> I never thought the AI player had a 'deep understanding' of Go any more than I think string sort routines have a deep understanding of all the alphabets in the world.
Go AI have networks that map a board state to probability of winning. A perfect AI would correctly know whether or not any given board is a win for white or black. In general, better human understanding of the game directly allows you to better estimate if a board is a win or a loss.
Katago without tree search is plays at a professional level. I would say that this corresponds to at least professional level human intuition.
String comparison algorithms lack an objective "correct" ordering so it is hard to say what a correct sort should do. But a string comparison using utf8 has a super human (compared to any given person) understanding of alphabets because of the many people involved in ratifying the characters included in unicode.
I have a different (and I think correct) definition of 'understand'
: to have understanding : have the power of comprehension. : to achieve a grasp of the nature, significance, or explanation of something. 3. : to believe or infer something to be the case. (Miriam)
String comparison routines can sort strings like a super human, but they don't understand what they are doing. Neither does the Go AI.
I guess people are using 'understand' in a more casual sense, like my "rice cooker automatically cooks my rice, so it can be said to understand how to cook rice."
You’ll notice that the definition of “understanding” is quite vague, and in particular it is untestable. In fact, I think we understand quite a bit less than we think we do. Grandmaster chess players will identify maybe 5 or so candidate moves in a position to assess, but the actual process of pruning the game tree is hardly deliberate. I assume Go is similar. Chess tactics are often “seen” quickly and subconsciously. Players are encouraged to practice chess tactics to train their subconscious in this way. Evaluating a position is an extremely qualitative exercise as well.
It seems that MCTS rollouts are a pretty good way of “understanding” a position. It is arguably one of the most unbiased ways. That fact alone suggests to me that it is at least very hard to judge. Maybe it is not interpretable by you or I, but I can’t say that rules out understanding.
I go back and forth on this. Machines aren’t introspective enough to have the kind of behavior most would call “understanding”. But then again, people aren’t either. And I don’t mean that in the most people are stupid but I’m very smart sense. I mean we quite literally don’t know why we have thoughts, we just do. We’re able to use words to provide justifications after the fact, but that’s not the same thing. The origin of any thought is a mystery to us.
everyone makes mistakes, but not everyone makes the same mistakes. depending on their mistake, players very much can and do conclude a lack of relevant understanding. this conclusion is an integral part of the learning process.
Go AI have networks that map a board state to probability of winning. A perfect AI would correctly know whether or not any given board is a win for white or black. In general, better human understanding of the game directly allows you to better estimate if a board is a win or a loss.
Katago without tree search is plays at a professional level. I would say that this corresponds to at least professional level human intuition.
String comparison algorithms lack an objective "correct" ordering so it is hard to say what a correct sort should do. But a string comparison using utf8 has a super human (compared to any given person) understanding of alphabets because of the many people involved in ratifying the characters included in unicode.