The Future of Computing

22 March 2009

Interview with

Chris Bishop

microprocessorBen - It's hard to think of any aspect of our lives that isn't dominated by computers, and that shows little sign of stopping.  Joining us now is Professor Chris Bishop, from Microsoft Research, to tell us about one way that computers may change in the future - they might become more intelligent.  But first, just what do we mean by Computer Science? 

Chris B - What I mean by computer science is really the set of underlying ideas; the mathematics of concepts that makes digital technology possible. I think it's also worth saying what computer science isn't.  To my mind, computer science is not really about how to use computers.  Something that we teach our children in school is ICT skills. We teach them how to use computers, how to use software. That's a very important set of skills. That itself is not computer science. It's not the top level, normal use of computers. The next level down is really how to modify computers, how to get them to do new things. That's really about programming and software development. That's something which I think would be great if we taught even more of that in schools. We've barely scratched the surface of that in school curriculum. It would be lovely to teach more of them. Underneath that again is this sort of third level, the foundation of the subject. That's computer science. That's the set of mathematics, the concepts that make it all possible. While computer technology evolves incredibly rapidly and next year you've a whole new set of gadgets and the year after it's another set of gadgets again. The concepts of computer science are much more fundamental. Some of them have been around for half a century and they evolve much more slowly. I think it would be just wonderful if we could show children: in particular a lot more of these basic long-term concepts.

Ben - It's really an understanding of the algorithms, the maths that lie at the very heart - completely regardless of the technology and what you're then doing with it. You need to understand how the numbers work in order to build up these layers that eventually get to a word processor or a shoot 'em up.

Chris B - That's exactly right. A famous computer scientist called Dijkstra once said that, "Computer science is no more about computers than astronomy is about telescopes." The ideas of computer science apply whether you're talking about silicon or whether you've built your computers out of gear wheels or whether you've built it out of DNA or chemistry in some way. These are very general concepts about the limits of computation and the capabilities of computation in the general sense.

Ben - As we've said you're the Chief Research Scientist at Microsoft Research. What is it that you do there? We think of Microsoft as being the people that make Windows that a lot of our computers run on. My own laptop runs on Windows. What do you do?

Chris B - Microsoft research which is about 1% of the whole in terms of people is the basic research components of Microsoft. You can think of us as, in many ways, functioning like academics. Our researchers have a complete freedom to choose the research they do, to publish it where and when they choose and to collaborate fully with academics and so on. At the same time they may also develop ideas that might have commercial relevance. Some of those ideas might be fed into the rest of Microsoft. Now, in Cambridge (which is the European Lab of Microsoft Research) we have about 120 scientists covering a very broad range of fields from many of the mainstream areas of computer science like security and systems and networking and programming language theory and so on. We're increasingly diverse. We have people in machine learning like myself. We have people in vision researchers, even ecologists and biologists these days. One of my main roles as chief research scientist is to promote cross-disciplinary interaction. There's a big danger in this very heterogeneous environment that everybody stays in their own little stove pipe and they don't really talk to each other. I try to find mechanisms to mix people up and get them to understand the ideas and problems in each other's fields. I hope that will promote interaction between the different disciplines because I'm a great believer that a lot of the really new ideas actually emerge at the intersection of different disciplines. Genuinely new ideas in science are actually quite rare but very hard to find. It's slightly less hard to take a good idea from someone else's field and import it into your own field where it may be quite novel and have quite an impact.

Ben - We had quite a good example of this on the show a few weeks ago with Alyssa Goodman who was using the software that's been developed for understanding and visualising MRI scans to look at deep space. It really is true this sort of multi-disciplinarianism is the way science is moving. Going back to something you said you were working on yourself: machine learning. The idea of intelligent machines has been around for a while but what do we really mean? How can we get a machine to learn?

Chris B - The idea of intelligence machines goes right back to the dawn of computing and certainly Alan Turing, the famous Cambridge mathematician who was one of the founders of modern computer science, was very interested in the idea of building intelligent machines. It's obviously enormously difficult problem. Technology in many respects has evolved incredibly quickly. You now have machines that are extremely good at multiplying numbers together, far better than humans. There are other tasks that people and animals are very good at that we tend to characterise as intelligence. Things such as visual recognition: simply looking at a visual scene and understanding what's going on in different objects, understanding what's happening has proved to be immensely difficult to build those capabilities into a computer. Over the years we've been making good progress. There's a long way to go before we have a machine that has genuine intelligence. In fact, although perhaps 30 years ago people sought to build machines that would be  intelligent in a high-level sense (machines you could have  a conversation with about philosophy or culture) we've sort of abandoned that. It's really just way too difficult. We've taken a bottom-up approach, more of a signal-processing approach. We say let's look at something of a simpler task that machines are not yet good at, let's say people aren't good at, and an example I gave already was recognising objects in the visual world. It's an immensely difficult problem. It appears to be trivial. I can turn around. I can say: well look there's a glass, there's a table, there's a chair, there's a microphone in front of me and so on. I can recognise these objects. Actually what your eye sees is a pattern of light and dark and colour but is miniscule-y variable. No two chairs look the same, the pattern of light in the retina varies as the chair is moved closer or farther away or rotated as light is scattered off other objects. It changes the colour of the light, changes the colour of the chair as this huge variability. Our brain, in think in some way we don't understand yet, mysteriously takes away all that variability and says, no that's still a chair. If we could build a similar capability into machines it would be very powerful technologically. It's an outstanding problem that we're just beginning to make good progress with. It's one of the fast-moving frontiers of machine intelligence at the moment.

Ben - It sounds exciting. It is true that we see a chair but there are so many different varieties that if we had to programme this into a computer it would be an enormous database of things they need to look at. That was Edinburgh University's Professor Chris Bishop.  So in the future our computers may more intelligent, but that probably wouldn't stop people getting distracted by games of minesweeper! 

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