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Enabling Zero-Shot Generalization in AI4Science
Anima Anandkumar, Caltech
ABSTRACT: Many scientific applications heavily rely on the use of brute-force numerical methods performed on high-performance computing (HPC) infrastructure. Can artificial intelligence (AI) methods augment or even entirely replace these brute-force calculations to obtain significant speed-ups? Can we make groundbreaking new discoveries because of such speed-ups? However, such AI4science often requires zero-shot generalization to entirely new scenarios not seen during training. I will present exciting recent advances that build new foundations in AI that are applicable to a wide range of problems such as fluid dynamics and quantum chemistry. On the other side of the coin, the use of simulations to train AI models can be very effective in applications such as robotics and autonomous driving. Thus, we will see a convergence of AI, Simulations and HPC in the coming years.
Anima Anandkumar is a Bren Professor at Caltech and Director of ML Research at NVIDIA. She was previously a Principal Scientist at Amazon Web Services. She has received several honors such as Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from DoD, and Faculty Fellowships from Microsoft, Google, Facebook, and Adobe. She is part of the World Economic Forum's Expert Network. She is passionate about designing principled AI algorithms and applying them in interdisciplinary applications. Her research focus is on unsupervised AI, optimization, and tensor methods.
Institute for Applied Computational Science (IACS)
Jackie Strom
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