In 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
Chapter 1: What is artificial intelligence and how is it different from machine learning?
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 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 computational techniques that allow you to solve various problems. There's a long history that approaches the problem from different perspectives.
What's always been throughout one of the threads, one of the communities, goes under the flag of machine learning, which is emphasizing in the AI space the task of learning. How do you make a machine that knows very little in the beginning, follow some kind of process and learns to become better and better at a particular task.
What's been most very effective in the recent about 15 years is a set of techniques that fall under the flag of deep learning that utilize neural networks. 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. Like 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
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Chapter 2: What is supervised vs self-supervised learning?
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, then your 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'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... 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 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 it's 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 new.
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. 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 uh children in this way human children is your parents only give one or two examples to teach a concept the the dream with self-supervised learning is that would be the same with
with machines that they would watch millions of hours of YouTube videos and then come to a human and be able to understand when the human shows them this is a cat. Like, remember, this is a cat. They will understand that a cat is not just a thing with pointy ears.
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Chapter 3: How does AI influence human-robot relationships?
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 won at Go, at AlphaZero, that won at 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.
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Chapter 4: What role does time play in human-robot interactions?
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 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 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 uh 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 uh i have questions but i'm learning so please keep going so that 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 other space, of human-robot interaction. When AI systems and humans work together to accomplish tasks. That dance to me is fascinating.
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 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, are flawed. And that's what makes life beautiful, that they're flawed. That's where learning happens at the edge of your capabilities.
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Chapter 5: Can robots help us understand our own emotions?
So you always have to figure out how can flawed robots and flawed humans interact together such that 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. Andrej Karpathy, who's the head of Autopilot, calls it the data engine. And this process applies for a lot of machine learning, which is you build a system that's pretty good at doing stuff. You send it out into the real world.
It starts doing the stuff, and then it runs into what are called edge cases, like failure cases, where it screws up. You know, we do this as kids. 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. 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, 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. 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'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, 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.
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Chapter 6: What is the future of companion robots?
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