May 7, 2024 - 4:00 pm
Bruno Olshausen, UC Berkeley
Abstract: The goal of building machines that can perceive and act in the world as humans and other animals do has been a focus of AI research efforts for over half a century. Over this same period, neuroscience has sought to achieve a mechanistic understanding of the brain processes underlying...
April 18, 2024 - 11:00 am
Online Webinar
Amnon Shashua, Sachs Professor of Computer Science at the Hebrew University of Jerusalem, Founder & CEO,...
*Event time is 11:00pm EDT / 5:00pm CET - Registration required!*
Register for the event here.
Join us on a journey through the history of artificial intelligence (AI) from its early conceptual foundations to today’s Gen AI breakthroughs and tomorrow’s potential futures with:
Amnon Shashua, Sachs...
Register for the event here.
Join us on a journey through the history of artificial intelligence (AI) from its early conceptual foundations to today’s Gen AI breakthroughs and tomorrow’s potential futures with:
Amnon Shashua, Sachs...
April 2, 2024 - 4:00 pm
Singleton Auditorium (46-3002)
Melanie Mitchell, Santa Fe Institute
Abstract: I will survey a current, heated debate in the AI research community on whether large pre-trained language models can be said to "understand" language—and the physical and social situations language encodes—in any important sense. I will describe arguments that have been made for and...
March 26, 2024 - 4:00 pm
Singleton Auditorium (46-3002)
Giorgio Metta, Istituto Italiano di Tecnologia (IIT)
Abstract: The iCub is a humanoid robot designed to support research in embodied AI. At 104 cm tall, the iCub has the size of a five-year-old child. It can crawl on all fours, walk, and sit up to manipulate objects. Its hands have been designed to support sophisticate manipulation skills. The iCub...
March 12, 2024 - 4:00 pm
Singleton Auditorium (46-3002)
Tom Griffiths, Princeton University
Abstract: Recent rapid progress in the creation of artificial intelligence (AI) systems has been driven in large part by innovations in architectures and algorithms for developing large scale artificial neural networks. As a consequence, it’s natural to ask what role abstract principles of...
February 14, 2024 - 2:00 pm
Singleton Auditorium (46-3002)
Alexander Borst, Max-Planck-Institute for Biological Intelligence, Martinsried, Germany
*Due to the forecast weather event for Cambridge, MA on Tuesday February 13th, this talk will be held on Wednesday February 14th at 2:00PM*
Abstract: Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival...
Abstract: Detecting the direction of image motion is important for visual navigation, predator avoidance and prey capture, and thus essential for the survival...
February 6, 2024 - 4:00 pm
Singleton Auditorium (46-3002)
Yael Niv, Princeton University
Abstract: No two events are alike. But still, we learn, which means that we implicitly decide what events are similar enough that experience with one can inform us about what to do in another. Starting from early work by Sam Gershman, we have suggested that this relies on parsing of incoming...
December 5, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Daniel Wolpert, Columbia University
Abstract: Humans spend a lifetime learning, storing and refining a repertoire of motor memories appropriate for the multitude of tasks we perform. However, it is unknown what principle underlies the way our continuous stream of sensorimotor experience is segmented into separate memories and how we...
December 4, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Dylan Hadfield-Menell (CSAIL)
Abstract: For AI systems to be safe and effective, they need to be aligned with the goals and values of users, designers, and society. In this talk, I will discuss the challenges of AI alignment and go over research directions to develop safe AI systems. I'll begin with theoretical results that...
November 14, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Peter Dayan, Max Planck Institute for Biological Cybernetics
Abstract:
Much existing work in reinforcement learning involves environments that are either intentionally neutral, lacking a role for cooperation and competition, or intentionally simple, when agents need imagine nothing more than that they are playing versions of themselves or are happily ...
Much existing work in reinforcement learning involves environments that are either intentionally neutral, lacking a role for cooperation and competition, or intentionally simple, when agents need imagine nothing more than that they are playing versions of themselves or are happily ...
September 12, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Prof. Michael Hasselmo; Director, Center for Systems Neuroscience; Boston University
Abstract: Recordings of neurons in cortical structures in behaving rodents show responses to dimensions of space and time relevant to encoding and retrieval of spatiotemporal trajectories of behavior in episodic memory. This includes the coding of spatial location by grid cells in entorhinal cortex...
May 18, 2023 - 2:00 pm
Singleton Auditorium (46-3002)
Dan Yamins, Stanford University
Abstract: The emerging field of NeuroAI has leveraged techniques from artificial intelligence to model brain data. In this talk, I will show that the connection between neuroscience and AI can be fruitful in both directions. Towards "AI driving neuroscience", I will discuss a new candidate...
May 9, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Eero Simoncelli, Silver Professor; Professor of Neural Science, Mathematics, Data Science and Psychology, NYU
Abstract: Inference problems in machine or biological vision generally rely on knowledge of prior probabilities, such as spectral or sparsity models. In recent years, machine learning has provided dramatic improvements in most of these problems using artificial neural networks, which are typically...
May 2, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Jeff Clune, Associate Professor, Computer Science, University of British Columbia; Canada CIFAR AI Chair and...
Abstract: Quality Diversity (QD) algorithms are those that seek to produce a diverse set of high-performing solutions to problems. I will describe them and a number of their positive attributes. I will summarize how they enable robots, after being damaged, to adapt in 1-2 minutes in order to...
April 25, 2023 - 4:00 pm
Singleton Auditorium (46-3002)
Leila Wehbe, Carnegie Mellon University
Abstract: Aligning neural network representations with brain activity measurements is a promising approach for studying the brain. However, it is not always clear what the ability to predict brain activity from neural network representations entails. In this talk, I will describe a line of work...