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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 a long 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, is 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 is 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 Siri, 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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