Videos
See AI2's full collection of videos on our YouTube channel.Viewing 11-20 of 243 videos
The University of Washington eScience Institute: a Home for Data-Intensive Discovery
September 27, 2023 | Sarah Stone, Executive Director, eScience Institute, University of WashingtonAbstract: The University of Washington eScience Institute, one of the nation's first university data science institutes, grew out of the Moore-Sloan Data Science Environment effort which was focused on identifying and tackling impediments to the broad and sustainable adoption of data-intensive discovery. With a…
Reliable Evaluation and High-Quality Data: Building Blocks for Helpful Question Answering Systems
September 26, 2023 | Ehsan KamallooAbstract: As models continue to rapidly evolve in complexity and scale, the status quo of how they are being evaluated and the quality of benchmarks has not significantly changed. This inertia leaves challenges in evaluation and data quality unaddressed, which results in the potential for erroneous conclusions…
Vision Without Labels
September 13, 2023 | Bharath Hariharan/Cornell UniversityBio: Bharath Hariharan is an assistant professor at Cornell University. He works on problems in computer vision and machine learning that defy the big data label. He did his PhD at University of California, Berkeley with Jitendra Malik. His work has been recognized with an NSF CAREER and a PAMI Young Researcher…
Skill-it! A Data-Driven Skills Framework for Understanding and Training Language Models
August 31, 2023 | Mayee Chen, PhD Student, Stanford UniversityBio: Mayee Chen is a PhD student in the Computer Science department at Stanford University advised by Professor Christopher Ré. She is interested in understanding and improving how models learn from data. Recently, she has focused on problems in data selection, data labeling, and data representations, especially…
From Compression to Convection: A Latent Variable Perspective
August 30, 2023 | Prof. Stephan Mandt/UC IrvineAbstract: Latent variable models have been an integral part of probabilistic machine learning, ranging from simple mixture models to variational autoencoders to powerful diffusion probabilistic models at the center of recent media attention. Perhaps less well-appreciated is the intimate connection between latent…
Avenging Polanyi's Revenge: Exploiting the Approximate Omniscience of LLMs in Planning without Deluding Yourself In the Process
August 21, 2023 | Subbarao KambhampatiAbstract: LLMs are on track to reverse what seemed like an inexorable shift of AI from explicit to tacit knowledge tasks. Trained as they are on everything ever written on the web, LLMs exhibit "approximate omniscience"--they can provide answers to all sorts of queries, with nary a guarantee. This could herald a…
Machine Learning in Climate Action
July 19, 2023 | David RolnickAbstract: Machine learning (ML) can be a useful tool in helping society reduce greenhouse gas emissions and adapt to a changing climate. In this talk, we will explore opportunities and challenges in ML for climate action, from optimizing electrical grids to monitoring crop yield, with an emphasis on how to…
Imaginative Vision Language Models
June 26, 2023 | Mohamed Elhoseiny, Assistant Professor/KAUSTBio: Mohamed Elhoseiny is an assistant professor of Computer Science at KAUST., He has become a senior member of IEEE since Fall 2021 and AAAI since Spring 2022. He is also a member of the international Summit community. Previously, he was a visiting Faculty at Stanford Computer Science department (2019-2020…
Do language models have coherent mental models of everyday things?
June 22, 2023 | Yuling GuWhen people think of everyday things like an egg, they typically have a mental image associated with it. This allows them to correctly judge, for example, that "the yolk surrounds the shell" is a false statement. Do language models similarly have a coherent picture of such everyday things? To investigate this, we…
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories
June 6, 2023 | Alex MallenPresentation of ACL 2023 main conference long paper "When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories". Alex Mallen*, Akari Asai*, Victor Zhong, Rajarshi Das, Daniel Khashabi, Hannaneh Hajishirzi Despite their impressive performance on diverse tasks, large…