TR: How so?
ML: Imagine that you took all the newspapers and books, and you cut out all the words, and put them in a black bag: you would have a random process. You would expect nothing but gobbledygook. But if we pick a real page of text, it’s not random: if we read the word “dog,” then the probability that you will see the word “walk” increases. The reason is that the process has been biased by something: the idea of the dog that was in the mind of the author of the sentence. By using Bayesian inference, you can, in fact, infer the existence of the idea behind the word and all its relationships. The wonderful thing is you inherently get context. With Bayesian systems, you understand that just because Nicole Kidman is a star doesn’t mean she’s a cosmic gas ball.
TR: Why can’t Google’s algorithms search unstructured information?
ML: Just because you’ve been very good at keyword-based, popularity-ranked search doesn’t actually buy you much advantage processing unstructured information where you have to understand meaning.
TR: You have philosophical as well as practical objections to the curatorial approach to search embraced by Wolfram Alpha (see “Search Me,” July/August 2009).
ML: Those methods can work very well in limited contexts. But there are some big philosophical problems with the idea that information is absolute in meaning and that you can classify it just one way. If you come from the probabilistic world, the first thing you learn is that you have to deal with people’s worldviews. A very simple example: a computer might classify the same news story differently if it was working for a Palestinian newspaper or an Israeli one. But there’s nothing wrong with that. This notion that all information should have the same meaning is something that we’ve been taught by the idea of objective science since the Reformation. But for lots of the tasks that people need to do, it’s perfectly acceptable that meaning should be in the eye of the beholder.