Explores how the 550-million-year-old evolution of biological vision triggered an arms race and inspired modern AI.

Details Hubel and Wiesel's Nobel-winning discovery of hierarchical edge detection in the primary visual cortex.

Covers early breakthroughs like Fukushima's Neocognitron and LeCun's LeNet-5 for automated feature learning.

Discusses the impact of ImageNet on deep models like AlexNet and the efficiency of Transfer Learning.

Introduces Natural Language Processing and contrasts traditional one-hot encoding with deep representation learning.

Explains the power of dense word vectors and semantic vector arithmetic based on the distributional hypothesis.

Synthesizes the common core of vision and language: learning hierarchical structure directly from data.