Humans and animals have a natural ability to autonomously learn and quickly adapt to their surroundings. How can we design AI systems that do the same? In this talk, I will present Bayesian principles to bridge such gaps between humans and AI. I will show that a wide variety of machine-learning algorithms are instances of a single learning-rule called the Bayesian learning rule. The rule unravels a dual perspective yielding new adaptive mechanisms for machine-learning based AI systems. My hope is to convince the audience that Bayesian principles are indispensable for an AI that learns as efficiently as we do.

Reference: M.E. Khan, H. Rue, The Bayesian Learning Rule [arXiv] [Tweet]