Noam Brown
speaker
1,199 appearances
1 recordings
1 series
first heard Dec 2022
last heard Dec 2022
Noam Brown’s voice in public audio — every appearance, attributed to the second.
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So even today, what, seven years after AlphaGo,
If you take out the Monte Carlo tree search that's being done at when playing against the human, the bots are not superhuman.
Nobody has made a raw neural net that is superhuman in Go.
That's right.
Yeah, and all these bots, they have what's called a policy network, where it will tell you, this is what the neural net thinks is the next best move.
And it's kind of like the intuition that a human has.
The human looks at the board and any Go or chess master will be able to tell you like, oh, instantly, here's what I think the right move is.
And the bot is able to do the same thing.
But just like how a human grandmaster can make a better decision if they have more time to think, when you add on this Monte Carlo tree search, the bot is able to make a better decision.
I have thought a lot about that, and I think it's a really important question.
So the AI in Alpha and AlphaGo or any of these Go AIs, they're all doing Monte Carlo tree search, which is a particular kind of search.
And it's actually a symbolic tabular search.
It uses the neural net to guide its search, but it isn't actually like full on neural net.
Now, that kind of search is very successful in these kinds of perfect information board games like chess and Go.
But if you take it to a game like poker, for example, it doesn't work.
It can't understand the concept of hidden information.
It doesn't understand the balance that you have to strike between the amount that you're raising versus the amount that you're calling.
And in every one of these games, you see a different kind of search.
And the human brain is able to plan for all these different games in a very general way.
Now, I think that's one thing that we're missing from AI today.
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