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It's not like humanity really needs another chess playing program 20 years after IBM solved that problem (but now utilizing 1000x more compute power). I just fi

It's not like humanity really needs another chess playing program 20 years after IBM solved that problem (but now utilizing 1000x more compute power). I just find all these game playing contraptions really uninteresting. There are plenty real world problems to be solved of much higher practicality. Moravec's paradox in full glow.

icc97 8y ago
Big Blue relied on humans to do all the training. Alpha Go zero didn't need humans at all to do the training. That's a pretty major shift for humanity.
- DonaldFisk 8y ago
  It's Deep Blue, not Big Blue. The parameters used by its evaluation function were tuned by the system on games played by human masters. But it's a mistake to think that a system learning by playing against itself is something new. Arthur Samuel's draughts (chequers) program did that in 1959.
  - sangnoir 8y ago
    > It's Deep Blue, not Big Blue. Big Blue is fine - it's referring to the company and not the machine. From Wikipedia "Big Blue is a nickname for IBM"
    - icc97 8y ago
      I meant Deep Blue, but yeah Deep Blue was a play on Big Blue.
  - icc97 8y ago
    Sorry, mix up, thanks for the correction. It's not that it's new, it's that they've achieved it. Chess was orders of magnitude harder than draughts. The solution for draughts didn't scale to chess but Alpha Go zero showed that chess was ridiculously easy for it once it had learned Go.
    - DonaldFisk 8y ago
      Both Samuel's chequer's program and Deep Blue used alpha-beta pruning for search, and a heuristic function. Deep Blue's heuristic function was necessarily more complex because chess is more complex than draughts. I think the reason master chess games were used in Deep Blue instead of self-play was the existence of a large database of such games, and because so much of its performance was the result of being able to look ahead so far.
canoebuilder 8y ago
I think for most people, the research interest in games of various sorts, is not simply a desire for a better and better game contraption, a better mousetrap. But rather the thinking is, "playing games takes intelligence, what can we learn about intelligence by building machines that play games?" Most games are also closed systems, and conveniently grokkable systems, with enumerable search spaces. Which gives us easily produceable measures of the contraptions' abilities. Whether this is the most effective path to understanding deeper questions about intelligence is an open question. But I don't think it's fair to say that deeper questions and problems are being foregone simply to play games. I think most 'games researchers' are pursuing these paths because they themselves and no one else has put forth any other suggestion that makes them think, "hmm, that's a really good idea, that seems like it might be viable and there is probably something interesting we could learn from it." Do you have any suggestions?
- Erlich_Bachman 8y ago
  This is so true, I can't understand why people miss this. The games are just games. It's intelligence that is the goal. And comparing Alpha Go Zero against those "other chess programs that existed for 30 years" is exactly missing the point also. Those programs were not constructed with zero-knowledge. They were carefully crafted by human players to achieve the result. Are we also going to count in all the brain processing power and the time spent by those researchers to learn to play chess? Alpha Go Zero did not need any of that, besides the knowledge about the basic rules of the game. Who compare compute requirements for 2 programs that have fundamentally different goals and achievements? One is carefully crafted by human intervention. The other one learns a new game without prior knowledge...
  - sgt101 8y ago
    It shows something about the game, but it's clear that humans don't learn in the way that alpha zero does, do i don't think that alpha zero illuminated any aspect of human intelligence.
    - Erlich_Bachman 8y ago
batmansmk 8y ago
I guess there are reasons why researchers build chess programs: it is easy to compare performance between algorithms. When you can solve chess, you can solve a whole class of decision-making problems. Consider it as the perfect lab.
- gaius 8y ago
  When you can solve chess, you can solve a whole class of decision-making problems If this were true, there would be a vast demand for grandmasters in commerce, government, the military... and there just isn’t. Poker players suffer from similar delusions about how their game can be generalised to other domains.
  - majewsky 8y ago
    I think batmansmk doesn't mean "when X is good at chess, X is automatically good at lots of other things", but "the traits that make you a good chess player (given enough training) also make you good at lots of other things (given enough training)".
    - EliRivers 8y ago
      I might suspect (but certainly cannot prove) that the traits that make a human good at playing chess are very different to the traits that make a machine good at playing chess, and as such I don't think we can assume that the machine skilled-chess-player will be good at lots of other things in an analagous way to the human skilled-chess-player.
    - SiempreViernes 8y ago
      And Gaius point stands before this argument as well, chess is seen as such a weak predictor that playing a game of chess or requesting an official ELO rating isn't used for hiring screening for instance. I suspect that chess as a metagame is just so far developed that being "good at chess" means your general ability is really overtrained for chess.
    - monktastic1 8y ago
kamaal 8y ago
>>20 years after IBM solved that problem We solved nothing. IBM Deep Blue doesn't exactly think like humans do. Most of our algorithms really are 'better brute force'. https://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ https://www.theatlantic.com/magazine/archive/2013/11/the-man...
- pdimitar 8y ago
  Exactly. To my not-very-well-informed self, even AlphaGo Zero is just a more clever way to brute-force board games. Side observers are taking joy in the risker plays that it did -- reminded them of certain grand-masters I suppose -- but that still doesn't mean AGZ is close to any form of intelligence at all. Those "riskier moves" are probably just a way to more quickly reduce the problem space anyway. It seriously reminds me more and more of religion, the AI area these days.
some_account 8y ago
Tell me about it. The brightest minds are working on ads, and we have AI playing social games. Can AI make the world better? It can, but it won't since we are humans, and humans will weaponize technology every chance it gets. Of course some positive uses will come, but the negative ones will be incredibly destructive.
- deleted 8y ago
  [deleted]
- hannasanarion 8y ago
  Just because you haven't seen humongous publicity stunts involving pratical uses of AI doesn't mean they aren't being deployed. My company using similar methods to warn hospitals about patients with high probability of imminent heart attacks and sepsis. The practical uses of these technologies don't always make national news. I'm sure you would also have scoffed at the "pointless impractical, wasteful use of our brightest minds" to make the the Flyer hang in the air for 30 yards at Kitty Hawk.
- iwintermute 8y ago
  Let's start with defining "better"
salty_biscuits 8y ago
It's not like the research on games is at the expense of other more worthy goals. It is a well constrained problem that lets you understand the limitations of your method. Great for making progress. Alpha zero didn't just play chess well, it learned how to play chess well (and could generalize to other games). I'd forgive it 10000 times the resources for that.
- ttctciyf 8y ago
  > It is a well constrained problem But attacking not-well-constrained problems is what's needed to show real progress in AI these days, right?
  - salty_biscuits 8y ago
    I'd say getting better sample efficiency is a bigger deal. It isn't like POMDP's are a huge step away theoretically from MDP's. But if you attach one of these things to a robot, taking 10^7 samples to learn a policy is a deal breaker. So fine, please keep using games to research with.
- joshgel 8y ago
  >it learned how to play chess well This. Learning to play a game is one thing. Learning how to teach computers to learn a game is another thing. Yes chess programs have been good before, but that's missing the point a little bit. The novel bit is not that it can beat another computer, but how it learned how to do so.
orwin 8y ago
The fact that it beat Stockfish9 is not what is impressive with AlphaZero. What was impressive was the way Stockfish9 was beaten. AlphaZero played like a human player, making sacrifices for position that stockfish thought were detrimental. When it played as white, the fact that is mostly started with the Queen pawn (despite that the King pawn is "best by test") and the way AlphaZero used Stockfish pawnstructure and tempo to basicaly remove a bishop from the game was magical. Yes, since its a game, it's "useless", but it allowed me (and i'm not the only one) to be a bit better at chess. It's not world hunger, not climate change, it's just a bit of distraction for some people. PS: I was part of the people thinking that Genetic algorithm+deep learning was not enough to emulate human logical capacities, AlphaZero vs Stockfish games made me admit i was wrong (even if i still think it only works inside well-defined environments)
- blub 8y ago
  Two observations: Just because Fischer preferred 1. e4, it doesn't make it better than other openings. https://en.chessbase.com/post/1-e4-best-by-test-part-1 https://en.chessbase.com/post/1-e4-best-by-test-part-1 Playing like a human for me also means making human mistakes. A chess-playing computer playing like a 4000 rated "human" is useless, one that can be configured to play at different ELOs is more interesting, although most can do that and there's no ML needed, nor huge amounts of computing power.
- dragontamer 8y ago
  > What was impressive was the way Stockfish9 was beaten. Without its opening database and without its endgame tablebase? Frankly, the Stockfish vs AlphaZero match was the beginning of the AI Winter in my mind. The fact that they disabled Stockfish's primary databases was incredibly fishy IMO and is a major detriment to their paper. Stockfish's engine is designed to only work in the midgame of Chess. Remove the opening database and remove the endgame database, and you're not really playing against Stockfish anymore. The fact that Stockfish's opening was severely gimped is not a surprise to anybody in the Chess community. Stockfish didn't have its opening database enabled... for some reason.

icc97 8y ago

Big Blue relied on humans to do all the training. Alpha Go zero didn't need humans at all to do the training. That's a pretty major shift for humanity.

DonaldFisk 8y ago
It's Deep Blue, not Big Blue. The parameters used by its evaluation function were tuned by the system on games played by human masters. But it's a mistake to think that a system learning by playing against itself is something new. Arthur Samuel's draughts (chequers) program did that in 1959.
- sangnoir 8y ago
  > It's Deep Blue, not Big Blue. Big Blue is fine - it's referring to the company and not the machine. From Wikipedia "Big Blue is a nickname for IBM"
  - icc97 8y ago
    I meant Deep Blue, but yeah Deep Blue was a play on Big Blue.
- icc97 8y ago
  Sorry, mix up, thanks for the correction. It's not that it's new, it's that they've achieved it. Chess was orders of magnitude harder than draughts. The solution for draughts didn't scale to chess but Alpha Go zero showed that chess was ridiculously easy for it once it had learned Go.
  - DonaldFisk 8y ago
    Both Samuel's chequer's program and Deep Blue used alpha-beta pruning for search, and a heuristic function. Deep Blue's heuristic function was necessarily more complex because chess is more complex than draughts. I think the reason master chess games were used in Deep Blue instead of self-play was the existence of a large database of such games, and because so much of its performance was the result of being able to look ahead so far.

canoebuilder 8y ago

I think for most people, the research interest in games of various sorts, is not simply a desire for a better and better game contraption, a better mousetrap. But rather the thinking is, "playing games takes intelligence, what can we learn about intelligence by building machines that play games?" Most games are also closed systems, and conveniently grokkable systems, with enumerable search spaces. Which gives us easily produceable measures of the contraptions' abilities. Whether this is the most effective path to understanding deeper questions about intelligence is an open question. But I don't think it's fair to say that deeper questions and problems are being foregone simply to play games. I think most 'games researchers' are pursuing these paths because they themselves and no one else has put forth any other suggestion that makes them think, "hmm, that's a really good idea, that seems like it might be viable and there is probably something interesting we could learn from it." Do you have any suggestions?

Erlich_Bachman 8y ago
This is so true, I can't understand why people miss this. The games are just games. It's intelligence that is the goal. And comparing Alpha Go Zero against those "other chess programs that existed for 30 years" is exactly missing the point also. Those programs were not constructed with zero-knowledge. They were carefully crafted by human players to achieve the result. Are we also going to count in all the brain processing power and the time spent by those researchers to learn to play chess? Alpha Go Zero did not need any of that, besides the knowledge about the basic rules of the game. Who compare compute requirements for 2 programs that have fundamentally different goals and achievements? One is carefully crafted by human intervention. The other one learns a new game without prior knowledge...
- sgt101 8y ago
  It shows something about the game, but it's clear that humans don't learn in the way that alpha zero does, do i don't think that alpha zero illuminated any aspect of human intelligence.
  - Erlich_Bachman 8y ago

batmansmk 8y ago

I guess there are reasons why researchers build chess programs: it is easy to compare performance between algorithms. When you can solve chess, you can solve a whole class of decision-making problems. Consider it as the perfect lab.

gaius 8y ago
When you can solve chess, you can solve a whole class of decision-making problems If this were true, there would be a vast demand for grandmasters in commerce, government, the military... and there just isn’t. Poker players suffer from similar delusions about how their game can be generalised to other domains.
- majewsky 8y ago
  I think batmansmk doesn't mean "when X is good at chess, X is automatically good at lots of other things", but "the traits that make you a good chess player (given enough training) also make you good at lots of other things (given enough training)".
  - EliRivers 8y ago
    I might suspect (but certainly cannot prove) that the traits that make a human good at playing chess are very different to the traits that make a machine good at playing chess, and as such I don't think we can assume that the machine skilled-chess-player will be good at lots of other things in an analagous way to the human skilled-chess-player.
  - SiempreViernes 8y ago
    And Gaius point stands before this argument as well, chess is seen as such a weak predictor that playing a game of chess or requesting an official ELO rating isn't used for hiring screening for instance. I suspect that chess as a metagame is just so far developed that being "good at chess" means your general ability is really overtrained for chess.
  - monktastic1 8y ago

kamaal 8y ago

>>20 years after IBM solved that problem We solved nothing. IBM Deep Blue doesn't exactly think like humans do. Most of our algorithms really are 'better brute force'. https://www.theatlantic.com/magazine/archive/2013/11/the-man-who-would-teach-machines-to-think/309529/ https://www.theatlantic.com/magazine/archive/2013/11/the-man...

pdimitar 8y ago
Exactly. To my not-very-well-informed self, even AlphaGo Zero is just a more clever way to brute-force board games. Side observers are taking joy in the risker plays that it did -- reminded them of certain grand-masters I suppose -- but that still doesn't mean AGZ is close to any form of intelligence at all. Those "riskier moves" are probably just a way to more quickly reduce the problem space anyway. It seriously reminds me more and more of religion, the AI area these days.

some_account 8y ago

Tell me about it. The brightest minds are working on ads, and we have AI playing social games. Can AI make the world better? It can, but it won't since we are humans, and humans will weaponize technology every chance it gets. Of course some positive uses will come, but the negative ones will be incredibly destructive.

deleted 8y ago
[deleted]
hannasanarion 8y ago
Just because you haven't seen humongous publicity stunts involving pratical uses of AI doesn't mean they aren't being deployed. My company using similar methods to warn hospitals about patients with high probability of imminent heart attacks and sepsis. The practical uses of these technologies don't always make national news. I'm sure you would also have scoffed at the "pointless impractical, wasteful use of our brightest minds" to make the the Flyer hang in the air for 30 yards at Kitty Hawk.
iwintermute 8y ago
Let's start with defining "better"

salty_biscuits 8y ago

It's not like the research on games is at the expense of other more worthy goals. It is a well constrained problem that lets you understand the limitations of your method. Great for making progress. Alpha zero didn't just play chess well, it learned how to play chess well (and could generalize to other games). I'd forgive it 10000 times the resources for that.

ttctciyf 8y ago
> It is a well constrained problem But attacking not-well-constrained problems is what's needed to show real progress in AI these days, right?
- salty_biscuits 8y ago
  I'd say getting better sample efficiency is a bigger deal. It isn't like POMDP's are a huge step away theoretically from MDP's. But if you attach one of these things to a robot, taking 10^7 samples to learn a policy is a deal breaker. So fine, please keep using games to research with.
joshgel 8y ago
>it learned how to play chess well This. Learning to play a game is one thing. Learning how to teach computers to learn a game is another thing. Yes chess programs have been good before, but that's missing the point a little bit. The novel bit is not that it can beat another computer, but how it learned how to do so.

orwin 8y ago

The fact that it beat Stockfish9 is not what is impressive with AlphaZero. What was impressive was the way Stockfish9 was beaten. AlphaZero played like a human player, making sacrifices for position that stockfish thought were detrimental. When it played as white, the fact that is mostly started with the Queen pawn (despite that the King pawn is "best by test") and the way AlphaZero used Stockfish pawnstructure and tempo to basicaly remove a bishop from the game was magical. Yes, since its a game, it's "useless", but it allowed me (and i'm not the only one) to be a bit better at chess. It's not world hunger, not climate change, it's just a bit of distraction for some people. PS: I was part of the people thinking that Genetic algorithm+deep learning was not enough to emulate human logical capacities, AlphaZero vs Stockfish games made me admit i was wrong (even if i still think it only works inside well-defined environments)

blub 8y ago
Two observations: Just because Fischer preferred 1. e4, it doesn't make it better than other openings. https://en.chessbase.com/post/1-e4-best-by-test-part-1 https://en.chessbase.com/post/1-e4-best-by-test-part-1 Playing like a human for me also means making human mistakes. A chess-playing computer playing like a 4000 rated "human" is useless, one that can be configured to play at different ELOs is more interesting, although most can do that and there's no ML needed, nor huge amounts of computing power.
dragontamer 8y ago
> What was impressive was the way Stockfish9 was beaten. Without its opening database and without its endgame tablebase? Frankly, the Stockfish vs AlphaZero match was the beginning of the AI Winter in my mind. The fact that they disabled Stockfish's primary databases was incredibly fishy IMO and is a major detriment to their paper. Stockfish's engine is designed to only work in the midgame of Chess. Remove the opening database and remove the endgame database, and you're not really playing against Stockfish anymore. The fact that Stockfish's opening was severely gimped is not a surprise to anybody in the Chess community. Stockfish didn't have its opening database enabled... for some reason.