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Timeline

A coarse chronology of turning points—tightened over time with articles and sources.

Also see: Glossary · Predictions , Compare.

Early ideas & foundations

  1. Turing’s “imitation game”

    Alan Turing’s paper in Mind reframes machine intelligence as behaviour indistinguishable from a human in dialogue—later popularised as the Turing test.

  2. Dartmouth AI workshop convenes

    Summer research workshop where John McCarthy proposed the name “artificial intelligence” and a shared research agenda—widely treated as the field’s public debut.

    Articles: The Dartmouth Summer Research Project on Artificial Intelligence (1956)

  3. Rosenblatt’s perceptron draws attention

    Frank Rosenblatt’s trainable single-layer classifier sparks optimism—and later debate—about whether neural networks can scale to general intelligence.

  4. ELIZA shows conversational pattern-matching

    Joseph Weizenbaum’s ELIZA script demonstrates how simple rules can feel like empathy—raising early questions about illusion, trust, and “understanding” in software.

Commercial AI & winters

  1. Expert systems go industrial

    Rule-based “expert systems” move from labs into product stories—promising domain capture without full learning, and shaping the 1980s AI business wave.

  2. Second AI winter begins

    After a boom in specialised AI tools, market expectations cool; funding tightens and many commercial projects stall—often dated to the late 1980s “winter.”

  3. Deep Blue defeats Kasparov

    IBM’s chess specialist beats world champion Garry Kasparov in a match—an iconic public moment for brute-force search plus hardware, even as broader AI still looked modest.

Deep learning at scale

  1. ImageNet seeds large-scale vision

    Fei-Fei Li’s team launches a vast labelled image dataset—later the benchmark that makes modern convolutional networks measurable and competitive.

  2. AlexNet wins ImageNet

    A deep convolutional network trained on GPUs cuts error rates dramatically on ImageNet—often cited as the spark of the modern deep-learning resurgence.

  3. AlphaGo beats Lee Sedol

    DeepMind’s AlphaGo wins four of five games against one of the world’s top Go players—showing that intuition-heavy games are not off limits to learning systems.

Transformers & large language models

  1. “Attention is all you need”

    The Transformer architecture replaces recurrence with self-attention—becoming the backbone of later large language models and multimodal stacks.

  2. GPT-3 scales few-shot prompting

    OpenAI’s 175B-parameter language model paper highlights in-context learning—tasks described in natural language without weight updates—reshaping product and research expectations.

  3. ChatGPT opens to the public

    A conversational wrapper around a large language model becomes a mass-market demo overnight—accelerating debates on safety, work, copyright, and access.

  4. GPT-4 ships as a multimodal flagship

    OpenAI positions GPT-4 as a safer, stronger successor—vision + text demos reset expectations for capability benchmarks and product roadmaps.

  5. Llama 2 weights go open(ish) for research & products

    Meta releases Llama 2 under a community license—widening who can fine-tune and ship local or hosted models, and accelerating the “open weights” ecosystem debate.

  6. Google launches Gemini 1.0

    Google rebrands and ships its multimodal family under the Gemini name—signalling tighter integration across Search, cloud APIs, and consumer hardware stories.

  7. EU AI Act adopted

    The European Parliament backs the world’s first broad AI rulebook—layering obligations by risk, transparency duties for general-purpose models, and timelines for compliance.

  8. OpenAI previews “reasoning” models (o1)

    A new line emphasises longer internal deliberation before answering—rekindling public talk about test-time compute, STEM benchmarks, and safety evaluation gaps.