My world this week has been agog with the Watson match on Jeopardy!. The victory by a computer over the game's two greatest champions may well signal a new era, in much the same way as the rise of the web and the advent of search. I'd like to collect my thoughts before writing anything detailed about the match itself.

In anticipation of match, last week my Intelligent Systems students and I began to talk about some of the techniques that were likely being used by Watson under the hood. When I realized how little they had learned in their AI course about reasoning in the face of uncertainty, I dusted off an old lecture from my AI course, circa 2001. With a few tweaks and additions, it held up well. Among its topics were probability and Bayes' Law. In many ways, this material is more timely today than it was then.

Early in the week, as I began to think about what this match would mean for computing and for the world, I was reminded by Peter Norvig's The Machine Age that, in so many ways, the Jeopardy! extravaganza heralds a change already in progress. If you haven't read it yet, you should.

The shift from classical AI to the data-driven AI that underlies the advances Norvig lists happened while I was in graduate school. I saw glimpses of it at conferences on expert systems in finance and accounting, where the idea of mining reams of data seemed to promise new avenues for decision makers in business. The data might be audited financial statements of public corporations or, more tantalizing, collected from grocery store scanners. But I was embedded pretty deeply in a particular way of thinking about AI, and I missed the paradigm shift.

What is most ironic for me is that my own work, which involved writing programs that could construct legal arguments using both functional knowledge of a domain and case law, was most limited by the problem that we see addressed in systems like Google and Watson: the ability to work with the staggering volume of text that makes up our case law. Today's statistical techniques for processing language make extending my work and seeing how well it works in large domains possible in a way I could only dream of. And I never even dreamed of doing it myself, such was my interest in classical AI. (I just needed a programmer, right?)

There is no question that data-driven, statistical AI has proven to be our most successful way to build intelligent systems, particularly those of a certain scale. As an engineering approach, it has won. It may well turn out to be the best scientific approach to understanding intelligence as well, but... For an old classicist like me, there is something missing. Google is more idiot savant than bon vivant; more Rain Man than Man on the Street.

There was a telling scene in one of the many short documentary films about Watson in which the lead scientist on the project said something like, "People ask me if I know why Watson gave the answer it did. I don't how it got the answer right. I don't know how it got the answer wrong." His point was that Watson is so complex and so data-rich it can surprise us with its answers. This is true, of course, and an important revelation to many people who think that, because a computer can only do what we program it to do, it can never surprise or create.

But immediately I was thinking about another sense of that response. When Watson asks "What is Toronto? in a Final Jeopardy! on "U.S. Cities", I want to ask it, "What in the world were you thinking?" If it were a human player, it might be able to tell about its reasoning process. I'd be able to learn from what it did right or wrong. But if I ask most computer programs based on statistical computations over large data sets "Why?" I can't get much more than the ranked lists of candidates we saw at the bottom of the screen on Jeopardy!

There is still a romantic part of me that wants to understand what it means to think and reason at a conscious level. Perhaps my introspection misleads me with explanations constructed post hoc, but it sure seems like I am able to think at a level above my neurons firing. That feeling is especially strong when I perform more complex tasks, such as writing a program or struggling to understand a new idea.

So, when it comes to the science of AI, I still hope for more. Maybe our statistical systems will become complex and data-rich enough that they will be able to explain their reasoning in a meaningful way. It's also possible that my desire is nothing more than a form of chauvinism for my own species, that the desire to sit around and talk about stuff, including how and why we think the way we do, is a quaint feature peculiar to humans. I don't know the answer to this question, but I can't shake the deep belief in an architecture of thought and intelligent behavior that accounts for metacognition.

In any case, it was fun to watch Watson put on such an impressive show!

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If you are interested in such things, you might want to (re-)read Newell and Simon's 1975 Turing Award lecture, Computer Science as Empirical Inquiry: Symbols and Search. It's a classic of the golden era of AI.