

you just let an AI system that's self supervised

run around the internet for awhile,

watch YouTube videos for millions and millions of hours,

and without any supervision be primed

and ready to actually learn with very few examples

once the human is able to show up.

We think of children in this way,

human children,

is your parents only give one or two examples

to teach a concept.

The dream with self-supervised learning

is that will be the same with machines.

That they would watch millions of hours of YouTube videos,

and then come to a human and be able to understand

when the human shows them, this is a cat.

Like, remember this' a cat.

They will understand that a cat

is not just the thing with pointy ears,

or a cat is a thing that's orange, or is furry,

they'll see something more fundamental

that we humans might not actually be able

to introspect and understand.

Like, if I asked you,

what makes a cat versus a dog,

you wouldn't probably not be able to answer that,

but if I showed you, brought to you a cat and a dog,

you'll be able to tell the difference.

What are the ideas that your brain uses

to make that difference?

That's the whole dream with self-supervised learning,

is it would be able to learn that on its own.

That set of commonsense knowledge,

that's able to tell the difference.

And then there's like a lot of incredible uses

of self-supervised learning,

very weirdly called self-play mechanism.

That's the mechanism behind

the reinforcement learning successes

of the systems that won at Go,

at, AlphaZero that won a chess.

- Oh, I see.

That play games?

- [Lex] That play games.

- Got it.

- So the idea of self-play is probably,

applies to other domains than just games.

Is a system that just plays against itself.

And this is fascinating in all kinds of domains,

but it knows nothing in the beginning.

And the whole idea is it creates

a bunch of mutations of itself

and plays against those versions of itself.

And the fascinating thing is when you play against systems

that are a little bit better than you,

you start to get better yourself.

Like learning,

that's how learning happens.

That's true for martial arts.

It's true in a lot of cases.

Where you want to be interacting with systems

that are just a little better than you.

And then through this process of interacting with systems

just a little better than you,

you start following this process

where everybody starts getting better and better and better

and better until you are several orders of magnitude

better than the world champion in chess, for example.

And it's fascinating because it's like a runaway system.

One of the most terrifying and exciting things

that David Silver, the creator of AlphaGo and AlphaZero,

one of the leaders of the team said,

to me is a,

they haven't found the ceiling for AlphaZero.

Meaning it could just arbitrarily keep improving.

Now, in the realm of chess,

that doesn't matter to us.

That it's like,

it just ran away with the game of chess.

Like it's like just so much better than humans.

But the question is what,

if you can create that in the realm that does have a bigger,

deeper effect on human beings and societies,

that can be a terrifying process.

To me, it's an exciting process

if you supervise it correctly,

if you inject, if what's called value alignment,

you make sure that the goals that the AI is optimizing

is aligned with human beings and human societies.

There's a lot of fascinating things to talk about

within the specifics of neural networks

and all the problems that people are working on.

But I would say the really big,

exciting one is self-supervised learning.

We're trying to get less and less human supervision,

less and less human supervision of neural networks.

And also just a comment and I'll shut up.

- No, please keep going.

I'm learning.

I have questions, but I'm learning.

So please keep going.

- So, to me what's exciting is not the theory,

it's always the application.

One of the most exciting applications

of artificial intelligence,

specifically neural networks and machine learning

is Tesla Autopilot.

So these are systems that are working in the real world.

This isn't an academic exercise.

This is human lives at stake.

This is safety-critical.

- These are automated vehicles.

Autonomous vehicles. - Semi-autonomous.

We want to be.

- Okay.

- We've gone through wars on these topics,

- Semi-autonomous vehicles.

- Semi-autonomous.

So, even though it's called a FSD, Full Self-Driving,

it is currently not fully autonomous,

meaning human supervision is required.

So, human is tasked with overseeing the systems.

In fact, liability-wise, the human is always responsible.

This is a human factor psychology question,

which is fascinating.

I'm fascinated by the whole space,

which is a whole 'nother space of human robot interaction

when AI systems and humans work together

to accomplish tasks.

That dance to me is one of the smaller communities,

but I think it will be one of the most

important open problems once they're solved,

is how the humans and robots dance together.

To me, semi-autonomous driving is one of those spaces.

So for Elon, for example, he doesn't see it that way,

he sees semi-autonomous driving as a stepping stone

towards fully autonomous driving.

Like, humans and robots can't dance well together.

Like, humans and humans dance and robots and robots dance.

Like, we need to,

this is an engineering problem,

we need to design a perfect robot that solves this problem.

To me forever,

maybe this is not the case with driving,

but the world is going to be full of problems

with always humans and robots have to interact,

because I think robots will always be flawed,

just like humans are going to be flawed, are flawed.

And that's what makes life beautiful,

that they're flawed.

That's where learning happens

at the edge of your capabilities.

So you always have to figure out,

how can flawed robots and flawed humans interact together

such that they, like the sum is bigger than the whole,

as opposed to focusing on just building the perfect robot?

- Mm-hmm.

- So that's one of the most exciting applications

I would say of artificial intelligence to me

is autonomous driving, the semi-autonomous driving.

And that's a really good example of machine learning

because those systems are constantly learning.

And there's a process there that maybe I can comment on,

the, Andrej Karpathy who's the head of autopilot

calls it the data engine.

And this process applies for a lot of machine learning,

which is you build a system that's pretty good

at doing stuff,

you send it out into the real world,

it starts doing the stuff

and then it runs into what are called edge cases,

like failure cases,

where it screws up.

We do this as kids.

That you have-

- You do this as adults.

- We do this as adults.

Exactly.

But we learn really quickly.

But the whole point,

and this is the fascinating thing about driving,

is you realize there's millions of edge cases.

There's just like weird situations that you did not expect.

And so the data engine process

is you collect those edge cases,

and then you go back to the drawing board

and learn from them.

And so you have to create this data pipeline

where all these cars,

hundreds of thousands of cars are driving around

and something weird happens.

And so whenever this weird detector fires,

it's another important concept,

that piece of data goes back to the mothership

for the training,

for the retraining of the system.

And through this data engine process,

it keeps improving and getting better and better

and better and better.

So basically you send out

a pretty clever AI systems out into the world

and let it find the edge cases,

let it screw up just enough to figure out

where the edge cases are,

and then go back and learn from them,

and then send out that new version

and keep updating that version.

- Is the updating done by humans?

- The annotation is done by humans.

The, so you have to,

the weird examples come back,

the edge cases,

and you have to label what actually happened in there.

There's also some mechanisms for automatically labeling,

but mostly,

I think you always have to rely on humans to improve,

to understand what's happening in the weird cases.

And then there's a lot of debate.

And this, the other thing,

what is artificial intelligence?

Which is a bunch of smart people

having very different opinions about what is intelligence.

So AI is basically a community of people

who don't agree on anything.

- And it seems to be the case.

First of all,

this is a beautiful description of terms

that I've heard many times among my colleagues at Stanford,

at meetings in the outside world.

And there are so many fascinating things.

I have so many questions,

but I do want to ask one question about the culture of AI,

because it does seem to be a community

where at least as an outsider,

where it seems like there's very little consensus

about what the terms

and the operational definitions even mean.

And there seems to be a lot of splitting happening now

of not just supervised and unsupervised learning,

but these sort of intermediate conditions

where machines are autonomous,

but then go back for more instruction

like kids go home from college during the summer

and get a little,

moms still feeds them

then eventually they leave the nest kind of thing.

Is there something in particular about engineers,

or about people in this realm of engineering

that you think lends itself to disagreement?

- Yeah, I think,

so, first of all, the more specific you get,

the less disagreement there is.

So there's lot of disagreement

about what is artificial intelligence,

but there's less disagreement about what is machine learning

and even less when you talk about active learning

or machine teaching or self-supervised learning.

And then when you get

into like NLP language models or transformers,

when you get into specific neural network architectures,

there's less and less and less disagreement

about those terms.

So you might be hearing the disagreement

from the high-level terms,

and that has to do with the fact that engineering,

especially when you're talking about intelligence systems

is a little bit of an art and a science.

So the art part is the thing that creates disagreements,

because then you start having disagreements

about how easy or difficult the particular problem is.

For example,

a lot of people disagree with Elon

how difficult the problem of autonomous driving is.

And so, but nobody knows.

So there's a lot of disagreement about,

what are the limits of these techniques?

And through that,

the terminology also contains within it the disagreements.

But overall,

I think it's also a young science

that also has to do with that.

So like it's not just engineering,

it's that artificial intelligence truly

is a large-scale discipline,

where it's thousands, tens of thousands,

hundreds of thousands of people working on it,

huge amounts of money being made as a very recent thing.

So we're trying to figure out those terms.

And, of course,

there's egos and personalities and a lot of fame to be made.

Like the term deep learning, for example,

neural networks have been around

for many, many decades since the '60s,

you can argue since the '40s.

So there was a rebranding of neural networks

into the word, deep learning,

term, deep learning,

that was part of the re-invigoration of the field,

but it's really the same exact thing.

- I didn't know that.

I mean, I grew up in the age of neuroscience

when neural networks were discussed,

computational neuroscience and theoretical neuroscience,

they had their own journals.

It wasn't actually taken terribly seriously

by experimentalists until a few years ago.

I would say about five to seven years ago.

Excellent theoretical neuroscientist like Larry Abbott

and other colleagues,

certainly at Stanford as well

that people started paying attention

to computational methods.

But these terms,

neural networks, computational methods,

I actually didn't know that neural network works

in deep learning where those have now become

kind of synonymous.

- No, they're always the same thing.

- Interesting.

It was, so.

- I'm a neuroscientist and I didn't know that.

- So, well, because neural networks

probably means something else

and neural science not something else,

but a little different flavor depending on the field.

And that's fascinating too,

because neuroscience and AI people

have started working together and dancing a lot more

in the recent,

I would say probably decade.

- Oh, machines are going into the brain.

I have a couple of questions,

but one thing that I'm sort of fixated on

that I find incredibly interesting

is this example you gave of playing a game

with a mutated version of yourself as a competitor.

- Yeah.

- I find that incredibly interesting

as a kind of a parallel or a mirror

for what happens when we try and learn as humans,

which is we generate repetitions

of whatever it is we're trying to learn,

and we make errors.

Occasionally we succeed.

In a simple example, for instance,

of trying to throw bulls eyes on a dartboard.

- Yeah.

- I'm going to have errors, errors, errors.

I'll probably miss the dartboard.

And maybe occasionally, hit a bullseye.

And I don't know exactly what I just did, right?

But then let's say I was playing darts

against a version of myself

where I was wearing a visual prism,

like my visual, I had a visual defect,

you learn certain things in that mode as well.

You're saying that a machine can sort of mutate itself,

does the mutation always cause a deficiency

that it needs to overcome?

Because of mutations in biology

sometimes give us super powers, right?

Occasionally, you'll get somebody

who has better than 2020 vision,

and they can see better than 99.9% of people out there.

So, when you talk about a machine playing a game

against a mutated version of itself,

is the mutation always say what we call a negative mutation,

or an adaptive or a maladaptive mutation?

- No, you don't know until you get,

so, you mutate first

and then figure out and they compete against each other.

- So, you're evolving,

the machine gets to evolve itself in real time.

- Yeah.

And I think of it,

which would be exciting

if you could actually do with humans.

It's not just.

So, usually you freeze a version of the system.

So, really you take on Andrew of yesterday

and you make 10 clones of them.

And then maybe you mutate, maybe not.

And then you do a bunch of competitions

of the Andrew of today, like you fight to the death,

and who wins last.

So, I love that idea

of like creating a bunch of clones of myself

from like from each of the day for the past year,

and just seeing who's going to be better

at like podcasting or science,

or picking up chicks at a bar or I don't know,

or competing in Jujitsu.

That's the one way to do it,

I mean, a lot of Lexes would have to die for that process,

but that's essentially what happens,

is in reinforcement learning

through the self-play mechanisms,

it's a graveyard of systems that didn't do that well.

And the surviving, the good ones survive.

- Do you think that, I mean, Darwin's Theory of Evolution

might have worked in some sense in this way,

but at the population level.

I mean, you get a bunch of birds with different shaped beaks

and some birds have the shaped beak

that allows them to get the seeds.

I mean, is a trivially simple example

of Darwinian in evolution,

but I think it's correct even though it's not exhaustive.

Is what you're referring to?

You essentially that normally this is done

between members of a different species,

lots of different members of species have different traits

and some get selected for,

but you could actually create multiple versions of yourself

with different traits.

- So, with, I should probably have said this,

but perhaps it's implied with machine learning,

with reinforcement learning through these processes.

One of the big requirements,

is to have an objective function, a loss function,

a utility function,

those are all different terms for the same thing,

is there's a like any equation that says what's good,

and then you're trying to optimize that equation.

So, there's a clear goal for these systems.

- Because it's a game, like with chess, there's a goal.

- But for anything.

Anything you want machine learning to solve,

there needs to be an objective function.

In machine learning, it's usually called Loss Function,

that you're optimizing.

The interesting thing about evolution,

it's complicated of course,

but the goal also seems to be evolving.

Like it's a, I guess, adaptation to the environment,

is the goal,

but it's unclear that you can convert that always.

It's like survival of the fittest.

It's unclear what the fittest is.

In machine learning,

the starting point,

and this is like what human ingenuity provides,

is that fitness function of what's good and what's bad,

which it lets you know which of the systems is going to win.

So, you need to have a equation like that.

One of the fascinating things about humans,

is we figure out objective functions for ourselves.

Like it's the meaning of life,

like why the hell are we here?

And a machine currently

has to have a hard-coded statement about why.

- It has to have a meaning of-

- Yeah.

- Artificial intelligence-based life.

- Right.

It can't.

So, like there's a lot of interesting explorations

about that function being more about curiosity,

about learning new things and all that kind of stuff,

but it's still hard coded.

If you want a machine to be able to be good at stuff,

it has to be given very clear statements

of what good at stuff means.

That's one of the challenges of artificial intelligence,

is you have to formalize the,

in order to solve a problem, you have to formalize it

and you have to provide

both like the full sensory information,

you have to be very clear about what is the data

that's being collected,

and you have to also be clear about the objective function.

What is the goal that you're trying to reach?

And that's a very difficult thing

for artificial intelligence.

- I love that you mentioned curiosity,

I am sure this definition falls short in many ways,

but I define curiosity

as a strong interest in knowing something,

but without an attachment to the outcome.

It's sort of a, it could be a random search,

but there's not really an emotional attachment,

it's really just a desire

to discover and unveil what's there

without hoping it's a gold coin under a rock,

you're just looking under rocks.

Is that more or less how the,

within machine learning,

it sounds like there are elements

of reward prediction and rewards.

The machine has to know when it's done the right thing.

So, can you make machines that are curious,

or are the sorts of machines that you are describing,

curious by design?

- Yeah, curiosity is a kind of a symptom, not the goal.

So, what happens,

is one of the big trade-offs in reinforcement learning,

is this exploration versus exploitation.

So, when you know very little, it pays off to explore a lot,

even suboptimal, like even trajectories

that seem like they're not going to lead anywhere,

that's called exploration.

The smarter and smarter and smarter you get,

the more emphasis you put on exploitation,

meaning you take the best solution, you take the best path.

Now, through that process,

the exploration can look like curiosity by us humans,

but it's really just trying to get out of the local optimal,

the thing it's already discovered.

From an AI perspective,

it's always looking to optimize the objective function,

it derives, and we can talk about the slot more,

but in terms of the tools of machine learning today,

it derives no pleasure from just the curiosity

of like, I don't know, discovery.

- So, there's no dopamine

for machine learning. - There's no dopamine.

- There's no reward, system chemical,

or I guess electronic-reward system.

- That said, if you look at machine learning literature

and reinforcement learning literature,

that will use,

like deep mind, we use terms like dopamine,

we're constantly trying to use the human brain

to inspire totally new solutions to these problems.

So, they'll think like,

how does dopamine function in the human brain,

and how can it lead to more interesting ways

to discover optimal solutions?

But ultimately currently,

there has to be a formal objective function.

Now, you could argue

the humans also has a set of objective functions

we try and optimize, we're just not able to introspect them.

- Yeah, we don't actually know what we're looking for

and seeking and doing.

- Well, like Lisa Feldman Barrett

who we spoken with at least on Instagram, I hope you-

- I met her through you, yeah.

- Yeah, I hope you actually have are on this podcast.

- Yes, she's terrific.

- So, she has a very,

it has to do with homeostasis like that.

Basically, there's a very dumb objective function

that the brain is trying to optimize,

like to keep like body temperature the same.

Like there's a very dom

kind of optimization function happening.

And then what we humans do with our fancy consciousness

and cognitive abilities, is we tell stories to ourselves

so we can have nice podcasts,

but really it's the brain trying to maintain a,

just like healthy state, I guess.

That's fascinating.

I also see the human brain,

and I hope artificial intelligence systems,

as not just systems that solve problems, or optimize a goal,

but are also storytellers.

I think there's a power to telling stories.

We tell stories to each other, that's what communication is.

Like when you're alone, that's when you solve problems,

that's when it makes sense to talk about solving problems.

But when you're a community,

the capability to communicate, tell stories,

share ideas in such a way that those ideas are stable

over a long period of time,

that's like, that's being a charismatic storyteller.

And I think both humans are very good at this.

Arguably, I would argue that's why we are who we are,

is we're great storytellers.

And then AI I hope will also become that.

So, it's not just about being able to solve problems

with a clear objective function,

it's afterwards, be able to tell like a way better,

like make up a way better story about why you did something,

or why you failed.

- So, you think that robots or, and/or machines of some sort

are going to start telling human stories?

- Well, definitely.

So, the technical field for that is called Explainable AI,

Explainable Artificial Intelligence,

is trying to figure out how you get the AI system

to explain to us humans why the hell it failed,

or why it succeeded,

or there's a lot of different sort of versions of this,

or to visualize how it understands the world.

That's a really difficult problem,

especially with neural networks

that are famously opaque,

that we don't understand in many cases,

why a particular neural network does what it does so well,

and to try to figure out where it's going to fail,

that requires the AI to explain itself.

There's a huge amount of money,

like there's a huge amount of money in this,

especially from government funding and so on.

Because if you want to deploy AI systems in the real world,

we humans at least, want to ask it a question like,

why the hell did you do that?

Like in a dark way,

why did you just kill that person, right?

Like if a car ran over a person,

we want to understand why that happened.

And now again, we're sometimes very unfair to AI systems

because we humans can often not explain why very well.

But that's the field of Explainable AI

that people are very interested in

because the more and more we rely on AI systems,

like the Twitter recommender system,

that AI algorithm that's, I would say impacting elections,

perhaps starting wars, or at least military conflict,

that algorithm, we want to ask that algorithm,

first of all, do you know what the hell you're doing?

Do you understand the society-level effects you're having?

And can you explain the possible other trajectories?

Like we would have that kind of conversation with a human,

we want to be able to do that with an AI.

And in my own personal level,

I think it would be nice to talk to AI systems

for stupid stuff, like robots when they fail to-

- Why'd you fall down the stairs?

- Yeah.

But not an engineering question,

but almost like an endearing question,

like I'm looking for,

if I fell and you and I were hanging out,

I don't think you need an explanation

exactly what were the dynamics,

like what was the under actuated system problem here?

Like what was the texture of the floor?

Or so on.

Or like, what was the-

- No, I want to know what you're thinking.

- That, or you might joke about like,

you're drunk again, go home, or something,

like there could be humor in it, that's an opportunity.

Like storytelling, isn't just explanation of what happened,

it's something that makes people laugh,

it makes people fall in love, it makes people dream,

and understand things

in a way that poetry makes people understand things

as opposed to a rigorous log

of where every sensor was, where every actuator was.

- I mean, I find this incredible

because one of the hallmarks

of severe autism spectrum disorders is,

a report of experience from the autistic person

that is very much a catalog of action steps.

It's like, how do you feel today?

And they'll say, well, I got up and I did this,

and then I did this, and I did this.

And it's not at all the way that a person

who doesn't have autism spectrum disorder would respond.

And the way you describe these machines

has so much humanism,

or so much of a human and biological element,

but I realized that we were talking about machines.

I want to make sure that I understand

if there's a distinction between a machine that learns,

a machine with artificial intelligence and a robot.

Like at what point does a machine become a robot?

So, if I have a ballpoint pen,

I'm assuming I wouldn't call that a robot,

but if my ballpoint pen can come to me

when I moved to the opposite side of the table,

if it moves by whatever mechanism,

at that point, does it become a robot?

- Okay, there's 1 million ways to explore this question.

It's a fascinating one.

So, first of all, there's a question of what is life?

Like how do you know something as a living form and not?

And it's to the question of when does sort of a,

maybe a cold computational system becomes a,

or already loading these words with a lot of meaning,

robot and machine,

So, one, I think movement is important,

but that's a kind of a boring idea

that a robot is just a machine

that's able to act in the world.

So, one artificial intelligence

could be both just the thinking thing,

which I think is what machine learning is,

and also the acting thing,

which is what we usually think about robots.

So, robots are the things that have a perception system

that's able to take in the world

however you define the world,

is able to think and learn

and do whatever the hell it does inside,

and then act on the world.

So, that's the difference

between maybe an AI system learning machine and a robot,

it's something that's able,

a robot is something that's able to perceive the world

and act in the world.

- So, it could be through language or sound,

or it could be through movement or both.

- Yeah.

And I think it could also be in the digital space

as long as there's a aspect of entity

that's inside the machine

and a world that's outside the machine.

And there's a sense in which the machine

is sensing that world and acting in it.

- So, we could,

for instance, there could be a version of a robot,

according to the definition that I think you're providing,

where the robot, where I go to sleep at night

and this robot goes and forges for information

that it thinks I want to see loaded onto my desktop

in the morning.

There was no movement of that machine,

there was no language,

but it essentially, has movement in cyberspace.

- Yeah, there's a distinction that I think is important

in that there's an element of it being an entity,

whether it's in the digital or the physical space.

So, when you have something like Alexa in your home,

most of the speech recognition, most of what Alexa is doing,

is constantly being sent back to the mothership.

When Alexa is there on its own, that's to me, a robot,

when it's there interacting with the world.

When it's simply a finger of the main mothership,

then the Alexa is not a robot,

then it's just an interaction device,

then may be the main Amazon Alexa AI,

big, big system is the robot.

So, that's important because there's some element,

to us humans, I think, where we want there to be an entity,

whether in the digital or the physical space,

that's where ideas of consciousness come in

and all those kinds of things

that we project our understanding

of what it means to be a being.

And so, to take that further,

when does a machine become a robot,

I think there's a special moment.

There's a special moment in a person's life

and in a robot's life where it surprises you.

I think surprise is a really powerful thing,

where you know how the thing works and yet it surprises you,

that's a magical moment for us humans.

So, whether it's a chess-playing program

that does something that you haven't seen before,

that makes people smile like, huh,

those moments happen with AlphaZero for the first time

in chess playing,

where grand masters were really surprised by a move.

They didn't understand the move

and then they studied and studied

and then they understood it.

But that moment of surprise,

that's for grandmasters in chess.

I find that moment of surprise really powerful,

really magical in just everyday life.

- Because it supersedes the human brain in that moment?

- So, it's not supersedes, like outperforms,

but surprises you in a positive sense.

Like I didn't think he could do that,

I didn't think that you had that in you.

And I think that moment is a big transition for a robot

from a moment of being a servant

that accomplishes a particular task

with some level of accuracy, with some rate of failure,

to an entity, a being that's struggling

just like you are in this world.

And that's a really important moment

that I think you're not going to find many people

in the AI community that talk like I just did.

I'm not speaking like some philosopher or some hippie,

I'm speaking from purely engineering perspective.

I think it's really important for robots to become entities

and explore that as a real engineering problem,

as opposed to everybody treats robots

in the robotics community,

they don't even call them or he or she,

they don't give them, try to avoid giving them names,

they've really want to see like a system, like a servant.

They see it as a servant that's trying to accomplish a task.

To me, and don't think I'm just romanticizing the notion,

I think it's a being, it's a currently perhaps a dumb being,

but in the long arc of history,

humans are pretty dumb beings too, so-

- I would agree with that statement.

[Andrew laughing]

- So, I tend to really want to explore this treating robots

really as entities, yeah.

So, like anthropomorphization,

which is the sort of the act

of looking at a inanimate object

and projecting onto it life-like features,

I think robotics generally sees that as a negative,

I see it as a superpower.

Like that, we need to use that.

- Well, I'm struck

by how that really grabs onto the relationship

between human and machine, or human and robot.

So, I guess the simple question is,

and I think you've already told us the answer,

but does interacting with a robot change you?

In other words, do we develop relationships to robots?

- Yeah, I definitely think so.

I think the moment you see a robot or AI systems

as more than just servants but entities,

they begin to change you, in just like good friends do,

just like relationships just to other humans.

I think for that,

you have to have certain aspects of that interaction.

Like the robot's ability to say no,

to have its own sense of identity,

to have its own set of goals,

that's not constantly serving you,

but instead, trying to understand the world

and do that dance of understanding

through communication with you.

So, I definitely think there's a,

I mean, I have a lot of thoughts about this as you may know,

and that's at the core of my life-long dream actually

of what I want to do,

which is I believe that most people

have a notion of loneliness in them

that we haven't discovered,

that we haven't explored, I should say.

And I see AI systems as helping us explore that

so that we can become better humans,

better people towards each other.

So, I think that connection

between human and AI, human and robot,

is not only possible, but will help us understand ourselves

in ways that are like several orders of magnitude

deeper than we ever could have imagined.

I tend to believe that [sighing]

well, I have very wild levels of belief

in terms of how impactful that will be, right?

- So, when I think about human relationships,

I don't always break them down into variables,

but we could explore a few of those variables

and see how they map to human-robot relationships.

One is just time, right?

If you spend zero time with another person at all

in cyberspace or on the phone or in person,

you essentially have no relationship to them.

If you spend a lot of time, you have a relationship,

this is obvious.

But I guess one variable would be time,

how much time you spend with the other entity,

robot or human.

The other would be wins and successes.

You enjoy successes together.

I'll give a absolutely trivial example this in a moment,

but the other would be failures.

When you struggle with somebody,

whether or not you struggle between one another,

you disagree,

like I was really struck by the fact

that you said that robot saying no,

I've never thought about a robot saying no to me,

but there it is.

- I look forward to you being one of the first people

I send this robots to.

- So do I.

So, there's struggle.

When you struggle with somebody, you grow closer.

Sometimes the struggles

are imposed between those two people,

so called trauma bonding,

they call it in the whole psychology literature

and pop psychology literature.

But in any case, I can imagine.

So, time successes together, struggle together,

and then just peaceful time,

hanging out at home, watching movies,

waking up near one another,

here, we're breaking down

the elements of relationships of any kind.

So, do you think that these elements

apply to robot-human relationships?

And if so, then I could see how,

if the robot has its own entity

and has some autonomy in terms of how it reacts you,

it's not just there just to serve you,

it's not just a servant,

it actually has opinions,

and can tell you when maybe your thinking is flawed,

or your actions are flawed.

- It can also leave.

- It could also leave.

So, I've never conceptualized

robot-human interactions this way.

So, tell me more about how this might look.

Are we thinking about a human-appearing robot?

I know you and I have both had intense relationships to our,

we have separate dogs obviously,

but to animals,

it sounds a lot like human-animal interaction.

So, what is the ideal human-robot relationship?

- So, there's a lot to be said here,

but you actually pinpointed one of the big, big first steps,

which is this idea of time.

And it's a huge limitation

in machine-learning community currently.

Now we're back to like the actual details.

Life-long learning is a problem space

that focuses on how AI systems

can learn over a long period of time.

What's currently most machine learning systems

are not able to do,

is to all of the things you've listed under time,

the successes, the failures,

or just chilling together watching movies,

AI systems are not able to do that,

which is all the beautiful, magical moments

that I believe are the days filled with,

they're not able to keep track of those together with you.

- 'Cause they can't move with you and be with you.

- No, no, like literally we don't have the techniques

to do the learning,

the actual learning of containing those moments.

Current machine-learning systems

are really focused on understanding the world

in the following way,

it's more like the perception system,

like looking around, understand like what's in the scene.

That there's a bunch of people sitting down,

that there is cameras and microphones,

that there's a table, understand that.

But the fact that we shared this moment of talking today,

and still remember that

for like next time you're doing something,

remember that this moment happened.

We don't know how to do that technique-wise.

This is what I'm hoping to innovate on

as I think it's a very, very important component

of what it means to create a deeper relationship,

that sharing of moments together.

- Could you post a photo of you in the robot,

like selfie with robot

and the robot sees that image

and recognizes that was time spent,

there were smiles, or there were tears-

- Yeah.

- And create some sort of metric of emotional depth

in the relationship and update its behavior?

- So.

- Could it...

It texts you in the middle of the night and say,

why haven't you texted me back?

- Well, yes, all of those things, but we can dig into that.

But I think that time element, forget everything else,

just sharing moments together, that changes everything.

I believe that changes everything.

Now, there's specific things

that are more in terms of systems that I can explain you.

It's more technical and probably a little bit offline,

'cause I have kind of wild ideas

how that can revolutionize social networks

and operating systems.

But the point is that element alone,

forget all the other things we're talking about

like emotions, saying no, all that,

just remembering sharing moments together

would change everything.

We don't currently have systems that share moments together.

Like even just you and your fridge,

just all those times, you went late at night

and ate thing you shouldn't have eaten,

that was a secret moment you have with your refrigerator.

You shared that moment,

that darkness or that beautiful moment

where you were just like heartbroken for some reason,

you're eating that ice cream or whatever,

that's a special moment.

And that refrigerator was there for you,

and the fact that it missed the opportunity

to remember that is tragic.

And once it does remember that,

I think you're going to be very attached to the refrigerator.

You're going to go through some hell with that refrigerator.

Most of us have, like in the developed world,

have weird relationships with food, right?

So, you can go through some deep moments

of trauma and triumph with food,

and at the core of that, is the refrigerator.

So, a smart refrigerator, I believe would change society.

Not just the refrigerator,

but these ideas in the systems all around us.

So, I just want to comment

on how powerful that idea of time is.

And then there's a bunch of elements

of actual interaction of allowing you as a human

to feel like you're being heard.

Truly heard, truly understood,

that we human, like deep friendship is like that, I think,

but there's still an element of selfishness,

there's still an element

of not really being able to understand another human.

And a lot of the times

when you're going through trauma together,

through difficult times and through successes,

you actually starting

to get that inkling of understanding of each other,

but I think

that could be done more aggressively, more efficiently.

Like if you think of a great therapist,

I think I've never actually been to a therapist,

but I'm a believer I used to want to be a psychiatrist.

- Do Russians go to therapists?

- No, they don't.

They don't.

And if they do, the therapist don't live to tell the story.

I do believe in talk therapy,

which friendship is to me, is it's talk therapy,

or like you don't even necessarily need to talk [laughing]

it's like just connecting through in the space of ideas

and the space of experiences.

And I think there's a lot of ideas

of how to make AI systems

to be able to ask the right questions

and truly hear another human.

This is what we try to do with podcasting, right?

I think there's ways to do that with AI.

But above all else,

just remembering the collection of moments

that make up the day, the week, the months,

I think you maybe have some of this as well.

Some of my closest friends

still are the friends from high school.

That's time, we've been through a bunch of together,

and that like we're very different people.

But just the fact that we've been through that,

and we remember those moments,

and those moments somehow create a depth of connection

like nothing else, like you and your refrigerator.

- I love that because my graduate advisor,

she unfortunately, she passed away,

but when she passed away,

somebody said at her at her memorial

all these amazing things she had done, et cetera.

And then her kids got up there,

and she had young children and that I knew

as when she was pregnant with them.

And so, it was really,

you're even now I can feel like your heart gets heavy,

thinking about this,

they're going to grow up without their mother.

And it was really amazing,

very strong young girls, and now the young women.

And what they said was incredible,

they said what they really appreciated most

about their mother, who was an amazing person,

is all the unstructured time they spent together.

- Mm-hmm.

- So, it wasn't the trips to the zoo,

it wasn't she woke up at five in the morning

and drove us to school.

She did all those things too.

She had two hour commute in each direction,

it was incredible, ran a lab, et cetera,

but it was the unstructured time.

So, on the passing of their mother,

that's what they remembered was that the biggest give

and what bonded them to her,

was all the time where they just kind of hung out.

And the way you describe the relationship to a refrigerator

is so, I want to say human-like,

but I'm almost reluctant to say that.

Because what I'm realizing as we're talking,

is that what we think of as human-like

might actually be the lower form of relationship.

There may be relationships that are far better

than the sorts of relationships

that we can conceive in our minds right now

based on what these machine relationship interactions

could teach us.

Do I have that right?

- Yeah, I think so.

I think there's no reason to see machines

as somehow incapable of teaching us something

that's deeply human.

I don't think humans have a monopoly on that.

I think we understand ourselves very poorly

and we need to have the kind of prompting from a machine.

And definitely part of that,

is just remembering the moments.

I think the unstructured time together,

I wonder if it's quite so unstructured.

That's like calling this podcast on structured time.

- Maybe what they meant, was it wasn't a big outing,

there was no specific goal,

but a goal was created through the lack of a goal.

Like we would just hang out

and then you start playing, thumb war,

and you end up playing thumb war for an hour.

So, it's the structure emerges from lack of structure.

- No, but the thing is the moments,

there's something about those times

that creates special moments,

and I think those could be optimized for.

I think we think of like a big outing as,

I don't know, going to Six Flags or something,

or some big, the Grand Canyon,

or go into some, I don't know, I think we would need to,

we don't quite yet understand, as humans,

what creates magical moments.

I think it's possible to optimize a lot of those things.

And perhaps like podcasting is helping people discover that,

like maybe the thing we want to optimize for

isn't necessarily like some sexy, like quick clips,

maybe what we want, is long-form authenticity.

- Depth.

- Depth.

So, we were trying to figure that out,

certainly from a deep connection

between humans and humans and NAS systems,

I think long conversations, or long periods of communication

over a series of moments like my new,

perhaps, seemingly insignificant to the big ones,

the big successes, the big failures,

those are all just stitching those together

and talking throughout.

I think that's the formula

for a really, really deep connection.

That from like a very specific engineering perspective,

is I think a fascinating open problem

that has been really worked on very much.

And for me, from a, if I have the guts

and, I mean there's a lot of things to say,

but one of it is guts, is I'll build a startup around it.

- So, let's talk about this startup

and let's talk about the dream.

You mentioned this dream before

in our previous conversations,

always as little hints dropped here and there.

Just for anyone listening,

there's never been an offline conversation about this dream,

I'm not privy to anything, except what Lex says now.

And I realized that there's no way

to capture the full essence of a dream

in any kind of verbal statement

in a way that captures all of it.

But what is this dream

that you've referred to now several times

when we've sat down together and talked on the phone?

Maybe it's this company, maybe it's something distinct.

If you feel comfortable,

it'd be great if you could share a little bit

about what that is. - Sure.

So, the way people express long-term vision,

I've noticed is quite different.

Like Elon is an example of somebody

who can very crisply say exactly what the goal is.

Also has to do with the fact that problems he's solving

have nothing to do with humans.

So, my long-term vision

is a little bit more difficult to express in words,

I've noticed, as I've tried,

it could be my brain's failure,

but there's a way to sneak up to it.

So, let me just say a few things.

Early on in life and also in the recent years,

I've interacted with a few robots

where I understood there's magic there.

And that magic could be shared by millions

if it's brought to light.

When I first met Spot from Boston Dynamics,

I realized there's magic there that nobody else is seeing.

- Is the dog.

- The dog, sorry.

The Spot is the four-legged robot from Boston Dynamics.

Some people might have seen it, it's this yellow dog.

And sometimes in life,

you just notice something that just grabs you.

And I believe that this is something

that this magic is something that could be

in every single device in the world.

The way that I think maybe Steve Jobs

thought about the personal computer,

laws didn't think about the personal computer this way,

but Steve did.

Which is like, he thought that the personal computer

should be as thin as a sheet of paper

and everybody should have one,

and this idea,

I think it is heartbreaking that we're getting,

the world is being filled up with machines

that are soulless.

And I think every one of them can have that same magic.

One of the things that also inspired me

in terms of a startup,

is that magic can engineered much easier than I thought.

That's my intuition

with everything I've ever built and worked on.

So, the dream is to add a bit of that magic

in every single computing system in the world.

So, the way that Windows Operating System for a long time

was the primary operating system everybody interacted with,

they built apps on top of it.

I think this is something that should be as a layer,

it's almost as an operating system

in every device that humans interacted with in the world.

Now, what that actually looks like,

the actual dream when I was officially a kid,

it didn't have this concrete form of a business,

it had more of a dream of exploring your own loneliness

by interacting with machines, robots.

This deep connection between humans and robots

was always a dream.

And so, for me,

I'd love to see a world where there's every home as a robot,

and not a robot that washes the dishes, or a sex robot,

or I don't know,

think of any kind of activity the robot can do,

but more like a companion.

- A family member.

- A family member, the way a dog is.

- Mm-hmm.

- But a dog that's able to speak your language too.

So, not just connect the way a dog does by looking at you

and looking away

and almost like smiling with its soul in that kind of way,

but also to actually understand what the hell,

like, why are you so excited about the successes?

Like understand the details, understand the traumas.

And that, I just think [sighing]

that has always filled me with excitement that I could,

with artificial intelligence, bring joy to a lot of people.

More recently, I've been more and more heartbroken

to see the kind of division, derision,

even hate that's boiling up on the internet

through social networks.

And I thought this kind of mechanism is exactly applicable

in the context of social networks as well.

So, it's an operating system that serves as your guide

on the internet.

One of the biggest problems

with YouTube and social networks currently,

is they're optimizing for engagement.

I think if you create AI systems

that know each individual person,

you're able to optimize for long-term growth

for a long-term happiness.

- Of the individual, or-

- Of the individual, of the individual.

And there's a lot of other things to say,

which is in order for AI systems

to learn everything about you,

they need to collect,

they need to just like you and I when we talk offline,

we're collecting data

about each others secrets about each other,

the same way AI has to do that.

And that allows you to,

and that requires you to rethink ideas of ownership of data.

I think each individual should own all of their data

and very easily be able to leave

just like AI systems can leave,

humans can disappear

and delete all of their data in a moment's notice.

Which is actually better than we humans can do,

is once we load the data into each other, it's there.

I think it's very important to be both,

give people complete control over their data

in order to establish trust that they can trust you.

And the second part of trust is transparency.

Whenever the data is used

to make it very clear what is being used for.

And not clear in a lawyerly legal sense,

but clear in a way that people

really understand what it's used for.

I believe when people have the ability

to delete all their data and walk away

and know how the data is being used, I think they'll stay.

- The possibility of a clean breakup,

is actually what will keep people together.

- Yeah, I think so.

I think, exactly.

I think a happy marriage

acquires the ability to divorce easily

without the divorce industrial complex or whatever.

Things currently going on

and then there's so much money to be made

from lawyers and divorce.

But yeah, the ability to leave

is what enables love, I think.

- It's interesting.

I've heard the phrase from a semi-cynical friend,

that marriage is the leading cause of divorce,

but now we've heard that divorce,

or the possibility of divorce

could be the leading cause of marriage.

- Of a happy marriage.

- Good point.

- Of a happy marriage.

So, yeah.

So, there's a lot of details there,

but the big dream

is that connection between AI system and a human.

And I haven't.

There's so much fear

about artificial intelligence systems and about robots

that I haven't quite found the right words

to express that vision

because the vision I have,

it's not like some naive, delusional vision

of like technology is going to save everybody,

it's I really do just have a positive view

of ways AI systems can help humans explore themselves.

- I love that positivity

and I agree that the stance everything

is doomed is equally bad

to say that everything's going to turn out all right,

there has to be a dedicated effort.

And clearly, you're thinking

about what that dedicated effort would look like.

You mentioned two aspects to this dream [clears throat]

and I want to make sure

that I understand where they connect if they do,

or if they are independent streams.

One was this hypothetical robot family member,

or some other form of robot

that would allow people to experience the kind of delight

that you experienced many times,

and that you would like the world to be able to have,

and it's such a beautiful idea of this give.

And the other is social media

or social network platforms that really serve individuals

and their best selves and their happiness and their growth.

Is there crossover between those,

or are these two parallel dreams?

- It's 100% of the same thing.

It's difficult to kind of explain

without going through details,

but maybe one easy way to explain

the way I think about social networks,

is to create an AI system that's yours.

It's not like Amazon Alexa that's centralized,

you own the data,

it's like your little friend

that becomes your representative on Twitter

that helps you find things that will make you feel good,

that will also challenge your thinking to make you grow,

but not get to that,

not let you get lost in the negative spiral of dopamine,

that gets you to be angry,

or most just get you to be not open to learning.

And so, that little representative

is optimizing your long-term health.

And I believe that that is not only good for human beings,

it's also good for business.

I think longterm, you can make a lot of money

by challenging this idea that the only way to make money,

is maximizing engagement.

And one of the things that people disagree with me on,

is they think Twitter's always going to win.

Like maximizing engagement is always going to win,

I don't think so.

I think people have woken up now to understanding that,

like they don't always feel good,

the ones who are on Twitter a lot,

that they don't always feel good at the end of the week.

- I would love feedback

from whatever this creature, whatever,

I don't know what to call it,

as to maybe at the end of the week,

it would automatically unfollow

some of the people that I follow

because it realized through some really smart data

about how I was feeling inside, or how I was sleeping,

or something that, I don't know,

that just wasn't good for me.

But it might also put things and people in front of me

that I ought to see,

is that kind of a sliver of what this look like?

- The whole point, because of the interaction,

because of sharing the moments and learning a lot about you,

you're now able to understand what interactions

led you to become a better version of yourself.

Like the person you yourself are happy with.

This isn't,

if you're into flat earth and you feel very good about it,

that you believe that earth is flat,

like the idea that you should sensor that is ridiculous.

If it makes you feel good

and you becoming the best version of yourself,

I think you should be getting

as much flat earth as possible.

Now, it's also good to challenge your ideas,

but not because the centralized committee decided,

but because you tell to the system

that you like challenging your ideas, I think all of us do.

And then, which actually YouTube doesn't do that well,

once you go down the flat-earth rabbit hole,

that's all you're going to see.

It's nice to get some really powerful communicators

to argue against flat earth.

And it's nice to see that for you,

and potentially, at least long-term to expand your horizons,

maybe the earth is not flat.

But if you continue to live your whole life

thinking the earth is flat,

I think, and you're being a good father or son or daughter,

and like you're being the best version of yourself

and you're happy with yourself, I think the earth is flat.

So, like I think this kind of idea,

and I'm just using that kind of silly, ridiculous example

because I don't like the idea of centralized forces

controlling what you can and can't see,

but I also don't like this idea

of like not censoring anything.

Because that's always the biggest problem with that,

is this there's a central decider,

I think you yourself can decide what you want to see and not,

and it's good to have a companion that reminds you

that you felt last time you did this,

or you felt good last time you did this.

- Man, I feel like in every good story,

there's a guide or a companion that flies out,

or forges a little bit further, a little bit differently

and brings back information that helps us,

or at least tries to steer us in the right direction.

- So, yeah, that's exactly what I'm thinking

and what I've been working on.

I should mention as a bunch of difficulties here,

you see me up and down a little bit recently.

So, there's technically a lot of challenges here.

Just like with a lot of technologies,

and the reason I'm talking about it

on a podcast comfortably as opposed to working in secret,

is it's really hard and maybe it's time has not come.

And that's something you have to constantly struggle with

in terms of like entrepreneurially as a startup,

like I've also mentioned to you, maybe offline,

I really don't care about money,

I don't care about business success,

all those kinds of things.

So, it's a difficult decision to make,

how much of your time do you want to go all in here

and give everything to this?

It's a big roll the dice.

Because I've also realized

that's working on some of these problems,

both with the robotics and the technical side

in terms of the machine-learning system that I'm describing,

it's lonely, it's really lonely.

Because both on a personal level and a technical level.

So, on the technical level,

I'm surrounded by people that kind of doubt me,

which I think all entrepreneurs go through.

And they doubt you in the following sense,

they know how difficult it is.

Like the people that the colleagues of mine,

they know how difficult life-long learning is,

they also know how difficult it is

to build a system like this,

to build the competitive social network.

And in general,

there's a kind of a loneliness

to just working on something on your own

for long periods of time.

And you start to doubt whether,

given that you don't have a track record of success,

like that's a big one.

But when you look in the mirror,

especially when you're young,

but I still have that, I'm most things,

you look in the mirror,

it's like, and you have these big dreams,

how do you know

you're actually as smart as you think you are?

Like how do you know

you're going to be able to accomplish this dream?

You have this ambition.

- You sort of don't,

but you're kind of pulling on a string

hoping that there's a bigger ball of yarn.

- Yeah.

[Andrew laughing]

But you have this kind of intuition.

I think I pride myself in knowing what I'm good at,

because the reason I have that intuition,

is 'cause I think I'm very good

at knowing all the things I suck at,

which is basically everything.

So, like whenever I notice like,

wait a minute, I'm kind of good at this,

which is very rare for me.

I think like that,

that might be a ball of yarn worth pulling at.

And the thing with in terms of engineering systems

that are able to interact with humans,

I think I'm very good at that.

And because we talk about podcasting and so on,

I don't know if I'm very good at podcasts.

- You're very good at podcasting, right?

- But I certainly don't,

I think maybe it is compelling for people

to watch a kindhearted idiot struggle with this form,

maybe that's what's compelling.

But in terms of like actual being a good engineer

of human-robot interaction systems, I think I'm good.

But it's hard to know until you do it,

and then the world keeps telling you you're not,

and it's full of doll, it's really hard.

And I've been struggling with that recently,

it's kind of a fascinating struggle.

But then that's where the Goggins thing comes in,

is like, aside from the state-hard motherfucker, is the,

like whenever you're struggling,

that's a good sign that if you keep going,

that you're going to be alone in the success, right?

- Well, in your case, however, I agree.

And actually David had a post recently

that I thought was among his many brilliant posts,

was one of the more brilliant

about how he talked about this myth

of the light at the end of the tunnel.

And instead, what he replaced that myth with,

was a concept that eventually, your eyes adapt to the dark.

That the tunnel, it's not about a light at the end,

that it's really about adapting to the dark of the tunnel.

It's very Goggins-

- I love him so much.

- Yeah.

You got share a lot in common knowing you both a bit,

share a lot in common.

But in this loneliness and the pursuit of this dream,

it seems to me,

it has a certain component to it that is extremely valuable,

which is that the loneliness itself

could serve as a driver

to build the companion for the journey.

- Well, I'm very deeply aware of that.

So, like some people can make,

'cause I talk about love a lot,

I really love everything in this world,

but I also love humans, friendship and romantic,

like even the cheesy stuff, just-

- You like romantic movies?

- Yeah, not those, not necessarily.

So, well, I got so much from Rogan about like,

was it the Tango scene from "Scent of a Woman,"

but I find like there's nothing better

than a woman in a red dress, just like the classy-

- You should move to Argentina my friend.

My father's Argentine, and you know what he said

when I went on your podcast for the first time?

He said, he dresses well.

Because in Argentina,

the men go to a wedding or a party or something,

in the U.S., by halfway through the night,

10 minutes in the night, all the jackets are off.

- Yeah.

- It looks like everyone's undressing for the party

they just got dressed up for.

And he said, you know, I liked the way he dresses.

And then when I started, he was talking about you.

And then when I started my podcast, he said,

why don't you wear a real suit like your friend Lex?

[laughing]

- I remember that.

- No, you can't.

But let's talk about this pursuit just a bit more,

because I think what you're talking about,

is building a, not just a solution for loneliness,

but you've alluded to the loneliness as itself,

an important thing.

And I think you're right.

I think within people,

there is like caverns of faults and shame,

but also just the desire to have resonance,

to be seen and heard.

And I don't even know

that it's seen and heard through language.

But these reservoirs of loneliness,

I think, well, they're interesting,

maybe you could comment a little bit about it.

Because just as often as you talk about love,

I haven't quantified it,

but it seems that you talk about this loneliness,

and maybe you just if you're willing,

you'll share a little bit more about that,

and what that feels like now

in the pursuit of building this robot-human relationship.

And you've been, let me be direct,

you've been spending a lot of time

on building a robot-human relationship, where's that at?

- Oh, in terms of business, in terms of systems?

- No, I'm talking about a specific robot.

- Oh [laughing]

so, okay, I should mention a few things.

So, one, is there's a startup with an idea

where I hope millions of people can use.

And then there's my own personal

like almost like Frankenstein explorations

with particular robots.

So, I'm very fascinated with the legged robots in my own,

private sounds like dark,

but like in of one experiments

to see if I can recreate the magic.

And that's been,

I have a lot of really good already perception systems

and control systems that are able to communicate affection

in a dog-like fashion.

So, I'm in a really good place there.

The stumbling blocks,

which also have been part of my sadness recently,

is that I also have to work with robotics companies

that I gave so much of my heart, soul

and love and appreciation towards Boston Dynamics,

but Boston Dynamics has also,

as a company, it has to make a lot of money

and they have marketing teams.

And they're like looking at this silly Russian kid

in a suit and tie,

it's like, what's he trying to do

with all this love and robot interaction

and dancing and so on?

So, there was a, I think let's say for now,

it's like, when you break up with a girlfriend or something,

right now, we decided to part ways on this particular thing.

They're huge supporters of mine, they're huge fans,

but on this particular thing,

Boston Dynamics is not focusing on,

or interested in human-robot interaction.

In fact, their whole business currently,

is keep the robot as far away from humans as possible

because it's an in the industrial setting

where it's doing monitoring in dangerous environments.

It's almost like a remote security camera,

essentially is its application.

To me, I thought it's still,

even in those applications, exceptionally useful

for the robot to be able to perceive humans, like see humans

and to be able to, in a big map,

localize where those humans are and have human intention.

For example, like this,

I did this a lot of work with pedestrians,

for a robot to be able to anticipate

what the how the human is doing, like where it's walking.

The humans are not ballistics object,

just because you're walking this way one moment,

it doesn't mean you'll keep walking that direction,

you have to infer a lot of signals,

especially with the head movement and the eye movement.

And so, I thought that's super interesting to explore,

but they didn't feel that.

So, I'll be working with a few other robotics companies

that are much more open to that kind of stuff,

and they're super excited and fans of mine.

And hopefully, Boston Dynamics, my first love,

like getting back with an ex-girlfriend, will come around.

But so, the algorithmically, it's basically a done there.

The rest, is actually getting some of these companies

to work with.

And then there's,

for people who'd worked with robots

know that one thing is to write software that works,

and the other is to have a real machine that actually works.

And it breaks down all kinds of different ways

that are fascinating.

And so, there's a big challenge there.

But that's almost,

it may sound a little bit confusing

in the context of our previous discussion

because the previous discussion

was more about the big dream,

how I hoped to have millions of people

enjoy this moment of magic.

This current discussion about a robot,

is something I personally really enjoy,

just brings me happiness,

I really try to do now everything that just brings me joy,

I maximize that 'cause robots are awesome.

But two, given my like little bit growing platform,

I want to use the opportunity to educate people.

Like robots are cool.

And if I think they're cool,

I hope be able to communicate why they're cool to others.

So, this little robot experiment

is a little bit of research project too,

there's a couple of publications with MIT folks around that.

But the other is just to make some cool videos

and explain to people how they actually work.

And as opposed to people being scared of robots,

they could still be scared, but also excited,

like see the dark side, the beautiful side,

the magic of what it means to bring,

for a machine to become a robot.

I want to inspire people with that.

But that's less,

it's interesting because I think the big impact

in terms of the dream does not have to do with embodied AI.

So, it does not need to have a body.

I think the refrigerator's enough,

that for an AI system just to have a voice and to hear you,

that's enough for loneliness.

The embodiment is just-

- By embodiment, you mean the physical structure.

- Physical instantiation of intelligence.

So, it's a legged robot, or even just a thing.

I have a few other of humanoid robot,

a little humanoid robot maybe I'll keep them on the table,

is like walks around,

or even just like a mobile platform

they can just like, turn around and look at you,

it's like we mentioned with the pen.

Something that moves and can look at you,

it's like that Butter Robot that asks, what is my purpose?

That is really, it's almost like art.

There's something about a physical entity that moves around,

that's able to look at you and interact with you,

that makes you wonder what it means to be human.

It like challenges you to think,

if that thing looks like he has consciousness,

what the hell am I?

And I like that feeling,

I think that's really useful for us,

it's humbling for us humans.

But that's less about research,

it certainly less about business

and more about exploring our own selves,

and challenging others to think like,

to think about what makes them human.

- I love this desire

to share the delight of an interaction with a robot.

And as you describe it,

I actually find myself starting to crave that

because we all have those elements from childhood where,

or from adulthood, where we experience something,

we want other people to feel that.

And I think that you're right,

I think a lot of people are scared of AI,

I think a lot of people are scared of robots.

My only experience, and of a robotic-like thing

is my Roomba Vacuum, where it goes about,

actually, it was pretty good at picking up Costello's hair

when he was shed and I was grateful for it.

But then when I was on a call or something,

and it would get caught on a wire or something,

I would find myself getting upset with the Roomba

in that moment, I'm like, what are you doing?

And obviously it's just doing what it does.

But that's a kind of mostly positive,

but slightly negative interaction.

But what you're describing,

it has so much more richness and layers of detail

that I can only imagine what those relationships are like.

- Well, there's a few, just a quick comment.

So, I've had, they're currently in Boston

and I have a bunch of Roombas from iRobot.

And I did this experiment-

- Wait, how many Roombas?

[Lex laughing]

It sounds like a fleet of Roombas.

- Yeah.

So, it's probably seven or eight, yeah.

- Well, it's a lot of Roombas.

This place is very clean.

- Well, so, this, I'm kind of waiting,

this is the place we're currently in Austin,

is way larger than I need.

But I basically got it to make sure I have room for robots.

- So, you have these seven or so Roombas,

you deploy all seven at once?

- Oh, no, I do different experience with them,

different experiments with them.

So, one of the things I want to mention, is this is a,

I think there was a YouTube video

that inspired me to try this,

is I got them to scream in pain and moan in pain

whenever they were kicked or contacted.

And I did that experiment to see how I would feel.

I meant to do like a YouTube video on it,

but then it just seemed very cruel.

- Did any Roomba rights activists come at you?

- Like I think if I released that video,

I think it's going to make me look insane,

which I know people know I'm already insane-

- Now, you have to release the video.

[Lex laughing]

- Sure.

Well, I think maybe if I contextualize it

by showing other robots

like to show why this is fascinating,

because ultimately,

I felt like there were human almost immediately.

And that display of pain was what did that.

- Giving them a voice.

- Giving them a voice,

especially a voice of dislike, of pain.

- I have to connect you to my friend Eddie Chang,

he studied speech and language, he's a neurosurgeon,

and we're life-long friends.

He studied speech and language,

but he describes some of these more primitive,

visceral vocalizations, cries, groans, moans of delight,

other sounds as well, use your imagination,

as such powerful rudders for the other,

for the emotions of other people.

And so, I find it fascinating,

I can't wait to see this video.

So, is the video available online?

- No, I haven't recorded it,

I just hit a bunch of Roombas

that are able to scream in pain in my Boston place.

[Andrew laughing]

So, like people already-

- Next podcast episode with Lex,

maybe we'll have that one, who knows?

- So, the thing is like people,

I've noticed because I talk so much about love

and it's really who I am,

I think they want to,

to a lot of people,

seems like there's there got to be a dark person

in there somewhere.

And I thought if I release videos of Roombas screaming

and they're like, yep, yep, that guy's definitely insane.

- What about like shouts of glee and delight,

you could do that too, right?

- Well, I don't know how to,

to me, delight is quiet, right?

- You're a Russian.

Americans are much louder than Russians.

- Yeah. - Yeah.

- Yeah.

But like I don't, unless you're talking about like,

I don't know

how you would have sexual relationships with the Roomba.

- Well, I wasn't necessarily saying a sexual delight, but-

- Trust me, I tried.

I'm just kidding, that's a joke, internet.

Okay [giggles]

but I was fascinated in the psychology

of how little it took.

'Cause you mentioned

you had a negative relationship with a Roomba.

- Well, I'd find that mostly, I took it for granted.

- Yeah.

- It just served me, it collected Costello's hair.

And then when it would do something I didn't like,

I would get upset with it.

So, that's not a good relationship, it was taken for granted

and I would get upset and then I'd park it again,

and I just like you're in the corner.

- Yeah.

- But there's a way to frame it being quite dumb

as almost cute.

You're almost connecting with it for its dumbness.

And I think that's artificial intelligence problem.

- [Andrew] It's interesting.

- I think flaws should be feature, not a bug.

- So, along the lines of this,

the different sorts of relationships

that one could have with robots,

and the fear, but also some of the positive relationships

that one could have,

there's so much dimensionality, there's so much to explore.

But power dynamics in relationships are very interesting,

because the obvious ones,

the unsophisticated view of this,

is one, there's a master and a servant, right?

But there's also manipulation,

there's benevolent manipulation,

children do this with parents, puppies do this.

Puppies turn their head and look cute

and maybe give out a little noise,

kids coup and parents always think that they're doing this

because they love the parent,

but in many ways,

studies show that those coups are ways

to extract the sorts of behaviors and expressions

from the parent that they want.

The child doesn't know it's doing this,

it's completely subconscious,

but it's benevolent manipulation.

So, there's one version of fear of robots

that I hear a lot about

that I think most people can relate to

where the robots take over

and they become the masters and we become the servants.

But there could be another version

that in certain communities

that I'm certainly not a part of,

but they call topping from the bottom,

where the robot is actually manipulating you

into doing things,

but you are under the belief that you are in charge,

but actually they're in charge.

And so, I think that's one

that if we could explore that for a second,

you could imagine it wouldn't necessarily be bad,

although it could lead to bad things.

The reason I want to explore this,

is I think people always default to the extreme,

like the robots take over and we're in little jail cells

and they're out having fun and ruling the universe.

What sorts of manipulation

can a robot potentially carry out, good or bad?

- Yeah.

Just so, there's a lot of good and bad manipulation

between humans, right?

Just like you said.

[Lex sighing]

To me [sighing] especially like you said [giggles]

topping from the bottom, is that the term?

- I think someone from MIT told me that term.

[Lex laughing]

It wasn't Lex.

- I think.

So, first of all, there's power dynamics in bed,

and power dynamics in relationships,

and power dynamics on the street,

and in the work environment, those are all very different.

I think power dynamics can make human relationships,

especially romantic relationships fascinating and rich

and fulfilling and exciting and all those kinds of things.

So, I don't think in themselves they're bad,

and the same goes with robots.

I really love the idea

that a robot will be at top or a bottom

in terms of like power dynamics.

And I think everybody should be aware of that.

And the manipulation is not so much manipulation,

but a dance of like pulling away, a push and pull,

and all those kinds of things.

In terms of control,

I think we're very, very, very far away from AI systems

that are able to lock us up.

To lock us up in,

like to have so much control

that we basically cannot live our lives

in the way that we want.

I think there's a, in terms of dangers of AI systems,

there's much more dangers

that have to do with autonomous weapon systems

and all those kinds of things.

So, the power dynamics as exercised in the struggle

between nations and war and all those kinds of things.

But in terms of personal relationships,

I think power dynamics are a beautiful thing.

Now, there's of course,

going to be all those kinds of discussions

about consent and rights and all those kinds of things.

- Well, here, we're talking about,

I always say in any discussion around this,

if we need to define a really the context,

it always should be consensual, age-appropriate,

context-appropriate, species-appropriate,

but now we're talking about human-robot interactions.

And so, I guess that-

- No, I actually was trying to make a different point,

which is I do believe

that robots will have rights down the line.

And I think in order for us

to have deep, meaningful relationships with robots,

we would have to consider them as entities in themselves

that deserve respect.

And that's a really interesting concept

that I think people are starting to talk about

a little bit more,

but it's very difficult for us to understand

how entities that are other than human.

I mean, the same as with dogs and other animals

can have rights on a level as humans.

- Well, yeah.

We can't and nor should we do whatever we want with animals,

we have a USDA, we have Department of Agriculture

that deal with animal care and use committees for research,

for farming and ranching and all that.

So, when you first said it, I thought, wait, why would,

there'll be a bill of robotic rights,

but it absolutely makes sense

in the context of everything we've been talking about

up until now.

If you're willing, I'd love to talk about dogs,

because you've mentioned dogs a couple of times,

a robot dog, you had a biological dog.

Yeah. - Yeah.

I had a Newfoundland named Homer for many years growing up.

- In Russia or in the U.S.?

- In the United States.

And he was about, he's over 200 pounds, that's a big dog.

- That's a big dog.

- People know Newfoundland.

So, he's this black dog that's a really a long hair

and just a kind soul.

I think perhaps that's true for a lot of large,

but he thought he was a small dog.

So, he moved like that, and-

- Was he your dog?

- Yeah, yeah.

- So, you had him since he was fairly young?

- Since the very, very beginning, till the very, very end.

And one of the things, I mean, he had this kind of a,

we mentioned like the Roombas,

he had kind-hearted dumbness about him

that was just overwhelming,

it's part of the reason I named him Homer,

because it's after Homer Simpson,

in case people are wondering which Homer I'm referring to.

[Andrew laughing]

And so, there's a.

Yeah, exactly.

There's a clumsiness that was just something

that immediately led to a deep love for each other.

And one of the,

I mean, he was always, it's the shared moments,

he was always there for so many a nights together,

that's a powerful thing about a dog

that he was there through all the loneliness,

through all the tough times, through the successes

and all those kinds of things.

And I remember,

I mean, that was a really moving moment for me,

I still miss him to this day.

- How long ago did he die?

- Maybe 15 years ago.

So, it's been awhile.

But it was the first time

I've really experienced like the feeling of death.

So, what happened, is he got a cancer.

And so, he was dying slowly.

And then there's a certain point he couldn't get up anymore.

Now, there's a lot of things that could say here

that I struggle with.

Maybe he suffered much longer than he needed to,

that's something I really think about a lot.

But I remember when I had to take him to the hospital

and the nurses couldn't carry him, right?

So, you're talking about a 200 pound dog

and I was really into power lifting at the time.

And I remember they tried to figure out

all these kinds of ways to.

So, in order to put them to sleep,

they had to take them into a room.

And so, I had to carry him everywhere.

And here's this dying friend of mine that I just had to,

first of all, that was really difficult to carry,

somebody that heavy when they're not helping you out.

And, yeah.

So, I remember it was the first time

seeing a friend laying there

and seeing life drain from his body.

And that realization that we're here for a short time

was made so real,

that here is friend that was there for me the week before,

the day before, and now he's gone.

And that was,

I don't know, that spoke to the fact

that he could be deeply connected with the dog.

Also spoke to the fact that the shared moments together

that led to that deep friendship will make life so amazing,

but it also spoke to the fact that death is a motherfucker.

So, I know you've lost Costello recently.

- Yeah.

- And you can- - And as you're saying this,

I'm definitely fighting back the tears.

I thank you for sharing that.

That I guess we're about to both cry over,

I don't want to say dogs [laughing]

that it was bound to happen

just given when this is happening.

Yeah, it's-

- How long did you know that Costello was not doing well?

- We'll, let's see, a year ago during the start of,

about six months into the pandemic,

he started getting abscesses and he was not,

his behavior change and something really changed.

And then I put him on testosterone

which helped a lot of things.

It certainly didn't cure everything,

but it helped a lot of things,

he was dealing with joint pain, sleep issues,

and then it just became a very slow decline

to the point where two, three weeks ago,

he had a closet full of medication.

I mean, this dog was, it was like a pharmacy.

It's amazing to me when I looked at it the other day,

I still haven't cleaned up and removed all those things,

'cause I can't quite bring myself to do it, but-

- Do you think he was suffering?

- Well, so, what happened, was about a week ago,

it was really just about a week ago, it's amazing.

He was going up the stairs, I saw him slip.

And he was a big guy,

he wasn't 200 pounds, but he was about 90 pounds,

he's a bulldog, he was pretty big and he was fit.

And then I noticed

that he wasn't carrying a foot in the back

like it was injured, it had no feeling at all.

He never liked me to touch his hind paws, and I could do,

that thing was just flopping there.

And then the vet found some spinal degeneration

and I was told that the next one would go.

Did he suffer?

Sure, hope not, but something changed in his eyes.

- Yeah. - Yeah, it's the eyes again,

I know you and I spend long hours on the phone

and tell you about like the eyes

and what they convey and what they mean

about internal states

and for sake of robots and biology of other kinds, but-

- You think something about him was gone in his eyes?

- I think he was real,

here, I am anthropomorphizing,

I think he was realizing that one of his great joys in life,

which was to walk and sniff and pee on things.

[Lex laughing]

This dog.

- The fundamental. - Loved to pee on things,

it was amazing.

I've wondered where he put it,

he was like a reservoir of urine, it was incredible.

I think, oh, that's Eddie,

he'd put like one drop on the 50 millionth plant.

And then we get to the 50 millionth in one plant,

and he just have, leave a puddle.

And here I am talking about Costello peeing.

He was losing that ability to stand up and do that,

he was falling down while he was doing that.

And I do think he started to realize.

And the passage was easy and peaceful,

but I'll say this, I'm not ashamed to say it,

I mean, I wake up every morning since then just,

I don't even make the conscious decision

to allow my self to cry, I wake up crying.

And I'm unfortunately able to make it through the day,

thanks to the great support of my friends

and you and my family, but I miss him, man.

- You miss him?

- Yeah, I miss him.

And I feel like, Homer, Costello,

the relationship to one's dog is so specific part.

- So, that part of you is gone?

- That's the hard thing.

What I think is different,

is that I made the mistake, I think.

Moreover, I hope it was a good decision,

but sometimes I think I made the mistake

of I brought Costello a little bit to the world

through the podcast or posting about him,

I anthropomorphized about him in public.

Let's be honest, I have no idea what his mental life was,

or his relationship to me.

And I'm just exploring all this for the first time

'cause he was my first dog,

but I raised him since he was seven weeks.

- Yeah, you got hold it together,

I noticed the episode you released on Monday,

you mentioned Costello.

Like you brought them back to life for me

for that brief moment.

- Yeah.

But he's gone.

- That's the,

he's going to be gone for a lot of people too.

- Well, this is what I'm struggling with.

I think that maybe you're pretty good at this.

Like, have you done this before?

[Andrew laughing]

This is the challenge.

Is that actually part of me.

I know how to take care of myself pretty well.

- Yeah.

- Not perfectly, but pretty well.

And I have good support.

I do worry a little bit about how it's going to land

and how people will feel.

I'm concerned about their internalization.

So that's something I'm still iterating on.

- And you have to watch you struggle,

which is fascinating.

- Right.

And I've mostly been shielding them from this,

but what would make me happiest

is if people would internalize

some of Costello's best traits.

And his best traits were that he was incredibly tough.

I mean, he was a,

22-inch-neck bulldog, the whole thing.

He was just born that way.

What was so beautiful is that his toughness

has never what he rolled forward.

It was just how sweet and kind he was.

And so if people can take that,

then there's a win in there someplace, so.

- I think there's some ways

in which she should probably live on in your podcast too.

You should,

I mean, it's such a,

one of the things I loved about his role in your podcast

is that he brought so much joy to you.

We mentioned the robots.

- Mm-hmm.

- Right?

I think that's such a powerful thing

to bring that joy into,

like, allowing yourself to experience that joy,

to bring that joy to others, to share it with others.

That's really powerful.

And I mean, not to,

this is like the Russian thing,

is [chuckles] it touched me when Lucy Kay had that moment

that I keep thinking about in this show "Louie,"

or like an old man was criticizing Louis

for whining about breaking up with his girlfriend,

and he was saying like the most beautiful thing about love,

they made a song that's catchy

now that's now making me feel horrible saying it,

but like, is the loss.

The loss really also is making you realize

how much that person,

that dog meant to you.

And like allowing yourself to feel that loss

and not run away from that loss is really powerful.

And in some ways that's also sweet,

just like the love was the loss is also sweet

because you know that you felt a lot for that

through your friend.

So I, and like continue to bring that joy.

I think it would be amazing to the podcast.

I hope to do the same with [laughing] robots,

or whatever else is the source of joy, right?

And maybe, do you think about one day getting another dog?

- Yeah, in time.

You're hitting on all the key buttons here.

I want that to,

we're thinking about ways to kind of immortalize Costello

in a way that's real,

not just creating some little logo or something silly.

Costello much like David Goggins is a person,

but Goggins also has grown into kind of a verb.

You're going to Goggins this or you're going to,

and there's an adjective.

Like that's extreme.

Like it,

I think that for me, Costello was all those things.

He was a being, he was his own being,

he was a noun, a verb and an adjective.

So, and he had this amazing super power

that I wish I could get,

which is this ability to get everyone else

to do things for you without doing a thing.

[Lex laughing]

The Costello effect,

as I call it.

- So is an idea I hope he lives on.

- Yes.

Thank you for that.

This actually has been very therapeutic for me,

which actually brings me to a question,

we're friends, we're not just co-scientists,

colleagues working on a project together

and in the world,

that's somewhat similar.

- Just two dogs.

- Just two dogs basically.

But let's talk about friendship,

because I think that I certainly know as a scientist

that there are elements that are very lonely

of the scientific pursuit.

There are elements of many pursuits that are lonely.

Music,

Math always seem to me like they're like

the loneliest people.

Who knows if that's true or not.

Also people work in teams.

And sometimes people are surrounded

by people interacting with people and they feel very lonely.

But for me, and I think as well for you,

friendship is an incredibly strong force

in making one feel like certain things are possible

or worth reaching for,

maybe even making us compulsively reach for them.

So, when you were growing up,

you grew up in Russia until what age?

- 13.

- Okay.

And then you moved directly to Philadelphia?

- To Chicago.

- [Andrew] Chicago.

- And then Philadelphia,

and San Francisco and Boston and so on.

But really to Chicago, that's where I went to high school.

- Do you have siblings?

- Older brother.

- But most people don't know that.

[Lex laughing]

- Yeah, he is a very different person.

But somebody I definitely look up to.

So he's a wild man.

He's extrovert,

he was into, I mean,

so he's also scientists, a bio engineer,

but he's, when we were growing up,

he was the person who did,

drank and did every drug.

And, but also as the life of the party.

And I just thought he was the,

when your older brother, five years older,

he was the coolest person that I was wanting to be him.

So for that,

he definitely had a big influence.

But I think for me in terms of friendship, growing up,

I had one really close friend.

And then when I came here I had another close friend,

but I'm very,

I believe,

I don't know if I believe,

but I draw a lot of strength from deep connections

with other people,

and just the small number of people.

Just a really small number of people.

That's when I moved to this country,

I was really surprised how,

like, there were

these large groups of friends, quote, unquote.

But the depth of connection was not there at all

from my sort of perspective.

Now, I moved to the suburb of Chicago, was Naperville.

It's more like a middle class, maybe upper middle class.

So it's like people that cared more