>I think this [AI beating humans at facial recognition] is scarier than most people give it credit for. It’s no big deal when computers beat humans at chess – h

>I think this [AI beating humans at facial recognition] is scarier than most people give it credit for. It’s no big deal when computers beat humans at chess – human brains haven’t been evolving specific chess modules. But face recognition is an adaptive skill localized in a specific brain area that underwent a lot of evolutionary work, and modern AI still beats it This could give us a new lower bound on the computation required for human-level AI. "Facial recognition," IIRC, is a well-localized cognitive function occupying some reasonably consistent fraction of a human's grey matter. The processing power required to recognize a stream of faces via ML techniques can be quantitized. Simply multiply the former ratio by the latter quantity and you have the processing power required to functionally emulate a human brain, no? Regarding his larger points: I don't have time to track down the reference, but there's fairly consistent stories coming out about how, for any sort of classification deep net, small adversarial changes to the input can cause arbitrary mis-classifications to arbitrary confidence. So even if you give your godlike AI a paperclip-recognizing deep net, the grounding problem could still bite you; your AI may learn to take over all economic production, or it may learn that if it pokes its camera just like this it suddenly looks like the universe is made of paperclips. Which matches the human case- since, certainly, we dissipate plenty of energy towards ends orthogonal to what we're supposedly optimizing. Even to the point of, ahem, directly subverting some of classifiers that are key to our reproductive cycle.