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In 1982, when he was still a student at MIT, Danny Hillis cofounded Thinking Machines, one of the most famous failures in the history of computing. A hive of wayward and brilliant researchers, Thinking Machines tried to build the world’s first artificial intelligence. But if the company did not succeed in “building a machine that will be proud of us” (its corporate motto), its Connection Machine demonstrated the practicality of parallel processing, the foundation of modern supercomputing. Today, Danny Hillis is cochair of Applied Minds, a design and invention company, and he is building the Clock of the Long Now, a mechanical timepiece meant to last 10,000 years.

TR: Why is creating an artificial intelligence so difficult?

Hillis: We look to our own minds and watch our patterns of conscious thought, reasoning, planning, and making analogies, and we think, “That’s thinking.” Actually, it’s just the tip of a very deep iceberg. When early AI researchers began, they assumed that hard problems were things like playing chess and passing calculus exams. That stuff turned out to be easy. But the types of thinking that seemed effortless, like recognizing a face or noticing what is important in a story, turned out to be very, very hard.

TR: Why did Thinking Machines fail to create a thinking machine?

Hillis: Well, the glib answer is that we just didn’t have enough time. But enough time would have been decades, maybe lifetimes. It is a hard problem, probably many hard problems, and we don’t really know how to solve them. We still have no real scientific answer to “What is a mind?”

TR: The Connection Machine was an effective platform for supercomputing. Why didn’t Thinking Machines prosper as a supercomputing company?

Hillis: Supercomputing turned out to be a technology, not a business. My friend Nathan Myhrvold, who was running Microsoft Research at the time, once told me, “It is at least as hard to make software for a supercomputer as for a PC, but you only have a few thousand customers, and we have billions. Not only that, but each of those customers actually expects you to give them exactly what they need.”

TR: What were the successful commercial applications of the research at Thinking Machines?

Hillis: The commercial applications were mostly chip design, data mining, text search, cryptology, computational chemistry, computer graphics, financial optimization, seismic processing, and fluid flow modeling. Scientific applications like astronomy, climate modeling, or quantum chromodynamics were exciting when they helped get a result on the cover of Nature, but we never made money on them.

TR: What happened to the patents from Thinking Machines? More than anyone else, you are responsible for massive parallel processing. You get credit, but no payment. Who gets it, and why?

Hillis: Well, first of all, I should be clear that I am just one of many people who contributed to developing massively parallel computing. As for the patents, one of the consequences of Thinking Machines’ failure is that I lost any rights to the technologies. In retrospect, that turned out to be a blessing, because it saved me from spending the next decade of my life in court.

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