Name: AIと論理誘導 - Computerphile (AI & Logical Induction - Computerphile)
Uploaded: 2021-01-14T10:16:16.000Z
Duration: 27 min 48 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

過去形

I thought we would talk a bit about logical induction paper out of the Machine Intelligence Research Institute.

動名詞

Very technical paper, very mathematical and can be a bit hard to get your head around, and we're not going to get too far into it for computer file.

I just want to explain, like why it's cool and why it's interesting and people who are into it and read the paper themselves and find out about it.

One of the things about logical induction as a paper is that it's not immediately obvious why it's ending.

Um, but like my perception off the way that married the way that the mission intelligence recessions to shoot is thinking about this is they're saying they're thinking like we're going to be producing at some point artificial general intelligence.

And as we talked about in previous videos, there are hundreds of ways really weird, subtle, difficult to predict ways that he's this kind of thing could go very badly wrong, and so we want to be confident about the behavior of our systems before we turn them on.

And that means ideally, we want to be making systems that we can, um, actually in the best case that we could actually prove theorems about write things that are well specified enough that we could actually write down and formally proved that will have certain characteristics.

And if you look at, like, current machine learning stuff old like date neural networks and that kind of thing, they're just very opaque.

Because we don't necessarily know exactly why it's doing what it's doing.

Yeah, and also just that the the system itself is very is very complex and very contingent on a lot of specifics about the training data on dhe, the architecture and like, there's just yet effectively.

We don't understand it well enough, but it's it's like, not formally specified enough, I guess.

And so they're trying to come up with the sort of mathematical foundations that we would need to be able to prove important things about powerful A i systems.

Before we were talking about hypothetical Future A.

I systems, we've got time to print more stamps, so maybe hijacks the world's stamp printing factories.

And when we were talking about the stamp collector, it was really useful tohave.

This framework of an agent and say this is what an agent is.

We can reason about the behavior of agents in general.

And then all we need to do is make these fairly straightforward assumptions about our A i system that it will behave like an agent.

So we have idealized forms of reasoning, like we have probability theory, which tells you the rules that you need to follow to have good beliefs about the state of the world.

And we have, like, like, rational choice theory about, uh, what what rules you need to follow.

It may not be like, actually possible to follow those rules, but we can at least formally specify.

Like if the thing has these properties, it will do this well.

And all we're trying to do is that they're thinking if we're building very powerful A.

I systems, the one thing we can expect from them is there going to be able to do thinking well.

And so if we can come up with some formalized method of of exactly what we mean by doing thinking, well, then if we do, if we reason about that.

That should give us some insight into how these systems will behave.

Then this is where we should have a demo.

And I can ask you was the probability that that is 51 in six, right?

One in six because you don't know it could be anything.

There was a time when people reasoned about the probabilities of things happening in a very sort of fuzzy way.

You'd be playing cards and you'd be like, Oh, you know, that card has shown up here.

So I guess it's less likely that he has this hand and people would, um, into it.

Yeah, and like there was some sense of, like, clearly we are doing something.

It's meaningful to say that this this outcome or that outcome is more or less likely by a bit, or buy a lotto or whatever But people didn't have a really good explanation of how that all worked until we had probability theory, right?

And like practically speaking, you often can't do the full probability theory stuff in your head for a game of cards, especially when you're trying to read people's faces and like stuff that's very hard to quantify.

But now we have this understanding that, like even when we can't actually do it, what's going on underneath this probability theory?

And now I'm getting now I'm gonna need a pen.

That's like the 10th digit after the decimal point off the square root of 17 is a five.

One in 10 seems like a totally reasonable thing to say, right.

We don't know which one, but if I You've got the paper here, you know you could do like division.

You could do this if you had long enough.

Like if I gave you say an hour with the paper and you could sit here and figure it out on Dhe.

Let's say you do it and you you come up and it seems to be like three.

Let's do the calculation about expression.

So I suppose you didn't have a calculator you were doing on paper I gave you, you know, half an hour an hour to do the like, long divisional.

Whatever is you would have to do to figure this out.

That statement is true Now what do you say?

But you've just done this in a big hurry on paper, right?

So what's the probability that you made a mistake you forgot to, like, carry the one or something could be one.

Um, and then if I left you in the room again, still with a piece of paper for 100 years.

1000 years, infinite time eventually, assuming that was correct.

Um, you come up with some, like, formal proof that this is This is false.

Um, whereas you imagine, if I leave you for infinite time with the cup and you can't look at you got look under the cup, it's one sick.

When you're playing cards, you've got these probabilities for which cards you think it depending on the game.

And as you observe new things, you're updating your probabilities for these different things as you observe new evidence, and then it may be eventually you'll actually see the thing, and then you can go to 100% 0.

Um, whereas in this case, all that's changing is your thinking.

But you're doing a similar sort of thing.

You are still updating your probabilities for things, But probability theory kind of doesn't have anything to say about this scenario.

Like probability is how you update when you've seen your evidence, and here you're not seeing any new evidence.

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AIと論理誘導 - Computerphile (AI & Logical Induction - Computerphile)

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