5 ms·

For your second question, of course, a grandmaster can only tell you why they think they made the move. It may be a postrationalization for the fact that they f

For your second question, of course, a grandmaster can only tell you why they think they made the move. It may be a postrationalization for the fact that they feel, based on deep learning they have acquired over years of practice, that that move is the right one to make. It doesn't seem too difficult to have an AI let you know which other strong moves it rejected, and to dig into its forecasts for how those moves play out compared to the chosen move to tell you why it makes particular scenarios more likely. But that would just be postrationalization too...

meric 11y ago
Perhaps postrationalizaton is still useful? Could empathy and mirror neurons help transfer some of that deep learning? Two weeks later the student faces a similar situation, and they get the same gut feeling their teacher did, and that helps them play better than if their teacher didn't postrationalize?
- jameshart 11y ago
  Absolutely! My point is that human intelligence doesn't actually have any deep insight into how it makes decisions, so we shouldn't be that disappointed that an AI doesn't, either. Humans can postrationalize - explore how they think they make decisions - but they can't tell you how they actually decided. Doing the same for an AI is interesting, but I don't think it's a necessary component of making good decisions in the first place to be able to explain why you made it.
  - cgio 11y ago
    On the other hand, not all actions are decisions. There is plenty of actions we would classify as decisions which are application of rules instead. There is a clear rational path in application of rules. To clarify terminology, for me decisions are actions in response to intractable computationally challenges, e.g. will that couch fit in my living room (when I am in a store, where size calculations are hard in an unfamiliar context) etc. This could mean that your action is my decision if we are not equally capable of calculating based on rules.
  - robbrown451 11y ago
    Although, when looking at the Deep Dream images that come as a by product (more or less) of image recognition AI, I get the impression that there ARE ways of communicating things about what a computer is "thinking" when trying to solve problems in areas where humans are traditionally better.
  - jibalt 11y ago
    Your point is completely and utterly wrong when it comes to chess.
Retric 11y ago
You are being pedantic. People have learned to communicate heuristics which is very useful for beginning players. A grand master may not be able to communicate nuances of the game to low level players, but they do benefit from working in groups which suggests they can share reasons for a given move not just propose moves and have other independently evaluate them. So, while people can't map out the networks of neurons that actually made a given choice, we can effectively communicate reasons for a given move.
kelseydh 11y ago
I play competitive chess, and I assure you most moves are made because of players proving in their minds that a move is objectively good. The reasons for why the player may think the move is objectively good can vary, but they are almost always linked to rational considerations. E.g. that the move increases their piece count, their control of center squares, their attacking opportunities, or is tactically necessary due to the position. My point being that when grandmaster players play chess, they are just as interested in finding objectively right moves as a computer is. Unless it's speed chess it's rarely a "I suspect this might be good" sort of thing. (That said, many grandmasters do avoid lines of play they consider too dangerous. World Champion Magnus Carlsen's "nettlesomeness" - his willingness to force games into difficult positions - has been one explanation for why he beats other Grandmasters.)
- jameshart 11y ago
  If the move's objectively good, there would be no variation in moves between players. Since there is variation, I assume different players apply different heuristics for 'good'. And whether the move increases their piece count is a fine justification, but why are you privileging increasing your piece count at this point in this game against this opponent? At some point the answer becomes 'because I learned to'.
  - kelseydh 11y ago
    Well almost every computer playing chess algorithm uses piece counts to evaluate the quality of chess positions, because barring an amazing tactical combination (which can usually be computationally eliminated past 5 moves) or a crushing positional advantage, a loss of pieces will mean the victory of the person with more pieces. I would argue you see far more pattern recognition at play in chess than you do of heuristics. Heuristics is more common at lower levels of play. When Grandmaster's rely on pattern recognition, they are using their vast repertoire of remembered positions as a way to identify opportunities of play. It's not that they think the move looks right, it's that they played a lot of tactical puzzles, and because of this pattern recognition, they are now capable of identifying decisive attacks that can then be objectively calculated within the brain to be seen as leading to checkmate or a piece advantage. They don't make the move because of the pattern or heuristic. They make the move because the pattern allowed them to see the objective advantage in making that move. ------ As for your point about a move being objectively good: Unless you completely solve the game of chess, there will never be always one move in every situation that's objectively the best. In many games (and you will see this in computer analysis), 2 or 3 moves will hold high promise, while others will hold less. From an objective standpoint all these three moves could be objectively better than all others, but it could be hard to justify that one is necessarily better than another. The reason for this is partly because between two objectively 'equal' moves, there may be a rational reason for me to justify one over the other based on personal considerations (e.g. because I am familiar with the opening, because I played and analyzed many games similar to this line, because I can play this end game well, etc.) Decisions based on those considerations are not what I would call heuristics, because they are based on objective reasons even if heuristics may have contributed to their formation within the mind.
Houshalter 11y ago
I think this is wrong. This idea comes from when humans first tried to program computers to do what we wanted them to do. We failed, and it turned out to be really hard. A grand master couldn't explain the algorithm he used exactly. But that doesn't mean he couldn't give any useful information at all. Think of it like describing a tiger. I have no idea how to describe complicated edge detection algorithms and exact shapes. But I can say something like "an animal with orange stripes", and that would be useful information to another human. Likewise a grandmaster can explain that pawns are valuable in situations like this, or that point to a certain position and say don't do that, etc. To a computer that information is useless, but to a human that's extremely useful. We already have some pattern recognition and intelligence, we just need some pointers. Not an exact description of the algorithm.
- RobertoG 11y ago
  "Likewise a grandmaster can explain that pawns are valuable in situations like this, or that point to a certain position and say don't do that, etc. To a computer that information is useless, but to a human that's extremely useful. " I think that the only reason this is true is because, humans, shared the same low (or intermediate in this case) level features of their models of the world and a common language to share them. Artificial neural networks understanding have evolved along other path, and they probable have another different organization between the different levels, but that doesn't make the fundamental mechanism different.

meric 11y ago

Perhaps postrationalizaton is still useful? Could empathy and mirror neurons help transfer some of that deep learning? Two weeks later the student faces a similar situation, and they get the same gut feeling their teacher did, and that helps them play better than if their teacher didn't postrationalize?

jameshart 11y ago
Absolutely! My point is that human intelligence doesn't actually have any deep insight into how it makes decisions, so we shouldn't be that disappointed that an AI doesn't, either. Humans can postrationalize - explore how they think they make decisions - but they can't tell you how they actually decided. Doing the same for an AI is interesting, but I don't think it's a necessary component of making good decisions in the first place to be able to explain why you made it.
- cgio 11y ago
  On the other hand, not all actions are decisions. There is plenty of actions we would classify as decisions which are application of rules instead. There is a clear rational path in application of rules. To clarify terminology, for me decisions are actions in response to intractable computationally challenges, e.g. will that couch fit in my living room (when I am in a store, where size calculations are hard in an unfamiliar context) etc. This could mean that your action is my decision if we are not equally capable of calculating based on rules.
- robbrown451 11y ago
  Although, when looking at the Deep Dream images that come as a by product (more or less) of image recognition AI, I get the impression that there ARE ways of communicating things about what a computer is "thinking" when trying to solve problems in areas where humans are traditionally better.
- jibalt 11y ago
  Your point is completely and utterly wrong when it comes to chess.

Retric 11y ago

You are being pedantic. People have learned to communicate heuristics which is very useful for beginning players. A grand master may not be able to communicate nuances of the game to low level players, but they do benefit from working in groups which suggests they can share reasons for a given move not just propose moves and have other independently evaluate them. So, while people can't map out the networks of neurons that actually made a given choice, we can effectively communicate reasons for a given move.

kelseydh 11y ago

I play competitive chess, and I assure you most moves are made because of players proving in their minds that a move is objectively good. The reasons for why the player may think the move is objectively good can vary, but they are almost always linked to rational considerations. E.g. that the move increases their piece count, their control of center squares, their attacking opportunities, or is tactically necessary due to the position. My point being that when grandmaster players play chess, they are just as interested in finding objectively right moves as a computer is. Unless it's speed chess it's rarely a "I suspect this might be good" sort of thing. (That said, many grandmasters do avoid lines of play they consider too dangerous. World Champion Magnus Carlsen's "nettlesomeness" - his willingness to force games into difficult positions - has been one explanation for why he beats other Grandmasters.)

jameshart 11y ago
If the move's objectively good, there would be no variation in moves between players. Since there is variation, I assume different players apply different heuristics for 'good'. And whether the move increases their piece count is a fine justification, but why are you privileging increasing your piece count at this point in this game against this opponent? At some point the answer becomes 'because I learned to'.
- kelseydh 11y ago
  Well almost every computer playing chess algorithm uses piece counts to evaluate the quality of chess positions, because barring an amazing tactical combination (which can usually be computationally eliminated past 5 moves) or a crushing positional advantage, a loss of pieces will mean the victory of the person with more pieces. I would argue you see far more pattern recognition at play in chess than you do of heuristics. Heuristics is more common at lower levels of play. When Grandmaster's rely on pattern recognition, they are using their vast repertoire of remembered positions as a way to identify opportunities of play. It's not that they think the move looks right, it's that they played a lot of tactical puzzles, and because of this pattern recognition, they are now capable of identifying decisive attacks that can then be objectively calculated within the brain to be seen as leading to checkmate or a piece advantage. They don't make the move because of the pattern or heuristic. They make the move because the pattern allowed them to see the objective advantage in making that move. ------ As for your point about a move being objectively good: Unless you completely solve the game of chess, there will never be always one move in every situation that's objectively the best. In many games (and you will see this in computer analysis), 2 or 3 moves will hold high promise, while others will hold less. From an objective standpoint all these three moves could be objectively better than all others, but it could be hard to justify that one is necessarily better than another. The reason for this is partly because between two objectively 'equal' moves, there may be a rational reason for me to justify one over the other based on personal considerations (e.g. because I am familiar with the opening, because I played and analyzed many games similar to this line, because I can play this end game well, etc.) Decisions based on those considerations are not what I would call heuristics, because they are based on objective reasons even if heuristics may have contributed to their formation within the mind.

Houshalter 11y ago

I think this is wrong. This idea comes from when humans first tried to program computers to do what we wanted them to do. We failed, and it turned out to be really hard. A grand master couldn't explain the algorithm he used exactly. But that doesn't mean he couldn't give any useful information at all. Think of it like describing a tiger. I have no idea how to describe complicated edge detection algorithms and exact shapes. But I can say something like "an animal with orange stripes", and that would be useful information to another human. Likewise a grandmaster can explain that pawns are valuable in situations like this, or that point to a certain position and say don't do that, etc. To a computer that information is useless, but to a human that's extremely useful. We already have some pattern recognition and intelligence, we just need some pointers. Not an exact description of the algorithm.

RobertoG 11y ago
"Likewise a grandmaster can explain that pawns are valuable in situations like this, or that point to a certain position and say don't do that, etc. To a computer that information is useless, but to a human that's extremely useful. " I think that the only reason this is true is because, humans, shared the same low (or intermediate in this case) level features of their models of the world and a common language to share them. Artificial neural networks understanding have evolved along other path, and they probable have another different organization between the different levels, but that doesn't make the fundamental mechanism different.