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How to create an intelligence


Programming an intelligence for solving complex problems
W Daniel Hillis Scientist
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One of the great things about doing this today is I don't have to start by building a parallel computer. There are plenty of people that building parallel computers. So I can actually just go use one that somebody else built. And they're building them for other reasons, but... so what was the big obstacle before, I can skip over now. So I could go straight into actually building this. Now the hardest thing would be to find a domain in which you could make steady progress from something very simple to something complicated. And so you could start... because I think that's also part of evolution, is it works out an idea in a simple case, and then it elaborates it to a complicated case. And so that lets you sort of build modules and put those modules together. So one of the things I'd do, of course, is I wouldn't try to start from nothing. We know a lot of the modules. We know a lot of good ideas about, for instance, neural network algorithms that work and things like that. So I wouldn't be shy at all about pre-loading it with the genetic material that we know is useful, but it could decide how much to use that, how much to modify it and so on. But we don't have to start from zero. So I would seed it with the knowledge that we already have about how to be intelligent or how to make good computations.

The problem that I would probably use is... I'll describe it to you in the abstract and then in the specific. But what I want to do is make an intelligence that would be actually be useful for us. Because I think it's not actually all that interesting to make another human intelligence, because we have better ways of doing that. And what would be much more interesting would be to have an intelligence that was intelligent at things that we're not very good at.

And in particular, there's something that we need to get good at that we're not good at, which is manipulating very complex systems that are too complicated for us to understand, like an economy, like an ecology. You know, we repeated have problems like our global climate and things like that, very complex systems that we have a certain ability to influence, but we don't really understand them. And so the question is how should we act in a way to keep them in a safe zone? So an economy is a great example. It's really... you know, economists have been reasonably successful lately at controlling the money supply in a way that we don't have an economic collapse, but they failed at it in the past. And they could easily fail in the future.

So that's a kind of problem we don't solve very well, because it doesn't lend itself to the simple storytelling that we do. We try to tell stories about it. So if you look... if the stock market goes up or down, then they can always come up with a story of why it did, but then it goes up the next day and they come up with a story why it went in the other direction, but they don't come up with the story until it already did what it did. So the story isn't good enough to be predictive.

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: Christopher Sykes George Dyson

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: intelligence, computer programming, modules, parallel computer

Duration: 3 minutes, 36 seconds

Date story recorded: October 2016

Date story went live: 05 July 2017