Artificial intelligence pioneer says we need to start over
Brief description of the issue for non-experts:<p>Supervised learning<p>You can judge the output of your network against ground truth. You say that's a cat? Nope, it's a dog! And then slightly adjust your network so it's less likely to give that wrong answer in the future. How exactly you adjust the network is what backpropagation describes (in combination with something called a learning rate).<p>Unsupervised learning<p>You need to learn without someone telling you what the answer is. Most of the time for biological intelligence there isn't an oracle describing the truth at every moment of life to judge actions/decisions against. If you don't have someone telling you you've made a mistake, how can you know when to adjust your network? And if you don't know what the truth is, exactly how to adjust the network becomes tricky.<p>Somehow biological brains work without that oracle, and there are lots of ideas about how it does that. But right now for artificial intelligence none of those ideas has been shown to work so amazingly well that it has taken off like backprop has in the supervised learning world.<p>Hinton wants to find that amazing algorithm.