Deep Learning Dawn
2006–2011 · 6 milestones
Geoffrey Hinton's breakthroughs reignited neural networks. GPU computing made deep networks trainable. The revolution was beginning.
Milestones
Deep Belief Networks: Hinton Revives Deep Learning
Geoffrey Hinton published 'A Fast Learning Algorithm for Deep Belief Nets,' showing that deep neural networks could be effectively trained by pre-training each layer as a restricted Boltzmann machine. This solved the long-standing problem of training networks with many layers.
The Netflix Prize
Netflix offered $1 million to anyone who could improve their recommendation algorithm by 10%. The competition attracted thousands of teams and ran for 3 years (won in 2009). It popularized collaborative filtering, matrix factorization, and ensemble methods.
ImageNet: The Dataset That Changed Everything
Fei-Fei Li and her team created ImageNet, a dataset of over 14 million hand-labeled images in 20,000+ categories. Starting in 2010, the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) became the benchmark for computer vision progress.
GPU Computing for Neural Networks
Researchers including Andrew Ng demonstrated that GPUs (graphics processing units) could train neural networks 10-70x faster than CPUs. NVIDIA's CUDA platform made GPU programming accessible. This hardware breakthrough removed the computational bottleneck that had held back deep learning.
IBM Watson Wins Jeopardy!
IBM's Watson system defeated the two greatest Jeopardy! champions, Ken Jennings and Brad Rutter, in a televised match. Watson used natural language processing, information retrieval, and machine learning to understand nuanced questions with puns and wordplay.
Apple Launches Siri
Apple introduced Siri as a built-in feature of the iPhone 4S — the first major voice assistant integrated into a mainstream consumer device. Users could ask questions, set reminders, and control their phone with natural speech.