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Allusions are fun and all, but I disagree. These are important problems that a lot of people have put their whole careers into researching. Silly names like these lack gravitas.
Always sad to see these projects suffer from A Shortfall of Gravitas
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I thought the protagonist wasn't nearly as talented as the culture AIs (even the ones that are not all that powerful)?
Is that clear from the text? Gurgeh supposedly perceives the result of the last game before the AIs so we’re led to believe he’s seeing deeper. Obviously he could have been wrong and still won. The AIs lied to and manipulated him the entire time so it’s hard to know, but it would seem a very odd weakness for an AI to have. I think Banks pretty quickly recanted on the subject of the Culture’s ‘referrers’ but I don’t t…
To me it felt like the whole point of the novel was that Gurgeh was a piece in an even larger game and he didn't even realize it. So the idea that the people playing the "bigger" game couldn't compete in the smaller game seems silly, and I think they mention that they used Gurgeh instead of an AI to make it appear fair to the inhabitants of the planet.
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Yeah, whatever. As someone who grew up playing chess and is almost certainly much better at it than you, this future sucks
I don't understand why this is so depressing? You can still play against humans though. It's probably more fun anyways than playing versus a computer as in most games, isn't it?
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Yeah, whatever. As someone who grew up playing chess and is almost certainly much better at it than you, this future sucks
Why?
I do acknowledge that this isn't a new phenomena Fischer complained about this before the computer engine era and came up with a chess variant to nullify deep opening prep!
This is clearly part of DeepMind's long-game plan to achieve world domination through board game mastery. Naming the new algorithm after the book is a real tip of their hand... https://en.wikipedia.org/wiki/The_Player_of_Games
Funny that most of the comments are about the name. What an excellent choice.
I really like seeing references to the Culture series when naming things: https://en.m.wikipedia.org/wiki/The_Player_of_Games
Allusions are fun and all, but I disagree. These are important problems that a lot of people have put their whole careers into researching. Silly names like these lack gravitas.
Earlier quoted context omitted.
Is that clear from the text? Gurgeh supposedly perceives the result of the last game before the AIs so we’re led to believe he’s seeing deeper. Obviously he could have been wrong and still won. The AIs lied to and manipulated him the entire time so it’s hard to know, but it would seem a very odd weakness for an AI to have. I think Banks pretty quickly recanted on the subject of the Culture’s ‘referrers’ but I don’t t…
My recollection is that by the end of the novel its clear that Gurgeh was never competitive with the ship, although he might have been competitive with his security drone (although even that isn't clear, since imply that the security drone is a better game player than it pretends to be). To me it felt like the whole point of the novel was that Gurgeh was a piece in an even larger game and he didn't even realize it. S…
The practical question is: How much computation do you need to get useful results? Alpha Go Zero is impressive mathematics, but who is willing to spend $1mio daily for months to train it? IMPALA (another Google one) can learn almost all Atari games, but you need a head node with 256 TPU cores and 1000+ evaluation workers to replicate the timings from the paper.
This is a great result, but you can see that it's more of a theoretical case because of this: "converging to perfect play as available computation time and approximation capacity increases." That is true for pretty much all current deep reinforcement learning algorithms. The practical question is: How much computation do you need to get useful results? Alpha Go Zero is impressive mathematics, but who is willing to sp…
Suppose you're a business that needs to play games. Most people seem to think that it's a matter of plugging in the settings from the paper, buying the same hardware, then clicking a button and waiting.
It's not. The specific settings matter a lot.
But my main point is that you'll get most of your performance pretty rapidly. The only reason to leave it running for so long is to get that last N%, which is nice for benchmarks but not for business.
DeepMind overspends. Actually, they don't; they're not paying anywhere close to the price of a 256 core TPU. (Many external companies aren't, either, and you can get a good deal by negotiating with the Cloud TPU team.)
But you don't need a 256 core TPU. Lots of times, these algorithms simply do not require the amount of compute that people throw at the problem.
On the other hand, you can also usually get access to that kind of compute. A 256 core TPU isn't beyond reach. I'm pretty sure I could create one right now. It's free, thanks to TFRC, and you yourself can apply (and be approved). I was. https://sites.research.google/trc/
It kills me that it's so hard to replicate these papers, which is most of the motivation for my comment here. Ultimately, you're right: "How much compute?" is a big unknown. But the lower bound is much lower than most people realize (and most researchers).