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Q&A: Bill Gates On Supercomputing, Software In Science, And More

Bill Gates talks to InformationWeek about how work done at Microsoft Research can apply to science, medicine, and engineering; how more powerful desktop processors can improve user interfaces; and his evolving role at Microsoft.

Nowhere is that more true than in biology, life sciences, where you're just gathering so much data. The ability to connect these data sources together using our very state-of-the-art Web service and visualization approaches is pretty exciting. So it's not like we woke up one day and said, "Oh, let's work on some non-software problems." It's like if you noticed that engineers were using math, and you said to the mathematicians, "When did you decide to help these poor engineers?" The mathematician would say, "No way we did." The engineers figured out that the only way to describe the ideas of material strength, and crystal fracturing, and all of these very complicated things, was [through] very deep mathematical techniques. Well, now, mathematics alone isn't enough. You need software that deals with vast amounts of data.

But, you're right that there has been a thing where our outreach to these scientists in these other fields to say, "Hey, here's what we're doing," is stronger. This is particularly true in Europe where there is a lot of good science. Some pure computer science, but, say, less than in the United States. Software is becoming important to them, to their productivity. And when you look at a lot of these poor scientists who are writing low-level code, and transferring data by hand, re-entering the data--I just painted the positive vision future [in my speech]. If I had more time and if somebody was willing, I would have shown an example of how poor it is today that scientists have all this data, but can't really bring it together and get insights into it. They spend a lot of their time, not thinking deep scientific thoughts, but rather re-entering data and writing code that they shouldn't have to.

When we say "science," think about people designing cars, think about people designing planes, think about people thinking through the design of a Web site. It's not just new medicines, although that alone would justify all this work. It's not just modeling the environment, although that alone is a supercritical thing that we absolutely need to do, and advanced computer software will play a key role there. It's sort of the digitization of the world applied to science and business and commerce.

InformationWeek: Is there anything more formal that you're doing in terms of engaging these researchers with outside domains? Jim Gray's work seems like it came largely through his own initiative. Is that generally the case, or is there some way you can formalize these collaborations?

Gates: Well, there are two ways to look at this. One thing that Microsoft Research has done a brilliant job at is we have super good relationships with the top computer science departments around the world. If you go to the computer science departments and say, "OK, list the companies that you work with on a collaborative way," they'll uniformly list Microsoft and talk about the breadth of things we do. When we do our faculty summits, we get some of those people to come in. Every one of those departments is doing interdisciplinary work. It's a super important thing.

At MIT, they're really working on life sciences, and some of the other groups are doing a lot of robotics work. It's hard to say if you consider that all within computer science or whether it's multidisciplinary. [Microsoft researcher] Butler Lampson I think four years ago said one of the goals of Microsoft Research should be that nobody ever dies in a car wreck ever again. That sounds like, "Wow what is that?" That's brilliant software--that's vision software, that's acquisition software, sensor data. It's a very good goal, because software should be able to achieve that. The sensors will be super, super cheap, and the benefits will be very dramatic. So we've often taken goals that are kind of wild like that. All these robotics challenges pull you into the vision, modeling, [machine] learning type of things. It's through these university relationships where we say to the university, "OK, what is your problem, how could software work with that?" Then we have individuals who themselves are multidisciplinary.

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