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by maest
2176 days ago
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For contrast, take this Hofstadter quote: > This, then, is the trillion-dollar question: Will the approach undergirding AI today—an approach that borrows little from the mind, that’s grounded instead in big data and big engineering—get us to where we want to go? How do you make a search engine that understands if you don’t know how you understand? Perhaps, as Russell and Norvig politely acknowledge in the last chapter of their textbook, in taking its practical turn, AI has become too much like the man who tries to get to the moon by climbing a tree: “One can report steady progress, all the way to the top of the tree.” My take is that there is something intelectually unsatisfying about solving a problem by simply throwing more computational power at it, instead of trying to understand it better. Imagine in a parallel universe where computational power is extremely cheap. In this universe, people solve integrals exclusively by numerical integrations so there is no incentive to develop any of the Analysis theory we currently have. I would expect that to be a net negative in the long run as theories like Gen Relativity would be almost impossible to develop without the current mathematical apparatus. |
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To play devil's advocate, I think retort to your comment about "intellectually satisfying" methods is "yeah, but, they work". And in any case, "intellectually satisfying" doesn't have a formal definition in computer science or AI so it can't very well be a goal, as such.
My own concern is exactly what Russel & Norvig seem to say in Hofstadter's comment: by spending all our resources on clmbing the tallest trees to get to the moon, we're falling behind from our goal, of ever getting to the moon. That's even more so if the goal is to use AI to understand our own mind, rather than to beat a bunch of benchmarks.