This is a clip from a conversation with George Hotz on the Artificial Intelligence podcast. You can watch the full conversation here: http://bit.ly/2YLIPom If you enjoy these, consider subscribing, sharing, and commenting below.

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George Hotz is the founder of Comma.ai, a machine learning based vehicle automation company. He is an outspoken personality in the field of AI and technology in general. He first gained recognition for being the first person to carrier-unlock an iPhone, and since then has done quite a few interesting things at the intersection of hardware and software.

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You said that the meaning of life is to win.

If you look 5 years into the future, what does winning look like?

I can go into technical depth to what I mean by that, to win.

I was criticized for that in the comments.

Doesn't this guy want to save the penguins in Antarctica?

Or like, oh man, listen to what I'm saying.

I'm not talking about like I have a yacht or something.

And I don't really know what my purpose is.

But if you're a reinforcement, if you're an intelligent agent and you're put into a world, what is the ideal thing to do?

Well, the ideal thing mathematically, you can go back to like Schmidhuber theories about this, is to build a compressive model of the world, to build a maximally compressive, to explore the world such that your exploration function maximizes the derivative of compression of the past.

And I took that kind of as like a personal goal function.

So what I mean to win, I mean like, maybe this is religious, but I think that in the future, I might be given a real purpose, or I may decide this purpose myself.

And then at that point, now I know what the game is and I know how to win.

I think right now I'm still just trying to figure out what the game is.

So you have imperfect information, you have a lot of uncertainty about the reward function and you're discovering it.

So The purpose is to maximize it while you have it a lot of uncertainty around it.

And you're both reducing the uncertainty and maximizing at the same time.

What is the, if you believe in the universal prior, What is the universal reward function?

I think I speak for everyone in saying that I wonder what that reward function is for you.

And I look forward to seeing that in 5 years and 10 years.

I think a lot of people including myself are cheering you on man.

So I'm happy you exist and I wish you You


Speaker 0: You said that the meaning of life is to win. If you look 5 years into the future, what does winning look like?

Speaker 1: So there's a lot of... I can go into technical depth to what I mean by that, to win. It may not mean... I was criticized for that in the comments. Doesn't this guy want to save the penguins in Antarctica?

Or like, oh man, listen to what I'm saying. I'm not talking about like I have a yacht or something. I am an agent. I am put into this world. And I don't really know what my purpose is.

But if you're a reinforcement, if you're an intelligent agent and you're put into a world, what is the ideal thing to do? Well, the ideal thing mathematically, you can go back to like Schmidhuber theories about this, is to build a compressive model of the world, to build a maximally compressive, to explore the world such that your exploration function maximizes the derivative of compression of the past. Schmidt-Huber has a paper about this. And I took that kind of as like a personal goal function. So what I mean to win, I mean like, maybe this is religious, but I think that in the future, I might be given a real purpose, or I may decide this purpose myself.

And then at that point, now I know what the game is and I know how to win. I think right now I'm still just trying to figure out what the game is. But once I know.

Speaker 0: So you have imperfect information, you have a lot of uncertainty about the reward function and you're discovering it. Exactly. But the

Speaker 1: purpose is- That's a better way to put

Speaker 0: it. So The purpose is to maximize it while you have it a lot of uncertainty around it. And you're both reducing the uncertainty and maximizing at the same time. And so that's at the technical level.

Speaker 1: What is the, if you believe in the universal prior, What is the universal reward function? That's the better way to put it.

Speaker 0: So that win is interesting. I think I speak for everyone in saying that I wonder what that reward function is for you. And I look forward to seeing that in 5 years and 10 years. I think a lot of people including myself are cheering you on man. So I'm happy you exist and I wish you You

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George Hotz: Winning - A Reinforcement Learning Approach | AI Podcast Clips