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> These kinds of predictions are almost always useless. Let's rephrase. For a long time, the expert consensus regarding Go was that it was extremely difficult

> These kinds of predictions are almost always useless. Let's rephrase. For a long time, the expert consensus regarding Go was that it was extremely difficult to write strongly-performing AI for. From the AlphaGo Paper: Go presents "difficult decision-making tasks; an intractable search space; and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function." For many years, the state-of-the-art Go AI stagnated or grew very slowly, reaching at most the amateur dan level. AlphaGo presents a huge and surprising leap. > Continued advancement doesn't mean that it is accelerating Over constant time increases, AI is tackling problems that appear exponentially more difficult. In particular, see Checkers (early '90s) vs Chess ('97) vs Go ('16). The human advantage has generally been understood to be the breadth of the game tree, nearly equivalent to the complexity of the game. If we let x be the maximum complexity of a task at which AI performs as well as a human, then I would argue that x has been growing at an accelerating pace over the past few decades.

ekianjo 11y ago
> If we let x be the maximum complexity of a task at which AI performs as well as a human, then I would argue that x has been growing at an accelerating pace over the past few decades. At ONE task, yes. But humans are average at many things but excel at being able to adapt to many different tasks, all the time. Typical AIs (as we know them now) cannot ever hope to replicate that.
- celticninja 11y ago
  This seems to have been linked to a lot recently but I feel it is relevant to the discussion on technology advances pertaining to AI. http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://waitbutwhy.com/2015/01/artificial-intelligence-revolu...
skj 11y ago
"and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function." To be clear, the above refers to specific concepts in Reinforcement Learning. A policy is a function from state (in Go, where all the stones are) to action (where to place the next stone). I agree that it is unlikely to have an effective policy function. At least one that is calculated efficiently (no tree search)... otherwise its not what a Reinforcement Learning researcher typically calls a policy function. A value function is is a function from state to numerical "goodness", and is more or less one step removed from a policy function: you can choose the action that takes you to the state with the highest value. It has the same representational problems found there.
xigency 11y ago
> AI is tackling problems that appear exponentially more difficult. The hardest AI problems are the ones that involve multiple disciplines in deep ways. Here's a top tier artificial intelligence problem: given a plain English description of a computer program, implement it in source code. There might be some cases where this is possible, and some cases are bound to fail. Those are the kind of difficult problems in AI, which combine knowledge, understanding, thought, intuition, inspiration, and perspiration - or demand invention. We would be lucky to make linear progress in this area let alone exponential growth. I think there's certainly an impression of exponential progress in AI in popular culture, but the search space is greater than factorial in size, and I think hackers should know that.
- dmoy 11y ago
  Wait what is the plan to brute force go? The search space is beyond immense...
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- the_af 11y ago
  > To be fair, in terms of the complexity of rules, checkers is easier to understand than go which is easier to understand than chess. And honestly, go seems like the kind of brute-force simple, parallel problem that we can solve now without too much programming effort Your intuition is mistaken. Go is indeed "easier to understand" than Chess in terms of its rules, but it is arguably harder to play well and has a way larger search space, which makes it less amenable to brute force, and this was precisely why people thought it'd be impossible for a computer to play it consistently at champion level. I don't think the achievement of AlphaGo is solely due to increased processing power, otherwise why did people think Go was such a hard problem?
  - xigency 11y ago

ekianjo 11y ago

> If we let x be the maximum complexity of a task at which AI performs as well as a human, then I would argue that x has been growing at an accelerating pace over the past few decades. At ONE task, yes. But humans are average at many things but excel at being able to adapt to many different tasks, all the time. Typical AIs (as we know them now) cannot ever hope to replicate that.

celticninja 11y ago
This seems to have been linked to a lot recently but I feel it is relevant to the discussion on technology advances pertaining to AI. http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://waitbutwhy.com/2015/01/artificial-intelligence-revolu...

skj 11y ago

"and an optimal solution so complex it appears infeasible to directly approximate using a policy or value function." To be clear, the above refers to specific concepts in Reinforcement Learning. A policy is a function from state (in Go, where all the stones are) to action (where to place the next stone). I agree that it is unlikely to have an effective policy function. At least one that is calculated efficiently (no tree search)... otherwise its not what a Reinforcement Learning researcher typically calls a policy function. A value function is is a function from state to numerical "goodness", and is more or less one step removed from a policy function: you can choose the action that takes you to the state with the highest value. It has the same representational problems found there.

xigency 11y ago

> AI is tackling problems that appear exponentially more difficult. The hardest AI problems are the ones that involve multiple disciplines in deep ways. Here's a top tier artificial intelligence problem: given a plain English description of a computer program, implement it in source code. There might be some cases where this is possible, and some cases are bound to fail. Those are the kind of difficult problems in AI, which combine knowledge, understanding, thought, intuition, inspiration, and perspiration - or demand invention. We would be lucky to make linear progress in this area let alone exponential growth. I think there's certainly an impression of exponential progress in AI in popular culture, but the search space is greater than factorial in size, and I think hackers should know that.

dmoy 11y ago
Wait what is the plan to brute force go? The search space is beyond immense...
- deleted 11y ago
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- deleted 11y ago
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the_af 11y ago
> To be fair, in terms of the complexity of rules, checkers is easier to understand than go which is easier to understand than chess. And honestly, go seems like the kind of brute-force simple, parallel problem that we can solve now without too much programming effort Your intuition is mistaken. Go is indeed "easier to understand" than Chess in terms of its rules, but it is arguably harder to play well and has a way larger search space, which makes it less amenable to brute force, and this was precisely why people thought it'd be impossible for a computer to play it consistently at champion level. I don't think the achievement of AlphaGo is solely due to increased processing power, otherwise why did people think Go was such a hard problem?
- xigency 11y ago