Successes in Speech, Machine Translation, medical diagnosis and data mining successes have all descended from sound theoretical research in statistics and infor

Successes in Speech, Machine Translation, medical diagnosis and data mining successes have all descended from sound theoretical research in statistics and information theory. For instance, Machine Translation (as we know it today) was originally inspired by the "noisy channel" for speech recognition that is based on Bayes Rule. In speech recognition, the probability of some words given some input waveform is proportional to the probability of someone saying those words times the probability of hearing some wave form given those rules. The same model led to statistical machine translation: if I speak to you in French, what I'm really doing is speaking to you in English, but the "channel" is so noisy that it comes out sounding like French. Today, the parallels are less clear (for instance, German and English have substantially different word order, and in speech you don't usually have reordering in the channel--though there's actually some cool new research in MT to bring it closer in line with modern speech processing!). Yes, there are huge amounts of specialization and hacks for each of these fields, but they are (mostly) based around core good statistical ideas. In fact, some people are worried that the AI community is so focused on log-linear models and the like that we're in some kind of local minimum, and that we're unlikely to work our way out any time soon. That said, Kurzweil is still wildly deluded, as you suggest.