| // personal opinion: I think machine learning as it currently stands is widely overhyped How is this the top comment? > I am starting to notice a pattern in these papers - Writing hyper-specific tokenizers for the target problem. This is merely expressing what they consider as part of a game state, which is entirely needed for what they set out to do. > I argue this is just ordinary programming "Ordinary programming" (what does that mean?) for such a task implies extraordinary chess intuition, capable of conjuring rules and heuristics for the task of comparing two game states and saying which one is "better" (what does better mean?). > How would this model perform if we made a small change to the rules of chess and continued using the same tokenizer? If by "small change" you are implying i.e. removing the ability to castle, then sure, the tokenizer would need to be rewritten. At the same time, the entire training dataset would need to be changed, such that the games are valid under your new ruleset. How is this controversial or unexpected? It feels like you are expecting that state of the art technology allows us to input an arbitrary ruleset and the mighty computer immediately plays an arbitrary game optimally. Unfortunately, this is not the case, but that does not take anything away from this paper. |