Last time we discussed and motivated the need for a modern theory of causal inference. We developed some of the basic principles necessary to develop this theory, but we have...
Perhaps we could use uncertainty estimation to detect where the model may be wrong and then correct for these potential errors without having to collect much more data. This uncertainty...
One of the inherent problems an agent faces in some arbitrary environment is how to decide whether to explore and discover more of the world around it, or to rather...
The active inference framework proposes agents act to maximise the evidence for a biased generative model, whereas in reinforcement learning the agent seeks to maximise the expected discounted cumulative reward....
But what is reinforcement learning? The field of reinforcement learning is at the crossroads between optimal control, animal psychology, artificial intelligence and game theory and has seen a surge of...
Hello! Today we’ll be discussing the mathematics of predictive processing - a modern theory for how much of the processing of information is done in the brain. This is also...
