Domain insights in the real world are often simply being told which general strategies will work best.
No, that’s not what domain insights are. Domain insights are just that, insights which are limited to a specific domain. So something like, “Trying to control the center of the board,” is a domain insight for chess. Another example of chess-specific domain insights is the large set of pre-computed opening and endgame books that engines like Stockfish are equipped with. These are specific to the domain of chess, and are not applicable to other games, such as Go.
An AI that can use more general algorithms, such as tree search, to effectively come up with new domain insights is more general than an AI that has been trained with domain specific insights. AlphaZero is such an AI. The rules of chess are not insights. They are constraints. Insights, in this context, are ideas about which moves one can make within the constraints imposed by the rules in order to reach the objective most efficiently. They are heuristics that allow you to evaluate positions and strategies without having to calculate all the way out to the final move (a task that may be computationally infeasible).
AlphaZero did not have any such insights. No one gave AlphaZero any heuristics about how to evaluate board positions. No one told it any tips or tricks about strategies that would make it more likely to end up in a winning position. It figured out everything on its own and did so at a level that was better than similar AIs that had been seeded with those heuristics. That is the true essence of the Bitter Lesson: human insights often make things worse. They slow the AI down. The best way to progress is just to add more scale, add more compute, and let the neural net figure things out on its own within the constraints that it’s been given.
No. That’s a foolish interpretation of domain insight. We have a massive number of highly general strategies that nonetheless work better for some things than others. A domain insight is simply some kind of understanding involving the domain being put to use. Something as simple as whether to use a linked list or an array can use a minor domain insight. Whether to use a monte carlo search or a depth limited search and so one are definitely insights. Most advances in AI to this point have in fact been based on domain insights, and only a small amount on scaling within an approach (though more so recently). Even the ‘bitter lesson’ is an attempted insight into the domain (that is wrong due to being a severe overreaction to previous failure.)
Also, most domain insights are in fact an understanding of constraints. ‘This path will never have a reward’ is both an insight and a constraint. ‘Dying doesn’t allow me to get the reward later’ is both a constraint and a domain insight. So is ‘the lists I sort will never have numbers that aren’t between 143 and 987’ (which is useful for and O(n) type of sorting). We are, in fact, trying to automate the process of getting domain insights via machine with this whole enterprise in AI, especially in whatever we have trained them for.
Even, ‘should we scale via parameters or data’ is a domain insight. They recently found out they had gotten that wrong (Chinchilla) too because they focused too much on just scaling.
Alphazero was given some minor domain insights (how to search and how to play the game), years later, and ended up slightly beating a much earlier approach, because they were trying to do that specifically. I specifically said that sort of thing happens. It’s just not as good as it could have been (probably).
And now we have the same algorithms that were used to conquer Go and chess being used to conquer matrix multiplication.
Are you still sure that AlphaZero is “domain specific”? And if so, what definition of “domain” covers board games, Atari video games, and matrix multiplication? At what point does the “domain” in question just become, “Thinking?”
No, that’s not what domain insights are. Domain insights are just that, insights which are limited to a specific domain. So something like, “Trying to control the center of the board,” is a domain insight for chess. Another example of chess-specific domain insights is the large set of pre-computed opening and endgame books that engines like Stockfish are equipped with. These are specific to the domain of chess, and are not applicable to other games, such as Go.
An AI that can use more general algorithms, such as tree search, to effectively come up with new domain insights is more general than an AI that has been trained with domain specific insights. AlphaZero is such an AI. The rules of chess are not insights. They are constraints. Insights, in this context, are ideas about which moves one can make within the constraints imposed by the rules in order to reach the objective most efficiently. They are heuristics that allow you to evaluate positions and strategies without having to calculate all the way out to the final move (a task that may be computationally infeasible).
AlphaZero did not have any such insights. No one gave AlphaZero any heuristics about how to evaluate board positions. No one told it any tips or tricks about strategies that would make it more likely to end up in a winning position. It figured out everything on its own and did so at a level that was better than similar AIs that had been seeded with those heuristics. That is the true essence of the Bitter Lesson: human insights often make things worse. They slow the AI down. The best way to progress is just to add more scale, add more compute, and let the neural net figure things out on its own within the constraints that it’s been given.
No. That’s a foolish interpretation of domain insight. We have a massive number of highly general strategies that nonetheless work better for some things than others. A domain insight is simply some kind of understanding involving the domain being put to use. Something as simple as whether to use a linked list or an array can use a minor domain insight. Whether to use a monte carlo search or a depth limited search and so one are definitely insights. Most advances in AI to this point have in fact been based on domain insights, and only a small amount on scaling within an approach (though more so recently). Even the ‘bitter lesson’ is an attempted insight into the domain (that is wrong due to being a severe overreaction to previous failure.)
Also, most domain insights are in fact an understanding of constraints. ‘This path will never have a reward’ is both an insight and a constraint. ‘Dying doesn’t allow me to get the reward later’ is both a constraint and a domain insight. So is ‘the lists I sort will never have numbers that aren’t between 143 and 987’ (which is useful for and O(n) type of sorting). We are, in fact, trying to automate the process of getting domain insights via machine with this whole enterprise in AI, especially in whatever we have trained them for.
Even, ‘should we scale via parameters or data’ is a domain insight. They recently found out they had gotten that wrong (Chinchilla) too because they focused too much on just scaling.
Alphazero was given some minor domain insights (how to search and how to play the game), years later, and ended up slightly beating a much earlier approach, because they were trying to do that specifically. I specifically said that sort of thing happens. It’s just not as good as it could have been (probably).
And now we have the same algorithms that were used to conquer Go and chess being used to conquer matrix multiplication.
Are you still sure that AlphaZero is “domain specific”? And if so, what definition of “domain” covers board games, Atari video games, and matrix multiplication? At what point does the “domain” in question just become, “Thinking?”