Artificial Intelligence And Go: (Alpha)Go Ahead, Move The Goalposts

In the summer of 1999, I attended my first ever professional academic philosophy conference–in Vienna. At the conference, one titled ‘New Trends in Cognitive Science’, I gave a talk titled (rather pompously) ‘No Cognition without Representation: The Dynamical Theory of Cognition and The Emulation Theory of Mental Representation.’ I did the things you do at academic conferences as a graduate student in a job-strapped field: I hung around senior academics, hoping to strike up conversation (I think this is called ‘networking’); I tried to ask ‘intelligent’ questions at the talks, hoping my queries and remarks would mark me out as a rising star, one worthy of being offered a tenure-track position purely on the basis of my sparking public presence. You know the deal.

Among the talks I attended–a constant theme of which were the prospects of the mechanization of the mind–was one on artificial intelligence. Or rather, more accurately, the speaker concerned himself with evaluating the possible successes of artificial intelligence in domains like game-playing. Deep Blue had just beaten Garry Kasparov in an unofficial chess-human world championship in 1997, and such questions were no longer idle queries. In the wake of Deep Blue’s success the usual spate of responses–to news of artificial intelligence’s advance in some domain–had ensued: Deep Blue’s success did not indicate any ‘true intelligence’ but rather pure ‘computing brute force’; a true test of intelligence awaited in other domains. (Never mind that beating a human champion in chess had always been held out as a kind of Holy Grail for game-playing artificial intelligence.)

So, during this talk, the speaker elaborated on what he took to be artificial intelligence’s true challenge: learning and mastering the game of Go. I did not fully understand the contrasts drawn between chess and Go, but they seemed to come down to two vital ones: human Go players relied, indeed had to, a great deal on ‘intuition’, and on a ‘positional sizing-up’ that could not be reduced to an algorithmic process. Chess did not rely on intuition to the same extent; its board assessments were more amenable to an algorithmic calculation. (Go’s much larger state space was also a problem.) Therefore, roughly, success in chess was not so surprising; the real challenge was Go, and that was never going to be mastered.

Yesterday, Google’s DeepMind AlphaGo system beat the South Korean Go master Lee Se-dol in the first of an intended five-game series. Mr. Lee conceded defeat in three and a half hours. His pre-game mood was optimistic:

Mr. Lee had said he could win 5-0 or 4-1, predicting that computing power alone could not win a Go match. Victory takes “human intuition,” something AlphaGo has not yet mastered, he said.

Later though, he said that “AlphaGo appeared able to imitate human intuition to a certain degree” a fact which was born out to him during the game when “AlphaGo made a move so unexpected and unconventional that he thought “it was impossible to make such a move.”

As Jean-Pierre Dupuy noted in his The Mechanization of Mind, a very common response to the ‘mechanization of mind’ is that such attempts merely simulate or imitate, and are mere fronts for machinic complexity–but these proposals seemingly never consider the possibility that the phenomenon they consider genuine or the model for imitation and simulation can only retain such a status as long as simulations and imitations remain flawed. As those flaws diminish, the privileged status of the ‘real thing’ diminishes in turn. A really good simulation,  indistinguishable from the ‘real thing,’ should make us wonder why we grant it such a distinct station.