Beyond Mamba AI: Vector Fields and Fluid Dynamics in Transformer Models - Session 6

Learn about advanced AI concepts in this 57-minute video that explores the intersection of fluid dynamics and transformer models. Delve into a groundbreaking perspective that views Transformers as flow maps, similar to fluid dynamics systems. Explore how this novel approach transcends traditional MAMBA S6 State Space Models by addressing their limitations in self-attention and in-context learning. Discover the evolution from discrete to continuous models in AI, examining how data flows through neural networks in ways analogous to fluid motion. Study the application of theoretical physics and mathematical frameworks to Transformer architectures, including concepts from fluid state equations and dynamical system flow maps. Examine how these principles enhance the understanding and optimization of AI models, supported by research from MIT and CNRS scholars as presented in their arxiv pre-print "Mathematical Perspective on Transformers." Master advanced mathematical constructs that blend fluid dynamics, theoretical physics, and probability measures to gain cutting-edge insights into AI model development.