It may be easier to learn “this sort of shape is better than everyone thought” than “in this particular position
Thing is, the way you build shape in Go isn’t a straightforward process; the 3 phases of a game, opening, middle game and end game usually involve different types of positional judgement, requiring different ratios of consideration between local position and global position.
Shape building occurs as game play progresses simply because of the aggregation of moves on the board over time, with the development of ‘good shape’ being desirable because it’s easy to defend and useful, and ‘bad shape’ being difficult to defend and a hindrance.
Most of the opening of the game and part of the middle game, shape is implied, and it is the potential of a shape to support a specific approach or tactic which develops into strategy over the game. It is the ability of the human player to correctly see the potential for shape especially in the opening, and to read out how it is likely to grow over the course of game play which makes the difference between a good player and a mediocre one.
Since a great endgame can never make up for a bad opening, especially when you consider many games between evenly matched players will result in a win with only a 0.5 point lead, a human has to be good at either the opening or the middle game in order to even have a chance of winning in the end game.
In human terms, Go bots seem to contemplate all 3 phases, opening, middle, and end game at the same time—from the beginning of the game—while the human player is only thinking about the opening. It seems this long view of AI leads the bots to play moves which at times seem like bad moves. Sometimes a potential rational becomes clear 10 or 15 moves later, but at times it is just plain impossible to understand why a Go bot plays a different move than the one preferred by professionals.
...what makes “this shape” better in a particular case may depend on quirks of the position in ways that look arbitrary and random to strong human go players.
At times yes. Trying to read out all the potential variations of a developing position—over time—from a seemingly arbitrary or random move a Go bot makes results in diminishing returns for a human player. Especially as AI moves move in and out of use in the Go world, like a fad. If no one is playing AI moves, then it doesn’t make sense to try and learn the sequences.
Thing is, the way you build shape in Go isn’t a straightforward process; the 3 phases of a game, opening, middle game and end game usually involve different types of positional judgement, requiring different ratios of consideration between local position and global position.
Shape building occurs as game play progresses simply because of the aggregation of moves on the board over time, with the development of ‘good shape’ being desirable because it’s easy to defend and useful, and ‘bad shape’ being difficult to defend and a hindrance.
Most of the opening of the game and part of the middle game, shape is implied, and it is the potential of a shape to support a specific approach or tactic which develops into strategy over the game. It is the ability of the human player to correctly see the potential for shape especially in the opening, and to read out how it is likely to grow over the course of game play which makes the difference between a good player and a mediocre one.
Since a great endgame can never make up for a bad opening, especially when you consider many games between evenly matched players will result in a win with only a 0.5 point lead, a human has to be good at either the opening or the middle game in order to even have a chance of winning in the end game.
In human terms, Go bots seem to contemplate all 3 phases, opening, middle, and end game at the same time—from the beginning of the game—while the human player is only thinking about the opening. It seems this long view of AI leads the bots to play moves which at times seem like bad moves. Sometimes a potential rational becomes clear 10 or 15 moves later, but at times it is just plain impossible to understand why a Go bot plays a different move than the one preferred by professionals.
At times yes. Trying to read out all the potential variations of a developing position—over time—from a seemingly arbitrary or random move a Go bot makes results in diminishing returns for a human player. Especially as AI moves move in and out of use in the Go world, like a fad. If no one is playing AI moves, then it doesn’t make sense to try and learn the sequences.