NeuralAtlas

Timeline

Eight decades of artificial intelligence

The field has died twice and come back stronger each time. Here is the compressed story — the ideas, the winters, and the breakout.

Era I

The foundations · 1943–1969

When the mathematical ideas arrived decades before the machines that could run them.

  • 1943

    The artificial neuron, on paper

    Neurophysiologist Warren McCulloch and logician Walter Pitts model a neuron as a simple logic unit — the mathematical seed of neural networks.

  • 1950

    The imitation game

    Alan Turing’s paper “Computing Machinery and Intelligence” proposes his famous test: if a machine’s conversation is indistinguishable from a human’s, can we call it intelligent?

  • 1956

    AI gets its name

    At the Dartmouth Summer Research Project, John McCarthy coins the term “artificial intelligence” and sets the new field’s agenda.

  • 1958

    The Perceptron

    Frank Rosenblatt builds a machine that learns to classify by adjusting weights — an early ancestor of today’s networks, and a media sensation.

  • 1966

    ELIZA

    Joseph Weizenbaum’s pattern-matching “therapist” shocks users with how human scripted empathy can feel — an early lesson for the chatbot era.

  • 1969

    A cold splash

    Minsky and Papert’s book Perceptrons documents the limits of single-layer networks, cooling enthusiasm and funding.

Era II

Winters and expert systems · 1970–1996

Hype outran capability, twice — but the engineering never stopped.

  • 1973

    The first winter

    The UK’s Lighthill Report judges AI to have failed its grand promises; governments retreat, and funding freezes spread.

  • 1980

    Expert systems arrive

    Rule-based systems like DEC’s XCON encode specialist knowledge for configuring computers — the first real commercial AI boom.

  • 1986

    Backpropagation goes mainstream

    Rumelhart, Hinton, and Williams show how to train multi-layer neural networks efficiently (Nature) — the key that deep learning would later turn.

  • 1987

    The second winter

    The specialized Lisp-machine market collapses, and “AI” becomes a word investors flinch at for over a decade.

Era III

The deep-learning revolution · 1997–2016

Data, GPUs, and stubborn researchers turn the old ideas into machines that win.

  • 1997

    Deep Blue defeats Kasparov

    IBM’s chess machine beats the reigning world champion — with brute-force search rather than learning. The lesson: success sells, whatever the method.

  • 2011

    Watson wins Jeopardy!

    IBM’s question-answering system beats champions Ken Jennings and Brad Rutter on live television.

  • 2012

    AlexNet’s shock

    A deep neural network trained on GPUs scores 15.3 percent top-5 error in the ImageNet contest — roughly half the runner-up’s 26.2. The modern era begins.

  • 2014

    GANs

    Ian Goodfellow’s generative adversarial networks pit a forger against a detective, enabling convincing synthetic images.

  • 2016

    AlphaGo defeats Lee Sedol 4–1

    DeepMind’s Go player plays moves humans found strange and beautiful — move 37 of game two becomes a landmark of the field.

Era IV

The generative era · 2017–today

One architecture, one scaling recipe — and a technology that met the public.

  • 2017

    “Attention Is All You Need”

    Vaswani et al. introduce the Transformer, an architecture built on attention that trains better at scale. Nearly every large model since uses it.

  • 2018–2020

    From BERT to GPT-3

    Pretrained language models explode. GPT-3 (2020) reaches 175 billion parameters and shows few-shot learning — abilities nobody explicitly programmed.

  • 2020

    AlphaFold 2 cracks protein folding

    At the CASP14 assessment, AlphaFold 2 predicts protein structures to near-experimental accuracy, helping settle a 50-year grand challenge of biology.

  • 2022

    The generative breakout

    Diffusion models (DALL-E 2, Stable Diffusion, Midjourney) make text-to-image mainstream. On November 30, ChatGPT turns an LLM into — at the time — the fastest-growing consumer app in history.

  • 2023

    Frontier and open models

    GPT-4 arrives multimodal; Meta’s Llama 2 brings strong open weights; governments convene safety summits and start writing rulebooks.

  • 2024

    Nobel recognition and new rules

    Hopfield and Hinton win the Physics Nobel for foundational neural-network work; Hassabis and Jumper share the Chemistry Nobel for AlphaFold. The EU AI Act — the first comprehensive AI law — enters into force, and “reasoning” models learn to spend more compute per answer.

  • 2025

    Reasoning goes mainstream

    OpenAI’s GPT-5 (August) and Google’s Gemini 3 (November) push frontier reasoning; agentic assistants that browse, code, and complete multi-step tasks spread to hundreds of millions of users.

Looking forward

What to watch next

  • Agentic systems — models that plan and act across many steps, with access to tools, files, and the web.
  • Small, efficient models — capable AI running on phones and laptops: faster, private, and always available offline.
  • Maturing regulation — the EU AI Act’s obligations phase in through 2026 and 2027, shaping products far beyond Europe.

The pattern across eighty years is consistent: winters follow hype that outruns capability, and revolutions follow engineering that catches up. Wherever you are reading this from, you are inside the latest chapter.