mathematics, for example: Whitehead and Russell’s Principia Mathematica in 1910 was the biggest showoff effort. There were previous attempts by Gottlob Frege and Giuseppe Peano that were a little more modest in their presentation. Ultimately, they were wrong in what they thought they should formalize: They thought they should formalize some process of mathematical proof, which turns out not to be what most people care about.
With regard to a modern analog of the Turing Test, it’s an interesting question. There’s still the conversational bot, which is Turing’s idea. That one hasn’t been solved yet. It will be solved—the only question is, What is the application for which it is solved? For along time I would ask, “Why should we care?”—because I thought the principal application would be customer service, which wasn’t particularly high on my list. But customer service, where you’ re trying to interface, 1s just where you need this conversational language.
One big difference between Turing’s time and ours is the method of communicating with computers. In his time, you typed something into the machine and it typed back a response. In today’s world, it responds with a screen—as for instance, when you want to buy a movie ticket. How is a transaction with a machine different from a transaction with a human? The main answer ts that there’s a visual display. It asks you something, and you press a button, and you can see the result immediately. For example, in Wolfram|Alpha, when it’s used inside Sin, if there’s a short answer, Siri will tell you the short answer. But what most people want is the visual display, showing the infographic of this or that. This is a nonhuman form of communication that turns out to be richer than the traditional spoken, or typed, human communication. In most human- to-human communication, we’re stuck with pure language, whereas in computer-to- human communication we have this much higher bandwidth channel—of visual communication.
Many of the most powerful applications of the Turing Test fall away now that we have this additional communication channel. For example, here’s one we’re pursuing right now. It’s a bot that communicates about writing programs: You say, “I want to write a program. I want it to do this.” The bot will say, “I’ve written this piece of program. This is what it does. Is this what you want?” Blah-blah-blah. It’s a back-and- forth bot. Devising such systems is an interesting problem, because they have to have a model of a human if they’re trying to explain something to you. They have to know what the human is confused about.
What has long been difficult for me to understand is, What’s the point of a conventional Turing Test? What’s the motivation? As a toy, one could make a little chat bot that people could chat with. That will be the next thing. The current round of deep learning—particularly, recurrent neural networks—is making pretty good models of human speech and human writing. We can type in, say, “How are you feeling today?” and it knows most of the time what sort of response to give. But I want to figure out whether I can automate responding to my email. I know the answer is “No.” A good Turing Test, for me, will be when a bot can answer most of my email. That’s a tough test. It would have to learn those answers from the humans the email is connected to. I might be a little bit ahead of the game, because I’ve been collecting data on myself for about twenty-five years. I have every piece of email for twenty-five years, every keystroke for twenty. I should be able to train an avatar, an AI, that will do what I can do—perhaps better than I could.
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