One of the harms of the modern version, I think (I’m not sure how widespread it is, but sure, a decent number of people believe the claim, whether or not they call it “Moravec’s paradox”) is that it creates complacency if you believe that “centaur” situations are the default expectation. They don’t seem to be. Sometimes they happen, but if AIs reach human level at all (which they usually don’t), then complete replacement or deskilling is what happens.
That is, nobody turned carriages into 1-horse+1-automobile-engine hybrids: you either kept using horses for various good reasons, or you used a car. For example, in chess, for all the energetic PR by Kasparov & Cowen, the ‘centaur’ era was short-lived and appears to be long over. In Go, it never existed (or the window was so brief as to have gone unmeasured): the best players at every point in team were either humans, or neural nets, and never humans+NNs. With protein-folding, AFAIK AlphaFold’s mistakes are on cases where the folding is genuinely extremely hard or unknown, and there are few or no cases where even a grad student-level expert can glance at it and instantly say what the structure obviously has to be. With tools like machine translation (or audio transcription), the most skilled labor doesn’t become centaur labor because of decent AI tooling, it just becomes a way to take the least-skilled translators and make them more productive by doing the easy work for them (and on the low end, substitutes entirely for human translators, like in e-commerce). And so on.
The regular Moravec’s paradox continues to hold true, I think. As awesome as DL is for suddenly giving perception capabilities incomparably better to what was available even a decade ago, there’s still a gap between computer vision and instantaneous human understanding of an image. It seems to be closing, but currently at too-high prices for many tasks like self-driving cars.
One of the harms of the modern version, I think (I’m not sure how widespread it is, but sure, a decent number of people believe the claim, whether or not they call it “Moravec’s paradox”) is that it creates complacency if you believe that “centaur” situations are the default expectation. They don’t seem to be. Sometimes they happen, but if AIs reach human level at all (which they usually don’t), then complete replacement or deskilling is what happens.
That is, nobody turned carriages into 1-horse+1-automobile-engine hybrids: you either kept using horses for various good reasons, or you used a car. For example, in chess, for all the energetic PR by Kasparov & Cowen, the ‘centaur’ era was short-lived and appears to be long over. In Go, it never existed (or the window was so brief as to have gone unmeasured): the best players at every point in team were either humans, or neural nets, and never humans+NNs. With protein-folding, AFAIK AlphaFold’s mistakes are on cases where the folding is genuinely extremely hard or unknown, and there are few or no cases where even a grad student-level expert can glance at it and instantly say what the structure obviously has to be. With tools like machine translation (or audio transcription), the most skilled labor doesn’t become centaur labor because of decent AI tooling, it just becomes a way to take the least-skilled translators and make them more productive by doing the easy work for them (and on the low end, substitutes entirely for human translators, like in e-commerce). And so on.
The regular Moravec’s paradox continues to hold true, I think. As awesome as DL is for suddenly giving perception capabilities incomparably better to what was available even a decade ago, there’s still a gap between computer vision and instantaneous human understanding of an image. It seems to be closing, but currently at too-high prices for many tasks like self-driving cars.