HN user
http://boostmydata.tumblr.com/
The normal approximation is notoriously [1][2] bad when the binomial proportion you're trying to estimate is small and you don't have a big n. If one still wants a closed-form out-of-the-box formula (to avoid the exact resolution you suggested), one can use the Wilson approximation for instance.
[1] http://en.wikipedia.org/wiki/Binomial_proportion_confidence_...
[2] http://projecteuclid.org/download/pdf_1/euclid.ss/1009213286
As you said the PageRank is just one of the many features used by the ranking algorithm. I highly doubt that there is any patent issue here (it's "just" an eigenvector computation), and there is a ton of literature and practical evidence that you can build very good web-search ranking technologies without PageRank. It would seem very surprising to me that one can patent a feature used in an algorithm for commercial purpose...
The work of Zubrzycki is impressive. For having studied acoustics quite a bit, I'm very impressed by the fact that the sound manages to have both a very short attack an a very good sustain (thanks to the wheel). Controlling the vibration of a bowed string must have been extremely difficult... For the interested reader, there's a more detailed article here: http://tygodnik.onet.pl/zmysly/the-da-vinci-tone-in-english/...
Well if I were Google, I would make sure that the Incognito mode of Chrome prevents targeting except for adwords/adsense. Pretty easy to do when you control the browser...
There are some gross simplifications here: 8. Wavelets are not "recursive Fourier analysis". If you want to make it simple, it's more like a spatially localized Fourier extension. I agree that they are under-used though. 9. SVM: the "kernel trick" is a big deal because sometimes defining a vector-space of linear features out of non-vector objects won't give you a good performance, and you'd better define a dot-product. 10. Deep Nets are not only bigger neural networks. The buzz is about how you train them. It's about improving how you train a net in general.
I would say that the next bing thing is more: Realizing even more that Neural Nets is an optimization problem, and instead of using some heuristics, wait for some Russian mathematician to derive the right SGD schedule / batch solver for the problem. Then what the 1,000 of Google computers have been able to do for the cat face detector, we'll be able to do it on a smartphone chip. People have to realize that Deep Learning is a bit of a "brute force" solution for the moment (each node is a linear model). We need to derive smarter algorithms.