Ted Talks on IBM Watson & Bayes’ rule in evolution

Some of our most read articles have been on IBM Watson, including suggestions & possible alternatives. We’ve pushed IBM several times to come up with better demos for Watson in a business context.

This author went to a demonstration by IBM of Watson in August 2014, an witnessed an overglorified AltaVista demonstrated by an engineer. (AltaVista was the dominant Internet search engine prior to Google. Both Google and AltaVista can handle natural language question-like syntax in search queries, although users tend not to use since it just adds boilerplate text. Similar to AltaVista two decades ago, this Watson demonstration would some respond to questions by coming up with partially-related excerpts from various web pages from a small medical database on the web. It had difficultly understanding many simple questions, and the clips selected weren’t always the most appropriate responses. It didn’t look like this thing was a Jeopardy! champion. As the engineer was told, this is bleeding-edge technology; if you want to sell it to businesses, you need to make the case. Businesses don’t want to invest in an AltaVista rehash from twenty years ago. The Watson engineer explained he had been in a hurry and had just loaded a small Watson demo that would fit onto his laptop.

In practice, the secret to IBM Watson is the same one this author discovered a decade ago while doing statistical inference research in academia: distance metrics. At the time, many artificial intelligence researchers believed symbolic reasoning was adequate for artificial intelligence. (From our results, we knew that statistical inference would quickly blow symbolic reasoning out of the water. It’s essentially a consequence of Moore’s law: statistical inference requires a lot more computing power than symbolic reasoning. Symbolic reasoning was an appropriate choice in the days when computers were much slower.)

So it’s nice to see that IBM has put together some Ted Talks about Watson, as well as used Watson to build a system capable of searching Ted Talks. (The example they show brings up the Geoffrey West Ted Talk that we also used in one of our first articles on the Singularity.)

IBM’s published papers on IBM Watson talk about how it uses a large number of different distance metrics. It uses the same one we used in our paper a decade ago: Smith-Waterman, to compare questions to answers in Wikipedia (or other data sources). A closer match between question text and a paragraph in Wikipeida implies greater statistical likelihood you’ve found Wikipedia text with the right answer. (You can then dynamically adjust the size of the text so that optimally balances conciseness with statistical probability of correctness. This author’s paper on statistical inference a decade did precisely such a dynamic clustering, in a different context, to optimize the informational richness of the answer against it’s likelihood of being correct.)

Continue reading on the next page for more videos….

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