Edward Feigenbaum

"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 consciousness-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, 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."

"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,"

"So at the beginning of 2000, I retired. 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 details 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."

"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"

"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."

"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."

"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 whatever. We just don’t do that today. Our AI programs are handcrafted and knowledge engineered. We will be forever doing that unless we can find out how to build programs that read text, understand text, and learn from text."

"Reading from text in general is a hard problem, because it involves all of common sense knowledge. But reading from text in structured domans I don’t think is as hard. It is a critical problem that needs to be solved."

"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 data-bases—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."