Huberman Lab - Essentials: Machines, Creativity & Love | Dr. Lex Fridman
Episode Date: May 29, 2025In this Huberman Lab Essentials episode my guest is Lex Fridman, PhD, a research scientist at the Massachusetts Institute of Technology (MIT), an expert in robotics and host of the Lex Fridman Podcast.... We discuss the development of artificial intelligence through machine learning, deep learning and self-supervised techniques. We also examine the growing significance of interactions between humans and robots, including their potential for companionship and emotional connection. This episode explores how AI is shifting from a technical tool into something that could reshape human relationships, emotions and society. Read the episode show notes at hubermanlab.com. Thank you to our sponsors AG1: https://drinkag1.com/huberman Maui Nui: https://mauinui.com/huberman Function: https://functionhealth.com/huberman David: https://davidprotein.com/huberman Timestamps 00:00:00 Lex Fridman; Artificial Intelligence (AI), Machine Learning, Deep Learning 00:02:23 Supervised vs Self-Supervised Learning, Self-Play Mechanism 00:09:06 Tesla Autopilot, Autonomous Driving, Robot & Human Interaction 00:14:26 Sponsors: AG1 & Maui Nui 00:17:47 Human & Robot Relationship, Loneliness, Time 00:22:38 Authenticity, Robot Companion, Emotions 00:27:55 Robot & Human Relationship, Manipulation, Rights 00:32:12 Sponsors: Function & David 00:35:14 Dogs, Homer, Companion, Cancer, Death 00:40:04 Dogs, Costello, Decline, Joy, Loss 00:47:31 Closing Disclaimer & Disclosures Learn more about your ad choices. Visit megaphone.fm/adchoices
Transcript
Discussion (0)
Welcome to Huberman Lab Essentials,
where we revisit past episodes
for the most potent and actionable science-based tools
for mental health, physical health, and performance.
And now my conversation with Dr. Lex Friedman.
We meet again.
We meet again.
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.
What is artificial intelligence and how is it different from things like machine learning
and robotics?
So I think of artificial intelligence first as a big philosophical thing.
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 a 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 those things are true. Of course, what AI really means is 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,
follows 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. 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 they're tasked with learning something interesting.
What that something interesting is
usually involves a particular task.
There's a lot of ways to talk about this
and break this down.
One of them is how much human supervision is required to teach this thing.
So supervised learning, this 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.
The question, there's a lot of fascinating questions
within that, which is how do you provide the truth?
When you've 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 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 a... the computer knows nothing about our three-dimensional world. It's
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, their attention, their overlapping, is what'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 models, natural language processing,
and now more and more is being successful in computer vision tasks.
And the idea there is, let the machine, without any ground truth annotation, just look at
pictures on the internet or look at text 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 common sense knowledge. And so the idea with self-supervised learning
is to build this common sense 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, you just let an AI system
that's self-supervised run around the internet for
a while, 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,
remember this is a cat.
They will understand that a cat is not just a thing
with pointy ears or a cat is a thing that's orange
or it's 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'd 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 common sense 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 want it to go
at AlphaZero, that want it to chess.
Oh, I see, that play games.
That play games. Got it.
So the idea of self play,
this 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 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 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 on societies
That can be a terrifying process.
To me, it's an exciting process if you supervise it correctly, if you inject 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 to 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.
Even though it's called 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 would be one of the most important open problems once
they're solved, is how do 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 a 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
where it's 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.
So that's one of the most exciting applications,
I would say, of artificial intelligence to me
is autonomous driving and 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.
Andre Capati, 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. You know, we do this as kids.
That's, you know, you have-
We 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 as you have to create this data pipeline
Where all these cars hundreds of thousands of cars are you're 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.
One of the fascinating things about humans is we figure out
objective functions for ourselves.
Like we're, 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
artificial intelligence based life.
Right, 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
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 is being collected and you have to also be clear about the objective
function, what is the goal that you're trying. And you have to also be clear about the objective function.
What is the goal that you're trying to reach?
Ultimately, currently, there has to be
a formal objective function.
Now you could argue that humans also has a set
of objective functions we're trying to optimize.
We're just not able to introspect them.
Yeah, we don't actually know what we're looking for
and seeking and doing.
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Does interacting with a robot change you?
Does it, in other words,
do we develop relationships to robots?
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.
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 time, how much time you spend with the other entity, robot or human.
The other would be wins and successes.
You enjoy successes together.
The other would be failures when you struggle with somebody.
When you struggle with somebody, you grow closer.
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?
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.
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 can explain you.
It's more technical and probably a little bit off-line
because 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
remember 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 the thing you shouldn't have eaten,
that was a secret moment you had with your refrigerator.
You shared that moment, that darkness
or that beautiful moment where you were just,
you know, 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 gonna be very attached to the refrigerator.
You're gonna go through some hell with that refrigerator.
Most of us have like, in a 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 that, 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.
allowing you as a human to feel like you're being heard,
truly heard, truly understood. And I think there's a lot of ideas
of how to make AI assistants 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 shit together.
And that, like we're very different people. But just the fact that We've been through a bunch of shit together. And that, like we've, 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.
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.
Maybe the thing we wanna optimize for
isn't necessarily like some sexy, like quick clips.
Maybe what we want is long form authenticity.
Depth. Depth.
Depth.
From a very specific engineering perspective
is I think a fascinating open problem
that hasn't been really worked on very much.
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.
Is the dog, sorry.
The Spot is the four-legged robot from Boston Dynamics.
Some people might have seen it.
It's this yellow dog.
This magic is something that could be
every single device in the world.
The way that I think maybe Steve Jobs
thought about the personal computer.
And so for me, I'd love to see a world
where there's every home has a robot
and not a robot that washes the dishes,
but more like a companion.
A family member.
A family member, the way a dog is.
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.
I love this desire to share the delight
of an interaction with a robot.
And as you describe it, I actually,
I find myself starting to crave that
because we all have those elements from childhood
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 of a robotic-like thing
is my Roomba vacuum where it goes about,
I actually was pretty good at picking up Costello's hair
when he was shed and I was grateful for it.
But then when it would, 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?
You know, and I, 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 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.
I have a bunch of Roombas from iRobot.
And I did this experiment.
Wait, how many Roombas?
Sounds like a fleet of Roombas.
So probably seven or eight.
That's a lot of Roombas.
So you have these seven or so Roombas,
you deploy all seven at once?
Oh no, I do different experiments with them.
Different experiments with them.
So one of the things I want to mention,
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 release that video,
I think it's gonna make me look insane,
which I know people know I'm already insane.
Now you have to release the video.
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 they 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.
So is the video available online?
No, I haven't recorded it.
I just had a bunch of rumors that are able to scream
in pain in my Boston, in my Boston place.
What about like shouts of glee and delight?
Well, I don't know how to, I don't, how to,
to me delight is quiet, right?
But there's a way to frame it's,
it being quite dumb as almost cute, you know,
almost connecting with it for its dumbness.
And I think that's a artificial intelligence problem.
That's interesting.
I think flaws should be a 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 that the unsophisticated view
of this is, you know, one, there's a master and a servant,
right, but there's also manipulation.
There's benevolent manipulation.
You know, children do this with parents, puppies do this. Puppies turn their head and look cute 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 coo.
Parents always think that they're doing this because they love the parent, but in many
ways studies show that those coos 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,
you know, 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, so there's a lot of good and bad manipulation
between humans, right?
Just like you said,
to me, especially like you said,
topping from the bottom,
is that the term?
So I think someone from MIT told me that term.
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 would be a 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, you know, like to have so much control
that we basically cannot live our lives
in the way that we want.
I think there's, 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. I do believe that
robots will have rights down the line. And I think in order
for us to have deep meaningful relationship 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.
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We can't and nor should we do
whatever we want with animals.
We have a USDA, we have departments 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 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 US?
In the United States.
And he was about, he was over 200 pounds.
That's a big dog.
That's a big dog.
If people know, people know Newfoundland,
so he's this black dog that's really long hair
and just a kind soul.
I think perhaps that's true for a lot of large dogs,
but he thought he was a small dog.
So he moved like that and-
Was he your dog?
Yeah, yeah. So you had him thought he was a small dog, so he moved like that. Was he your dog?
Yeah.
Yeah.
So you had him since he was fairly young?
Oh, since, yeah, since the very, very beginning to the very, very end.
And one of the things, I mean, he had this kind of, we mentioned, like the Roombas, he
had a 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.
I'm not, you know.
So 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 nights together.
That's a powerful thing about a dog.
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 a while, but it was the first time
I've really experienced like the feeling of death.
So what happened is he got cancer and so he was dying slowly
and then at a certain point he couldn't get up anymore.
There's a lot of things I could say here that I struggle with
that maybe he suffered much longer than he needed to to that's something I really think about a lot, but I remember
what I had to take him to the hospital and
The nurses couldn't carry him right so you talk about 200 pound dog
I was really into powerlifting at the time and And I remember, like 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 them everywhere.
And here's this dying friend of mine
that I just had to, first of all,
it's 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's a 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 you
could be deeply connected with the dog. Also spoke to the fact that the shared moments
together that led to that deep friendship is what
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've been going.
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 our dead dogs.
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?
Well, 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 changed and something really changed.
And then I put him on testosterone
because, which helped a lot of things. It certainly didn't cure everything, but 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,
still haven't cleaned up and removed all his things
because 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 dog, he wasn't 200 pounds,
but he was about 90 pounds, but he's a bulldog, that saw him slip. And he was a big dog. He wasn't 200 pounds, but he was about 90 pounds.
He's a bulldog, that's 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 spent long hours on the phone
and talking about like the eyes and how,
what they convey and what they mean about internal states
and forsaker robots and biology of other kinds.
But-
Do 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.
This dog loved to pee on things.
It was amazing.
I wondered where he put it. He was like a reservoir of things. It was amazing. I wondered where he put it.
He was like a reservoir of urine.
It was incredible.
I think, oh, that's it.
He's just, he'd put like one drop on the 50 millionth plant
and then we get to the 50 millionth in one plant
and he'd just have, you know, 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 myself to cry.
I wake up crying.
And I'm fortunately 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 he, you know, Homer Costello,
you know, the relationship to One's Dog is so specific,
but- Yeah.
So that party is gone.
That's the hard thing.
You know, um.
What's, what I think is different is that
I made the mistake, I think.
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, through posting about him.
I gave, 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
because he was my first dog,
but I raised him since he was seven weeks.
Yeah, you gotta hold it together.
I noticed the episode you released on Monday,
you mentioned Costello, like you brought him back to life for me
for that brief moment.
Yeah, but he's gone.
Well, that's the, he's going to be gone
for a lot of people too.
Well, this is what I'm struggling with.
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 gonna 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, they have to watch you struggle
with just 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'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.
But what was so beautiful is that his toughness
is 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 he 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'll mention the robots, 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,
And I mean, not to, this is like the Russian thing is,
it touched me when Louis CK had that moment that I keep thinking about in his show, Louis,
where 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, the song beautiful thing about love,
they mean 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,
for your friend.
So I, you know, and I continue bringing that joy.
I think it would be amazing to the podcast.
I hope to do the same with 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, you know,
ways to kind of immortalize Costello in a way that's real,
not just, you know, creating some little logo
or something silly.
You know, 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 Goggins this, and there's an adjective.
That's extremely 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 superpower
that I wish I could get, which was this ability
to get everyone else to do things for you
without doing a damn thing.
The Costello effect, as I call it. So as an idea, I hope he lives on.
There's a saying that I heard when I was a graduate student
that's just been ringing in my mind
throughout this conversation in such a,
I think appropriate way, which is that,
Lex, you are in a minority of one.
You are truly extraordinary in your ability
to encapsulate so many aspects of science,
engineering, public communication about so many topics,
martial arts, and the emotional depth that you bring to it,
and just the purposefulness.
And I think if it's not clear to people,
it absolutely should be stated, but I think if it's not clear to people, it absolutely should be stated,
but I think it's abundantly clear that just the amount
of time and thinking that you put into things is,
it is the ultimate mark of respect.
So I'm just extraordinarily grateful for your friendship
and for this conversation.
I'm proud to be your friend and I just wish you showed me
the same kind of respect
by wearing a suit and make your father proud,
maybe next time.
Next time, indeed.
Thanks so much, my friend.
Thank you.
Thank you, Andrew.
Thank you.
Thank you.
Thank you.
Thank you.
Thank you.