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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It's a good estimate for if you're making the right decision.
Yeah, I think a lot of people under... This is true for the general public, and I think it's true for the AI community.
A lot of people underestimate the importance of search for these kinds of game AI results.
An example of this is TD Gammon that came out in 1992.
This was the first real instance of a neural net being used in a game AI.
It's a landmark achievement.
It was actually the inspiration for AlphaZero.
And it used search.
It used two-ply search to figure out its next move.
You got Deep Blue, there it was very heavily focused on search, looking many, many moves ahead, farther than any human could.
And that was key for why it won.
And then even with something like AlphaGo, I mean, AlphaGo is commonly hailed as a landmark achievement for neural nets, and it is, but there's also this huge component of search, Monte Carlo tree search to AlphaGo, that was key, absolutely essential for the AI to be able to beat top humans.
I think a good example of this is you look at the latest versions of AlphaGo.
It was called AlphaZero.
And there's this metric called ELO rating, where you can compare different humans and you can compare bots to humans.
Now, a top human player is around 3,600 ELO, maybe a little bit higher now.
AlphaZero, the strongest version, is around 5,200 ELO.
But if you take out the search that's being done at test time, and by the way, what I mean by search is the planning ahead, the thinking of like, oh, if I move my, if I place this stone here and then he does this, and then you look like five moves ahead and you see like what the board state looks like.
That's what I mean by search.
If you take out the search that's done during the game, the ELO rating drops to around 3000.
Showing 401–420 of 1,199 · page 21 of 60
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