Category: History
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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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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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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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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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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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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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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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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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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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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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"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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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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AlexNet: the night deep learning came back (2012)
A deep neural network trained on two gaming GPUs crushed the ImageNet benchmark and ended a long winter for neural networks almost overnight.
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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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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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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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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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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.
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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.