von Neumann and others on computers came directly not from Turing Machines but through this bypath of neural networks. But simple neural networks didn’t do much. Frank Rosenblatt invented a learning device he called the perceptron, which was a one-layer neural network. In the late sixties, Marvin Minsky and Seymour Papert wrote a book titled Perceptrons, in which they basically proved that perceptrons couldn’t do anything interesting, which is correct. Perceptrons could only make linear distinctions between things. So the idea was more or less dropped. People said, “These guys have written a proof that neural networks can’t do anything interesting, therefore no neural networks can do anything interesting, so let’s forget about neural networks.” That attitude persisted for some time. Meanwhile, there were a couple of other approaches to AI. One was based on understanding, at a formal level, symbolically, how the world works; and the other was based on doing statistics and probabilistic kinds of things. With regard to symbolic AI, one of the test cases was, Can we teach a computer to do something like integrals? Can we teach a computer to do calculus? There were tasks like machine translation, which people thought would be a good example of what computers could do. The bottom line is that by the early seventies, that approach had crashed. Then there was a trend toward devices called expert systems, which arose in the late seventies and early eighties. The idea was to have a machine learn the rules that an expert uses and thereby figure out what to do. That petered out. After that, AI became little more than a crazy pursuit. I had been interested in how you make an AJI-like machine since I was a kid. I was interested particularly in how you take the knowledge we humans have accumulated in our civilization and automate answering questions on the basis of that knowledge. I thought about how you could do that symbolically, by building a system that could break down qu