Enter Wolfram|Alpha: 3 - target public

Who will use Wolfram|Alpha? What is ideally its target audience? Certainly it's useful for statisticians and engineers, who deal with numeric data, can understand normalization and have experience with various graphical formats for data and results.

The big question here is: Will it be a big hit for general netizens? A major improvement on how ordinary people surf the interweb? In other words: will grandma use Wolfram Alpha? Should she?

I think not, or more specifically, not now. Lay people are happy with Google (at least I assume, citation needed), and although there's an overlap on the uses for both services, they are obviously not competing in their main focus, since they have different purposes (despite some sites painting a non-existent competition scenario).

This fictitious struggle with google shouldn't even be mentioned, since it's obviously a non-issue. It does not use similar technology, does not use similar data sets and does not tackle the same problem. It's actually hard to compare the two services, although in some cases, possible.

Wolfram Alpha is an answer engine, using Web Semantics technology for parsing and interpreting queries, and accesses a curated database. Google is like a big grep on steroids - it also parses the input query and do some normalization (notably accentuation and spell check), but what it does is basic text search on a huge database of text collected automatically over the web. It doesn't do something new (web search dates back to gopher), it innovates in the sorting algorithm (which as many other great ideas, it seems awfully simple once explained). In a nutshell, Google lets you find results over the web that best match your query, Wolfram Alpha tries to provide interpretation of quantitative answers to quantitative questions. It's all about number crunching.

Not all is sweet in the media hype, and I've seen at least two "bad" reviews in the wild (Ryan Tate for gawker and John Timmer for ArsTechnica) worth reading. Both give valid points, but I think they miss the key point: it is not supposed to be *the* answer machine, but the first big one (not even the first one, mind you - examples in wikipedia here, and here, and a nice pointer to a sciam article for more general approach to the topic).

It's not for me to prophetize that one day we will interact with the computer using solely human language, because of the excess ambiguities both in written and spoken language. Ordinary languages are not well suited for this, as Ryan Tate summarizes:

human language itself lacks the precision to enable what Wolfram is attempting

I expect WA to improve over the next years, both with acquisition of new data for the existing data sets and new unrelated data sets, and in the code linking data sets to each other and parsing human language.

I also expect other technologies to improve in parallel, and will not be surprised if other big answer engines appear, especially in the Open Source world. Standardization is key for these technologies, and open source developers often show an aptitude to crunch and implement open standards in drafts usable by tech-savvy people. But for now this is just wishful thinking.