InformationWeek: Microsoft chief technical officer Craig Mundie published an article this month in which he talked about positioning Microsoft Research where it hasn't been historically: on broad, societal problems outside of computer science. How can Microsoft Research technology or the intellect of those people be applied to these broad problems in science, medicine, or engineering?

Gates: Microsoft Research has always had a pretty broad set of activities. We're growing Microsoft research activities faster than the company as a whole because of the great results we've had. It's both growing the individual research centers we have, and then this year we added our fourth center, which is the one in India. Some of the people like [Eric] Horvitz and [David] Heckerman who came to Microsoft Research came--they're MDs, and they're machine learning experts. There's a technique, a Bayesian [statistical] technique, in which Heckerman or Horvitz are two of the leading people. When those guys came, we were always interested in applying machine learning to see what drugs work, and what lifestyles work, things like that. And they applied their things even to big data mining problems in business, where you say, "OK, which are my most profitable customers, or what promotion techniques are working well?" They've taken some of their techniques against clickstreams to figure out how you should design the Web, or how searches work. Search is an amazing example where we relied somewhat on an outside company, Inktomi, which Yahoo bought, then decided to build our own search effort essentially from scratch. Now, in a very short period of time, we will actually have more than matched the kind of relevance that Google can deliver. The role of Microsoft Research in that has been phenomenal.

InformationWeek: Are there areas outside of computer science where Microsoft Research intellect might be applied?

Gates: Yeah. But, OK, an important point about this--it's not so much about saying "Let's just work on some other problem." It's that software is needed. So all these genetic algorithms, like we're using for the AIDS vaccine [project], we invented those, those are software techniques. We're seeing fields of science that have so much data that without our ability to data mine and [manage] work flow and visualize, they can't make progress. The Sky Server example is sort of typical. In astronomy, historically, you wanted to be lucky enough to be gazing at the stars on a night when something interesting happened, and then you wrote a paper about quasars or something. Today, there are thousands of observation points around the world at different locations, at different wavelengths, different resolutions. There are a couple of satellites--lots of things up in the sky. And if you, as an astronomer, want to say, "Well, galaxies cluster like this, or these light sources work like this"--in order to test that hypothesis, there are thousands of databases in different formats that you have to pull data out of and look at and see if they're consistent with your hypothesis. What [Microsoft researcher] Jim [Gray] did is he got the astronomers together to see how you could use Web services to create essentially what we call Sky Server, one logical database. It doesn't mean all the data has to be copied into one place, but you can query it, and it goes out and pulls in the right information. That was a smashing success, but it was based on Jim's view that there's so much data in the sciences that without the kind of software management that we have, both in our products and in our research, that they won't be able to make the rapid advances that they should.