> Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. I would disagree with this characteriza

> Victory at chess was at one time considered to be the indicator of the emergence of true 'intelligence' in computing. I would disagree with this characterization. I believe at the time, it was (a) a problem that a machine had not yet conquered, (b) a problem that it seemed feasible that a machine might conquer, and (c) a problem that, once conquered, would point the way to general artificial intelligence. I would point at (c) as the assumption that proved to be erroneous. Deep Blue was clever algorithmic and hardware engineering (with a healthy budget) but led to... what? AlphaGo is a fundamentally different approach, which shows signs of being more adaptable. Point being, that winning a game is not sufficient evidence that a given approach will scale to winning all games, much less generalized intelligence. To put it in terms of the fallacy I read in an article linked on HN (paraphrased), 'The public assumes that if a machine can perform a task that humans can perform, the machine must be human-like, and therefore able to perform all tasks that humans can perform.' But in the same way that we use rendering tricks to go beyond-state-of-hardware-art in graphics rendering (by abusing hidden limitations), so do we often build ml systems. I believe the most optimistic point against me was the slide in this year's GTC keynote pointing to the "Cambrian explosion" in the diversity of ml approaches this time around.