Comment by afthonos
11 hours ago
Important to note that KataGo was double-handicapped. 20 seconds per move maximum; it couldn’t read deep. Against an amateur, it doesn’t matter, but against a historically strong pro it matters a lot.
11 hours ago
Important to note that KataGo was double-handicapped. 20 seconds per move maximum; it couldn’t read deep. Against an amateur, it doesn’t matter, but against a historically strong pro it matters a lot.
...on a 4x 3090 rig. The game ran 299 moves, giving katago 100 minutes if it exhausted time on each move (which must be the optimal strategy under that time control). Shin used about 205 minutes, over twice as much time and of course had leeway to spend more time on difficult moves.
Based on the youtube video, it looks like katago was only using 16 seconds per move, is that right? https://www.youtube.com/watch?v=-86zF4mTWOY
Is 20 seconds on that hardware really overkill and well into the diminishing-returns curve, as a top-level comment suggested, or is it plausible katago could have played better if given 40 seconds per move?
match details: https://gostonebase.com/blog/shin-jinseo-vs-katago-kishin-ma...
As another question, does it not operate similarly to the top chess engines? The way the neural network systems work is by using the probabilistic matching paired with a Monte Carlo simulation. So you can get to extreme depth very rapidly. Obviously the breadth is going to be limited, but if the neural network side is well tuned (so high probability hits are indeed generally the most challenging moves), then that's not such a problem.
And you can run a huuuuuuuge number of sims in 16 seconds.
I believe the basic idea is similar, but there's an enormous difference in the space of possible moves between chess and Go. Go has a larger board and moves are less restricted. There was a major breakthrough in Go playing programs a decade or two ago when good Monte Carlo methods were developed for it. But still I think the ability to simulate many moves is less powerful in Go.