hakuseki

The reductions in your linked paper [1] are not polynomial-time reductions.

The article contains a misleading description of NP-hardness:

> Computing the Kolmogorov complexity for a sequence X of N bits length is an NP-hard problem, since the search space of possible programs producing any sequence of length N is growing exponentially with respect to the input size N

NP-hardness would actually mean that any NP problem, e.g. a traveling salesman problem, could be "reduced" within polynomial time to calculating the Kolmogorov Complexity of a given string.

Is NNUE used by any Go AI? I've only heard of it being used for shogi and chess.

Computational complexity seems like a useful frame for thinking about such distinctions. If you can calculate the state of a system at time t in O(log t), that's a lot like having a closed-form solution. On the other hand if the calculation takes O(t) time, then it seems much less like a closed-form solution and more like a step-by-step simulation.

Actually this is an example of ambiguity, not context sensitivity (in the formal languages sense). Context sensitivity means context is needed to construct a grammatical sentence, not merely to parse one. It's actually pretty hard to find examples of this in natural language.

I believe the parent was asking in what sense these models were trained on human data.

> The more important takeaway: dozens of technologists at IBM spent more than three years and untold millions of dollars building the program specifically trained for Jeopardy! prowess. Less than 10 years later, a general-purpose open-sourced technology without the massive mainframe or cooling fans can compete on the same level.

This statement from the article confused me. GPT-3 is general-purpose, but not open source, nor does it run without massive hardware, nor was it developed without spending millions of dollars.

> It could even be written in a way where the order of arguments didn’t matter

Well, 'hello'.join(', ') does not produce the same output as ', '.join('hello'), so the order of the arguments does matter.

Yes, you can just `pip install katrain` followed by `python -m katrain` to get started. Personally I would recommend at least reading abut the rules first (unlike MuZero).

I think the strength or lack thereof of your opponent is actually much less important than the strength of the AI you use to review your games. After each game you should study the AI's advice and learn the moves it recommends.