It’s now twenty years since the Deep Blue chess computer beat Garry Kasparov, one the greatest ever human players of the game.
Chess playing software has had two decades to get better. Until last week, the best computer player in the world (and therefore the best player period) was a programme called Stockfish 8. But that was before it met AlphaZero, which played 100 games against the reigning champion, winning or drawing all of them.
What’s truly remarkable about AlphaZero’s achievement is that it taught itself how to play chess to this world-beating standard in just four hours. To many people, this is deeply disconcerting – if an AI system can pull off such a feat, what can’t it do?
Almost everything, as it happens — but that doesn’t mean it isn’t time to get smart about Machine Learning (which is a better focus for our interest than the fuzzier concept of Artificial Intelligence).
An excellent place to start is a recent Harvard Business Review briefing by Erik Brynjolfsson and Andrew McAfee. The authors pack a lot into a few pages, but a few key points stand out.
First of all, just because a Machine Learning (ML) system is good at one thing it doesn’t mean that it is good at everything:
“…ML systems are trained to do specific tasks, and typically their knowledge does not generalize. The fallacy that a computer’s narrow understanding implies broader understanding is perhaps the biggest source of confusion, and exaggerated claims, about AI’s progress. We are far from machines that exhibit general intelligence across diverse domains.”
With that out of the way, we can get what is truly powerful about ML:
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