#Neural Networks

The full narrative of AI's second great contraction — told from the inside, through the experiences of the researchers who built the boom and watched it collapse. The collapse of the expert systems industry, the dissolution of the LISP machine market, the reduction of DARPA funding, and the underground movement that kept the neural network approach alive through the cold.

The personal story of Hinton, LeCun, Bengio, and their collaborators during the years when neural network research was marginalised, underfunded, and dismissed. The decades of intellectual courage, the specific ideas that kept the research programme alive, and the slow accumulation of results that eventually made the field pay attention. How the most important research programme in modern AI survived by going underground.

The most important algorithm in modern AI was discovered at least three times before anyone paid attention. The full story of backpropagation — its multiple independent inventions, the decades it spent in the shadows of the symbolic AI winter, and the specific confluence of people, ideas, and timing that produced the 1986 paper that changed everything. How an algorithm refused to die, and what happened when the world finally listened.

The expert systems boom collapsed almost as quickly as it had risen. The LISP machine market imploded overnight. DARPA cut funding. And for the second time in AI's history, the field contracted, researchers left, and the most pessimistic observers began to wonder whether the project was fundamentally misguided. The full story of AI's second near-death experience — and the underground movement that kept the neural network flame alive through the cold.

He built the Perceptron — the first machine that could learn. The press called it a thinking machine. Marvin Minsky called it a dead end. History called Minsky right for fifteen years and then spent the next fifty years proving Rosenblatt correct. The most underrated figure in the history of AI, whose ideas now run the world.

He kept working on neural networks when everyone told him it was a dead end. He developed backpropagation, built the research group that produced AlexNet, and triggered the deep learning revolution. Then he quit Google to warn the world that he was afraid of what he had built. The full story of the most important AI researcher of his generation — and the journey from stubborn optimist to frightened sage.

The third Godfather of Deep Learning — his foundational contributions to recurrent networks, attention mechanisms, and generative models, his evolution from pure researcher to the field's most prominent voice for AI safety and ethics, and why the man who spent his career building increasingly powerful AI systems has become one of the most persuasive voices for ensuring those systems are safe.