And here is a discussion of the difference between the two by Stephen Wolfram from his blog:
IBM’s basic approach has a long history, with a lineage in the field of information retrieval that is in many ways shared with search engines. The essential idea is to start with textual documents, and then to build a system to statistically match questions that are asked to answers that are represented in the documents. (The first step is to search for textual matches to a question—using thesaurus-like and other linguistic transformations. The harder work is then to take the list of potential answers, use a diversity of different methods to score them, and finally combine these scores to choose a top answer.)
Early versions of this approach go back nearly 50 years, to the first phase of artificial intelligence research. And incremental progress has been made—notably as tracked for the past 20 years in the annual TREC (Text Retrieval Conference) question answering competition. IBM’s Jeopardy system is very much in this tradition—though with more sophisticated systems engineering, and with special features aimed at the particular (complex) task of competing on Jeopardy.
Wolfram|Alpha is a completely different kind of thing—something much more radical, based on a quite different paradigm. The key point is that Wolfram|Alpha is not dealing with documents, or anything derived from them. Instead, it is dealing directly with raw, precise, computable knowledge. And what’s inside it is not statistical representations of text, but actual representations of knowledge.
The input to Wolfram|Alpha can be a question in natural language. But what Wolfram|Alpha does is to convert this natural language into a precise computable internal form. And then it takes this form, and uses its computable knowledge to compute an answer to the question.
There’s a lot of technology and new ideas that are required to make this work. And I must say that when I started out developing Wolfram|Alpha I wasn’t at all sure it was going to be possible. But after years of hard work—and some breakthroughs—I’m happy to say it’s turned out really well. And Wolfram|Alpha is now successfully answering millions of questions on the web and elsewhere about a huge variety of different topics every day.