Eight-Month-Old Infants’ Social Evaluations of Agents Who Act on False Beliefs
Eight-Month-Old Infants’ Social Evaluations of Agents Who Act on False Beliefs. Proceedings of the Annual Meeting of the Cognitive Science Society 44, (2022).
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Artificial intelligence insights into hippocampal processing
Artificial intelligence insights into hippocampal processing. Frontiers in Computational Neuroscience 16, (2022).
Quest | CBMM Seminar Series: A Fruitful Reciprocity: The Neuroscience-AI Connection
May 18, 2023 - 2:00 pm Venue:
Singleton Auditorium (46-3002) Speaker/s:
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 universal principal for functional organization in the brain, based on recent advances in self-supervised learning, that explains both fine details as well as large-scale organizational structure in the vision system, and perhaps beyond. In the direction of "neuroscience guiding AI", I will present a novel cognitively-grounded computational theory of perception that generates robust new learning algorithms for real-world scene understanding. Taken together, these ideas illustrate how neural networks optimized to solve cognitively-informed tasks provide a unified framework for both understanding the brain and improving AI.
Bio: Dr. Yamins is a cognitive computational neuroscientist at Stanford University, an assistant professor of Psychology and Computer Science, a faculty scholar at the Wu Tsai Neurosciences Institute, and an affiliate of the Stanford Artificial Intelligence Laboratory. His research group focuses on reverse engineering the algorithms of the human brain to learn how our minds work and build more effective artificial intelligence systems. He is especially interested in how brain circuits for sensory information processing and decision-making arise by optimizing high-performing cortical algorithms for key behavioral tasks. He received his AB and PhD degrees from Harvard University, was a postdoctoral researcher at MIT, and has been a visiting researcher at Princeton University and Los Alamos National Laboratory. He is a recipient of an NSF Career Award, the James S. McDonnell Foundation award in Understanding Human Cognition, and the Sloan Research Fellowship. Additionally, he is a Simons Foundation Investigator.
This will be an in-person only event.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.eduQuest | CBMM Seminar Series: Photographic Image Priors in the Era of Machine Learning
May 9, 2023 - 4:00 pm Venue:
Singleton Auditorium (46-3002) Address:
43 Vassar St.
Cambridge, MA 02139
Speaker/s:
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 optimized using nonlinear regression to provide direct solutions for each specific task. As such, the prior probabilities are implicit, and intertwined with the tasks for which they are optimized. I'll describe properties of priors implicitly embedded in denoising networks, and describe methods for drawing samples from them. Extensions of these sampling methods enable the use of the implicit prior to solve any deterministic linear inverse problem, with no additional training, thus extending the power of a supervised learning for denoising to a much broader set of problems. The method relies on minimal assumptions, exhibits robust convergence over a wide range of parameter choices, and achieves state-of-the-art levels of unsupervised performance for deblurring, super-resolution, and compressive sensing. It also can be used to examine perceptual implications of physiological information processing.
Bio: Eero received a BA in Physics from Harvard (1984), a Certificate of Advanced study in Math(s) from University of Cambridge (1986), and a MS and PhD in Electrical Engineering and Computer Science from MIT (1988/1993). He was an assistant professor in the Computer and Information Science Department at the University of Pennsylvania from 1993 to 1996, and then moved to NYU as an assistant professor of Neural Science and Mathematics (later adding Psychology, and most recently, Data Science). Eero received an NSF CAREER award in 1996, an Alfred P. Sloan Research Fellowship in 1998, and became an Investigator of the Howard Hughes Medical Institute in 2000. He was elected a Fellow of the IEEE in 2008, and an associate member of the Canadian institute for Advanced Research in 2010. He has received two Outstanding Faculty awards from the NYU GSAS Graduate Student Council (2003/2011), two IEEE Best Journal Article awards (2009/2010) and a Sustained Impact Paper award (2016), an Emmy Award from the Academy of Television Arts and Sciences for a method of measuring the perceptual quality of images (2015), and the Golden Brain Award from the Minerva Foundation, for fundamental contributions to visual neuroscience (2017). His group studies the representation and analysis of visual images in biological and machine systems.
This will be an in-person only event.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.eduQuest | CBMM Seminar Series: Improving Deep Reinforcement Learning via Quality Diversity, Open-Ended, and AI-Generating Algorithms
May 2, 2023 - 4:00 pm Venue:
Singleton Auditorium (46-3002) Address:
43 Vassar St.
Cambridge, MA 02139
Speaker/s:
Jeff Clune, Associate Professor, Computer Science, University of British Columbia; Canada CIFAR AI Chair and Faculty Member, Vector Institute; Senior Research Advisor, DeepMind
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 continue performing their mission. I will next describe our QD-based Go-Explore algorithm, which dramatically improves the ability of deep reinforcement learning algorithms to solve previously unsolvable problems wherein reward signals are sparse, meaning that intelligent exploration is required. Go-Explore solved all unsolved Atari games, including Montezuma’s Revenge and Pitfall, considered by many to be a grand challenges of AI research. I will next motivate research into open-ended algorithms, which seek to innovate endlessly, and introduce our POET algorithm, which generates its own training challenges while learning to solve them, automatically creating a curricula for robots to learn an expanding set of diverse skills. Finally, I’ll argue that an alternate paradigm—AI-generating algorithms (AI-GAs)—may be the fastest path to accomplishing our field’s grandest ambition of creating general AI, and describe how QD, Open-Ended, and unsupervised pre-training algorithms (e.g. our recent work on video pre-training/VPT) will likely be essential ingredients of AI-GAs.
Bio: Jeff Clune is an Associate Professor of computer science at the University of British Columbia and a Canada CIFAR AI Chair at the Vector Institute. Jeff focuses on deep learning, including deep reinforcement learning. Previously he was a research manager at OpenAI, a Senior Research Manager and founding member of Uber AI Labs (formed after Uber acquired a startup he helped lead), the Harris Associate Professor in Computer Science at the University of Wyoming, and a Research Scientist at Cornell University. He received degrees from Michigan State University (PhD, master’s) and the University of Michigan (bachelor’s). More on Jeff’s research can be found at http://www.JeffClune.com or on Twitter (@jeffclune). Since 2015, he won the Presidential Early Career Award for Scientists and Engineers from the White House, had two papers in Nature and one in PNAS, won an NSF CAREER award, received Outstanding Paper of the Decade and Distinguished Young Investigator awards, and had best paper awards, oral presentations, and invited talks at the top machine learning conferences (NeurIPS, CVPR, ICLR, and ICML). His research is regularly covered in the press, including the New York Times, NPR, NBC, Wired, the BBC, the Economist, Science, Nature, National Geographic, the Atlantic, and the New Scientist.
This will be an in-person only event.
Organizer: Hector Penagos Organizer Email: cbmm-contact@mit.eduPages
