So muchÂ sci-fi coming to earthÂ this last week.
AlphaGo beat Lee Sedol and convincingly. It’s important because the game was thought to be too “big” for traditional computerÂ strategiesÂ based onÂ simulating playing the game forward a few rounds and seeing what happens (like chess programs often do). Go hasÂ 10^761 possible games compared to the estimated 10^120 for chess.
For me the most astonishing thing about AlphaGo is that it was not designed to play Go. It is a generic learning engine that was trained on 30m Go positionsÂ from public databases and then played itself across 50 computers to reinforce its learnings. It has improved steadily over time and now plays “a little strange, but a very strong player, a real person”. Lots more in this Nature article.Â Lee Sedol won a remarkable game four where a stunning move from the 9p seemed to break the computer’s learning and caused it to play weak move after weak move leading to an eventual resignation from AlphaGo.
Just imagine what a pattern engine could do when applied to other endeavours for example the law (pattern: winning vs losing cases) and risk management (pattern: successful vs failed contracts), medical image diagnosisÂ (pattern: life-years saved vs lost). Sadly, the Go learning can’t easily be transferred to another field of endeavour: AlphaGo is now a Go specialist and nothing else.
Even so, the speed at which these artificial general intelligences learn outpaces humans by orders of magnitude. We’re toast.
Separately, scientists have shown that rat cyborgs are better at solving mazes than rats left toÂ their own devices. Maybe that’s our way back in?