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Interesting article! However, Deep Blue, Watson, and AlphaGo are very different from one another. I don't think anyone deemed beating humans at chess or jeopard

by shas3 11y ago

Interesting article! However, Deep Blue, Watson, and AlphaGo are very different from one another. I don't think anyone deemed beating humans at chess or jeopardy impossible at the time Deep Blue and Watson were built. On AlphaGo the point about generalizing AI is valid, yet I think the author doesn't fully appreciate the novelty of approach described in the AlphaGo paper. Their work advances the field, has more general utility than Deep Blue's chess or Watson's jeopardy programs. AlphaGo paper specifically represents an advance in machine learning algorithms for games in general. As I understand, Watson's new NLP algorithm is PRISMATIC [1]. PRISMATIC is a rule-based NLP system and AlphaGo is more statistical inference/neural networks. Even if AlphaGo's 'policy-network- value-network' framework is not too generalizable, the philosophical implication is that we can build AIs that can mimic 'human intuition'. Jeopardy and chess have lesser components of 'human intuition' than Go. They are apples and oranges. So, I wonder if the author errs in bringing Deep Blue and Watson-Jeopardy into the picture. In my opinion while Watson-Jeopardy and Deep Blue were 'over-fitting' for Jeopardy and chess respectively, AlphaGo algorithms are more general and over-fits for the larger category of 'games'. [1] http://brenocon.com/watson_special_issue/05%20automatic%20knowledge%20extration.pdf http://brenocon.com/watson_special_issue/05%20automatic%20kn...

jmilloy 11y ago
I really think Gary agrees with you, and you're just picking an unnecessary fight. Having advocated for this kind of approach decades ago, he fully appreciates the novelty of the approach. He points out the simplicity of Jeopardy and Chess as reasons why they haven't translated to the real world well, and maintains optimism about DeepMind. I don't read "the real question is whether the technology developed there can be taken out of the game world and into the real world" as a criticism, but praise; the question is worth considering, whereas so often it is not.
- shas3 11y ago
  I don't intend to pick a fight. I am pointing out one deficiency I perceive in this widely shared article. On the philosophical questions: I think even with the greatest technological advances, the question remains: will we adopt AI? I have seen too many situations where machine learning is not adopted even though algorithms can enable great functionality: usually the reason is lack of financial incentives or simple entrenched human interests (low-grade Ludditism). This resistance will lead to more (to use terminology from the AI debate) paperclip maximizers and fewer general AIs. This is already happening, the future of 2001 didn't happen to be HAL 9000, but an ingenious model and linear algebra algorithm to deliver better search results.
wslh 11y ago
One of the main points of the article is the hybrid approach of AI against the "neural networks/deep learning solve everything" mantra.
fspeech 11y ago
If AlphaGo eventually beats humans at go I think it says more about the game (or the way humans play the game) than about AI in general. Its strength comes from mcts with better heuristics. Previous ConvNet work posted here https://news.ycombinator.com/item?id=10558831 https://news.ycombinator.com/item?id=10558831 was already impressive in generating human like moves and combining it with search was an obvious next step. Unbeknownst to all Google's DeepMind team had already succeeded in doing so at that time.
- empath75 11y ago
  > If AlphaGo eventually beats humans at go I think it says more about the game (or the way humans play the game) than about AI in general. AI problems are always mysterious until they're solved. People say this sort of thing every time an AI achieves some task which had previously been deemed impossible.
  - romaniv 11y ago
    There is another way of saying the same thing. A lot of seemingly groundbreaking progress in AI usually happens when people (people, not machines) discover a clever way of mapping a new unsolved problem to another - well-solved - problem. That's a valid and insightful observation that you shouldn't dismiss with such an ease.
    - empath75 11y ago
      Why 'seemingly'? Why is that not 'actual' groundbreaking progress? The achievement of any level of AI will by necessity require the chaining together of processes which are not themselves 'intelligent'. It has to be bootstrapped somehow. On the day they build a walking, talking AI who can converse fully with a human, write a symphony, design a building, feel and express emotions and all the rest of the things we define as being essentially in the domain of humain intelligence, everyone will say, "But of course, none of those things required intelligence at all. This is all an elaborate collection of illusions and ugly hacks." And they'll be right, but I suspect that the brain is the same way.
sp332 11y ago
I don't think Watson was over-fitting for Jeopardy. It took a lot of modification to even get it to play Jeopardy.
joe_the_user 11y ago
Go ranks are in "stones" that one player can give another as a handicap - except at the professional level where there's more relative equality of play and the stone-ranks are honorary and ELO ratings are more accurate. Monte Carlo tree search had advanced computer Go from 2-3K to 6-7 dan, a gain of 8 stones. Alpha Go apparently has advanced to 9-10 stones, professional level, by using neural networks to enhance the final position and search policy used in Monte Carlo tree search. In many ways, it seems to me that Monte Carlo Tree Search was the primary answer to how computers could deal with Go [1]. Chess essentially conquered through a combination of red-black and other smart pruning algorithms, incremental advances in hand-tuned final position policy and improved hardware [2]. So it seems like the "conquest" of Go has involved a more generalized, self-learned version of the original approach to chess (tree-search strategies plus final position heuristics). That might enough for just about any deterministic game one can find. It should be noted that Arimaa, a game designed specifically to be hard for computers without having the large board of Go, was "conquered" last year but without any neural net techniques (apparently). [1]https://en.wikipedia.org/wiki/Computer_Go https://en.wikipedia.org/wiki/Computer_Go [2]https://en.wikipedia.org/wiki/Computer_chess https://en.wikipedia.org/wiki/Computer_chess [3]https://en.wikipedia.org/wiki/Computer_Arimaa#Techniques_used_in_Arimaa_bots https://en.wikipedia.org/wiki/Computer_Arimaa#Techniques_use...
- drakenot 11y ago
  I wonder if there is any work going on to go back to Chess and try these same techniques that were used in AlphaGo. It would be interesting to see how well this more generalized approach fairs against the hand-tuned final position policy code that you spoke about.
- harryjo 11y ago
  Is MCTS algorithmically interesting, or is it only powerful because it is embarrassingly parallel and so can leverage all the computing power as you have availalbe?
Gibbon1 11y ago
I'll get hammered for this statement but. Traditional AI suffers from a love of solving parlor tricks. Solving tick tack toe, checkers, chess, poker, Jeopardy(tm), are parlor tricks. It seems important because frankly humans just suck at parlor trick type problems and other forms of intelligence like say a cat brain don't even get parlor tricks. SO then we found that computers were really good at parlor tricks and it seemed like we were really onto something here. But nope. On the other hand playing Go is not really a parlor trick, it's actually hard simple symbolic logic totally fails to grasp the problem at the first level.
- grmarcil 11y ago
  Can you provide a firm definition of parlor trick? Not saying I agree or disagree with you, but without a real definition of parlor trick you have a wide open no-true-Scotsman defense.
  - colllectorof 11y ago
    Parlor trick is something that looks interesting only as long as you make incorrect assumptions about how it's being done. For example, a card that seems to appear out of nowhere while in reality it is held behind the hand between little and index finger. Or a program that seems to engage in planning when in reality it simply iterates over all possible solutions.
  - resu_nimda 11y ago
    Well that's the crux of the problem - the corollary to your question is "can you provide a firm definition of intelligence?" Nobody can yet, so it's all speculation and subjective opinion. The reason that I personally consider all these things parlor tricks (including, hypothetically, complete mastery of Go) is that I see no path from these particular types of systems to general intelligence. A human can take in arbitrary sensory data and make all sorts of conclusions and associations with it. Does this particular system have the capability to get to a point where it can see an apple falling and posit a theory of gravity? Will it ever be able to read subtle cues in facial/oral/bodily expression and combine them with all sorts of other data, instantaneously, to achieve compelling real-time social interaction? Will this system ever invent the game of Go, or anything else, because it felt like it? No, it has absolutely no framework to do any of those things, or countless other things humans can do. It's a machine built with a single purpose in mind, and it can only serve that purpose. It's a glorified function call. I don't think this type of machine will just wake up one day after digging deeper and deeper into these "hard" tasks. We need breadth, not depth.
argonaut 11y ago
How is that overfitting? That term is not applicable in this context.
- shas3 11y ago
  It does: like an overfit (verb) model, Deep Blue and Jeopardy Watson don't generalize well to other problems.
  - argonaut 11y ago
    Overfitting concerns itself with generalization to out of training data on the same task. I think the term you are looking for is that it's too domain-specific. Or "it uses too much feature engineering and domain-specific engineering."
    - shas3 11y ago
      I see quotes to use it as an analogy. I'm not claiming that their algorithm over fits. It is just another way of saying the system is too specific and optimized to chess to be generalizable.
nl 11y ago
PRISMATIC isn't Watson. PRISMATIC is a single, rules based component for deriving the LAT (lexiconal answer type) of a question. Watson wasn't deep learning based, but it has a lot more than PRISMATIC

by shas3 11y ago

jmilloy 11y ago

I really think Gary agrees with you, and you're just picking an unnecessary fight. Having advocated for this kind of approach decades ago, he fully appreciates the novelty of the approach. He points out the simplicity of Jeopardy and Chess as reasons why they haven't translated to the real world well, and maintains optimism about DeepMind. I don't read "the real question is whether the technology developed there can be taken out of the game world and into the real world" as a criticism, but praise; the question is worth considering, whereas so often it is not.

shas3 11y ago
I don't intend to pick a fight. I am pointing out one deficiency I perceive in this widely shared article. On the philosophical questions: I think even with the greatest technological advances, the question remains: will we adopt AI? I have seen too many situations where machine learning is not adopted even though algorithms can enable great functionality: usually the reason is lack of financial incentives or simple entrenched human interests (low-grade Ludditism). This resistance will lead to more (to use terminology from the AI debate) paperclip maximizers and fewer general AIs. This is already happening, the future of 2001 didn't happen to be HAL 9000, but an ingenious model and linear algebra algorithm to deliver better search results.

wslh 11y ago

One of the main points of the article is the hybrid approach of AI against the "neural networks/deep learning solve everything" mantra.

fspeech 11y ago

If AlphaGo eventually beats humans at go I think it says more about the game (or the way humans play the game) than about AI in general. Its strength comes from mcts with better heuristics. Previous ConvNet work posted here https://news.ycombinator.com/item?id=10558831 https://news.ycombinator.com/item?id=10558831 was already impressive in generating human like moves and combining it with search was an obvious next step. Unbeknownst to all Google's DeepMind team had already succeeded in doing so at that time.

empath75 11y ago
> If AlphaGo eventually beats humans at go I think it says more about the game (or the way humans play the game) than about AI in general. AI problems are always mysterious until they're solved. People say this sort of thing every time an AI achieves some task which had previously been deemed impossible.
- romaniv 11y ago
  There is another way of saying the same thing. A lot of seemingly groundbreaking progress in AI usually happens when people (people, not machines) discover a clever way of mapping a new unsolved problem to another - well-solved - problem. That's a valid and insightful observation that you shouldn't dismiss with such an ease.
  - empath75 11y ago
    Why 'seemingly'? Why is that not 'actual' groundbreaking progress? The achievement of any level of AI will by necessity require the chaining together of processes which are not themselves 'intelligent'. It has to be bootstrapped somehow. On the day they build a walking, talking AI who can converse fully with a human, write a symphony, design a building, feel and express emotions and all the rest of the things we define as being essentially in the domain of humain intelligence, everyone will say, "But of course, none of those things required intelligence at all. This is all an elaborate collection of illusions and ugly hacks." And they'll be right, but I suspect that the brain is the same way.

sp332 11y ago

I don't think Watson was over-fitting for Jeopardy. It took a lot of modification to even get it to play Jeopardy.

joe_the_user 11y ago

Go ranks are in "stones" that one player can give another as a handicap - except at the professional level where there's more relative equality of play and the stone-ranks are honorary and ELO ratings are more accurate. Monte Carlo tree search had advanced computer Go from 2-3K to 6-7 dan, a gain of 8 stones. Alpha Go apparently has advanced to 9-10 stones, professional level, by using neural networks to enhance the final position and search policy used in Monte Carlo tree search. In many ways, it seems to me that Monte Carlo Tree Search was the primary answer to how computers could deal with Go [1]. Chess essentially conquered through a combination of red-black and other smart pruning algorithms, incremental advances in hand-tuned final position policy and improved hardware [2]. So it seems like the "conquest" of Go has involved a more generalized, self-learned version of the original approach to chess (tree-search strategies plus final position heuristics). That might enough for just about any deterministic game one can find. It should be noted that Arimaa, a game designed specifically to be hard for computers without having the large board of Go, was "conquered" last year but without any neural net techniques (apparently). [1]https://en.wikipedia.org/wiki/Computer_Go https://en.wikipedia.org/wiki/Computer_Go [2]https://en.wikipedia.org/wiki/Computer_chess https://en.wikipedia.org/wiki/Computer_chess [3]https://en.wikipedia.org/wiki/Computer_Arimaa#Techniques_used_in_Arimaa_bots https://en.wikipedia.org/wiki/Computer_Arimaa#Techniques_use...

drakenot 11y ago
I wonder if there is any work going on to go back to Chess and try these same techniques that were used in AlphaGo. It would be interesting to see how well this more generalized approach fairs against the hand-tuned final position policy code that you spoke about.
harryjo 11y ago
Is MCTS algorithmically interesting, or is it only powerful because it is embarrassingly parallel and so can leverage all the computing power as you have availalbe?

Gibbon1 11y ago

I'll get hammered for this statement but. Traditional AI suffers from a love of solving parlor tricks. Solving tick tack toe, checkers, chess, poker, Jeopardy(tm), are parlor tricks. It seems important because frankly humans just suck at parlor trick type problems and other forms of intelligence like say a cat brain don't even get parlor tricks. SO then we found that computers were really good at parlor tricks and it seemed like we were really onto something here. But nope. On the other hand playing Go is not really a parlor trick, it's actually hard simple symbolic logic totally fails to grasp the problem at the first level.

grmarcil 11y ago
Can you provide a firm definition of parlor trick? Not saying I agree or disagree with you, but without a real definition of parlor trick you have a wide open no-true-Scotsman defense.
- colllectorof 11y ago
  Parlor trick is something that looks interesting only as long as you make incorrect assumptions about how it's being done. For example, a card that seems to appear out of nowhere while in reality it is held behind the hand between little and index finger. Or a program that seems to engage in planning when in reality it simply iterates over all possible solutions.
- resu_nimda 11y ago
  Well that's the crux of the problem - the corollary to your question is "can you provide a firm definition of intelligence?" Nobody can yet, so it's all speculation and subjective opinion. The reason that I personally consider all these things parlor tricks (including, hypothetically, complete mastery of Go) is that I see no path from these particular types of systems to general intelligence. A human can take in arbitrary sensory data and make all sorts of conclusions and associations with it. Does this particular system have the capability to get to a point where it can see an apple falling and posit a theory of gravity? Will it ever be able to read subtle cues in facial/oral/bodily expression and combine them with all sorts of other data, instantaneously, to achieve compelling real-time social interaction? Will this system ever invent the game of Go, or anything else, because it felt like it? No, it has absolutely no framework to do any of those things, or countless other things humans can do. It's a machine built with a single purpose in mind, and it can only serve that purpose. It's a glorified function call. I don't think this type of machine will just wake up one day after digging deeper and deeper into these "hard" tasks. We need breadth, not depth.

argonaut 11y ago

How is that overfitting? That term is not applicable in this context.

shas3 11y ago
It does: like an overfit (verb) model, Deep Blue and Jeopardy Watson don't generalize well to other problems.
- argonaut 11y ago
  Overfitting concerns itself with generalization to out of training data on the same task. I think the term you are looking for is that it's too domain-specific. Or "it uses too much feature engineering and domain-specific engineering."
  - shas3 11y ago
    I see quotes to use it as an analogy. I'm not claiming that their algorithm over fits. It is just another way of saying the system is too specific and optimized to chess to be generalizable.

nl 11y ago

PRISMATIC isn't Watson. PRISMATIC is a single, rules based component for deriving the LAT (lexiconal answer type) of a question. Watson wasn't deep learning based, but it has a lot more than PRISMATIC