7 ms·

there needs to be work done and intuition developed in order for a human to look any amount of moves ahead. we do not look ahead in the same way a computer alg

there needs to be work done and intuition developed in order for a human to look any amount of moves ahead. we do not look ahead in the same way a computer algorithm does we look ahead in ways like "doing this leaves this area weak, and the opponent has resources that can take advantage of that, and i cannot intervene on those resources in time" or "if i create a strong threat then the opponent will be forced to react to it, here are the ways they can react that make any sort of sense, here is what i can do in each of those situations" they are not doing things like "let me simulate moving every one of my pieces right now, and then every one of my opponents pieces in response to each of those moves, and then my options again, and review 10,000 possible scenarios in my mind individually for the best min/max situation" like a classical computer engine does. so i always find the "X moves ahead" phrase misleading at best. but as i originally stated, it is useful to know how many moves of perfect play are necessary for someone to convert a winning position when reviewing the players confidence going into that position. and even then you dont know if they got lucky or earned that confidence by looking at just the one game alone. Over the course of their career the amount of time that their confidence pays off or not tells the story there

thaumasiotes 4y ago
> there needs to be work done and intuition developed in order for a human to look any amount of moves ahead. we do not look ahead in the same way a computer algorithm does I mean, that's just plain wrong on both counts. You need to do work to do a good job at looking ahead. You don't need to do work just to be able to imagine what the board might look like after a particular move is made. > they are not doing things like "let me simulate moving every one of my pieces right now, and then every one of my opponents pieces in response to each of those moves, and then my options again, and review 10,000 possible scenarios in my mind individually for the best min/max situation" like a classical computer engine does. You don't understand what the computer is doing. Pruning its options is just as important for the computer as it is for the humans.
- cupofpython 4y ago
  you're missing the point of my post. yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position. without work, as in developing an intuition for making sense of the game, a human looking ahead doesnt provide value. so im not sure why you think i was trying to say humans cant move pieces around in their mind. you also completely ignored that i was mainly addressing the scope difference of positions analyzed by a human player vs a computer, and that the talking point was someone looking "64 moves ahead" and trying to explain that no this guy did not literally see all variations 64 moves out - but that through (arguably more impressive) reasonable human ability he was able to to be accurate and confident in a decision that he had won while there was still 64 moves of depth left in the game
  - thaumasiotes 4y ago
    > yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position. No, pruning is by far the majority of the work the computer does. It is very much not trying to review all possible scenarios indiscriminately, because that can't be done, because there are too many possible scenarios. And "pruning" and "making sense of the position" refer to the same thing. Interpreting the position is how you prune.
    - kcexn 4y ago
      I think what many people in this thread are trying to stumble over is that the way a computer prunes moves and the way humans prune moves is qualitatively different. A computer prunes its moves from either an explicit or implicit (implicit when it's say a trained neural net) database of known positions, with some quantitative sense of strength (usually a probability to win or something like that). A human needs to assign a narrative to particular branching pathways. These are qualitative instead of quantitative assessments. A human isn't saying, if I make a certain move there is an 85% chance of winning, and so that makes it my best bet. They're assigning arbitrary structures and narratives to positions, hence why many positions, tactics, and strategies in chess and other games are given colourful names. The two approaches are very different and have different strengths and weaknesses. Which is why the best play outcome is to combine the computer generated moves with the human generated moves. The human approach is very good at generalising new information very quickly. Assigning unusual or unfamiliar information in a broader qualitative framework about what good play looks like, think about players who are trying to create certain structures, shapes and patterns on the board. The computer is very good at applying knowledge about individual moves at great depths. But cannot combine it with any external information. All information about the success rates of moves are determined from the database of all past moves. The computer can't condition those probabilities on things like, does my opponent need to win, or only draw. Do they have a tendency to be aggressive or defensive. Probabilities of success only make sense when taking a population view of the computers input data (a literally impossible task if your talking about the kinds of neural nets used in chess). So a hybrid approach lets good players condition computer generated moves based on external information. Maybe the computer generates a line of play with 80% confidence of winning, but the human can see that because of certain qualitative structures on the board, the opposing player is more likely to see the solution than the computers population, and so can recondition the lines of play on this new information, even if the human has no idea why the line of play should work 80% of the time. Lines of play that would otherwise have very similar success rates (differing by only a few percentage points say) can be re-ordered based on human judgement. Both the computer and the human can tell obviously bad from obviously good moves. But their approach is very different when nuance is required.

thaumasiotes 4y ago

> there needs to be work done and intuition developed in order for a human to look any amount of moves ahead. we do not look ahead in the same way a computer algorithm does I mean, that's just plain wrong on both counts. You need to do work to do a good job at looking ahead. You don't need to do work just to be able to imagine what the board might look like after a particular move is made. > they are not doing things like "let me simulate moving every one of my pieces right now, and then every one of my opponents pieces in response to each of those moves, and then my options again, and review 10,000 possible scenarios in my mind individually for the best min/max situation" like a classical computer engine does. You don't understand what the computer is doing. Pruning its options is just as important for the computer as it is for the humans.

cupofpython 4y ago
you're missing the point of my post. yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position. without work, as in developing an intuition for making sense of the game, a human looking ahead doesnt provide value. so im not sure why you think i was trying to say humans cant move pieces around in their mind. you also completely ignored that i was mainly addressing the scope difference of positions analyzed by a human player vs a computer, and that the talking point was someone looking "64 moves ahead" and trying to explain that no this guy did not literally see all variations 64 moves out - but that through (arguably more impressive) reasonable human ability he was able to to be accurate and confident in a decision that he had won while there was still 64 moves of depth left in the game
- thaumasiotes 4y ago
  > yes the computer prunes, but fundamentally it is attempting to review all possible scenarios indiscriminately as opposed to a human who is trying to make some sort of sense of the position. No, pruning is by far the majority of the work the computer does. It is very much not trying to review all possible scenarios indiscriminately, because that can't be done, because there are too many possible scenarios. And "pruning" and "making sense of the position" refer to the same thing. Interpreting the position is how you prune.
  - kcexn 4y ago
    I think what many people in this thread are trying to stumble over is that the way a computer prunes moves and the way humans prune moves is qualitatively different. A computer prunes its moves from either an explicit or implicit (implicit when it's say a trained neural net) database of known positions, with some quantitative sense of strength (usually a probability to win or something like that). A human needs to assign a narrative to particular branching pathways. These are qualitative instead of quantitative assessments. A human isn't saying, if I make a certain move there is an 85% chance of winning, and so that makes it my best bet. They're assigning arbitrary structures and narratives to positions, hence why many positions, tactics, and strategies in chess and other games are given colourful names. The two approaches are very different and have different strengths and weaknesses. Which is why the best play outcome is to combine the computer generated moves with the human generated moves. The human approach is very good at generalising new information very quickly. Assigning unusual or unfamiliar information in a broader qualitative framework about what good play looks like, think about players who are trying to create certain structures, shapes and patterns on the board. The computer is very good at applying knowledge about individual moves at great depths. But cannot combine it with any external information. All information about the success rates of moves are determined from the database of all past moves. The computer can't condition those probabilities on things like, does my opponent need to win, or only draw. Do they have a tendency to be aggressive or defensive. Probabilities of success only make sense when taking a population view of the computers input data (a literally impossible task if your talking about the kinds of neural nets used in chess). So a hybrid approach lets good players condition computer generated moves based on external information. Maybe the computer generates a line of play with 80% confidence of winning, but the human can see that because of certain qualitative structures on the board, the opposing player is more likely to see the solution than the computers population, and so can recondition the lines of play on this new information, even if the human has no idea why the line of play should work 80% of the time. Lines of play that would otherwise have very similar success rates (differing by only a few percentage points say) can be re-ordered based on human judgement. Both the computer and the human can tell obviously bad from obviously good moves. But their approach is very different when nuance is required.