I have not posted to this blog for a long time partly because I lost my focus on the Numenta program within the Machine Understanding arena, and partly because I have been distracted by the need to earn money, and even the political silly season. On a good note my friend Dan Hamburg got elected to be Mendocino County Supervisor, and the government of California can now approve budgets with a majority vote. Which does not mean they will start doing budgets on time, or balance them, or run the state well; but they could.
I found, slogging through Judea Pearl's Probabilistic Reasoning in Intelligent Systems
and Terrence Fine's Probability and Probabilistic Reasoning for Electrical Engineering
, that my mind kept fuzzing up. Maybe I am slowing down in my old age, but the real problem was that my last formal training in probability was when I was 19, and interpreting clinical trial p values is guestimate work. I had to regress to a simpler text than what I used in college, and I can recommend Finite Mathematics with Applications
by A. W. Goodman and J.S. Ratti for introductions to simple probability, conditional probability, Bayes' Theorem, and even Markov Chains that were simple enough for me to feel I really understood easy examples and the concepts themselves.
But my wanderings have been further afield than that. I continue to be fascinated with tensors, and got a lot out of Introduction to Tensor Calculus, Relativity and Cosmology
by D. F. Lawdon. Again, I never got to tensors in college (I ended up a Political Science major), and thinking I was brighter than I really was (brightness is mainly a function of prepartion, I now know), started off with mathematical treatments that were too abstract for me to do more than pretend to follow.
I have even got stuck on Maxwell's equations for electromagnetism. Now we all should admit that if we read broadly in math and science we don't take the time to really understand everything; we trust our fellows to have done their homework before a set of facts or an equation is presented in a paper or textbook. We may like to feel we agree with quantum physics, but who except for professional physicists have the time to really look at the data and the math in detail? I have always assumed that Maxwell's equations are correct, and that if I needed to I could look up the definitions of curl, etc., and do the math. But that is not the same thing as the deep understanding one gets from working in electromagnetics on a regular basis.
I have wandered farther afield than that, to Lie groups and Galois theory, which may have nothing to do with machine understanding. Nevertheless, I wander. And I keep coming back to what is known about the structure of the cortex, of the actual tangles of nerve cells themselves, and in particular to the way pyramidal cells span multiple layers of the cortex with their intricate axons and dendrites. How do you create a math that represents such a tangle? Skipping that, you can do funtional units as Numenta does, or you can try the AI tradition with its tradition trying to get the end results without understanding the details of how neurons actually get stuff done.
Right now I have little paid work going on, so I may be writing in the blog more often. If paid work becomes available, there will be more delays.