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I agree completely. People are too ready to leap at results that say, "Well, machines can never do X, and I can do X, therefore human-level AI is impossible."

by deong 10y ago

I agree completely. People are too ready to leap at results that say, "Well, machines can never do X, and I can do X, therefore human-level AI is impossible." In general, my counter to that would be "you probably can't do X either". It's just that you can do it well enough. You don't encounter the weird cases where your ability fails. But if that's allowed as an argument, then of course machines are more powerful too. I can absolutely solve the halting problem if "solve" can be interpreted as "almost always come up with the correct answer for the kinds of problems people encounter in the real world".

YeGoblynQueenne 10y ago
That's basically learning (and more generally, operating) in restricted settings. I agree that much progress can be made in that way, but there are problems that are really, really hard to solve and that both humans and computers are crap at. Then again there are entire classes of problems that humans are very good at, but computers are crap at and the other way around. Also, don't forget what Terry Pratchett said, about how thousand-to-one odds crop up one time out of ten. The problems with AI in the past started when people tried to attempt to solve anything beyond the trivial, toy-problems they could demonstrate were solvable "in the lab". Like SHRDLU, which solved blocks world, but could not really scale. The same goes with machine learning today: some problems are solvable in restricted settings, or in a lab environment, under controlled conditions and with safe assumptions etc, but once you try to do the same thing in the wild... You can even look at the big success of Deep nets for examples of that: image processing and speech recognition. Whereas it's possible to get really good results in both fields, image processing still relies very much on having good quality images to work with, and speech recognition is really crap if you have a bit of an accent, or a speech impediment etc. (at least that's what I gather- these are not my specialties).

by deong 10y ago

YeGoblynQueenne 10y ago

That's basically learning (and more generally, operating) in restricted settings. I agree that much progress can be made in that way, but there are problems that are really, really hard to solve and that both humans and computers are crap at. Then again there are entire classes of problems that humans are very good at, but computers are crap at and the other way around. Also, don't forget what Terry Pratchett said, about how thousand-to-one odds crop up one time out of ten. The problems with AI in the past started when people tried to attempt to solve anything beyond the trivial, toy-problems they could demonstrate were solvable "in the lab". Like SHRDLU, which solved blocks world, but could not really scale. The same goes with machine learning today: some problems are solvable in restricted settings, or in a lab environment, under controlled conditions and with safe assumptions etc, but once you try to do the same thing in the wild... You can even look at the big success of Deep nets for examples of that: image processing and speech recognition. Whereas it's possible to get really good results in both fields, image processing still relies very much on having good quality images to work with, and speech recognition is really crap if you have a bit of an accent, or a speech impediment etc. (at least that's what I gather- these are not my specialties).