Stephen Wolfram, of Mathematica fame, previews his Wolfram|Alpha QA engine, as something new under the QA sun -- not wholly and IR solution and not a translate-everything-to-logic-or-RDF solution. What is it? We'll be able to try it out in May, 2009:
Wolfram Blog : Wolfram|Alpha Is Coming!.
Fifty years ago, when computers were young, people assumed that they’d quickly be able to handle all these kinds of things. And that one would be able to ask a computer any factual question, and have it compute the answer.
But it didn’t work out that way. Computers have been able to do many remarkable and unexpected things. But not that. I’d always thought, though, that eventually it should be possible. And a few years ago, I realized that I was finally in a position to try to do it.
I had two crucial ingredients: Mathematica and NKS [Wolfram's New Kind of Science, his 2002 study of computational systems such as cellular automata] . With Mathematica, I had a symbolic language to represent anything—as well as the algorithmic power to do any kind of computation. And with NKS, I had a paradigm for understanding how all sorts of complexity could arise from simple rules.
But what about all the actual knowledge that we as humans have accumulated? A lot of it is now on the web—in billions of pages of text. And with search engines, we can very efficiently search for specific terms and phrases in that text. But we can’t compute from that. And in effect, we can only answer questions that have been literally asked before. We can look things up, but we can’t figure anything new out. So how can we deal with that?
Well, some people have thought the way forward must be to somehow automatically understand the natural language that exists on the web. Perhaps getting the web semantically tagged to make that easier.
But armed with Mathematica and NKS I realized there’s another way: explicitly implement methods and models, as algorithms, and explicitly curate all data so that it is immediately computable. It’s not easy to do this. Every different kind of method and model—and data—has its own special features and character. But with a mixture of Mathematica and NKS automation, and a lot of human experts, I’m happy to say that we’ve gotten a very long way.
