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This episode is sponsored by SysAid.

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In this episode of the Eye on AI podcast, join us as we delve into the cutting-edge world of AI and high-performance computing with Brian Spears, Director of the AI Innovation Incubator at Lawrence Livermore National Laboratory.

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Brian shares his experience in driving AI into national security science and managing the nation\u2019s nuclear stockpile. With a PhD in mechanical engineering, his expertise spans nonlinear dynamical systems and high-dimensional topology, making him uniquely positioned to lead groundbreaking projects in fusion ignition and AI integration.

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Discover how Lawrence Livermore National Laboratory achieved fusion ignition for the first time, harnessing the power of AI to elevate simulation models with precise experimental data. Brian explains how this approach is paving the way for commercially viable fusion energy and advancing stockpile stewardship.

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Explore the relationship between high-performance computing and AI as Brian discusses the Department of Energy's FAST initiative. Brian also touches on the importance of public-private partnerships, ethical considerations in AI development, and the future potential of quantum computing.

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Tune in to understand how the US is leading the global race in AI and computing technology, setting the stage for unprecedented advancements in science and security.

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Don't forget to like, subscribe, and hit the notification bell for more insights into the technologies driving the AI revolution.

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Craig Smith Twitter: https://twitter.com/craigss

Eye on A.I. Twitter: https://twitter.com/EyeOn_AI

(00:00) Preview

(01:52) Introducing Brian Spears

(03:14) Fusion Ignition and AI Integration

(06:00) Predictive Models and Experimental Data

(08:05) Challenges in Fusion Energy

(12:03) Inertial Confinement Fusion Explained

(14:12) Future of Fusion Energy

(17:15) US Leadership in AI and Computing

(19:22) Global AI Competition

(22:33) High-Performance Computing Infrastructure

(26:08) DOE\u2019s FAST Initiative

(28:55) Transformational AI Applications

(34:01) AI Ethics and Safety

(36:24) Scientific Models and Large Language Models

(39:30) 3D Molecular Modeling

(42:47) National AI Research Resource (NAR)

(45:18) Recruitment Challenges in AI

(48:09) Comparison with China

(52:30) DOE\u2019s Role and Future Vision

(54:19) Quantum Computing