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
Is general pattern recognition
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
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
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
In my view the science that we call
AI, maybe better called computational
intelligence, is the manifest destiny of
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.
It will indeed. Stay tuned.
Len Shustek ( email@example.com) is the
chairman of the Computer History Museum.