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Totally agree, and very happy to see this statement at the top of HN. I'm against hyping up AI or misinterpreting it just as much as the next guy (especially co

Totally agree, and very happy to see this statement at the top of HN. I'm against hyping up AI or misinterpreting it just as much as the next guy (especially coming from a position where dealing with a lot of higher-management people has made me very wary of articles and vision-pieces from the other end of the spectrum that are all like "invest now in AI or lose it all"), but this is just clickbait for the sake of enjoying playing devil's advocate. AlphaGo solved one of the hardest challenges which we believed to be very far out in the future before it came along. I'd say the field is on the right -- and very exciting -- track; people just need to learn to approach it in a clear-headed manner. Good on IEEE though for welcoming opposing viewpoints.

dwringer 10y ago
I wouldn't say the challenge has been "solved" - the system may have only been able to win consistently because the programmers made adjustments and improvements to it between each game. Fitting its heuristics to a specific opponent might actually be overfitting unless it can consistently win against all challengers without being modified between rounds. [edited to clarify uncertainty about "why" it was able to win]
- kirrent 10y ago
  AlphaGo played plenty of games online after the Lee Seedol match. It went 60-0 against some of the worlds best players. Granted they were fast games but that's a challenge for AlphaGo as well as its adversaries.
  - dwringer 10y ago
    Thank you for this information, it will definitely be interesting to see how the future of Go plays out. For example, will players be able to train with AlphaGo to the point of being able to beat it?
    - kirrent 10y ago
      Even more interesting to me is the question of if Go players will be able to apply the novel strategies of AlphaGo which break with decades of conventional wisdom or if there's something in their execution that requires such perfect play (but leads to a more certain outcome) that they're only feasibly played by AlphaGo.
      - pps43 10y ago
        I'm not sure about human players, but it might be possible to build an adversarial neural network that beats AlphaGo (and only AlphaGo).
        Dzugaru 10y ago
        I thought AlphaGo is trained in an adversarial manner already (it plays against itself).
        pps43 10y ago
        The version that played against Fan Hui was reportedly trained that way, but if I recall correctly the more recent one that played Lee Sedol did't rely on self-play as much. Also the objective was different - they tried to train a network that would play well against any opponent. It should be easier to train a network that exploits some very narrow weakness in specific AlphaGo version. For example, my phone consistently beats me at chess. But I found a sequence of moves in one opening where is always makes the same mistake, and I after that I can win easily.
      - dwringer 10y ago
        That's the sort of thing I have wondered about. Chess continues to evolve since the famous Kasparov vs. Deep Blue matches, and from a quick look around human players do continue to draw matches against the strongest computers (even though they are regularly beaten, as well).
    - dwaltrip 10y ago
      Humans will never defeat the strongest go playing programs ever again. AlphaGo is now clearly ahead of the top pros, and such programs will only get stronger. Humans should learn some things from the machines, but we will definitely not be able to extract the full totality of understanding, and thus will naturally continue to fall behind.
- deleted 10y ago
  [deleted]
- tazjin 10y ago
  I believe that there was a sort of "learning freeze" for AlphaGo during the Lee Sedol games and there was no manual modification of its strategy either. If I recall correctly this was part of the commentary during the matches so I can't find a good source for it right now.
  - eutectic 10y ago
    https://youtu.be/l-GsfyVCBu0?t=2866 https://youtu.be/l-GsfyVCBu0?t=2866
    - tazjin 10y ago
      Thank you, that's exactly what I was looking for!
      - dwringer 10y ago
        Thanks for bringing this up. I was recalling more recent articles that did state there were modifications each night between matches. Perhaps those were in error; I would not have commented in that case. It's a shame nobody pointed this out sooner, I could have removed the misinformation.

dwringer 10y ago

I wouldn't say the challenge has been "solved" - the system may have only been able to win consistently because the programmers made adjustments and improvements to it between each game. Fitting its heuristics to a specific opponent might actually be overfitting unless it can consistently win against all challengers without being modified between rounds. [edited to clarify uncertainty about "why" it was able to win]

kirrent 10y ago
AlphaGo played plenty of games online after the Lee Seedol match. It went 60-0 against some of the worlds best players. Granted they were fast games but that's a challenge for AlphaGo as well as its adversaries.
- dwringer 10y ago
  Thank you for this information, it will definitely be interesting to see how the future of Go plays out. For example, will players be able to train with AlphaGo to the point of being able to beat it?
  - kirrent 10y ago
    Even more interesting to me is the question of if Go players will be able to apply the novel strategies of AlphaGo which break with decades of conventional wisdom or if there's something in their execution that requires such perfect play (but leads to a more certain outcome) that they're only feasibly played by AlphaGo.
    - pps43 10y ago
      I'm not sure about human players, but it might be possible to build an adversarial neural network that beats AlphaGo (and only AlphaGo).
      - Dzugaru 10y ago
        I thought AlphaGo is trained in an adversarial manner already (it plays against itself).
        pps43 10y ago
        The version that played against Fan Hui was reportedly trained that way, but if I recall correctly the more recent one that played Lee Sedol did't rely on self-play as much. Also the objective was different - they tried to train a network that would play well against any opponent. It should be easier to train a network that exploits some very narrow weakness in specific AlphaGo version. For example, my phone consistently beats me at chess. But I found a sequence of moves in one opening where is always makes the same mistake, and I after that I can win easily.
    - dwringer 10y ago
      That's the sort of thing I have wondered about. Chess continues to evolve since the famous Kasparov vs. Deep Blue matches, and from a quick look around human players do continue to draw matches against the strongest computers (even though they are regularly beaten, as well).
  - dwaltrip 10y ago
    Humans will never defeat the strongest go playing programs ever again. AlphaGo is now clearly ahead of the top pros, and such programs will only get stronger. Humans should learn some things from the machines, but we will definitely not be able to extract the full totality of understanding, and thus will naturally continue to fall behind.
deleted 10y ago
[deleted]
tazjin 10y ago
I believe that there was a sort of "learning freeze" for AlphaGo during the Lee Sedol games and there was no manual modification of its strategy either. If I recall correctly this was part of the commentary during the matches so I can't find a good source for it right now.
- eutectic 10y ago
  https://youtu.be/l-GsfyVCBu0?t=2866 https://youtu.be/l-GsfyVCBu0?t=2866
  - tazjin 10y ago
    Thank you, that's exactly what I was looking for!
    - dwringer 10y ago
      Thanks for bringing this up. I was recalling more recent articles that did state there were modifications each night between matches. Perhaps those were in error; I would not have commented in that case. It's a shame nobody pointed this out sooner, I could have removed the misinformation.