IEEE Spectrum: We read about Deep Learning in the news a lot these days. What’s your least favorite definition of the term that you see in these stories?
Yann LeCun: My least favorite description is, “It works just like the brain.” I don’t like people saying this because, while Deep Learning gets an inspiration from biology, it’s very, very far from what the brain actually does. And describing it like the brain gives a bit of the aura of magic to it, which is dangerous. It leads to hype; people claim things that are not true. AI has gone through a number of AI winters because people claimed things they couldn’t deliver.
https://spectrum.ieee.org/automaton/artificial-intelligence/...
Also, like I say above, it doesn't matter how many images a human sees- what matters is how many she needs to see before learnign to recognise a thing. The example of an unchanging, two-dimensional caricature of a horse is evidence enough that, even if we do see billions of "images" as you say (I'm not sure it makes sense to speak of "images" int the sense you use it) we don't need to see all those billions of them before we learn what things look like.
Just a ball park figure for how many “pictures” they have seen. Probably off by a few OOM, but point holds.
I think there is a very good reason to think machine learning and human learning are connected. First, I’d say it’s largely true the people who have made the most impact in the former have all studied the way the mind works (as far as science allows), and tried to emulate that in machinery. Second, experimentally the phenomena observed are increasingly similar (deep dream, for example).