6 ms·

I find it very interesting that to a layperson, the idea of a computer being able to beat a human at a logic game is pretty much expected and uninteresting. Yo

by skc 11y ago

I find it very interesting that to a layperson, the idea of a computer being able to beat a human at a logic game is pretty much expected and uninteresting. You try and share this story with a non-technical person and they will likely say "Well, duh..it's a computer".

KennyCason 11y ago
I encountered this many times over the past couple of days haha. I then have to explain that previous AIs, like those in chess largely used brute force computation to simulate each move, while these AIs "actually learn, similar to our brain". Probably not the most scientific explanation, but I feel your sentiment.
- Scea91 11y ago
  There is still tree search going on. However I don't know the details about it. Is it that the deep net works mainly as an evaluation function of the current position? I guess it does more than that right?
  - umanwizard 11y ago
    There's also tree search in my brain when I play chess or go. (Consciously!)
  - Terribledactyl 11y ago
    They are using Monte Carlo Methods for looking around, more like a tiny sampling, it's an incomprehensibly large space. They have both value and policy (deep) neural nets. Train one to get the sense of good/bad individual board states (value) and one to get the sense of good/bad trajectories (policy, evaluates chains of moves). Chess has a fairly straight forward ranking of pieces, fairly well established ranking of piece power. A knight is more or less always a knight. Go, you kinda make up your "pieces" from scratch and there isn't any single common ranking. This makes the problem of even evaluating the board difficult (value), which is a sub step of making your higher plans (policy). The output of the two dNN help cut off, or prevent the sampling of the bad game states and encourage looking into the fruitful areas. Here is a (edit: not old) new paper, https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
- sobellian 11y ago
  Well, it's a bit more complicated than that. Neither chess engines nor go engines use brute-force (that is, exhaustive search). Although go does have a much higher branching factor and that does affect the search algorithm used, the biggest challenge is being able to write good evaluation and move-guessing heuristics.
kqr 11y ago
Pitch it as a creativity game rather than a logic game. I mean, it's a little bit of both, right?
- Radim 11y ago
  It's a creative game only to the degree your logic (reading) is not strong enough :) I mean, deep down, Go is a perfect-information-zero-sum-discrete-2-player game. I think a more successful pitch is showing that the strategies and skills coming from Go (human or robot) can be useful outside the game, as I argue here: http://rare-technologies.com/go_games_life/ http://rare-technologies.com/go_games_life/
- jholman 11y ago
  To me, "creative" is not the right word. Maybe try "intuitive". As Radim says, of course, the intuition is only relevant when logic fails. However, no computer, including AlphaGo, has sufficient processing power to take the logic-only approach to the, uh, logical extreme (more board states than atoms in the universe, etc etc). So both humans and computers play by a combination of logic and intuition. Surely Lee Sedol must be incredibly good at both. Perhaps AlphaGo is better than Lee Sedol at the logic part (or perhaps not, but just suppose for the sake of argument). In this light, what is interesting is that AlphaGo is sufficiently good at intuition, a domain we might have considered uniquely human[0], to complement its ferocious logical power. [0] I find it foolish to consider anything uniquely human, but a humanist essentialist might make such a claim.
  - Jach 11y ago
    The other interesting bit is the combination, the intuition guiding (and evaluating the results of) the logical searching. I'm still holding out for the discovery of a proof of winning strategy like other combinatorial games. That to me is the logical extreme, not magic evaluation of all states. There are two interesting books on analyzing Go with combinatorial game theory, they found a neat system of scoring in the endgame and can create example boards with one best move that can be found by mechanically applying their system but stumps 9p players (who use whatever systems).
mmahemoff 11y ago
Most people operate on a moving definition of intelligence as "whatever humans can do and machines can't" (thus ruling out AI by definition). If software started writing bestselling novels, it would soon become a "duh that's just what computers do" matter of fact.
- Certhas 11y ago
  That's why task based notions for general intelligence are rubbish. Here is what humans can do: When presented with pretty much any task, specified however poorly, they can design hardware and algorithms that can beat themselve at that task. That's a decent measure of intelligence. A decent measure of creativity is coming up with tasks that make the intelligence part as interesting as possible.
  - irascible 11y ago
    Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is an extension of the same algo that could beat all atari games sight unseen..
  - irascible 11y ago
    Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is based on the same algo that could beat all atari games sight unseen.. satisfying the 'novel situation' requirement you set forth..
  - irascible 11y ago
    Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is based on the same algo that could beat all atari games sight unseen.. satisfying the 'novel situation' requirement you set forth..
- fahadkhan 11y ago
  Lovelace Test is pretty old though.
- eru 11y ago
  We still have the Turing test.
conanbatt 11y ago
Go is a lot more psychological and emotional than playing tic-tac-toe. Its a strategy game, which undecided outcomes, where ones style and vision changes how you play and see the game. The computer was able to overcome the computational difficulty humans compensated with abstractions and strategic concepts. We still use those to understand whats going on in the game, but the computer is oblivious to them and only uses a tactical view of it.
hsshah 11y ago
As always, there is a relevant XKCD about this phenomenon. http://xkcd.com/1425/ http://xkcd.com/1425/

by skc 11y ago

KennyCason 11y ago

I encountered this many times over the past couple of days haha. I then have to explain that previous AIs, like those in chess largely used brute force computation to simulate each move, while these AIs "actually learn, similar to our brain". Probably not the most scientific explanation, but I feel your sentiment.

Scea91 11y ago
There is still tree search going on. However I don't know the details about it. Is it that the deep net works mainly as an evaluation function of the current position? I guess it does more than that right?
- umanwizard 11y ago
  There's also tree search in my brain when I play chess or go. (Consciously!)
- Terribledactyl 11y ago
  They are using Monte Carlo Methods for looking around, more like a tiny sampling, it's an incomprehensibly large space. They have both value and policy (deep) neural nets. Train one to get the sense of good/bad individual board states (value) and one to get the sense of good/bad trajectories (policy, evaluates chains of moves). Chess has a fairly straight forward ranking of pieces, fairly well established ranking of piece power. A knight is more or less always a knight. Go, you kinda make up your "pieces" from scratch and there isn't any single common ranking. This makes the problem of even evaluating the board difficult (value), which is a sub step of making your higher plans (policy). The output of the two dNN help cut off, or prevent the sampling of the bad game states and encourage looking into the fruitful areas. Here is a (edit: not old) new paper, https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf https://gogameguru.com/i/2016/03/deepmind-mastering-go.pdf
sobellian 11y ago
Well, it's a bit more complicated than that. Neither chess engines nor go engines use brute-force (that is, exhaustive search). Although go does have a much higher branching factor and that does affect the search algorithm used, the biggest challenge is being able to write good evaluation and move-guessing heuristics.

kqr 11y ago

Pitch it as a creativity game rather than a logic game. I mean, it's a little bit of both, right?

Radim 11y ago
It's a creative game only to the degree your logic (reading) is not strong enough :) I mean, deep down, Go is a perfect-information-zero-sum-discrete-2-player game. I think a more successful pitch is showing that the strategies and skills coming from Go (human or robot) can be useful outside the game, as I argue here: http://rare-technologies.com/go_games_life/ http://rare-technologies.com/go_games_life/
jholman 11y ago
To me, "creative" is not the right word. Maybe try "intuitive". As Radim says, of course, the intuition is only relevant when logic fails. However, no computer, including AlphaGo, has sufficient processing power to take the logic-only approach to the, uh, logical extreme (more board states than atoms in the universe, etc etc). So both humans and computers play by a combination of logic and intuition. Surely Lee Sedol must be incredibly good at both. Perhaps AlphaGo is better than Lee Sedol at the logic part (or perhaps not, but just suppose for the sake of argument). In this light, what is interesting is that AlphaGo is sufficiently good at intuition, a domain we might have considered uniquely human[0], to complement its ferocious logical power. [0] I find it foolish to consider anything uniquely human, but a humanist essentialist might make such a claim.
- Jach 11y ago
  The other interesting bit is the combination, the intuition guiding (and evaluating the results of) the logical searching. I'm still holding out for the discovery of a proof of winning strategy like other combinatorial games. That to me is the logical extreme, not magic evaluation of all states. There are two interesting books on analyzing Go with combinatorial game theory, they found a neat system of scoring in the endgame and can create example boards with one best move that can be found by mechanically applying their system but stumps 9p players (who use whatever systems).

mmahemoff 11y ago

Most people operate on a moving definition of intelligence as "whatever humans can do and machines can't" (thus ruling out AI by definition). If software started writing bestselling novels, it would soon become a "duh that's just what computers do" matter of fact.

Certhas 11y ago
That's why task based notions for general intelligence are rubbish. Here is what humans can do: When presented with pretty much any task, specified however poorly, they can design hardware and algorithms that can beat themselve at that task. That's a decent measure of intelligence. A decent measure of creativity is coming up with tasks that make the intelligence part as interesting as possible.
- irascible 11y ago
  Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is an extension of the same algo that could beat all atari games sight unseen..
- irascible 11y ago
  Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is based on the same algo that could beat all atari games sight unseen.. satisfying the 'novel situation' requirement you set forth..
- irascible 11y ago
  Optimizing compilers do this already and they aren't 'intelligent'. Iirc alpha go is based on the same algo that could beat all atari games sight unseen.. satisfying the 'novel situation' requirement you set forth..
fahadkhan 11y ago
Lovelace Test is pretty old though.
eru 11y ago
We still have the Turing test.

conanbatt 11y ago

Go is a lot more psychological and emotional than playing tic-tac-toe. Its a strategy game, which undecided outcomes, where ones style and vision changes how you play and see the game. The computer was able to overcome the computational difficulty humans compensated with abstractions and strategic concepts. We still use those to understand whats going on in the game, but the computer is oblivious to them and only uses a tactical view of it.

hsshah 11y ago

As always, there is a relevant XKCD about this phenomenon. http://xkcd.com/1425/ http://xkcd.com/1425/