4 ms·

It uses intuition to play, which is how humans play, but it does it by considering way more positions that humans.

by iopq 10y ago

hueving 10y ago
>It uses intuition to play, Not by the normal definition of intuition. It reasons about millions of moves.
- kirrent 10y ago
  Could you provide a citation for that figure? It's not one I've read but I don't necessarily disbelieve it. It is worth considering this perspective from one of the Deepmind Nature papers: "During the match against Fan Hui, AlphaGo evaluated thousands of times fewer positions than Deep Blue did in its chess match against Kasparov; compensating by selecting those positions more intelligently, using the policy network, and evaluating them more precisely, using the value network—an approach that is perhaps closer to how humans play. Furthermore, while Deep Blue relied on a handcrafted evaluation function, the neural networks of AlphaGo are trained directly from gameplay purely through general-purpose supervised and reinforcement learning methods"
- mistercow 10y ago
  Those are two independent sentences which are not mutually exclusive.
- joesb 10y ago
  Your intuition is probably based on millions of events happened to you and your ancestor (parts that are developed through evolution and social construct).
  - manquer 10y ago
    Equivalent on the side would be million of games in the past either simulation or real human ones, both of which such systems use
- iopq 10y ago
  The policy network (intuition) is already high dan level, which is stronger than many strong amateurs. That's right, with zero reading it's already something like 5d level. The other part that increases its strength is the positional evaluation through the value network. It's ALSO intuition based on which points are going to belong to which player. Those two combined give it a high dan level, maybe around 8d. Only the last component (Monte-Carlo tree search) is not quite as similar because it uses random game roll-outs to ALSO give an evaluation. AFAIK, value network evaluation is an overestimate of territories, while the MCTS is an underestimate so using both it's stronger than professional level. The levels I put is the version that played Lee Sedol. The version that went 50-0 on the Internet is clearly stronger and might already be professional level without MCTS.
kirrent 10y ago
Not only does it use a form of intuition, but it's an intuition that it's developed itself, rather than just copying others. Professional Go players are learning from the creative strategies of AlphaGo and if that doesn't imply some level of weak intelligence then I'm not sure why anyone talks about a weak/strong AI divide to begin with. Like Scott Aaronson said, "You can look at any of these examples -- Deep Blue, the Robbins conjecture, Google -- and say, that's not really AI. That's just massive search, helped along by clever programming. Now, this kind of talk drives AI researchers up a wall. They say: if you told someone in the sixties that in 30 years we'd be able to beat the world grandmaster at chess, and asked if that would count as AI, they'd say, of course it's AI! But now that we know how to do it, now it's no longer AI. Now it's just search."

It uses intuition to play, which is how humans play, but it does it by considering way more positions that humans.

by iopq 10y ago

It uses intuition to play, which is how humans play, but it does it by considering way more positions that humans.

hueving 10y ago

>It uses intuition to play, Not by the normal definition of intuition. It reasons about millions of moves.

kirrent 10y ago
Could you provide a citation for that figure? It's not one I've read but I don't necessarily disbelieve it. It is worth considering this perspective from one of the Deepmind Nature papers: "During the match against Fan Hui, AlphaGo evaluated thousands of times fewer positions than Deep Blue did in its chess match against Kasparov; compensating by selecting those positions more intelligently, using the policy network, and evaluating them more precisely, using the value network—an approach that is perhaps closer to how humans play. Furthermore, while Deep Blue relied on a handcrafted evaluation function, the neural networks of AlphaGo are trained directly from gameplay purely through general-purpose supervised and reinforcement learning methods"
mistercow 10y ago
Those are two independent sentences which are not mutually exclusive.
joesb 10y ago
Your intuition is probably based on millions of events happened to you and your ancestor (parts that are developed through evolution and social construct).
- manquer 10y ago
  Equivalent on the side would be million of games in the past either simulation or real human ones, both of which such systems use
iopq 10y ago
The policy network (intuition) is already high dan level, which is stronger than many strong amateurs. That's right, with zero reading it's already something like 5d level. The other part that increases its strength is the positional evaluation through the value network. It's ALSO intuition based on which points are going to belong to which player. Those two combined give it a high dan level, maybe around 8d. Only the last component (Monte-Carlo tree search) is not quite as similar because it uses random game roll-outs to ALSO give an evaluation. AFAIK, value network evaluation is an overestimate of territories, while the MCTS is an underestimate so using both it's stronger than professional level. The levels I put is the version that played Lee Sedol. The version that went 50-0 on the Internet is clearly stronger and might already be professional level without MCTS.

kirrent 10y ago

Not only does it use a form of intuition, but it's an intuition that it's developed itself, rather than just copying others. Professional Go players are learning from the creative strategies of AlphaGo and if that doesn't imply some level of weak intelligence then I'm not sure why anyone talks about a weak/strong AI divide to begin with. Like Scott Aaronson said, "You can look at any of these examples -- Deep Blue, the Robbins conjecture, Google -- and say, that's not really AI. That's just massive search, helped along by clever programming. Now, this kind of talk drives AI researchers up a wall. They say: if you told someone in the sixties that in 30 years we'd be able to beat the world grandmaster at chess, and asked if that would count as AI, they'd say, of course it's AI! But now that we know how to do it, now it's no longer AI. Now it's just search."