5 ms·

Humans tend to progress by learning from each other. The AI will teach us.

asdfasgasdgasdg 8y ago
Whether the AI teaches us or not, once it surpasses us, we will not catch up. This is the case for every game AI that has ever surpassed human performance so far, and there's no reason to expect that this will be different in the case of DotA.
- ethbro 8y ago
  > game AI that has ever surpassed human performance so far Am I missing something, or does that set consist of Checkers, Chess, and Go so far? (presumably with analogous misc games of comparable complexity) Discounting the reaction time wins, I'd say the sample size is too limited to generalize to eventual AI behavior in more complex / open-ended games. Extrapolation was the cause of the last AI winter.
  - tialaramex 8y ago
    Poker. Although Heads Up Limit Texas Hold 'Em has been essentially solved the No Limit variant (in which you actually decide how much to bet) is very far from solved. An AI beat the best players in the world anyway. It plays crazy, but it plays crazy in a way they weren't able to exploit at all. Doug Polk (also one of the best in the world in this narrow specialty) did some funny videos but the humans got crushed. So that's a pretty different game, it's got a big luck factor and has asymmetrical information and still the AI just kept getting better and the humans... didn't
  - TangoTrotFox 8y ago
    I think you're looking at things with a sort of hindsight bias. Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. The reason is that it's an extremely open, creative, and strategic game spattered with a minefield of tactical nuance. Nobody, human or computer, is getting even remotely close to scratching the depth of the game from a numeric point of view. There are some specifics on the numeric complexity of the game here [1]. The reason I point out the unfathomable numeric complexity is that it makes the games, from the perspective of an AI, effectively infinite. AIs are calculating, but to an extremely superficial degree relative to the depth of the game. E.g. - when a chess program says it's calculated to 30 ply (15 moves for both sides) what it really says is that it's seen up to 15 moves deep after intentionally ignoring or pruning 99.9999999999% of moves which it thinks probably aren't good -- something it still often gets wrong, but its 'understanding' of what is 'not wrong' is strong enough that it still results in a phenomenally strong level of play, compared to humans. There's no doubt that perfect play in chess would still go 1 billion - 0 against something like AlphaZero. So what matters is not the number of decisions to be made but the individual complexity of the decisions to be made. And in most games we consider complex the individual decisions are not really that complex, and complex systems can often be broken down into very simple games. For instance a great example of this is a 4x game. Taken as a whole they seem complex, but they're really just a large number of relatively simple components that are mostly independent. E.g. - Given this state, where do you explore next? Given this state, what do you research next? Etc. Another benefit for AIs in that in games we consider more complex, the value of any given mistake often becomes diminished. If you make a single bad move in chess, it's enough to lose the game. In a 4x game the weight of individual decisions is not so high, it's all about the big picture. But as perhaps computer success in Go shows most clearly, actually seeing the big picture is not really necessary to produce play like you do. This, I think, is why research has moved more onto real time competitive games. Crushing humans at chess, go, and now poker as well is a pretty solid proof of concept for computers beating humans at any turn based game. When you start adding bunches of different layers to games I think it's more likely to handicap the human than the computer. Imagine playing some sort of 100x100 chess. We can only speculate, but I imagine the distance between the top AIs and humans would be far greater than it is in 8x8 chess. [1] - https://en.wikipedia.org/wiki/Shannon_number https://en.wikipedia.org/wiki/Shannon_number
    - ethbro 8y ago
      > Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. I would disagree with this characterization. I believe at the time, it was (a) a problem that a machine had not yet conquered, (b) a problem that it seemed feasible that a machine might conquer, and (c) a problem that, once conquered, would point the way to general artificial intelligence. I would point at (c) as the assumption that proved to be erroneous. Deep Blue was clever algorithmic and hardware engineering (with a healthy budget) but led to... what? AlphaGo is a fundamentally different approach, which shows signs of being more adaptable. Point being, that winning a game is not sufficient evidence that a given approach will scale to winning all games, much less generalized intelligence. To put it in terms of the fallacy I read in an article linked on HN (paraphrased), 'The public assumes that if a machine can perform a task that humans can perform, the machine must be human-like, and therefore able to perform all tasks that humans can perform.' But in the same way that we use rendering tricks to go beyond-state-of-hardware-art in graphics rendering (by abusing hidden limitations), so do we often build ml systems. I believe the most optimistic point against me was the slide in this year's GTC keynote pointing to the "Cambrian explosion" in the diversity of ml approaches this time around.
  - red75prime 8y ago
    Statistics is not everything. Which hypothesis can lead to the outcome "AIs will not be able to beat humans in every game"? I can see following hypotheses (in no particular order): 1. Human brain is the optimal solution in the space of all computational devices capable of playing games, and we can only approach it. 2. To do computation human brain employs some physical processes, we will not be able to replicate in the foreseeable future. 3. Human brain do not produce general intelligence, so we will not be able to replicate it as such a task is outside of the scope of our limited intelligence (while playing games isn't). 4. Human brain uses metaphysical abilities to do cognition, we will not be to replicate them at all. 5. Human brain is a local optimum, but the space of potential AIs' constructions is too huge to explore in the lifetime of our civilization, so we will be stuck at this local optimum with marginal improvements. I don't see any of them as sufficiently likely, but your mileage may vary.
    - ethbro 8y ago
      I'd point to a combination of (2) and (5) as the maximally likely reason we'd fail to build a generalized game playing system. I believe there exists a combination of hardware and software capable of beating humans in all games. However, I also believe victory in a single game gives us minimal information on whether or not the system generalizes to many games (to say nothing of non-game, e.g. more complex, ruleless systems).
- douglaswlance 8y ago
  Depends how you define, "surpass." There will be a time where we learn from the AI and the AI learns from us, where we trade victories and defeats as we adapt to each other. Don't discount the ability of humans. They figured out how to exploit the 1v1 bot in a few days and soon humans had a 100% win rate using that strategy.

Whether the AI teaches us or not, once it surpasses us, we will not catch up. This is the case for every game AI that has ever surpassed human performance so far, and there's no reason to expect that this will be different in the case of DotA.

ethbro 8y ago
> game AI that has ever surpassed human performance so far Am I missing something, or does that set consist of Checkers, Chess, and Go so far? (presumably with analogous misc games of comparable complexity) Discounting the reaction time wins, I'd say the sample size is too limited to generalize to eventual AI behavior in more complex / open-ended games. Extrapolation was the cause of the last AI winter.
- tialaramex 8y ago
  Poker. Although Heads Up Limit Texas Hold 'Em has been essentially solved the No Limit variant (in which you actually decide how much to bet) is very far from solved. An AI beat the best players in the world anyway. It plays crazy, but it plays crazy in a way they weren't able to exploit at all. Doug Polk (also one of the best in the world in this narrow specialty) did some funny videos but the humans got crushed. So that's a pretty different game, it's got a big luck factor and has asymmetrical information and still the AI just kept getting better and the humans... didn't
- TangoTrotFox 8y ago
  I think you're looking at things with a sort of hindsight bias. Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. The reason is that it's an extremely open, creative, and strategic game spattered with a minefield of tactical nuance. Nobody, human or computer, is getting even remotely close to scratching the depth of the game from a numeric point of view. There are some specifics on the numeric complexity of the game here [1]. The reason I point out the unfathomable numeric complexity is that it makes the games, from the perspective of an AI, effectively infinite. AIs are calculating, but to an extremely superficial degree relative to the depth of the game. E.g. - when a chess program says it's calculated to 30 ply (15 moves for both sides) what it really says is that it's seen up to 15 moves deep after intentionally ignoring or pruning 99.9999999999% of moves which it thinks probably aren't good -- something it still often gets wrong, but its 'understanding' of what is 'not wrong' is strong enough that it still results in a phenomenally strong level of play, compared to humans. There's no doubt that perfect play in chess would still go 1 billion - 0 against something like AlphaZero. So what matters is not the number of decisions to be made but the individual complexity of the decisions to be made. And in most games we consider complex the individual decisions are not really that complex, and complex systems can often be broken down into very simple games. For instance a great example of this is a 4x game. Taken as a whole they seem complex, but they're really just a large number of relatively simple components that are mostly independent. E.g. - Given this state, where do you explore next? Given this state, what do you research next? Etc. Another benefit for AIs in that in games we consider more complex, the value of any given mistake often becomes diminished. If you make a single bad move in chess, it's enough to lose the game. In a 4x game the weight of individual decisions is not so high, it's all about the big picture. But as perhaps computer success in Go shows most clearly, actually seeing the big picture is not really necessary to produce play like you do. This, I think, is why research has moved more onto real time competitive games. Crushing humans at chess, go, and now poker as well is a pretty solid proof of concept for computers beating humans at any turn based game. When you start adding bunches of different layers to games I think it's more likely to handicap the human than the computer. Imagine playing some sort of 100x100 chess. We can only speculate, but I imagine the distance between the top AIs and humans would be far greater than it is in 8x8 chess. [1] - https://en.wikipedia.org/wiki/Shannon_number https://en.wikipedia.org/wiki/Shannon_number
  - ethbro 8y ago
    > Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. I would disagree with this characterization. I believe at the time, it was (a) a problem that a machine had not yet conquered, (b) a problem that it seemed feasible that a machine might conquer, and (c) a problem that, once conquered, would point the way to general artificial intelligence. I would point at (c) as the assumption that proved to be erroneous. Deep Blue was clever algorithmic and hardware engineering (with a healthy budget) but led to... what? AlphaGo is a fundamentally different approach, which shows signs of being more adaptable. Point being, that winning a game is not sufficient evidence that a given approach will scale to winning all games, much less generalized intelligence. To put it in terms of the fallacy I read in an article linked on HN (paraphrased), 'The public assumes that if a machine can perform a task that humans can perform, the machine must be human-like, and therefore able to perform all tasks that humans can perform.' But in the same way that we use rendering tricks to go beyond-state-of-hardware-art in graphics rendering (by abusing hidden limitations), so do we often build ml systems. I believe the most optimistic point against me was the slide in this year's GTC keynote pointing to the "Cambrian explosion" in the diversity of ml approaches this time around.
- red75prime 8y ago
  Statistics is not everything. Which hypothesis can lead to the outcome "AIs will not be able to beat humans in every game"? I can see following hypotheses (in no particular order): 1. Human brain is the optimal solution in the space of all computational devices capable of playing games, and we can only approach it. 2. To do computation human brain employs some physical processes, we will not be able to replicate in the foreseeable future. 3. Human brain do not produce general intelligence, so we will not be able to replicate it as such a task is outside of the scope of our limited intelligence (while playing games isn't). 4. Human brain uses metaphysical abilities to do cognition, we will not be to replicate them at all. 5. Human brain is a local optimum, but the space of potential AIs' constructions is too huge to explore in the lifetime of our civilization, so we will be stuck at this local optimum with marginal improvements. I don't see any of them as sufficiently likely, but your mileage may vary.
  - ethbro 8y ago
    I'd point to a combination of (2) and (5) as the maximally likely reason we'd fail to build a generalized game playing system. I believe there exists a combination of hardware and software capable of beating humans in all games. However, I also believe victory in a single game gives us minimal information on whether or not the system generalizes to many games (to say nothing of non-game, e.g. more complex, ruleless systems).
douglaswlance 8y ago
Depends how you define, "surpass." There will be a time where we learn from the AI and the AI learns from us, where we trade victories and defeats as we adapt to each other. Don't discount the ability of humans. They figured out how to exploit the 1v1 bot in a few days and soon humans had a 100% win rate using that strategy.