>Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. Here's the important differe

>Human or superhuman performance in one task is not necessarily a stepping-stone towards near-human performance across most tasks. Here's the important difference about NNs. They are incredibly general. The same algorithms that can do object recognition can also do language tasks, learn to play chess or go, control a robot, etc. With only slightly modifications to the architecture and otherwise no domain information. That's a hugely different thing than brute force game playing programs. Not only could they not learn the rules of the game from no knowledge, they couldn't even play games with large search spaces like Go. They couldn't do anything other than play games with well defined rules. They are not general at all. Current neural networks have limits. But there is no reason to believe that those limits can't be broken as more progress is made. For example, the author references that neural networks overfit. They can't make predictions when they have little data. They need huge amounts of data to do well. But this is a problem that has already been solved to some extent. There has been a great deal of work into bayesian neural networks that avoid overfitting entirely. Including some recent papers on new methods to do them efficiently. There's the invention of dropout, which is believed to approximate bayesian methods, and is very good at avoiding overfitting. There are some tasks that neural network can't do, like episodic memory, and reasoning. And there has been recent work exploring these tasks. We are starting to see neural networks with external memory systems attached to them, or ways of learning to store memories. Neuroscientists have claimed to have made accurate models of the hippocampus. And deepmind said that was their next step. Reasoning is more complicated and no one knows exactly what is meant by it. But we are starting to see RNNs that can learn to do more complicated "thinking" tasks, like attention models, and neural turing machines, and RNNs that are taught to model programming languages and code.