Are you an AI expert, or are you planning to be? There are three fundamental challenges that will underlie the major AI evolutions over the next decade. These are the three areas where you NEED to understand the fundamentals – before AI moves so fast that you’ll never catch up. Let them guide your deep study for the year ahead. Check them out in this new YouTube post: Live free or die, my friend – AJ Maren Live free or…
He might be getting a Nobel prize some day. But – no one can understand him. You don’t believe me? Have a quick glance at Scott Alexander’s article, “God Help Us, Let’s Try To Understand Friston On Free Energy”. We’re referring, of course, to Karl Friston. I’ve spent the past three-and-a-half years studying Friston’s approach to free energy, which he treats as the guiding principle in the brain. He has extended the classic variational Bayes treatment (frontier-material in machine learning)…
Karl Friston, whom we all admire, has written some lovely papers that are both enticing and obscure. Cutting to the chase, what we really want to understand is this equation: In a Research Digest article, Peter Freed writes: … And today, Karl Friston is not explaining [the free energy principle] in a way that makes it usable to your average psychiatrist/psychotherapist on the street – which is frustrating. I am not alone in my confusion, and if you read the…
Variational Free Energy I spent some time trying to figure out the derivation for the variational free energy, as expressed in some of Friston’s papers (see citations below). While I made an intuitive justification, I just found this derivation (Kokkinos; see the reference and link below): Other discussions about variational free energy: Whereas maximum a posteriori methods optimize a point estimate of the parameters, in ensemble learning an ensemble is optimized, so that it approximates the entire posterior probability distribution…