Anima Anandkumar on Using Generative AI to Tackle Global Challenges - Ep. 203

Sep 11, 2023

Anima Anandkumar, Bren Professor at Caltech and senior director of AI research at NVIDIA, discusses the potential of generative AI in scientific research, including predicting dangerous coronavirus variants and extreme weather events. She explains the challenges of working with genomes and the importance of fine-tuning models. They also explore the concept of model cards for transparency and automated testing for bias in AI models. Anandkumar provides advice for aspiring individuals in technology and science fields, emphasizing continuous learning and asking the right questions.

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Introduction

00:00 • 6min

Using Generative AI Models for Languages: English and Genomes

05:56 • 3min

Leveraging AI for Global Scale Issues

08:37 • 22min

Exploring the Concept of Model Cards and the Need for Automated Testing

30:28 • 2min

The Impact of AI on Education and Advice for Young People in Technology and Science

32:27 • 7min

Generative AI-based models can not only learn and understand natural languages — they can learn the very language of nature itself, presenting new possibilities for scientific research. Anima Anandkumar, Bren Professor at Caltech and senior director of AI research at NVIDIA, was recently invited to speak at the President’s Council of Advisors on Science and Technology. At the talk, Anandkumar says that generative AI was described as “an inflection point in our lives,” with discussions swirling around how to “harness it to benefit society and humanity through scientific applications.” On the latest episode of NVIDIA’s AI Podcast, host Noah Kravitz spoke with Anandkumar on generative AI’s potential to make splashes in the scientific community. It can, for example, be fed DNA, RNA, viral and bacterial data to craft a model that understands the language of genomes. That model can help predict dangerous coronavirus variants to accelerate drug and vaccine research. Generative AI can also predict extreme weather events like hurricanes or heat waves. Even with an AI boost, trying to predict natural events is challenging because of the sheer number of variables and unknowns. However, Anandkumar explains that it’s not just a matter of upsizing language models or adding compute power — it’s also about fine-tuning and setting the right parameters. “Those are the aspects we’re working on at NVIDIA and Caltech, in collaboration with many other organizations, to say, ‘How do we capture the multitude of scales present in the natural world?’” she said. “With the limited data we have, can we hope to extrapolate to finer scales? Can we hope to embed the right constraints and come up with physically valid predictions that make a big impact?” Anandkumar adds that to ensure AI models are responsibly and safely used, existing laws must be strengthened to prevent dangerous downstream applications. She also talks about the AI boom, which is transforming the role of humans across industries, and problems yet to be solved. “This is the research advice I give to everyone: the most important thing is the question, not the answer,” she said.