History of Neural Networks - Key Milestones

McCulloch and Pitts create the first mathematical model of a neural network, laying the foundation for artificial neurons with binary threshold logic.

Frank Rosenblatt develops the Perceptron, the first algorithm that could learn from data through supervised learning, inspiring dreams of thinking machines.

Minsky and Papert publish "Perceptrons," highlighting fundamental limitations like the XOR problem, leading to dramatically reduced funding and research interest.

Rumelhart, Hinton, and Williams popularize backpropagation, enabling training of multi-layer networks and solving the XOR problem that ended the golden age.

AlexNet wins ImageNet competition by a massive margin (15.3% vs 26.2% error), marking the beginning of the deep learning revolution and renewed industrial interest.

Large Language Models like GPT-4 and ChatGPT demonstrate unprecedented capabilities in natural language understanding, reasoning, and generation, reaching mainstream adoption.