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Explore the fundamental principles underlying artificial intelligence systems in this 18-minute conference talk that demystifies AI by examining its mathematical, physical, and hardware foundations. Discover how AI models operate at their core through basic linear algebra, optimization techniques, and high-dimensional geometry rather than through mystical processes. Learn how vectors and matrices represent meaning in AI systems, understand how gradient descent enables neural networks to learn and improve, and grasp why high-dimensional geometry makes embeddings effective for processing information. Examine the physics-driven constraints of modern hardware and see how GPUs and tensor cores accelerate the mathematical computations that power AI. Gain insight into the clear connections between mathematics, physics, and computing that enable modern transformer models and large language models to perform tasks like code generation, image creation, and language understanding.
