That’s a very strong claim, though, given that we’re explicitly talking about hypothetical future systems as opposed to “existing neural network systems”. Afte

That’s a very strong claim, though, given that we’re explicitly talking about hypothetical future systems as opposed to “existing neural network systems”. After all, taking the definition of “machine learning” to the extreme, it could include a system based on simulating an entire human brain. So the statement would necessarily imply that either: (a) brain simulation is fundamentally impossible for some reason (insert your favorite argument for why this should be the case), or (b) epsilon adversarial examples exist that fool humans (we just haven’t found any). Personally, I think (b) is more likely than (a), but it seems much more likely that neither is true: that the human visual system is resilient at least to some extent (...for some epsilon), and could hypothetically be simulated by a machine. It would stand to reason that building a resilient system doesn’t require simulating a human, either, or nearly as much computational power as that would take (given that emulation is inherently very inefficient, and that visual processing only takes up a part of the human brain and seems to be done pretty well by animals with simpler brains). But it might require much more computational power than we have today… On the other hand, if you limit computational power to around today’s level, and consider only potential architectural changes, the statement seems more plausible to me.