Description
Date and Time: March 1st 3:00pm-5:00pm (JST)
Venue: Zoom webinar
Language: English
3:00pm – 4:00pm
Speaker: Aurelio Cortese (Advanced Telecommunication Research Institute International, ATR)
Title: Metacognitive resources for efficient learning
Abstract: A central issue in reinforcement learning (RL) is the ‘curse-of-dimensionality’, arising when the degrees-of-freedom of the problem are much larger than the number of available training samples. In the brain, higher cognitive functions such as metacognition or abstraction can provide a biological solution by generating low dimensional representations on which RL can operate. In this talk I will discuss our work in which we used neuroimaging and computational modeling to investigate the neural basis of efficient RL. We found that people can learn remarkably complex task structures non-consciously, but also that metacognition appears tightly coupled to this learning ability. Furthermore, when people use an explicit (conscius) policy to select relevant information, learning is accelerated by abstractions. The prefrontal part of the brain is differentially involved in separate aspects of learning: dorsolateral prefrontal cortex pairs with metacognitive processes, while ventromedial prefrontal cortex with valuation and abstraction. I will discuss the implications of these findings and focus on a new model framework toward metacognitive AI.
4:00pm – 5:00pm
Speaker: Emtiyaz Khan (RIKEN AIP)
Title: The Bayesian Learning Rule
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 mechanisms for knowledge transfer in 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.
