Stephen Wolfram Talks Bing Partnership, Software Strategy, and the Future of Knowledge Computing

enterprise Wolfram Alpha stuff. It has been recognized remarkably quickly by CEOs and CIOs: “What can you do with the terabytes of data we have at our company?” There’s not anything announced, but a whole bunch of companies we’re working with. The big challenge for us is ramping up. From the point of view of software engineering. We just delivered the first two Wolfram Alpha “appliances”—little pieces of our data center that can sit in someone else’s data center. From a business point of view, that’s a big growth direction for us.

X: What about from a technology standpoint? What’s next?

SW: In Wolfram Alpha, a lot of what it works out is “old science” based. There is an existing model for such and such economic process [for example]. These models are based on equations and mathematical kinds of things. But can we not only compute on the fly, can we also invent and create on the fly? That brings us into the world of searching programs and NKS. I simply don’t know if today’s computers are fast enough to pull this off in a useful way. We have created musical forms using this, and it has been picked up by serious composers. But there are lots of domains. Until you try it, you really don’t know. There’s a tremendous range of applications and lots of different business directions. My priority right now is trying to ramp up our business.

X: How mainstream will Wolfram Alpha become, compared with search engines like Google or Bing?

SW: These are complementary kinds of things. It’s like asking, how successful is science going to be in the world? It’s saying, what can you compute in the world? How could search engines become so important? When it becomes sufficiently easy to be a reference librarian hundreds of times a day.

I think the set of people for whom Wolfram Alpha is useful is very broad. It’s a sobering comment on the human condition what people are actually typing in [to search engines]. We don’t see the porn, the celebrity gossip, but we do see lots of stuff where people try to figure out, in a machine shop, what size of drill should they use to make a hole of a certain size. Or how far is it from here to there, or how does this compare to that.

I expect in time, the things we’re doing will become commonplace. My children are playing with Wolfram Alpha; it’s trivial to find out things. Gradually, they become well absorbed into the culture, and things become assumed. Even with NKS, in a different direction, I wrote in the preface, all these things that when the book comes out will seem shocking, in time will seem completely obvious and commonplace.

X: Shifting gears to big science: Are physicists at the Large Hadron Collider (LHC) using your computational techniques?

SW: There’s a lot of Mathematica usage. I’d expect LHC people would use [NKS] on their laptops for searching the space of models. It’s for the future of NKS to figure out if something bizarre is seen at LHC.

X: Ultimately, what do you think they will find? (A potential discovery would be the Higgs boson, the so-called “God” particle that gives matter its mass—and the only element of the Standard Model of particle physics yet to be observed.)

SW: I’ll be disappointed if the Higgs boson is discovered. I’ve never been very keen on that theory.

Author: Gregory T. Huang

Greg is a veteran journalist who has covered a wide range of science, technology, and business. As former editor in chief, he overaw daily news, features, and events across Xconomy's national network. Before joining Xconomy, he was a features editor at New Scientist magazine, where he edited and wrote articles on physics, technology, and neuroscience. Previously he was senior writer at Technology Review, where he reported on emerging technologies, R&D, and advances in computing, robotics, and applied physics. His writing has also appeared in Wired, Nature, and The Atlantic Monthly’s website. He was named a New York Times professional fellow in 2003. Greg is the co-author of Guanxi (Simon & Schuster, 2006), about Microsoft in China and the global competition for talent and technology. Before becoming a journalist, he did research at MIT’s Artificial Intelligence Lab. He has published 20 papers in scientific journals and conferences and spoken on innovation at Adobe, Amazon, eBay, Google, HP, Microsoft, Yahoo, and other organizations. He has a Master’s and Ph.D. in electrical engineering and computer science from MIT, and a B.S. in electrical engineering from the University of Illinois, Urbana-Champaign.