In my early discussions and presentations regarding Wolfram|Alpha I often used Computational Journalism as the initial non-engineering use case.   Most folks weren’t quite sure what I meant initially by Computational Journalism until I explained how, as a toe in the water step, one could easily and automatically enhance articles and features with generated knowledge and visuals.   It seems I won’t need to explain in great depth the utility and inevitability of computational journalism because enough conference summaries, op-eds and journalists are starting to popularize the concept.

Here’s a great piece from PBS.

A new set of tools would help reporters find patterns in otherwise unstructured or unsearchable information. For instance, the Obama administration posted letters from dozens of interest groups providing advice on issues, but the letters were not searchable. A text-extraction tool would allow reporters to feed PDF documents into a Web service and return a version that could be indexed and searched. The software might also make it easy to tag documents with metadata such as people’s names, places and dates. Another idea is to improve automatic transcription software for audio and video files, often available (but not transcribed) for government meetings and many court hearings.

Wired UK goes a bit deeper into some specific companies and projects.

And here’s a nice presentation by Kurt Cagle that gives a good overview of some of the computational foundational technology out there.

I don’t think it’s unreasonable to think that the vast majority of daily news will be completely machine generated and machine broadcast.  Journalists will be increasingly involved in bigger, deeper features and defining the computational logic to generate the news stream.