Communications - June 2010

and (b) it’s not what you want anymore. They just didn’t know how to contract for cyclical development. Well, I think we were able to help them figure out how to do that.

What happened after your “tour
of duty” in Washington?

It was a rather unsettling experience to
come back to Stanford. After playing a
role on a big stage, all of a sudden you
come back and your colleagues ask,
“What are you going to teach next year?
Intro to AI?”
So at the beginning of 2000, I re-
tired. Since then I have been leading a
wonderful life doing whatever I please.
Now that I have a lot more time than
I had before, I’m getting geekier and
geekier. It feels like I’m 10 years old
again, getting back involved with de-
tails of computing.

The great thing about being retired is not that you work less hard, but that what you do is inner-directed. The world has so many things you want to know before you’re out of here that you have a lot to do.

Why is history important?

When I was younger, I was too busy for history and not cognizant of the importance of it. As I got older and began to see my own career unfolding, I began to realize the impact of the ideas of others on my ideas. I became more and more of a history buff.

That convinced me to get very serious about archives, including my own. If you’re interested in discoveries and the history of ideas, and how to manufacture ideas by computer, you’ve got to treat this historical material as fundamental data. How did people think? What alternatives were being considered? Why was the movement from one idea to another preposterous at one time and then accepted?

You are a big fan of using heuristics
not only for AI, but also for life. What
are some of your life heuristics?

˲ ˲ Pay a lot of attention to empirical data, because in empirical data one can discover regularities about the world.

˲ ˲ Meet a wonderful collaborator— for me it was Joshua Lederberg—and work with that collaborator on meaningful problems

˲ ˲ It takes a while to become really,
really good at something. Stick with it.
Focus. Persistence, not just on prob-
lems but on a whole research track, is
really worth it. Switching in the middle,
flitting around from problem to prob-
lem, isn’t such a great idea.

How far have we come in your quest to have computers think inductively? Our group, the Heuristic Programming Project, did path-breaking work in the large, unexplored wilderness of all the great scientific theories we could possibly have. But most of that beautiful wilderness today remains largely unexplored. Am I am happy with where we have gotten in induction research? Absolutely not, although I am proud of the few key steps we took that people will remember.

Is general pattern recognition the answer? I don’t believe there is a general pattern recognition problem. I believe that pattern recognition, like most of human reasoning, is domain specific. Cognitive acts are surrounded by knowledge of the domain, and that includes acts of inductive behavior. So I don’t really put much hope in “general anything” for AI. In that sense I have been very much aligned with Marvin Minsky’s view of a “society of mind.” I’m very much oriented toward a knowledge-based model of mind.

How should we give
computers knowledge?

I think the only way is the way human culture has gotten there. We transmit our knowledge via cultural artifacts called texts. It used to be manuscripts, then it was printed text, now it’s electronic text. We put our young people through a lot of reading to absorb the knowledge of our culture. You don’t go out and experience chemistry, you study chemistry.

We need to have a way for computers
to read books on chemistry and learn
chemistry. Or read books on physics
and learn physics. Or biology. Or what-
ever. We just don’t do that today. Our AI
programs are handcrafted and knowl-
edge engineered. We will be forever do-
ing that unless we can find out how to
build programs that read text, under-
stand text, and learn from text.

Why is AI important?

There are certain major mysteries that are magnificent open questions of the greatest import. Some of the things computer scientists study are not. If you’re studying the structure of databases—well, sorry to say, that’s not one of the big magnificent questions.

I’m talking about mysteries like the initiation and development of life. Equally mysterious is the emergence of intelligence. Stephen Hawking once asked, “Why does the universe even bother to exist?” You can ask the same question about intelligence. Why does intelligence even bother to exist?

We should keep our “eye on the prize.” Actually, two related prizes. One is that when we finish our job, whether it is 100 years from now or 200 years from now, we will have invented the ultra-intelligent computer. The other is that we will have a very complete model of how the human mind works. I don’t mean the human brain, I mean the mind: the symbolic processing system.

In my view the science that we call AI, maybe better called computational intelligence, is the manifest destiny of computer science.

For the people who will be out there years from now, the question will be: will we have fully explicated the theory of thinking in your lifetime? It would be very interesting to see what you people of a hundred years from now know about all of this.