Computer Specs for Go Review with AI

[jlt]

So inferior alternatives may not get more than 100 visits. That's low for sure, but again the point is the difference between LZ and Kata behavior.

Anyway I wouldn't trust any bot's judgment on a move that gets less than 100 visits, and in a complex fight more visits are needed. LZ may be better than KataGo in some cases, but on low visits, 15-block LZ easily misreads ladders...

[jann]

Well, the alternative is 0 visits - I either do a quick review or not.

On second thought there may be more to this. During the next few moves the top move does have a significant part of the 10k, still the drop only happens later... Will take a closer look next time.

[xela]

Yes, there is more to it. It's to do with the way confidence bounds are used in the search. You can have a second-choice move where the initial evaluation is 55%, after a couple of thousand playouts it's gone down to 50% while the first-choice move is still up at 52%. Perhaps another thousand playouts would reveal that the second choice move is really a 40% move, but why do those playouts once you're confident that the move is second-best? The goal of the algorithm is to identify the best move, not to accurately rank all the moves. If you do want an accurate evaluation of each move, you have to click on each move and evaluate them one by one (or else used a modified version of the software, as we've discussed elsewhere).

In my list of ladder positions that I'll post once I have an uninterrupted couple of hours for writing about go software, I've got a concrete example of this. Sorry about the delay...

[dino1019]

I have a quote with an ASUS GPU with 1066 Supper chipset now, how many visits per second with LZ266 should I expect?

On the other hands, Based on a China benchmark post for LZ up to 24x, 2060 has the highest performace to cost ratio, moreover, the 20 series has tensor core fearure, which boosts AI performance.Haven't seen similar comments in English.

[jann]

It's to do with the way confidence bounds are used in the search.

But this is not during search. As I wrote above the effect/drop occurred after next few moves actually played in the game (all the bot's top choices).

[xela]

It's to do with the way confidence bounds are used in the search.

But this is not during search. As I wrote above the effect/drop occurred after next few moves actually played in the game (all the bot's top choices).

Ah, I was thinking of the case where the second choice move turns out to be much worse than advertised. But you're describing something different: if you play out the top choice move, the evaluation still changes. Sorry for the misundestanding.

Are you able to show us a specific position where this happens?

[jann]

No, I just wrote this from memory (but will take a better look next time). I used to use LZ for these quick reviews, only recently tried switching to Kata, then a few times I run into this.

Basically, I search for significant mistakes only, where the reason and the better alternative is easy to explain when seen. So if I see a searched but not chosen move, with reasonably close eval, I go on. If the move was actually a serious mistake it should drop on next move. But instead I find nothing major, and keep moving on a few more moves (mostly top choices), then the eval is much worse. After some time consuming back and forth, the best explanation I find is this earlier move.

Besides the above policy differences, another part of the reason may be LZ eval (trained partially on Elf data) is more saturated than Kata, so drops are faster and more recognizable.