Lex Fridman Podcast
#333 – Andrej Karpathy: Tesla AI, Self-Driving, Optimus, Aliens, and AGI
29 Oct 2022
Chapter 1: What is the background of Andrej Karpathy?
The following is a conversation with Andrey Kapathy, previously the director of AI at Tesla, and before that at OpenAI and Stanford. He is one of the greatest scientists, engineers, and educators in the history of artificial intelligence. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast.
We got Eight Sleep for naps, BetterHelp for mental health, Fundrise for real estate investing, and Athletic Greens for nutrition. Choose wisely, my friends. And now onto the full ad reads. As always, no ads in the middle. I try to make this interesting, but if you skip them, please still check out our sponsors. I enjoy their stuff. Maybe you will too.
This episode is sponsored by Eight Sleep and its new Pod 3 mattress. I'm recording this in a hotel. In fact, given some complexities of my life, this is the middle of the night, 4 a.m. I'm sitting in an empty hotel room yelling at a microphone. This, my friends, is my life. I do usually feel good about myself at 4 a.m., but not with two cups of coffee in me.
Chapter 2: How do sponsors play a role in the podcast?
And the reason I feel good is because I'm going to go to sleep soon and I've accomplished a lot. This is true today, except for the sleep soon part because I think I'm going to an airport at some point soon. It doesn't matter. What matters is I'm not even going to sleep here. And that's great because in a hotel, I don't have an eight sleep bed that can cool itself. At home, I do.
And that's where I'm headed. I'm headed home. Anyway, check it out and get special savings when you go to eightsleep.com slash Lex. This episode is also brought to you by BetterHelp, spelled H-E-L-P, help. I'm a huge fan of talk therapy. I think of podcasting as a kind of talk therapy. So I'm a huge fan of listening to podcasts. In fact, that's how I think of doing a podcast myself.
I just get to have front row seats to a thing I love. And it's actually just the process of talking that reveals something about the mind. I think that's what good talk therapy is, is it guided by a professional therapist. It helps you reveal to yourself something about your mind. Just lay it all out on the table.
So yeah, you should definitely use the best method of talk therapy, the best meaning the most accessible, at least to try it. If not, to make it a regular part of your life. That's what BetterHelp does. Check them out at betterhelp.com slash Lex and save on your first month. This episode is also brought to you by Fundrise, spelled F-U-N-D-R-I-S-E.
It's a platform that allows you to invest in private real estate. We live in hard times, folks, for many different reasons, but one of them is financial. And one way to protect yourself in difficult times is to diversify your investments. Private real estate is one of the things, I believe, you should diversify into.
And when you do, you should use tools that look like they're made in the 21st century, which a lot of investment, even like online investment websites and services, seem to be designed by the same people that designed the original ATMs. That's not the case with Fundrise. Super easy to use, accessible, over 150,000 investors use it. Their team vets and manages all their real estate projects.
You can track your portfolio's performance on their website and see updates as properties across the country are acquired, improved, and operated. Anyway, check out Fundrise. It takes just a few minutes to get started at fundrise.com. This show is brought to you by Athletic Greens and its AG1 drink, which is an all-in-one daily drink to support better health and peak performance.
I have to be honest, I completely forgot to bring Athletic Greens with me as I'm traveling now, and I miss it. It's not just good for my nutritional base and needs, it's good for my soul. It's part of the daily habit of life. And when you don't have that habit, the routine stuff is off.
So it's good to just put that into your daily routine to make sure that you're getting the vitamins, the nutrition that you need, no matter the dietary. the workload, the athletic endeavors that you partake in. I don't know. It's kind of incredible. And yeah, that's what Athletic Greens is for me. They'll give you one month's supply of fish oil when you sign up at athleticgreens.com slash Lex.
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Chapter 3: What are the key features of neural networks?
Sure, but NPCs inside a video game don't observe any divine intervention either. We might just be all NPCs writing a kind of code.
Maybe eventually they will. Currently, NPCs are really dumb, but once they're running GPTs, maybe they will be like, hey, this is really suspicious. What the hell?
So you famously tweeted, it looks like if you bombard Earth with photons for a while, it can emit a roadster. So if like in Hitchhiker's Guide to the Galaxy, we would summarize the story of Earth. So in that book, it's mostly harmless. What do you think is all the possible stories, like a paragraph long or a sentence long, that Earth could be summarized as? Once it's done, it's computation.
So like all the possible full... if Earth is a book, right, probably there has to be an ending. I mean, there's going to be an end to Earth, and it could end in all kinds of ways. It can end soon, it can end later. What do you think are the possible stories? Well, definitely there seems to be, yeah, you're sort of,
It's pretty incredible that these self-replicating systems will basically arise from the dynamics, and then they perpetuate themselves and become more complex, and eventually become conscious and build a society. And I kind of feel like, in some sense, it's kind of like a deterministic wave that kind of just happens on any sufficiently well-arranged system like Earth.
And so I kind of feel like there's a certain sense of inevitability in it. And it's really beautiful.
And it ends somehow, right? So it's a chemically diverse environment where complex dynamical systems can evolve and become more further and further complex. But then there's a certain, what is it? There's certain terminating conditions, right?
Yeah, I don't know what the terminating conditions are, but definitely there's a trend line of something, and we're part of that story. And where does it go? So we're famously described often as a biological bootloader for AIs. And that's because humans, I mean, we're an incredible biological system, and we're capable of computation and... and love and so on.
But we're extremely inefficient as well. We're talking to each other through audio. It's just kind of embarrassing, honestly, that we're manipulating seven symbols serially. We're using vocal cords. It's all happening over multiple seconds. It's just kind of embarrassing when you step down to the frequencies at which computers operate or are able to operate on.
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Chapter 4: How does the discussion shift towards the impact of AI on society?
Definitely the zeitgeist today is just pushing. Basically, right now, the zeitgeist is do not touch the transformer. Touch everything else. So people are scaling up the data sets, making them much, much bigger. They're working on the evaluation, making the evaluation much, much bigger. And they're basically keeping the architecture unchanged.
And that's the last five years of progress in AI, kind of.
What do you think about one flavor of it, which is language models? Have you been surprised? Has your sort of imagination been captivated by, you mentioned GPT and all the bigger and bigger and bigger language models. And what are the limits of those models, do you think? So just the task of natural language.
Basically, the way GPT is trained is you just download a massive amount of text data from the internet, and you try to predict the next word in the sequence, roughly speaking. You're predicting little word chunks, but roughly speaking, that's it. And what's been really interesting to watch is, basically, it's a language model. Language models have actually existed for a very long time.
There's papers on language modeling from 2003, even earlier.
Can you explain in that case what a language model is?
Yeah, so language model, just basically the rough idea is just predicting the next word in a sequence, roughly speaking. So there's a paper from, for example, Benjio and the team from 2003, where for the first time they were using a neural network to take, say, like three or five words and predict the next word. And they're doing this on much smaller data sets.
And the neural net is not a transformer. It's a multi-layer perceptron. But it's the first time that a neural network has been applied in that setting. But even before neural networks, there were language models, except they were using n-gram models. So n-gram models are just count-based models.
So if you start to take two words and predict a third one, you just count up how many times you've seen any two-word combinations and what came next. And what you predict as coming next is just what you've seen the most of in the training set. And so language modeling has been around for a long time. Neural networks have done language modeling for a long time.
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Chapter 5: What are Andrej Karpathy's thoughts on the impact of movies on AI perception?
Didn't you mention Terminator 2? Okay. Okay. That's like Terminator 2 was a little bit later, 1990.
No, I think Terminator 2 was in the 80s.
And I like Terminator 1 as well. So, okay. So like a few exceptions, but by and large, for some reason, I don't like movies before 1995 or something. They feel very slow. The camera is like zoomed out. It's boring. It's kind of naive.
Chapter 6: How does Andrej Karpathy envision the future of robots in society?
It's kind of weird.
And also Terminator was very much ahead of its time.
Yes. And The Godfather, there's like no AGI.
Yeah.
I mean, but you have Good Will Hunting was one of the movies you mentioned. And that doesn't have any AGI either. I guess that's mathematics.
Yeah.
I guess occasionally I do enjoy movies that don't feature. Or like Anchorman. Anchorman is so good. I don't understand. Speaking of AGI, because I don't understand why Will Ferrell is so funny. It doesn't make sense. It doesn't compute. There's just something about him. And he's a singular human. Because you don't get that many comedies.
these days, and I wonder if it has to do about the culture or the machine of Hollywood, or does it have to do with just we got lucky with certain people in comedy that came together, because he is a singular human. That was a ridiculous tangent, I apologize. But you mentioned human or robot, so what do you think about Optimus, about TeslaBot?
Do you think we'll have robots in the factory and in the home in 10, 20, 30, 40, 50 years?
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Chapter 7: What advice does Andrej Karpathy give for beginners in machine learning?
They would be able to sit down in chairs, potentially even drive cars. Basically, the world is designed for humans. That's the form factor you want to invest into and make work over time. I think there's another school of thought, which is, okay, pick a problem and design a robot to it.
But actually designing a robot and getting a whole data engine and everything behind it to work is actually an incredibly hard problem. So it makes sense to go after general interfaces that, okay, they are not perfect for any one given task, but they actually have the generality of just with a prompt with English able to do something across.
And so I think it makes a lot of sense to go after a general interface in the physical world. And I think it's a very difficult project. I think it's going to take time. But I've seen no other company that can execute on that vision. I think it's going to be amazing. Basically, physical labor. If you think transportation is a large market, try physical labor. It's insane.
But it's not just physical labor. To me, the thing that's also exciting is social robotics. So the relationship we'll have on different levels with those robots. That's why I was really excited to see Optimus. People have criticized me for the excitement. But I've worked with a lot of research labs that do humanoid-legged robots, Boston Dynamics, Unitree.
There's a lot of companies that do legged robots. but that's the elegance of the movement is a tiny, tiny part of the big picture.
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Chapter 8: How does the conversation address the meaning of life and consciousness?
So integrating the two big, exciting things to me about Tesla doing humanoid or any legged robots is, Clearly integrating it into the data engine. So the data engine aspect, so the actual intelligence for the perception and the control and the planning and all that kind of stuff, integrating into the fleet that you mentioned, right?
And then speaking of fleet, the second thing is the mass manufacturers, just knowing... culturally driving towards a simple robot that's cheap to produce at scale. And doing that well, having experience to do that well, that changes everything. That's why that's a very different culture and style than Boston Dynamics, who, by the way, those robots...
The way they move, it'll be a very long time before Tesla can achieve the smoothness of movement. But that's not what it's about. It's about the entirety of the system, like we talked about, the data engine and the fleet. That's super exciting. Even the initial sort of models. But that, too, was really surprising. that in a few months you can get a prototype.
Yep. And the reason that happened very quickly is, as you alluded to, there's a ton of copy-paste from what's happening on the autopilot. A lot. The amount of expertise that came out of the woodworks at Tesla for building the human robot was incredible to see. Like... Basically, Elon said at one point, we're doing this. And then next day, basically, all these CAD models started to appear.
And people talking about the supply chain and manufacturing. And people showed up with screwdrivers and everything the other day and started to put together the body. And I was like, whoa. All these people exist at Tesla. And fundamentally, building a car is actually not that different from building a robot. And that is true, not just for the hardware pieces.
And also, let's not forget hardware, not just for a demo, but... manufacturing of that hardware at scale. It's like a whole different thing. But for software as well, basically this robot currently thinks it's a car.
It's going to have a midlife crisis at some point.
It thinks it's a car. Some of the earlier demos, actually, we were talking about potentially doing them outside in the parking lot because that's where all of the computer vision was working out of the box instead of inside. But all the operating system, everything just copy-pastes. Computer vision, mostly copy-pastes.
I mean, you have to retrain the neural nets, but the approach and everything and data engine and offline trackers and the way we go about the occupancy tracker and so on, everything copy-pastes. You just need to retrain the neural nets. And then the planning control, of course, has to change quite a bit. But there's a ton of copy-paste from what's happening at Tesla.
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