#Computer Vision

The story of the deep learning revolution from the AlexNet moment through the development of the Transformer, told as a narrative of rapid, cascading discovery in which one breakthrough enabled the next. The fastest scientific revolution in the history of AI — and what it actually means that machines can now do what only humans could do before.

The Stanford professor who assembled fourteen million labelled images when everyone told her the project was impossible, built the benchmark that made the deep learning revolution possible, went to Google as Chief AI Scientist, returned to academia to lead the movement for human-centered AI, and became one of the most important voices connecting technical capability to human values. The complete portrait of the creator of ImageNet.

How Fei-Fei Li assembled fourteen million labelled images over three years of painstaking work, created the most important dataset in the history of AI, and built the benchmark that made the deep learning revolution possible. The full story of ImageNet — the years of labour, the unconventional methodology, the competition that changed everything in 2012, and why one woman's conviction that data was as important as algorithms turned out to be exactly right.

The full story of the ImageNet competition in 2012 — the weeks of training on two gaming GPUs, the submission that shocked the computer vision world, the researchers who remember exactly where they were when the results came in, and why a single paper changed the trajectory of artificial intelligence. The starting gun of the modern AI era.