Rafal and I discuss many of the ways back-propagation could be approximated in brains as detailed in his recent Trends in Cognitive Sciences review. We also cover how brains and machines learn, the free energy principle with its predictions and implications related to back-prop and understanding brains in general, and more.
Francisco and I discuss language and brains, his company cortical.io that uses his Semantic Folding Theory about how brains process language to perform natural language processing on text for many purposes, and the world of making and running companies like his own.
Jay and I discuss his rather ambitious project to implement mathematical reasoning in an AI agent. Plus his prominent role in and experience of the history of parallel distributed processing and neural network architectures taking over symbolic “good ol’ fashioned artificial intelligence” in the 1980s, and lots more.
Paul, Zac, and I discuss the philosophy of emergence, the neuroscience and philosophy of mind wandering and spontaneous thought, and how both of these may fit in the realm of artificial intelligence and consciousness. Plus, we talk about the role of philosophy in neuroscience and AI, and more.
Sam and I talk about the Free Energy Principle and how it relates to the Bayesian Brain Hypothesis, all under the realm of predictive coding. We also discuss some of the ingredients currently missing from deep learning approaches if we want machines to learn and think more like humans.