Anil Ananthaswamy

Anil Ananthaswamy, a science writer and former New Scientist editor, explores the intricate mathematics behind artificial intelligence. He dives into topics like neural networks, the challenges of training models on planetary motion, and the significance of algorithms like backpropagation. The conversation highlights the elegance of the perceptron proof and discusses the complexities of high-dimensional spaces and transformer architectures. Anil also reflects on the implications of AI's mathematical foundations for scientific discovery.

Anil Ananthaswamy, award-winning science writer and author of Why Machines Learn, explores the math behind modern AI. He discusses why learning works despite overparameterization, how high-dimensional spaces shape behavior, the risks of data-driven lock-in, limits of current LLMs like hallucinations, and future paths such as continual learning and neuromorphic alternatives.