In this episode of pplpod, we explore the complex world of Neural Networks, the architecture that bridges the gap between neuroscience and modern Artificial Intelligence. Join us as we break down how groups of interconnected units—whether they are biological cells or mathematical models—collaborate to perform complex tasks.

Topics covered in this episode:

• Biological vs. Artificial: We compare the physical structure of the brain, where neurons connect via synapses to send electrochemical signals, against the mathematical models used in machine learning to approximate nonlinear functions.
• How AI Learns: Discover the anatomy of an artificial network, from input layers to hidden layers, and learn how systems are "trained" by modifying weights through processes like backpropagation.
• Deep Learning & Generative AI: We explain what makes a neural network "deep" (having more than three layers) and how these structures power today’s facial recognition, predictive modeling, and Generative AI.
• The History of Connectionism: From Alexander Bain’s theories in 1873 and Donald Hebb’s Hebbian learning to the invention of the Perceptron in 1943, we trace the evolution of the field,.

Tune in to understand how the study of the human nervous system evolved into the software that powers our digital world.