Predicting Human Players' Moves

     With the long holiday weekend now over it's time to get back to chess. With my neck of the woods seeing a surge of Covid, our usually large family get together didn't happen this year and everybody had a quiet meal at home with only their immediate family attending. 

     Also, we had the last good weather until next spring. The weather forecast for today is crappy turning crappier. The day dawned with an all day drizzling rain that will be changing to a predicted 6-10 inches of snow tonight. 

This is ugly

     I had a nice post already to go, but accidentally deleted it and don't feel like trying to reconstruct it, so today I thought I would post a link to an interesting (and scary) article from a paper I just read titled Aligning Superhuman AI with Human Behavior: Chess as a Model System. Download pdf of the paper HERE.

     There is a program called Maia, a version of Leela, which has the goal of having the engine play not necessarily the best move, but human-like moves over 50 percent of the time. According to Maia's website the program is learning to predict moves made by online human players that are based on the players' ratings. 

     Predicting situations where humans err is the main goal behind this joint project of the University of Toronto, Cornell University and Microsoft Research, but it can potentially make a big difference to the statistical evidence tools to detect cheaters. 

     Engines have already destroyed correspondence chess for all but a handful of players dedicated to doing very deep research on powerful computers. I was just wondering what's in the future for the game when engines can predict a GMs move half the time?