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.Por qué falló en su intento de desarrollar una "máquina de pensar":
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?"Sobre la aplicación de computación a la biología:
TR: You were ahead of your time in applying computation to immunology, genetics, and neurobiology. Today, computation is ubiquitous in biology. What will this mean?
Hillis: I am excited that computational biology is coming into its own. It feels like the field of computing did in 1970. Everything seems possible, and the only constraint is our imagination. There are still so many basic, simple questions that are unanswered: "How are memories encoded?" "How does the immune system have a sense of ‘self'?"
I am especially interested in what will come of computational models of evolution, although I have to admit that the field seems a bit stuck right now. Most current models of evolution reduce it to a very weak kind of search algorithm, but I have always felt that there is something more to it than that. It is not that the biologists are wrong about the mechanisms, but rather that the models are much simpler than the biology. It may be that the interaction of evolution and development is the key, or behavior and environment, or something like that.