(Mein Deutsch ist leider nicht so gut mehr, weil ich nicht mehr in der Schweiz wohne und darum es so selten spreche, aber lesen ist immer noch kein Problem.)
I think it's fairly safe to say that you could make something like AlphaZero that plays great chess on the Titan V ($3000, just announced), but whether it would be as strong as AlphaZero (or Stockfish on a 32-core) is much less certain.
Actually, at this point they are being heavily driven by efforts to unlock cryptocurrency...
A lot of it is correct, but I feel some of it is also missing the mark: Yes, sure, it's not the latest git master, but that's only something like 20 Elo, IIRC. And who really cares about time management when engines are generally used for analysis?
Game 3 video is nice. My view remains the same, once the crude, blunt and beginners material concept got junked, replaced effectively by "mobility" and all it's ramifications (which are many and complex) then chess is back to Tal with the bean counter programs left for dead. As A0 now proves. One can only tremble at what this technology can do when given more than four hours to learn.
Second factor is we can also see the rating leaps of Stockfish as basically incestuous. Like has been playing like down dead end street. We can predict a catch up by strong players who've had Stockfish's weaknesses shown them.
I want to know the amount of games it self-played
is already above 95%.
How could all those brilliant and enthusiastic chess programmers had missed it for decades
And yes, I was also one of those who thought it won't work for chess, even after the Go experience.
Could it be that alphabeta,minmax are somehow encoded, hidden in the neural net ?
Could it be, that a certain big amount of hardware is _needed_ to implement it ?
what Elo do we estimate for AZ on a normal PC
of 2000 , Intel Pentium or AMD K6 , 200MHz , 256MB RAM one core
or 2010 , 2GB RAM , 2GHz , one core
or 2017 , 16GB RAM , 16 cores at 4GHz
there are several techniques I can try that will probably drastically improve Giraffe,
when I can't [easily] try them [while working at Deepmind] because they are still [maybe] trade secret
Machine learning has defeated hand-crafted systems in just about every other field.
That will happen in chess, too, and the only question is when.
help understanding it (which might be still valid : "feel free to ping me")
, but an understanding of neural nets were required.
> You do need to have fairly good understanding of neutral networks and TD learning to begin with, though.
I speculate that chessprogramming interested people usually lack that understanding,
while people interested in neural networks and TD usually are not interested in
It's not too late ... Alpha Zero will probably not be released, so that is no competitor.
Now we have "proof" that much improvement is possible and hopefully someone
might improve Giraffe and give it to the rating lists.
ML machine learning
NN neural net (neutral net must be a typo)
TD Temporal Difference
reinforcement learning with deep neural networks
> In the last few years, neural networks have become hugely powerful thanks to two advances.
so maybe this was not possible 5 years ago, as you also suggested above
presumably the availability of big RAM at low prices, which improved a lot since 2000,
much more than computation power
His network consists of four layers that together examine each position
on the board in three different ways.
The first looks at the global state of the game, such as the number and type
of pieces on each side, which side is to move, castling rights and so on.
The second looks at piece-centric features such as the location of each piece
on each side, while the final aspect is to map the squares that each piece attacks and defends.
so counting material _is_ one basic way of examining positions
> 175 million positions
> Strategic Test Suite, which consists of 1,500 positions
matches the best chess engines in the world. [in that test]
> Giraffe takes about 10 times longer than a conventional chess engine to search the
> same number of positions.
> opening and end game phases, where it plays exceptionally well.
no program can change that
AZ just comes too late to play better chess, its done, like checkers
i think in 12 games i will win 2 against AZ
it has weaknesses in the opening
Currently we have only first generation Alpha Zero. It beats SF by 64%. Second, third, fourth generation Alpha Zero will win by much bigger margins. Especially if they feed SF to the Alpha Zero learning process with the aim of beating it by the highest margin.
The game has been changed by this new method. In 10-20 years the NN programs will do the same to the current top engines as the latter ones are doing to Shredder, Fritz and Junior of 2002-2007.
corr chess is solved and no program can do better as it is now
only quick games can (and will) be improved by AZ
That's what they also said 10 years ago :-).
Of course a much stronger NN program can do better. Namely a NN program that finds interesting things in the 99.999999% subset of the tree that SF is not looking at.
i didnt say it 10 years ago, but 3 years ago it was also right
Deepmind/Google isn't much interested into chess. It they were, that wouldn't be hard to teach AlphaZero take into account what Stockfish or anyone else thinks. :)
Such "AlphaCentaur" would kick asses of us all.
That could be enough, I believe, even without consulting with Stockfish or anyone else. That would be superhuman level.
Of course, it can be modified into supercentaur, too. :)
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