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Recreating evolution inside a computer

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The complexity of human intelligence
W Daniel Hillis Scientist
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There's another kind of intelligence, which is... we call it maybe symbolic intelligence. That's the kind of story in your head. It's like words, symbols, mathematics, and that's the kind of intelligence where we can explain logical reasoning and we can think out different possibilities of the future and do planning and things like that. So by the time I got to MIT, people had sort of given up a bit on this neural intelligence, and they were working mostly on the symbolic intelligence. And the symbolic intelligence is kind of what it feels like to think, because it's the part of intelligence that's very visible to us. And so when I got there, people were doing these things that seemed to require a lot of intelligence, like getting a computer to play chess, which required planning and imagining what the other person would do and reasoning like that. Or planning how to solve a little puzzle that involved moving blocks around so that you could get to another block or doing an analogy test and an IQ test. So Evans wrote this program that, you know, A is to B as C is to what?

And so those kinds of symbolic tasks were what the AI lab was doing mostly when I arrived. And in fact they turned out to be surprisingly easy. We've made lots of progress very quickly, and those actually, although they were hard for us, because we tend to lose track of all the details of the different chess positions and things like that, they were actually pretty easy for computers. And so computers did very well at them, and in fact very quickly became better than humans at many of those kinds of things. And that's also, certainly, a part of our intelligence.

But in fact, I think what we've learned is there's many parts of our intelligence. For instance, there's a visual intelligence, where we can imagine how things fit together, and we sort of do that by creating almost an analogue of an image in our head. And we can imagine things fitting or the combinatorics of it. And we may use that in planning, and certainly use that in designing things. There are emotional intelligences of sort of... maybe those are built out of these neural network things and recognising little clues, or maybe they have some symbolic things, or maybe they're something completely different. We just don't know. And so I think one of the things we're realising is intelligence is multidimensional. And so just as I was arriving at the AI lab, people were beginning to understand that intelligence was kind of more complicated. And Marvin [Minsky] kind of led that effort. And he started thinking of intelligence as a kind of society of little intelligences that interoperated with each other. And so I was very lucky to actually be living with Marvin while he developed those ideas. And so I got to talk with him about it as they were emerging. So that's the easiest way to understand an idea, you see it in the simplest form, and then you see it get more complicated and more complicated, and it's easier to follow the complexity, the idea, when you see it build up like that. I think it would... it seems, now, if you read Society of Mind, it seems... I think it would be very difficult to digest, just from the start, but watching it grow up from these simple ideas, it made a lot of logical sense. And so now I think I very much see intelligence that way. I see it as a complicated set of intelligences interacting, and our visible intelligence, our conscious intelligence, is just a small part of it. I'd call it our storytelling intelligence. And I think that as we develop AI, we're going to develop more and more of these types of intelligence, but we're also going to develop an architecture of how they work together. And in fact, if you look at the very best state of the art of AI right now, for instance the program that beats humans at go that was recently developed, it's actually a combination of those two kinds of intelligences. It has a neural network that kind of recognises patterns on the go board, and sort of intuits that it should play there, but it also has a planner that sort of guesses what the other person's going to do and looks ahead and so on, and it uses those two things together. So I think that's more and more the way of the future of artificial intelligence.

W Daniel Hillis (b. 1956) is an American inventor, scientist, author and engineer. While doing his doctoral work at MIT under artificial intelligence pioneer, Marvin Minsky, he invented the concept of parallel computers, that is now the basis for most supercomputers. He also co-founded the famous parallel computing company, Thinking Machines, in 1983 which marked a new era in computing. In 1996, Hillis left MIT for California, where he spent time leading Disney’s Imagineers. He developed new technologies and business strategies for Disney's theme parks, television, motion pictures, Internet and consumer product businesses. More recently, Hillis co-founded an engineering and design company, Applied Minds, and several start-ups, among them Applied Proteomics in San Diego, MetaWeb Technologies (acquired by Google) in San Francisco, and his current passion, Applied Invention in Cambridge, MA, which 'partners with clients to create innovative products and services'. He holds over 100 US patents, covering parallel computers, disk arrays, forgery prevention methods, and various electronic and mechanical devices (including a 10,000-year mechanical clock), and has recently moved into working on problems in medicine. In recognition of his work Hillis has won many awards, including the Dan David Prize.

Listeners: George Dyson Christopher Sykes

Christopher Sykes is an independent documentary producer who has made a number of films about science and scientists for BBC TV, Channel Four, and PBS.

Tags: Society of Mind, Marvin Minsky

Duration: 5 minutes, 3 seconds

Date story recorded: October 2016

Date story went live: 05 July 2017