The day before the asteroid hits, people will still be writing thinkpieces called "But What About The Metaphorical Asteroid...Of Capitalism? Or Did I Just Blow Your Mind?"
Also, some of the stuff going on with machine vision now seems very different from just symbolic rules / concrete pattern matching. Ability to replicate vague artistic styles seems like good example of moving beyond the easily discretized. Obviously still far from human level.
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What neural nets do is approximate a mathematical functions: a mapping from a set of bits to another. I can see the artistic styles used in the experiment being expressed as a mathematical transform: apply some filters, turn a few knobs on your image editor.
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AI has not progressed constantly but rather by fits and starts. And each time the view of what was possible has been over-optimistic, on the "natural language solved by next summer" scale.
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What we see currently, I think: the low-hanging fruits of a new technique (actually an old technique finally made practical) and the higher-hanging fruits of painstakingly combining it with other well established AI building blocks (Alpha Go).
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Computing is, as a rule, not that surprising. Solving Go is certainly not outside the realm of the imaginable. Neural nets were conceived decades ago. But the vaguely-defined superintelligence, that is something else, more akin to making you 20 mph cars reach Mach 1.
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Many things that the FAQ describes it as capable off, I think can be achieved - even with today's ideas given enough work. But to think of techniques that would enable all of them, and go beyond, I haven't an inkling how that could be done. No one has.
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Everything in AI seems boring once it's been accomplished. People used to say that beating humans at chess would mean an AI had true creative thought; now even making beautiful art doesn't. When AIs are superintelligent, that'll look like "just math" too.
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Although there has definitely been a lot of hype about AI, a lot of the "natural language solved by next summer" style stories are urban legends. See https://www.openphilanthropy.org/focus/global-catastrophic-risks/potential-risks-advanced-artificial-intelligence/what-should-we-learn-past-ai-forecasts … .
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My impression from talking to AI researchers is people generally underestimated pace of AI progress over past 5-10 years. EG just before AlphaGo, people predicted would be decades before AIs could beat people at Go. Some aspects of vision/speech recognition equally impressive.
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