Scale By The Bay 2020: Anima Anandkumar, Keynote: Next-generation frameworks for Large-scale AI

ai.bythebay.io Nov 2025, Oakland, full-stack AI conference Abstract: The deep-learning revolution has achieved impressive progress through the convergence of data, algorithms, and computing infrastructure. The availability of web-scale labeled data and parallelism of GPUs enabled us to harness the power of neural networks. However, for further progress, we cannot solely rely on bigger models. We need to reduce our dependence on labeled data, and design algorithms that can incorporate more structure and domain knowledge. Examples include tensors, graphs, physical laws, and simulations. I will describe efficient frameworks that enable developers to easily prototype such models, e.g. Tensorly to incorporate tensorized architectures, NVIDIA Isaac to incorporate physically valid simulations and NVIDIA RAPIDS for end-to-end data analytics. I will then lay out some outstanding problems in this area. Anima Anandkumar Caltech and NVIDIA Professor, Director of AI Anima Anandkumar holds dual positions in academia and industry. She is a Bren professor at Caltech CMS department and a director of machine learning research at NVIDIA. At NVIDIA, she is leading the research group that develops next-generation AI algorithms. At Caltech, she is the co-director of Dolcit and co-leads the AI4science initiative, along with Yisong Yue. She has spearheaded the development of tensor algorithms, first proposed in her seminal paper. They are central to effectively processing multidimensional and multimodal data, and for achieving massive parallelism in large-scale AI applications. Prof. Anandkumar…