---
title: Artificial Intelligence History
tags: [artificial-intelligence, machine-learning, deep-learning, neural-networks]
status: active
confidence: high
created: 2026-04-09
sources:
  - file: public-domain/mccorduck-machines-who-think.txt
    hash: placeholder
    size: 0
    ingested: 2026-04-09
aliases: []
---

# Artificial Intelligence History

Artificial intelligence — the endeavour to build machines that exhibit intelligent behaviour — traces directly to [[alan-turing]]'s 1950 paper proposing the Turing Test. The field has cycled through periods of optimism, underfunding ("AI winters"), and transformative breakthroughs.

## Dartmouth Conference (1956)

John McCarthy, Marvin Minsky, Claude Shannon, and Nathaniel Rochester organised a two-month workshop at Dartmouth College in 1956, coining the term "artificial intelligence." The attendees believed that every aspect of intelligence could in principle be simulated by a machine. Early successes — game-playing programs, theorem provers, LISP — fuelled enormous optimism.

## First AI Winter (1974–1980)

Progress stalled as the limits of early approaches became clear. The Lighthill Report (UK, 1973) criticised AI research as failing to deliver on its promises. Funding dried up in the US and UK. The core problem was combinatorial explosion: search-based methods that worked on toy problems failed catastrophically at real-world scale.

## Expert Systems and Second AI Winter (1980s)

The 1980s saw a commercial revival through expert systems — rule-based programs encoding domain expert knowledge. Companies built billion-dollar businesses on systems like XCON (Digital Equipment Corp). But expert systems were brittle, expensive to maintain, and couldn't learn. A second AI winter followed in the late 1980s as their limitations became apparent.

## Neural Networks and Machine Learning (1986–2000s)

Geoffrey Hinton, David Rumelhart, and Ronald Williams popularised backpropagation in 1986, enabling multi-layer neural networks to learn from data. Support Vector Machines (Vapnik, 1995) and random forests provided strong statistical learning alternatives. The shift was epistemological: instead of hand-coding rules, systems would learn patterns from data.

## Deep Learning Breakthrough (2012)

AlexNet, a deep convolutional neural network by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, won the ImageNet competition in 2012 with an error rate that shocked the field. The key enablers: large labelled datasets, GPU compute (see [[transistor-and-microchip]]), and improved training techniques. Deep learning then swept speech recognition, machine translation, and game-playing (DeepMind's AlphaGo, 2016).

## Large Language Models (2017–present)

The transformer architecture (Vaswani et al., 2017) — "Attention Is All You Need" — replaced recurrent networks for language tasks. Scaled up with massive datasets and GPU clusters, transformers became large language models (GPT series, BERT, Claude, Gemini). These systems exhibit emergent capabilities that were not explicitly programmed, reopening fundamental questions about the nature of intelligence that [[alan-turing]] first posed.

See also: [[programming-languages-overview]] for Python's role as the dominant AI research language; [[internet-origins]] for the web-scale data that enabled deep learning.
