タイムライン

A logician and a neurophysiologist show networks of simplified neurons can compute logic — the founding abstraction.

'Computing Machinery and Intelligence' reframes 'can machines think?' as a testable question.

McCarthy, Minsky, Shannon and colleagues coin 'artificial intelligence' and set its agenda.

The first trainable neural network hardware; the Navy predicts thinking machines. The hype-and-winter cycle begins.

A proof of single-layer limits helps freeze neural-network funding for a decade — the first AI winter's seed.

Rumelhart, Hinton and Williams show multi-layer networks can learn internal representations.

Convolutional networks deployed on US mail — deep learning's first industrial success.

Brute-force search takes chess; the win is narrow but the symbolism is not.

Fei-Fei Li's 14-million-image dataset bets that data is the bottleneck.

Two consumer GPUs halve the error rate. Deep learning's era — and NVIDIA's second act — begins.

Goodfellow's adversarial networks and Google's sequence-to-sequence models open generation and translation.

Deep RL takes Go a decade early; move 37 enters folklore. 280M people watch.

Eight Googlers publish the transformer. Everything on this site descends from it.

OpenAI and Google show pre-training + fine-tuning beats task-specific models.

Protein structure solved to experimental accuracy; 200M structures follow.

DeepMind: most models are undertrained on data. Every training plan changes.

Hinton (Physics) and Hassabis/Jumper (Chemistry) — the academy ratifies the era.