bers extracted from Mycin the core of it and called it E-Mycin for Essential Mycin, or Empty Mycin. That rule-based software shell was widely distributed.

What is the meaning of all those experiments that we did from 1965 to 1968? The Knowledge-Is-Power Hypothesis, later called the Knowledge Principle, which was tested with dozens of projects. We came to the conclusion that for the “reasoning engine” of a problem solving program, we didn’t need much more than what Aristotle knew. You didn’t need a big logic machine. You need modus ponens, backward and forward chaining, and not much else in the way of inference. Knowing a lot is what counts. So we changed the name of our laboratory to the “Knowledge System Lab,” where we did experiments in many fields.

What other AI models did you use?

AI people use a variety of underlying
problem-solving frameworks, and
combine a lot of knowledge about the
domain with one of these frameworks.
These can either be forward-chain-
ing—sometimes called generate and
test—or they could be backward-chain-
ing, which say, for example, “here’s the
theorem I want to prove, and here’s
how I have to break it down into pieces
in order to prove it.”
I began classified research on de-
tecting quiet submarines in the ocean
by their sound spectrum. The problem
was that the enemy submarines were
very quiet, and the ocean is a very noisy
place. I tried the same hypothesis for-
mation framework that had worked
for DENDRAL, and it didn’t even come
close to working on this problem.

Fortunately Carnegie Mellon people—Reddy, Erman, Lesser and Hayes-Roth—had invented another framework they were using for understanding speech, the Blackboard Framework. It did not work well for them, but I picked it up and adapted it for our project. It worked beautifully. It used a great deal of knowledge at different “levels of abstraction.” It allowed flexible combination of top-down and bottom-up reasoning from data to be merged at those different levels. In Defense Department tests, the program did better than people.

But that research was classified as “secret.” How could ideas be pub-

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

lished from a military classified project? The Navy didn’t care about the blackboard framework; that was computer science. So we published the ideas in a paper on a kind of hypothetical: “how to find a koala in eucalyptus trees,” which was a non-cassified problem drawn from my personal experience in an Australian forest!

Talk about being an entrepreneur
as well as an academic.

There was a very large demand for the software generalization of the MYCIN medical diagnosis expert system “shell,” called EMYCIN. So a software company was born called Teknowledge, whose goal was to migrate EMYCIN into the commercial domain, make it industrial strength, sell it, and apply it. Teknowledge is still in existence.

Our Stanford MOLGEN project was the first project in which computer science methods were applied to what is now called computational molecular biology. Some MOLGEN software turned out to have a very broad applicability and so was the basis of the very first company in computational molecular biology, called Intellige-netics, later Intellicorp. They had lots of very sophisticated applications. During the dot-com bust they went bust, but they lasted, roughly speaking, 20 years.

In the 1980s you studied the Japanese
government’s major effort in AI.
The Japanese plan was very ambitious.
They organized a project to essentially
do knowledge-based AI, but in a style
different from the style we were accus-
tomed to in this country. For one thing,
they wanted to do it in the “I-am-not-
LISP style,” because the Japanese had
been faulted in the past for being imi-
tators. So they chose Prolog and tried
formal methods. And they included
parallel computing in their initiative.

How did you come to work
for the U.S. government?

In 1994 an amazing thing happened. The phone rings and it is Professor Sheila Widnall of the Department of Aeronautics and Astronautics of MIT. She said, “Do you know anyone who wants to be Chief Scientist of the Air Force? And by the way, if you are interested let me know.” She had been chosen to be Secretary of the Air Force, and she was looking for her Chief Scientist. I thought about it briefly, told her yes, and stayed for three years.

My job was to be a window on science for the Chief of Staff of the Air Force. I was the first person to be asked to be Chief Scientist who was not an Aero-Astro person, a weapons person, or from the physical sciences. There had not been any computer scientists before me.

I did two big things. One was con-sciousness-raising in the Air Force about software. The one big report I wrote, at the end of my term, was a report called, It’s a Software-First World. The Air Force had not realized that. They probably still do not think that. They think it is an airframe-based world.

The other was on software development. The military up to that point believed in, and could only imagine, a structured-programming top-down world. You set up requirements, you get a contractor to break down the requirements into blocks, another contractor breaks them down into mini-blocks, and down at the bottom there are some people writing the code. It takes years to do. When it all comes back up to the top, (a) it’s not right,