= 27d567bc2d48d9f40e9ccc20ea370b15)
EHeller comments on AI risk, executive summary - Less Wrong
You are viewing a comment permalink. View the original post to see all comments and the full post content.
= 783df68a0f980790206b9ea87794c5b6)
Loading…= b715d02e85f7b3b4ae65ca3d3e450868)
Subscribe to RSS Feed
Powered by Reddit
= f037147d6e6c911a85753b9abdedda8d)
You are viewing a comment permalink. View the original post to see all comments and the full post content.
Comments (51)
While monte carlo search helps, Go AIs still must be given the maximum handicap to be competitive with high level players, and still consistently lose. Although the modern go algorithms are highly parallel, so maybe its just an issue of getting larger clusters.
Also, I wonder if these sorts of examples cause people to downgrade in risk- its hard to imagine how a program that plays Go incredibly well poses any threat.
I saw some 4 stone games against experts recently, and thought that 8 stones was the maximum normal handicap.
It looks like I'm out of date. A search revealed that a few Go bots have risen to 4-dan on various online servers, which implies a 4 or 5 stone handicap against a top level player (9 dan).
Found what I was thinking of: here are some game records of a human 9 dan playing two computers with 4 stone handicaps, winning to one and losing to the other (and you can also see a record of the two computers playing each other).