The F=ma of Artificial Intelligence [Backpropagation]

The podcast discusses Paul Werbos's discovery of backpropagation, a method for training multilayer neural networks, and its initial rejection by Marvin Minsky. It highlights backpropagation's eventual success in training AI models for various tasks, including driving cars and image classification, and its current widespread use in modern AI. The episode uses Meta's LLAMA 3.2 large language model as an example, visually demonstrating how backpropagation updates the model's parameters. It simplifies the concept by applying it to a smaller GPS model predicting city locations based on coordinates, explaining the math behind the algorithm, including softmax and cross-entropy loss. The podcast also includes a sponsorship message for Incogni and a personal appeal from the speaker for Patreon support to continue creating educational content on Welch Labs.