Essentials: Machines, Creativity & Love | Dr. Lex Fridman

0:00Welcome 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. With me 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.

0:46At 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.

1:27What'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 in 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 an output.

2:11They know nothing in the beginning and their task 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. Supervised learning, this broad category, is the neural network knows nothing in the beginning, and then it's given a bunch of examples of 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.

2:52When 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'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 bonding 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.

3:34So 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.

4:17So self -supervised learning is... It's been very successful in the domain of language model, natural language processing, and now more and more is being successful in computer vision task. And what 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 we have this giant base of knowledge on top of which we build more sophisticated knowledge.

5:02We have this kind of common sense knowledge. And so the idea of 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, as your parents only give one or two examples to teach a concept.

5:48The dream with self -supervised learning is that would 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. 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 your cat and a dog, you would be able to tell the difference.

6:27What 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 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 wanted to go at Alpha Zero, that wanted to ask. Oh, I see. That play games. That play games. So the idea of self -play is probably applies to other domains than just games is a system that just plays against itself.

7:13And 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 Alpha Zero, one of the leaders of the team said to me, they haven't found the ceiling.

7:58It's fascinating for Alpha Zero, 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 could 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.

8:41There'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 they're really big, exciting one is self -supervised learning where 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, I'm machine learning is Tesla autopilot.

9:23So these are systems that are working in the real world. This isn't an academic exercise. This is human lives a stake. Even though it's called the 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 am fascinated by the whole space, which is a whole another 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 do humans and robots dance together.

10:15To 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. 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, our flawed.

10:58And 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 summer 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 the Andre Kapati who's the head of autopilot calls it the data engine.

11:48And this process applies for a lot of machine learning, which is you build the 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 you know, you do this as adults with adults exactly, but we learn really quickly, but the whole point, and this is the fascinating 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 processes, you collect those edge cases and then you go back to the drawing board and learn from them.

12:30And 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.

13:15One of the fascinating things about humans is we figure out objective functions for us. 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. 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.

14:01And you have to also be clear about the objective function. What is the goal that you're trying to reach ultimately currently the there has to be a formal objective function. Now you could argue that humans also has a set of objective functions would try to optimize or just not able to introspect them. Yeah, we don't actually know what we're looking for and seeking and doing. As many of you know, I've been taking AG1 daily for more than 13 years. However, I've now found an even better vitamin mineral probiotic drink. That new and better drink is the new and improved AG1, which just launched this month.

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17:42Again, that's Maui NewiVenison .com slash Huberman 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.

18:34So 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 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 know, you enjoy successes together.

19:11The 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. 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. There's specific things that are more in terms of systems that I can explain you.

19:57It's more technical and probably a little bit offline 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, 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 just, you know, like hardbroken for some reason you're eating that ice cream or whatever.

20:47That'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 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.

21:30So 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. Truly heard, truly understood. And I think there's a lot of ideas of how to make a assistance 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, the makeup, 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.

22:20That'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. 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.

22:59I 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 want to optimize for isn't necessarily like some sexy, like quick clips. Maybe what we want is long form authenticity depth depth from a very specific engineering perspective is I think a fascinating open problem that has 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, what I first met spot from Boston Dynamics, I realized this magic there that nobody else is seeing is the dog is the dog.

23:59Sorry, the spot is the four -legged robot from Boston Dynamics. Some people might have seen it as a 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 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.

24:53I 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 of a robotic like thing is my room of vacuum where it goes about actually was pretty good at picking up Costello's hair when he was shed. And then, 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 room book in that moment.

25:36I'm like, what are you doing? You know, and I, and obviously it's just doing what it does. But, 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 there not currently embossed in that. I have a bunch of Roombas from I robot. And I did this experiment. Wait, how many Roombas? Sounds like a fleet of Roombas. Yeah. So it's probably seven or eight. Yeah. A lot of Roombas. So you're going to, so you have the seven or so Roombas you deploy all seven at once?

26:15Oh, no, I do different experience with them. A different experiments with them. So one of the things I want to mention, I got them to the 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 out? I think if I release 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. 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.

27:04And that display of pain was what did that? Giving them a voice, giving them a voice, especially a voice of this like of pain. So is the video available online? No, I haven't recorded it. I just hit a bunch of Roombas that are able to scream and pain in my Boston, in my Boston place. What about like a shout so glee and delight? Well, I don't know how I don't how to do me delight is quiet, right? But there's a way to frame it's it being quite dumb as almost cute. You know, you're almost connecting with it for its dumbness. And I think that's an artificial intelligence problem. I think flaws are should be a feature, not a bug.

27:54So along the lines of this, the different sorts of relationships that one could have with robots and the fear, but also that some of the positive relationships that one could have. There's so much dimensionality there so much to explore. But power dynamics and relationships are very interesting because the obvious ones that the unsophisticated view of this is, you know, one, there's a master in a servant. But there's also manipulation. There's benevolent manipulation. You know, children do this with parents. Puppy's do this. Puppy's turn their head and look cute and maybe give out a little noise.