Articles
All published articles on the history of AI.
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DARPA Grand Challenge 2005: Stanley crosses the desert (2005)
On 8 October 2005 Stanford's autonomous vehicle Stanley won the DARPA Grand Challenge, completing a 212 km Mojave Desert course in 6 hours 53 minutes after no vehicle finished the 2004 race.
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LeCun's zip code nets: when convolution met backpropagation (1989)
In 1989 Yann LeCun and colleagues at AT&T Bell Laboratories published a convolutional neural network trained with backpropagation on handwritten US Postal Service zip codes, an early proof that local filters could scale to real mail sorting.
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Neural language models: Bengio's fight against the curse of dimensionality (2003)
In 2003 Yoshua Bengio and colleagues published A Neural Probabilistic Language Model in the Journal of Machine Learning Research, learning distributed word representations to escape the limits of huge n-gram tables.
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Random forests: Leo Breiman's ensemble of decision trees (2001)
In October 2001 Leo Breiman published Random Forests in Machine Learning, combining many decorrelated decision trees trained on bootstrap samples to create a powerful general-purpose classifier.
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Support vector machines: maximum-margin classifiers with kernels (1995)
In 1995 Corinna Cortes and Vladimir Vapnik published support-vector networks in Machine Learning, combining maximum-margin linear separation with the kernel trick for nonlinear boundaries.
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TD-Gammon: learning backgammon by playing itself (1992)
Gerald Tesauro at IBM built TD-Gammon, a neural network that learned strong backgammon through temporal-difference reinforcement learning and self-play, reaching near expert-level play.
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Boltzmann machines: when neural networks borrowed statistical physics (1985)
In 1985 David Ackley, Geoffrey Hinton and Terrence Sejnowski published a learning algorithm for Boltzmann machines: stochastic binary units, an energy function from physics, and slow sampling that later methods would try to avoid.
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Deep belief networks: when deep nets became trainable again (2006)
In July 2006 Geoffrey Hinton and colleagues showed that stacking restricted Boltzmann machines layer by layer, then fine-tuning with backpropagation, could train deep networks. The result helped restart the deep-learning revival.
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Backpropagation: how neural networks learned from their mistakes (1986)
In 1986 David Rumelhart, Geoffrey Hinton and Ronald Williams showed that multilayer neural networks trained with backpropagation learn their own internal representations. The method is still the standard way to train most neural networks.
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GANs: when neural networks learned to invent images (2014)
In June 2014 Ian Goodfellow and colleagues introduced generative adversarial networks: two neural networks trained against each other. The idea made realistic image synthesis possible and raised the question of fakes early on.
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Watson on Jeopardy!: when a machine won the quiz (2011)
In February 2011 IBM's Watson defeated the two most successful Jeopardy! champions in a televised match. The system showed how far natural language processing had come, and IBM then set out to sell the technology to medicine and business.
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AI reaches gold at the International Mathematical Olympiad (2025)
In July 2025, language models from Google DeepMind and OpenAI solve five of six IMO problems for 35 of 42 points, writing proofs in natural language within the exam time limit.
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GPT-5 makes reasoning the default (2025)
On August 7, 2025, OpenAI releases GPT-5 as a unified system that decides when to answer quickly and when to think longer, and ships it to every ChatGPT user; a bumpy rollout shows how attached people have become to individual models.
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The New Delhi AI Impact Summit brings the US back to the table (2026)
At the first global AI summit held in the Global South, more than 80 countries including the US, China and the UK endorse a non-binding declaration on democratising AI, while Washington openly rejects any global governance of the technology.
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AlphaFold wins the 2024 Nobel Prize in Chemistry
One day after AI took the physics prize, the 2024 Chemistry Nobel honored AlphaFold, the DeepMind model that predicts protein structures, and computational protein design.
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DeepSeek-R1 and the efficiency shock (2025)
A Chinese startup released an open-weights reasoning model that rivaled the best at a fraction of the reported cost, and on January 27, 2025 it wiped a record sum off Nvidia's value.
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Gemini 2.0 and the start of the agentic era (2024)
Google's Gemini 2.0, launched in December 2024, was pitched as a model for the agentic era: systems that do not just answer, but take multi-step actions on your behalf.
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The 2024 Nobel Prize in Physics goes to neural networks
John Hopfield and Geoffrey Hinton won the 2024 Nobel Prize in Physics for foundational work on artificial neural networks, the machinery behind today's AI.
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The Paris AI Action Summit and a governance split (2025)
In February 2025 Paris hosted the AI Action Summit. Some 60 countries signed a declaration on inclusive, sustainable AI, but the United States and United Kingdom refused, exposing a rift over how to govern the technology.
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The Stargate Project: a $500 billion bet on AI infrastructure (2025)
In January 2025, OpenAI, SoftBank, Oracle and MGX announced Stargate, a plan to invest up to $500 billion in US AI data centers, just as DeepSeek was questioning the cost of compute.
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"Attention is all you need": the Transformer arrives (2017)
A 2017 paper replaced the sequential machinery of earlier networks with pure attention. The Transformer became the architecture behind almost every large language model since.
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AlexNet: the night deep learning came back (2012)
A deep neural network trained on two gaming <abbr title="Graphics processing unit">GPU</abbr>s crushed the ImageNet benchmark and ended a long winter for neural networks almost overnight.
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AlphaGo vs. Lee Sedol: the machine that learned to play (2016)
DeepMind's AlphaGo beat one of the greatest Go players 4–1, winning not by brute-force search like Deep Blue but by learning intuition from data and self-play.
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ChatGPT: the week AI went mainstream (2022)
A free chat demo built on a fine-tuned language model reached a hundred million users in about two months and turned AI from a research topic into a public obsession.
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Gemini 1.0: Google's multimodal answer (2023)
Google rebranded its AI efforts under Gemini, a natively multimodal family meant to rival GPT-4, and learned how closely launch demos would now be scrutinised.
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GPT-3: when scale became the strategy (2020)
OpenAI's 175-billion-parameter model showed that a big enough language model could learn new tasks from a few examples in the prompt, no retraining required.
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GPT-4: the flagship that raised the bar (2023)
OpenAI's GPT-4 could read images as well as text and scored well on exams built for humans, cementing a fierce commercial race, and a secrecy that unsettled researchers.
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ImageNet: the dataset that would teach machines to see (2006)
Fei-Fei Li bet that data, not just cleverer algorithms, would unlock computer vision. ImageNet became the benchmark that lit the fuse for the deep-learning era.
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Llama 2: open(ish) weights change the game (2023)
Meta released a capable language model whose weights anyone could download and build on, pushing the debate over 'open' AI to the center of the industry.
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OpenAI o1: models that think before they answer (2024)
OpenAI previewed a model line trained to reason through problems step by step before responding, trading speed for accuracy and reviving the idea that thinking longer can pay off.
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The EU AI Act: the first broad rulebook for AI (2024)
The European Parliament approved the world's first comprehensive AI law, sorting systems by risk and adding transparency duties for the general-purpose models behind modern chatbots.
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The first AI winter: when the hype froze (1974–1980)
The Lighthill Report, DARPA cuts, and the perceptron backlash drained funding from neural networks and overambitious AI, setting the stage for a decade of scepticism.
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Deep Blue vs. Kasparov: the match that shook the world (1997)
IBM's chess computer defeated the world champion in six games, a media spectacle that proved machines could outplay humans, and sparked a debate that never quite ended.
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The second AI winter: how the boom turned to bust (1987–1993)
Expert systems collapsed, Lisp machines became obsolete, governments cut funding, and AI entered years of disillusionment that nearly killed the field.
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Expert systems: when AI became a business (1980s)
Rule-based expert systems promised to bottle human expertise into software, they fuelled a billion-dollar boom, then collapsed into the second AI winter.
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ELIZA: the chatbot that was never meant to be one (1966)
Joseph Weizenbaum's ELIZA used simple pattern-matching to simulate therapy, and accidentally proved that humans project understanding onto machines.
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Rosenblatt's perceptron: the machine that learned (1958)
Frank Rosenblatt's perceptron was the first machine to learn from experience, celebrated by the press, attacked by critics, and vindicated decades later.
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Computing Machinery and Intelligence: Turing's imitation game (1950)
Alan Turing's 1950 paper replaced 'Can machines think?' with a practical test, and shaped how we still talk about machine intelligence.
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The Dartmouth Summer Research Project on Artificial Intelligence (1956)
A six-to-eight-week workshop that coined the term "artificial intelligence" and launched it as a recognised field of research.