内容简介
The Deep Learning Revolution traces the unlikely ascent of neural networks from academic exile to the defining technology of the twenty-first century. Challenging narratives of sudden genius, this history argues that deep learning’s dominance resulted not from discovering how minds work, but from the collision of 1980s ideas with massive data and computing power. Spanning the wilderness years of AI winter through the 2012 ImageNet shock, AlphaGo’s public spectacle, and the Transformer era, the book examines how scale repeatedly triumphed over algorithmic elegance. It chronicles the structural consolidation of talent and compute while honestly presenting unresolved debates regarding ingenuity versus brute force. From Geoffrey Hinton’s marginal research to ChatGPT’s mainstream arrival, this strict nonfiction account reveals how an old idea finally met its moment through engineering and infrastructure rather than theoretical breakthroughs alone.