The Princeton AI Club is excited to announce its next guest speaker – [ https://emtiyaz.github.io/ | Prof. Emtiyaz Khan ] from the RIKEN Center for AI Project! His talk will focus on: How can we design AI systems that imitate humans and animals to quickly adapt to their surroundings? If you want to know the answer to this intriguing question, sign-up for his virtual talk [ https://forms.gle/ghtmESmGr2Lr52na9 | here ] , and join us on June 27 th , at 10 am Eastern Time (ET) ! The full details are below. Speaker: Prof. Emtiyaz Khan Date: Monday 27th June 2022, 10:00 AM ET Title: The Bayesian Learning Rule for Adaptive AI Abstract : 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: The Bayesian Learning Rule, (Preprint) M.E. Khan, H. Rue [ [ https://arxiv.org/abs/2107.04562 | arXiv ] ] [ [ https://twitter.com/EmtiyazKhan/status/1414498922584711171?s=20 | Tweet ] ] Bio: Emtiyaz Khan (also known as Emti) is a team leader at the RIKEN center for Advanced Intelligence Project (AIP) in Tokyo where he leads the Approximate Bayesian Inference Team. He is also an external professor at the Okinawa Institute of Science and Technology (OIST). Previously, he was a postdoc and then a scientist at Ecole Polytechnique Fédérale de Lausanne (EPFL), where he also taught two large machine learning courses and received a teaching award. He finished his PhD in machine learning from University of British Columbia in 2012. The main goal of Emti’s research is to understand the principles of learning from data and use them to develop algorithms that can learn like living beings. For more than a decade, his work has focused on developing Bayesian methods that could lead to such fundamental principles. The approximate Bayesian inference team now continues to use these principles, as well as derive new ones, to solve real-world problems.