I don't know why you'd expect to be able to read the board as a non-player. If I watch a surgeon at work a lot of the individual motions are inscrutable to me. It's like looking at sheet music if you don't play an instrument. You just don't have the mental schema to see what is interesting about even the most interesting parts.
I was watching this game with my go club and we all instantly saw the significance of 37, it was audible in the room. 78 felt tangibly different, some of us immediately read it as a clear misplay, some were taking longer to come to any conclusion, just puzzled. Our most experienced player, at the time 5 dan, gasped when he got it. But it still took him time to even intuit what it was doing. Now that it is well understood, moves of that type are common even in intermediate level play. Changed the game forever.
> Now that it is well understood, moves of that type are common even in intermediate level play. Changed the game forever.
That's an important takeaway from the AlphaGo saga:
It played moves that (at the time, for human players) seemed weird. And while playing those, outperformed humans.
But as understanding of how/why of such moves grew, it showed humans new ways of doing things. And in doing so, become better players themselves.
AlphaGo broke new ground, humans followed. And like you said: changed the game forever.
Also, the subtlety of what makes a win:
Humans, before AlphaGo: try & grab as much territory as possible to beat your opponent.
AlphaGo: just try to grab more territory than opponent (so, not necessarily much more). End up with only 1 point advantage = still a win.
Different viewing angle, different strategy, different outcome.
You misunderstood my criticism. I have zero idea about Go, and I know it.
What I would have liked is for the video to take a minute to explain how a single move so early in a game was immediately obvious to players as amazing, given so much focus was put on the fact that there are more move options than atoms in the universe.
Let me rephrase: Mathematically, what was it about move 37 that reduced the quintillions+ of possible outcomes down to a perceived guaranteed win?
My assumption is that there are far fewer combinations of practical moves, which constrains the calculations considerably. I would have liked to have known more about that.
Oh, you're right I did misunderstand you, sorry.
I don't think there's really a framework for that sort of analysis yet. Go players talk about influence and structure but they aren't thinking of a move shrinking the problem space in that way, even though of course it does.
And mathematical analysis has so far mostly (afaik) been about the broader game. Trying to use computation to understand the value of individual moves in this way is pretty much exactly the dead end that caused deepmind to wind up using the approach they did. An approach that certainly wins games, but so far it has been up to normal go players to explain why, and they use traditional go player tools to do so.
If you find anything let me know bc it's super interesting. But I think what you're looking for is an as-yet-unwritten math or CS thesis written by a serious go playing phd candidate.