Dr. Lex Fridman on Huberman Lab: How AI Learns to Love

1. 0:00 – 2:23

   Lex Fridman; Artificial Intelligence (AI), Machine Learning, Deep Learning

   1. AHAndrew Huberman0:00

      (peaceful music) 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 Fridman.

   2. LFLex Fridman0:15

      We meet again.

   3. AHAndrew Huberman0:16

      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?

   4. LFLex Fridman0:32

      So, I think of artificial intelligence first as a big philosophical thing. It's our longing to create other intelligent 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 as a community, as a set of researchers and engineers, it's a set of tools, a set of, uh, computational techniques that allow you to solve various problems. There's a long history that, uh, approaches the problem from different perspectives. What's, uh, 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- the task of learning. How do you make a machine that knows very little in the beginning f-follow some kind of process and learns to become better and better in- in a particular task? What's been most, uh, 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, uh, these architectures have an input and an 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. The-

2. 2:23 – 9:06

   Supervised vs Self-Supervised Learning, Self-Play Mechanism

   1. LFLex Fridman2:23

      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, this broad category, is, uh, the- the neural network knows nothing in the beginning, and then it's given a bunch of examples of, uh, in computer vision that would 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, uh, the neural network is able to learn by example. That's called supervised learning. The quest- there's a lot of fascinating questions within that, which is, how do you provide the truth? When you're 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 a ... (laughs) The- the computer knows nothing about our three-dimensional world. It's just looking at a set of pixels. So, uh, 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 the- 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, uh, what 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, into, uh, into the task. So, self-supervised learning is, uh, more, uh, it's been very successful in the domain of, uh, language model, natural language processing, and now more and more it's being successful in computer vision task. And what's 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, uh, generalizable about the ideas that are at the core of language or at the core of vision. And based on that, we humans at- 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, but 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- the dream there is the- (laughs) you just- you just let an AI system that's, uh, 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. With- we think of, uh, children in this way, human children, is your parents only give one or two examples-

   2. AHAndrew Huberman5:46

      Mm-hmm.

   3. LFLex Fridman5:46

      ... to teach a concept. The- the dream with self-supervised learning is that would be the same with- with, uh, machines, that they would, uh, watch millions of hours of, uh, 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 a thing with pointy ears or a ca- cat is a thing that's orange or is furry. They'll, 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 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? Uh, that's the whole dream of 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, s- uh, very weirdly called self-play mechanism. That's the mechanism behind the, uh, uh, the reinforcement learning successes of, uh, the systems that won at, uh, Go, at, uh, AlphaZero, uh, that won at chess.

   4. AHAndrew Huberman7:02

      Wha- Oh, I see. Th- that play games.

   5. LFLex Fridman7:04

      That play games.

   6. AHAndrew Huberman7:05

      Got it.

   7. LFLex Fridman7:05

      So the idea of self-play, this probably applies, uh, to other domains than just games, is a system that just plays against itself. And this is fascinating in all kinds of domains. But, uh, it knows nothing in the beginning and the whole idea is it creates a bunch of mutations of itself and plays against those, uh, 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 'cause it's like a runaway system. O- one of the most terrifying and exciting things that, uh, David Silver, the creator of AlphaGo and AlphaZero, one of the leaders of the team, said, uh, to me is, uh, they haven't found a 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, uh, bigger, deeper effect on human beings and societies, uh, that can be a terrifying process. To me, it's an exciting process if you supervise it correctly, if you inject, uh, if, uh, what's called, uh, value alignment, you, uh, 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 d- uh, specifics of neural networks and a- all the problems that people are, 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, uh, l- uh, l- less and less human sup- supervision of neural networks. And also, just to comment, and I'll

3. 9:06 – 14:26

   Tesla Autopilot, Autonomous Driving, Robot & Human Interaction

   1. LFLex Fridman9:06

      shut up-

   2. AHAndrew Huberman9:07

      No, please keep going.

   3. LFLex Fridman9:07

      (laughs)

   4. AHAndrew Huberman9:08

      I'm, I'm learning. Uh, I have questions, but I'm learning, so please keep going.

   5. LFLex Fridman9:11

      So the... 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, uh, FSD, f- 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, the, the whole space, which is a whole nother space, of human-robot interaction, when AI systems and humans work together to accomplish task. That dance, to me, is, uh, 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 do humans and robots dance together? To me, semi-autonomous driving is one of those spaces. So for, uh, for Elon, for example, he doesn't see it that way. He sees, uh, f- 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, uh, robots and robots dance. Like, we need to s- 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, uh, like, the, the sum is bigger than the whole, as opposed to focusing on just building the perfect robot.

   6. AHAndrew Huberman11:23

      Mm-hmm.

   7. LFLex Fridman11:24

      So th- 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, uh, there's a, there's a process there that maybe I can comment on. Uh, the, Andrej Karpathy, who's the head of Autopilot, calls it the data engine. And thi- this process applies for a lot of machine learning, which is you build a system that's pretty good at doing stuff, you send any... 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, you know, you, you have-

   8. AHAndrew Huberman12:07

      We do this as adults. (laughs)

   9. LFLex Fridman12:08

      We do this as adults, exactly. But we learn really quickly. But the, the whole point, and this is the fascinating thing about driving, is you realize there's millions of edge cases. Uh, there's just, like, weird situations that you did not expect. And so what 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 as you're driving around, and something weird happens. And so whenever this weird detector fires, it's another important concept, uh, y- that piece of data goes back, uh, to the mothership for the, 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 ourself. Like, we are, um... it's the meaning of life. Like, why the hell are we here? And, uh, a machine currently has to have, uh, a hard-coded statement about why.

   10. AHAndrew Huberman13:34

       It has to have a meaning of-

   11. LFLex Fridman13:35

       Yeah.

   12. AHAndrew Huberman13:36

       ... artificial intelligence-based life.

   13. LFLex Fridman13:38

       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, uh, 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? Ultimately, currently, the, uh, there has to be a f- 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. Uh-

   14. AHAndrew Huberman14:20

       We, yeah, we don't actually know what we're looking for and seeking and doing. I think

4. 14:26 – 19:18

   Human & Robot Relationship, Loneliness, Time

   1. AHAndrew Huberman14:26

      you've already told us the answer, but does interacting with a robot change you? Does it, in other words, do, do we develop relationships to robots?

   2. LFLex Fridman14:36

      I believe that most people have, uh, a notion of loneliness in them that we haven't discovered, that, 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, uh, better people towards each other. So I think that connection between human and, and AI, human and robot, uh, is, is not only possible, but, uh, will help us understand ourselves in ways that are, like, several orders of magnitude, uh, deeper than we ever could have imagined.

   3. AHAndrew Huberman15:15

      So, uh, when I think about human relationships, I, I don't, um, always break them down into variables, but we could explore a fl- a few of those variables and see how they map to human/robot relationships. Um, one is just time, right? If you spend zero time with another person, uh, at all in, 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, um, wins and successes. You know, you enjoy successes together. The other would be failures. When you struggle with somebody, you know, when you struggle with somebody, you grow closer. So I've never conceptualized robot/human interactions this way. Uh, um, so tell me more about how this might look. Are we thinking about, um, a human-appearing robot? Um, what is the ideal human/robot relationship?

   4. LFLex Fridman16:14

      So there's, uh, 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, um... it's, it's more technical and probably a little bit offline 'cause I have kind of wild ideas how that can revolutionize, um, social networks and, um, and operating systems. But th- the point is that element alone, forget all the other things we're talking about h- hu- like, emotions, um, saying no, all that stuff, just remember sharing moments together would change everything. We don't currently have systems that, uh, uh, share, share moments together. Like, even just you and your fridge. Just all those times you went late at night and, 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 just, uh, 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, uh, is, is, is tragic, and once it does remember that, I think you're gonna be very attached to the refrigerator. You'll, you're gonna go through some sh- through some hell with that refrigerator. Most of us have, like, in, in, uh, in the developed world have weird relationships with food, right? So you can go through some, uh, some deep moments of trauma and triumph with food-

   5. AHAndrew Huberman17:59

      Absolutely.

   6. LFLex Fridman17:59

      ... and at the core of that is the refrigerator. So a smart refrigerator, I believe, would, uh...... change society, not just the refrigerator, but the, these ideas in the systems all around us. So that, I- I just wanna comment on how powerful the idea of time is.

   7. AHAndrew Huberman18:15

      Mm-hmm.

   8. LFLex Fridman18:16

      And then there is a bunch of elements of actual interaction of, uh, 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 systems to be able to ask the right questions and truly hear another human. This is what we try to do with podcasting, right? Uh, I think there's ways to do that with AI. But above all else, just remembering the collection of moments they make up the day, the week, the months. I think, uh, 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'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.

5. 19:18 – 24:34

   Authenticity, Robot Companion, Emotions

   1. LFLex Fridman19:18

   2. AHAndrew Huberman19:18

      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?

   3. LFLex Fridman19:32

      Yeah, I think so. I think there's no reason to see machines as, uh, somehow, uh, incapable of teaching us something that's deeply human. I, I don't think, uh, humans have a monopoly on that. I think we understand ourselves very poorly and we need the, to have the kind of, uh, uh, prompting from, um, from a machine. Uh, maybe the thing we wanna optimize for isn't necessarily, (laughs) uh, like some, uh, sexy, uh, like quick clips, maybe w- what we want is long form authenticity.

   4. AHAndrew Huberman20:05

      Depth.

   5. LFLex Fridman20:06

      Depth. From a ver- like a very specific engineering perspective is, um, I think a fascinating open problem that hasn't been really worked on, uh, very much. Early on in life, uh, in, in, 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, uh, brought to light. When I first met Spot from Boston Dynamics, I realized there's magic there that nobody else is seeing.

   6. AHAndrew Huberman20:38

      Is the dog.

   7. LFLex Fridman20:39

      Is the dog, sorry. The, Spot is the four-legged, uh, 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, the way that I think, uh, 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, way-

   8. AHAndrew Huberman21:08

      A family member.

   9. LFLex Fridman21:09

      A family member. The way a dog is.

   10. AHAndrew Huberman21:11

       Mm-hmm.

   11. LFLex Fridman21:12

       But a dog that's al- a- able to speak your language too. So not just connect the way a dog does by looking at you and looking away and go, almost like smiling with its soul in that kind of way, um, but also to actually understand what the hell, like, why are you so excited about the successes? Like understand the details, understand the traumas.

   12. AHAndrew Huberman21:34

       I love this, um, desire to share the delight of an interaction-

   13. LFLex Fridman21:38

       Yeah.

   14. AHAndrew Huberman21:38

       ... with a robot. And as you describe it, I actually, I find myself starting to crave that-

   15. LFLex Fridman21:42

       Yeah.

   16. AHAndrew Huberman21:42

       ... because, uh, we all have those elements from childhood where, or from adulthood, where we experience something, we want other people-

   17. LFLex Fridman21:49

       Yeah.

   18. AHAndrew Huberman21:49

       ... 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 an- of a robotic-like thing, uh, is my Roomba vacuum, where it goes about, actually was pretty good at picking up Costello's hair when he would shed, uh, and then, um, 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, 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-

   19. LFLex Fridman22:18

       Yeah.

   20. AHAndrew Huberman22:18

       And obviously, it's just doing what it does.

   21. LFLex Fridman22:21

       Yeah.

   22. AHAndrew Huberman22:21

       But, but that's a kind of, um, mostly positive but slightly negative interaction. Um, but what you're describing has so much more richness and layers of detail that I can only imagine what those relationships are like.

   23. LFLex Fridman22:35

       Well, there's a few, just a quick comment. So I've had, they're n- currently in Boston, I have a bunch of Roombas from iRobot. And I did this experiment-

   24. AHAndrew Huberman22:43

       Wait, how many Roombas?

   25. LFLex Fridman22:45

       (laughs)

   26. AHAndrew Huberman22:46

       Sounds like a fleet of Roombas.

   27. LFLex Fridman22:47

       Yeah. So I, uh, probably seven or eight.

   28. AHAndrew Huberman22:50

       Wow.

   29. LFLex Fridman22:50

       Yeah.

   30. AHAndrew Huberman22:50

       That's a lot of Roombas. So you're gonna... So you have these seven or so Roombas, you deploy all seven at once?
6. 24:34 – 29:19

   Robot & Human Relationship, Manipulation, Rights

   1. LFLex Fridman24:34

   2. AHAndrew Huberman24:34

      So along the lines of this, um, 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, uh, there's so much dimensionality there, so much to explore. But, uh, power dynamics in relationships are very interesting because the, the obvious ones that, um, 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, uh, children do this with parents. Puppies do this. Puppies turn their head and look cute and maybe give out a little, little, um, noise. Kids coo, and parents always think that they're, you know, they're doing this because, you know, they, 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, um, uh, you know, in certain communities that I'm certainly not a part of but they call topping from the bottom-

   3. LFLex Fridman25:52

      (laughs)

   4. AHAndrew Huberman25:52

      ... where the robot is actually manipulating you into doing things, but it... you are under the belief that you're in charge, but actually, they're in charge. And so I think that's one that, um, uh, if we could explore that for a second, you could imagine it wouldn't necessarily be bad, although it, it could lead to bad things. Um, the reason I wanna explore this is I think people always, uh, default to the, the extreme, like the robots take over, and we're in little jail cells, and they're out having fun and, and ruling the universe. Uh, what, uh, what, what sorts of manipulation can a robot potentially carry out, good or bad?

   5. LFLex Fridman26:32

      Yeah, so there's a lot of good and bad manipulation between humans, right? J- just like you said. (sighs) To me, (sighs) especially, uh, like you said, (laughs) uh, topping from the bottom. Is that the term? Uh, so-

   6. AHAndrew Huberman26:47

      I think someone from MIT told me that term.

   7. LFLex Fridman26:49

      (laughs)

   8. AHAndrew Huberman26:50

      It wasn't Lex.

   9. LFLex Fridman26:53

      Uh, I think... So first of all, there's power dynamics, uh, in bed and power dynamics in relationships and power dynamics on the street and in the work environment. Those are all very different. Uh, I think, um, I think power dynamics can make human relationships, especially romantic, romantic relationships, uh, fascinating and rich and fulfilling and exciting and all those kinds of things. So I don't, uh, 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, uh, and I think everybody should be aware of that, and the manipulation is not so much manipulation but, uh, a dance of, like, pulling away, push and pull, and all those kinds of things. Uh, in terms of control, I, I think we're very, very, very far away from AI systems that are able to, uh, lock us up. They, uh, uh, to lock us up in, uh, in a... 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, uh, in terms of dangers of AI systems, there's much more dangers that have to do with autonomous weapons 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, in order for us to have deep meaningful relationship with robots, we would have to consider them as entities in themselves that, uh, deserve respect, and that's a really interesting concept that, uh, 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, uh, other animals, can have rights on the level as humans.

   10. AHAndrew Huberman28:51

       We can't and nor should we do whatever we want with animals. We have a USDA with the way of Department of, uh, of Agriculture that deal with, um, you know, animal care and use committees for research, for agri... you know, for farming and ranching and all that. So I, I... while I... when you first said it, I thought, "Wait, why would have... there be a bill of robotic rights?" But it absolutely makes sense, um, in the context of everything we've been talking about up until now.

7. 29:19 – 33:18

   Dogs, Homer, Companion, Cancer, Death

   1. AHAndrew Huberman29:19

      Uh, let's, um... I- if you're willing, I'd love to talk about dogs because you've mentioned dogs a couple times, a robot dog. Um, you had a, a biological dog. Yeah.

   2. LFLex Fridman29:32

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

   3. AHAndrew Huberman29:39

      In Russia or in the US?

   4. LFLex Fridman29:41

      In the United States, and, uh, he was about... he was over 200 pounds. He was a big dog.

   5. AHAndrew Huberman29:46

      That's a big dog.

   6. LFLex Fridman29:47

      (laughs) If people know, people know Newfoundlands, uh, he's this black dog that's, uh, really, uh, long hair and just, uh, a kind soul. I think perhaps that's true for a lotta large dogs, but he thought he was a small dog, so he moved like that and-

   7. AHAndrew Huberman30:02

      Was he your dog?

   8. LFLex Fridman30:03

      Yeah, yeah.

   9. AHAndrew Huberman30:04

      So you had him since he was fairly young?

   10. LFLex Fridman30:06

       Oh, for since... yeah, since the very, very beginning to the very, very end. And o- one of the things... I mean, he had this kind of, uh... we mentioned, like, the Roombas. He had, uh, kindhearted dumbness about him that was just overwhelming.... part of the reason, uh, I named him Homer because they've, it's after Homer Simpson. In case people are wondering (laughs) which Homer I'm referring to. I'm not s- f- you know. (laughs) So, the, there's a clum-

   11. AHAndrew Huberman30:34

       Not the Odyssey.

   12. LFLex Fridman30:35

       This, yeah, that, exactly. Uh, there was a cl- clumsiness that was just, uh, 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, uh, nights together. That's a, that's a powerful thing about a dog that, um, he was there through all the loneliness, through all the tough times, through the successes and all those kinds of things. And I remember, um, I mean, that was a really moving moment for me. I still miss him to this day.

   13. AHAndrew Huberman31:07

       How long ago did he die?

   14. LFLex Fridman31:10

       Um, maybe 15 years ago. So, it's, it's been a while. But it was the first time I've really experienced, like, the feeling of death. 'Cause, so what happened is, uh, he, uh, he got c- uh, cancer and so he wa- uh, dying slowly and then at a certain point, he couldn't get up anymore. Uh, there's a lot of things I could say here, uh, you know, the, that I struggle with that maybe, um, maybe he suffered much longer than he needed to. That's something I really think about a lot. But I remember w- I had to take him to the hospital and (laughs) the nurses couldn't carry him, right? So, you're talking about a 200 pound dog and I was really into powerlifting at the time and I remember, like, they, they, they tried to figure out all these kinds of ways to, um... so in order to put him to sleep, they had to take, um, um, into, into a room. And so, I had to carry him everywhere. And here's this, uh, dying friend of mine that I just had to, uh, first of all, it's really difficult to carry somebody that heavy when they're not helping you out. And, um, 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 it, here's a friend that was there for me the week before, the day before, and now he's gone. And that was, um, I don't know, it, that, that spoke to the fact that you could be deeply connected with a dog. Also spoke to the fact that, uh, the, the shared moments together that led to that deep friendship is, um, are, that will make life s- so amazing. But also spoke to the fact that death is a motherfucker. (laughs)

8. 33:18 – 41:07

   Dogs, Costello, Decline, Joy, Loss

   1. LFLex Fridman33:18

      Um, so I know you've, uh, lost Costello recently.

   2. AHAndrew Huberman33:21

      Yeah, I-

   3. LFLex Fridman33:21

      And you've been going-

   4. AHAndrew Huberman33:22

      And as you're saying this, I'm definitely fighting back, uh, the tears. I, um, I, uh, thank you for sharing that. That, uh, I guess we're about to both cry over (laughs) our dead dogs.

   5. LFLex Fridman33:33

      (laughs)

   6. AHAndrew Huberman33:33

      (laughs) Uh, that it was, uh, it was bound to happen just given when this is, when this is happening. Um, yeah. It's, uh...

   7. LFLex Fridman33:40

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

   8. AHAndrew Huberman33:44

      Um, well, let's see. A year ago during the start of, uh, m- about six months into the pandemic, I, he started getting abscesses and he was not, i- his behavior changed and something really changed. And then, um, I put him on testosterone because, uh, which helped a lot of things, it certainly didn't cure everything, but it c- helped a lot of things. He was dealing with joint pain, sleep issues. And then it just became a, a very slow decline to the point where, you know, two, three weeks ago, he had, you know, a closet full of medication. I mean, this dog was, you know, 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 his things 'cause I can't quite bring myself to do it. But, um, he-

   9. LFLex Fridman34:31

      Do you think he was suffering?

   10. AHAndrew Huberman34:33

       Well, so what happened was about a week ago, it was really just about a week ago, it's amazing, uh, he was going up the stairs and 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's pretty big and he was fit.

   11. LFLex Fridman34:46

       Mm-hmm.

   12. AHAndrew Huberman34:46

       Um, and then I noticed that he wasn't carrying the, 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 just, that thing was just flopping there. And then, uh, the vet found some spinal degeneration and I was told that the next one would go. Did he suffer? Uh, sure hope not. Um, but something changed in his eyes.

   13. LFLex Fridman35:07

       Yeah.

   14. AHAndrew Huberman35:07

       Yeah, it's the eyes again. I know you and I spend long hours on the phone and talking about, like, the eyes and how, what they convey and what they mean about internal states and for sake of robots and biology of other kinds. But, um-

   15. LFLex Fridman35:18

       Do you think, uh, something about him was gone in his eyes?

   16. AHAndrew Huberman35:23

       I th- 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. (laughs)

   17. LFLex Fridman35:38

       (laughs) The fundamentals.

   18. AHAndrew Huberman35:40

       This dog loved to pee on things. It was amazing. I have wondered where he put it.

   19. LFLex Fridman35:44

       Yeah.

   20. AHAndrew Huberman35:44

       He was like a reservoir of urine. It was incredible.

   21. LFLex Fridman35:47

       Yeah.

   22. AHAndrew Huberman35:48

       I'd think, "Oh, that's it." He's just, he'd put, like, one drop on the 50 millionth plant and then when we get to the 50 millionth and one plant-

   23. LFLex Fridman35:54

       Always more?

   24. AHAndrew Huberman35:54

       ... and he'd just have, you know, leave a puddle. And here I am talking about Costello peeing. Um, he was losing that ability to stand up and do that. He was falling down while he was doing that. And I, I do think he started to realize and the, the passage was easy and peaceful, but, um, uh, you know, I'll say this, uh, I'm not ashamed to say it. I mean, I wake up every morning since then just, I, I don't even make the conscious decision to allow myself to cry. I wake up crying. And, uh, I'm fortunately able to make it through the day thanks to the great support of my friends and, and you and my family, but, um-I miss him, man.

   25. LFLex Fridman36:29

       You miss him?

   26. AHAndrew Huberman36:30

       Yeah. I miss him and I feel like, uh, he ... You know, Homer, Costello, you know, the relationship to one's dog is so specific, but ...

   27. LFLex Fridman36:38

       Yeah.

   28. AHAndrew Huberman36:39

       Um ...

   29. LFLex Fridman36:39

       So that, that part of you is gone? That's the hard thing.

   30. AHAndrew Huberman36:45

       You know, um, what's, what- what I think is different is that I made the mistake, I think ... Well, I hope it was a good decision, but sometimes I think I made the mistake of I brought Costello a- a little bit to the world through the podcast, through posting about him.
9. 41:07 – 42:16

   Closing

   1. LFLex Fridman41:07

   2. AHAndrew Huberman41:07

      Um, there's a saying that I heard when I was a graduate student that I ... That's just been ringing in my mind throughout this conversation in such a, I think, appropriate way, which is that, uh, "Lex, you are in a minority of one. You are truly, uh, extraordinary in your ability to encapsulate so many aspects of science, engineering, public communication, about so many topics, uh, 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'm thinking it's abundantly clear that just the amount of time and thinking that you put into things is, uh ... It- it is the ultimate mark of respect. Um, so I'm just extraordinarily grateful for your friendship and for this conversation.

   3. LFLex Fridman41:59

      I'm, uh, proud to be your friend, and I just wish you showed me the same kind of respect by wearing a suit and make your, uh, father proud. Maybe next time.

   4. AHAndrew Huberman42:07

      (laughs) Next time, indeed. Thanks so much, my friend.

   5. LFLex Fridman42:10

      Thank you. Thank you, Andrew. (upbeat music)

Episode duration: 42:17