The title is a little misleading. 
A better one would be “Comparing human players thoughts and AI programming and making use of that for avoiding tsumegos” but it ain’t that appealing! 
I’m not gonna say anything but obvious, but I thought about my personal thread of thoughts when I play and compared that to what I know about Go AIs. I think that could be a starting point for some conversations.
AFAIK Go AIs are mainly made of two pieces: a Neural Network (NN) and some sort of MonteCarlo (MC) algorithm.
The MC part is just brute force exploring of branches in the variations tree. It’s a overwhelming task when done on all possible variations, even for a computer.
That’s why the NN is key: it looks at the board and generates a bunch of possible moves, only a few, that will then be passed to the MC, reducing the variations tree to a smaller and manageable size.
Of course, for each move in each variation these two parts are interacting: NN comes out with some possible moves, MC start exploring a branch which brings to a new board configuration and new possible moves by the NN, and so on.
I miss a piece: the evaluation part.
At some point in this process, there’s something that states the hypotetical win rate for both players. This is necessary for pruning the variations tree, removing variations that aren’t useful. IDK if this task is done by one of the above pieces or if it’s on its own.
This isn’t very important for what I’m discussing here. Let’s just think that we (both humans and AIs) just know when a variation is good or not for us.
My point is: when comparing AIs with human thoughts, NN is about instinct and imagining possible reasonable moves, MC is about reading.
Peronally, I heavily rely on instinct. My reading skills are weak and I don’t want to improve them by doing specific training (tsumego). I am old, I am tired, I have almost no time to play, so I have to pick just what’s most important for me: playing. And I have to do that in my spare time (that’s why I only play correspondence online games, making my moves when I’m on the bus, at the toilet, in a waiting room and so on).
When I drop any chance of improving reading, I can only try to improve instinct.
I already know that my imagination about possible moves is weak. I check that every time I try solving a puzzle: when I get to “my strenght puzzles” I mainly fail because I don’t even recognize the solution as a possible move. I just discard it before starting reading it out.
I tried once to solve a simple tsumego by writing down all possible variations. It’s a load of work even for a simple puzzle. Brute force is exausting and frustrating because it brings you to all possible silly and useless variations of which, in our beloved game, there’s a plenty. You get to the solution by excluding everything else, which is a drag!
So instinct is key to prune the tree, but it becomes harmful when you prune the good branch!
My training on that is using OGS AI to review my games.
It’s very easy, because for each move you’re presented with few highlited possible next moves. That’s just it: a handful of best options, already sorted by effectiveness. What I must do then is try to understand the underlying logic (it’s better to play elsewhere, I should place another stone on that group, I should defend, I should attack and so on) and get used to shapes (jumps, extensions, boxes and tables, hane and caps and so on).
I do this in two ways:
- while I play, I write down in comments (Malkovich) when I’m very doubtful about direction of play. “Where should I play next?” or “Am I safe now? Can I play elsewhere?” or “That group seems unsettled, should I attack it?” is something I leave in my Malko log as a note for my later analysis with the help of AI. Then I can see if I was right or wrong and make use of it in my next game.
- after the game, I scroll the full game comparing my choices with AI suggestions. Sometimes I’m spot on, sometimes I’m pretty near (trying to achieve the same goal with a less effective move), sometimes I’m completely out (neglecting urgent moves or, more frequently, adding stones where there’s no need to).
That’s my experience so far.
If you want to do the same, you must remember that strong AIs live on the edge: they have a very fighting style and they leave many positions unsettled because they can deeply understand full board balance. I personally like a safer style and most of the time I check for second or third options, when AI evaluates that a safe move is a small error compared to the best one. When there’s a prudent move that is less than 2 points from a “fighting best choice”, I like to choose that instead. At my level (OGS 4k) 2 pts mistakes are very good moves! 
I prefer points instead of winrate because I can always understand what N points means, while I don’t always understand what a N% change in winrate means. I’m just more comfortable with it and I think that winrate becomes more important when you’re very strong and must work on honing your skills instead of building them fron nowhere!
What do you think?
Am I missing something crucial?
Could this piece of advice be useful?
