Past Events

The presentation will begin with short introductions to the question from the artificial and natural intelligence perspectives. Then it will dig a bit into three research results: a Bayesian approach to 3D perception, a method for efficient planning under the resulting uncertainty over world models, and a study of the role of spatial cognition in human behavior in a VR object-search task. The presentation will conclude with a discussion. 

Mitchell will survey a current, heated debate in the AI research community on whether large pre-trained language models "understand" language—and the physical and social situations language encodes—in any important sense. She will describe arguments for and against such understanding and, more generally, will discuss methods to evaluate understanding and intelligence in AI systems.

The iCub is a humanoid robot designed to support research in embodied AI. At 104 cm tall, the iCub is the size of a five-year-old child, and can crawl on all fours, walk, and sit up. Its hands support sophisticated manipulation skills. The iCub is distributed as Open Source following the GPL licenses. More than 50 robots have been built so far which are available in laboratories across Europe, US, Korea, Singapore, and Japan.

Large language models are fundamental building blocks in many modern AI systems—for language processing, as well as robotics, computer vision, software engineering, and more. For models trained on text to be useful for general AI and scientific applications, they must understand not just the structure of language, but the structure of the world; moreover, their language, reasoning, and world knowledge capabilities must align with those in humans.

Tom Griffiths develops mathematical models of higher-level cognition to understand the formal principles underlying our ability to solve everyday computational problems. His current focus on inductive problems — probabilistic reasoning, learning causal relationships, acquiring and using language, and inferring the structure of categories — is addressed by comparing human behavior to optimal computational solutions.

Special Research Talk, Arash Afraz, Ph.D. Dr. Afraz received his MD from Tehran University of Medical Sciences in 2003 and his PhD in Psychology from Harvard University in 2009. He joined NIMH at NIH as a principal investigator in 2017 to lead the unit on Neurons, Circuits and Behavior.

Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival of all animals that have eyes. However, the direction of motion is not explicitly represented at the level of the photoreceptors: it rather needs to be computed by subsequent neural circuits.

The Niv lab focuses on the neural and computational processes underlying reinforcement learning and decision-making, studying the ongoing day-to-day processes by which animals and humans learn from trial and error. Of particular interest is how attention and memory processes interact with reinforcement learning.