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and you turn that special capability to a human purpose, to something you want technology to do. In the case of magnetic materials, there are plenty of ways to do that. In terms of programs, it’s the same story. There are all kinds of programs out there, even tiny programs that do complicated things. Could we entrain them for some useful human purpose?

And how do you get AIs to execute your goals? One answer is to just talk to them, in the natural language of human utterances. It works pretty well when you’re talking to Siri. But when you want to say something longer and more complicated, it doesn’t work well. You need a computer language that can represent sophisticated concepts in a way that can be progressively built up and isn’t possible in natural language. What my company spent a lot of time doing was building a knowledge-based language that incorporates the knowledge of the world directly into the language. The traditional approach to creating a computer language is to make a language that represents operations that computers intrinsically know how to do: allocating memory, setting values of variables, iterating things, changing program counters, and so on. Fundamentally, you’re telling computers to do things in your own terms. My approach was to make a language that panders not to the computers but to the humans, to take whatever a human thinks of and convert it into some form that the computer can understand. Could we encapsulate the knowledge we’d accumulated, both in science and in data collection, into a language we could use to communicate with computers? That’s the big achievement of my last thirty years or so—being able to do that.

Back in the 1960s, people would say things like, “When we can do such-and-such, we’ll know we have AI. When we can do an integral from a calculus course, we’ll know we have AI. When we can have a conversation with a computer and make it seem human...,” et cetera. The difficulty was, “Well, gosh, the computer just doesn’t know enough about the world.” You’d ask the computer what day of the week it was, and it might be able to answer that. You’d ask it who the President was, and it probably couldn’t tell you. At that point, you’d know you were talking to a computer and not a person. But now when it comes to these Turing Tests, people who’ve tried connecting, for example, Wolfram|Alpha to their Turing Test bots find that the bots lose every time. Because all you have to do is start asking the machine sophisticated questions and it will answer them! No human can do that. By the time you’ve asked it a few disparate questions, there will be no human who knows all those things, yet the system will know them. In that sense, we’ve already achieved good AI, at that level.

Then there are certain kinds of tasks easy for humans but traditionally very hard for machines. The standard one is visual object identification: What is this object? Humans can recognize it and give some simple description of it, but a computer was just hopeless at that. A couple of years ago, though, we brought out a little image-identification system, and many other companies have done something similar—ours happens to be somewhat better than the rest. You show it an image, and for about ten thousand kinds of things, it will tell you what it is. It’s fun to show it an abstract painting and see what it says. But it does a pretty good job.

It works using the same neural-network technology that McCulloch and Pitts imagined in 1943 and lots of us worked on in the early eighties. Back in the 1980s, people successfully did OCR—optical character recognition. They took the twenty-six letters of the alphabet and said, “OK, is that an A? Is that a B? Is that a C?” and so on.

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