{"course":{"slug":"ai-actually-explained","title":"AI, Actually Explained","url":"https://addaly.com/learn/ai-actually-explained","level":"beginner","track":"start"},"lesson":{"slug":"seventy-years-in-one-page","title":"Seventy years in one page","url":"https://addaly.com/learn/ai-actually-explained/seventy-years-in-one-page","position":42,"of":92,"minutes":8,"answer":"The field has run in three booms and two winters since 1956, and each boom was the same two ideas (write the rules or learn them from examples) taking their turn.","keyIdea":"The field has run in three booms and two winters since 1956, and each boom was the same two ideas (write the rules or learn them from examples) taking their turn."},"body":{"format":"markdown","text":"## The name is older than the transistor radio\n\nIn 1950 Alan Turing published a paper asking whether a machine could think, and proposed a test: could a person, typing questions, tell the machine from a human? He called it the imitation game. He did not use the words \"artificial intelligence\". Nobody had yet.\n\nThose words were written in 1955, in a funding proposal for a summer workshop at Dartmouth College in the United States. The proposal, by John McCarthy and three colleagues, said that ten people working for two months could make \"a significant advance\" on getting machines to use language and improve themselves. The workshop ran in 1956. The advance did not arrive in two months. The name stuck.\n\nThat proposal is worth remembering for one reason: the gap between what was promised and what was delivered is the oldest feature of the field, older than any of its methods.\n\n## Two families, taking turns\n\nFrom the start there were two ways to build a thinking machine, and you met the distinction in the first module of this course. You can **write the rules**, or you can **learn them from examples**.\n\nThe rule-writers dominated early. The Logic Theorist in 1956 proved theorems from a book of mathematics. ELIZA, in 1966, held a conversation by matching patterns and reflecting them back, and its author was disturbed by how many people confided in it.\n\nThe learners started at the same time and were sidelined for decades. In 1958 Frank Rosenblatt built the perceptron, a machine that adjusted its own connections from examples. The *New York Times* reported that the US Navy expected it to walk, talk, see, write and be conscious of its existence. In 1969 Marvin Minsky and Seymour Papert published a book showing what a single layer of such units could not learn, and funding for the learning approach dried up for a generation.\n\nBoth sides were right about something. The rule-writers were right that you could build a working system in a week. The learners were right about where it would end.\n\n## The pattern\n\nPut the whole seventy years on one line and it repeats:\n\n- **1956 to 1973.** Symbolic programs, big claims, government money. Then a British government report in 1973 concluded the promises had not been met, and funding on both sides of the Atlantic fell.\n- **1980 to 1987.** Expert systems: hand-written rules for narrow jobs like configuring computers or suggesting antibiotics. Real companies, real revenue. Then the cost of maintaining thousands of hand-written rules caught up, and the market collapsed.\n- **2012 onward.** Learned systems, first in image recognition, then speech, then language. This is the boom you are living in.\n\nEach boom began with a demonstration, attracted money on the strength of a promise, and cooled when the promise met the long tail of cases the demonstration had not covered. The next two lessons look inside those winters, because the mechanism of a winter is more useful to know than the dates.\n\n> Figure: Three booms, two winters, one repeating shape. Timeline: 1950, Turing proposes the imitation game. The words 'artificial intelligence' do not yet exist; 1956, The Dartmouth workshop names the field in a funding proposal. First boom begins; 1973, A British government report finds the promises unmet. First winter; 1980s, Expert systems: hand-written rules, real revenue, then ten thousand rules to maintain; 1987, The specialised-hardware market collapses in a year. Second winter; 2012, A network trained on two gaming cards halves the error on a labelled-image competition; 2017, The transformer design makes training parallel, and therefore purchasable; 2022, A chatbot on a public web page. Access, not capability, is what changed that November. Each boom began with a demonstration, attracted money on a promise, and cooled when the promise met the cases the demonstration had not covered. The method for training multi-layer networks was published in the middle of the second winter and waited twenty-five years for the data and the chips.\n\n## A game a machine won without learning\n\nOne event confuses the pattern and is worth placing correctly. In 1997 IBM's Deep Blue beat Garry Kasparov, the world chess champion. It was reported as the arrival of machine intelligence.\n\nDeep Blue did not learn. It examined about two hundred million board positions a second and scored them with rules that chess experts had written by hand. It belongs to the rule-writing family, and it was that family's high-water mark. The learning family's equivalent came nineteen years later. In 2016 AlphaGo, trained on human games and then on millions of games against itself, beat Lee Sedol, one of the greatest Go players of his generation, by four games to one, at a game where the positions are too many to search. A year later it beat the world number one, Ke Jie.\n\nThe lesson to carry from this: \"a machine beat a human at X\" tells you nothing about *how*, and how is the only thing that predicts what it will do next.\n\n## A timeline to keep\n\n1950 Turing's paper. 1956 the Dartmouth workshop. 1958 the perceptron. 1966 ELIZA. 1973 the first winter begins. 1980s expert systems. 1986 the method for training multi-layer networks is popularised. 1997 Deep Blue. 2012 a learned system wins the ImageNet competition by a wide margin. 2016 a learned system wins at Go. 2017 the transformer design. 2020 GPT-3. 2022 a chatbot on a public web page.\n\n## What the pattern does and does not tell you\n\nIt tells you that a demonstration is not a deployment, and that money arrives before proof. It does not tell you when, or whether, the current boom cools. Two things are new this time and pull in opposite directions: real revenue exists, and so does capital spending on a scale that requires returns nobody has yet demonstrated. Anyone who tells you confidently which way it goes is pattern-matching on a sample of two."},"check":{"question":"A 1997 headline said a computer had beaten the world chess champion. What does that result show about machine learning at the time?","options":[{"text":"That neural networks trained on games had matured enough to master something as hard as chess against a world champion.","correct":false,"tempting":"Assumes a machine beating a human must have learned to do it; Deep Blue's evaluation rules were written by people."},{"text":"That the learning approach had finally displaced hand-written rules in serious systems.","correct":false,"tempting":"Reverses the history; 1997 was the rule-writing family's high point, and learning did not take over until 2012."},{"text":"Nothing directly: Deep Blue scored searched positions with hand-written rules, so it was written, not trained.","correct":true},{"text":"That the second AI winter had ended, because a famous success brings funding back.","correct":false,"tempting":"Treats a headline as a funding event; the second winter had already thawed slowly through the 1990s for unrelated reasons."}]},"prev":{"slug":"weaker-in-your-language","title":"It is measurably weaker in most languages","url":"https://addaly.com/learn/ai-actually-explained/weaker-in-your-language"},"next":{"slug":"the-two-winters","title":"Two winters, and what actually froze","url":"https://addaly.com/learn/ai-actually-explained/the-two-winters"},"authorship":"Founder and series editor: Dr Mustafa Mun. Written by AI models to a written standard. Exam keys checked blind by a second model: 3,665 of 3,666 agreed. Academic review invited.","review":{"status":"Draft, open for academic review"},"cite":"Addaly (2026) 'Seventy years in one page', in AI, Actually Explained. Edited by M. Mun. Addaly. Available at: https://addaly.com/learn/ai-actually-explained/seventy-years-in-one-page (Accessed: 13 October 2026).","licence":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"reader":{"schema":1,"title":"Seventy years in one page","courseTitle":"AI, Actually Explained","module":{"index":5,"slug":"how-it-got-here","title":"How it got here, and what changed in 2022"},"answer":null,"keyIdea":"The field has run in three booms and two winters since 1956, and each boom was the same two ideas (write the rules or learn them from examples) taking their turn.","hero":{"kind":"figure","figure":{"t":"figure","label":"Figure 5.1","figure":{"type":"timeline","title":"Three booms, two winters, one repeating shape","events":[{"when":"1950","what":"Turing proposes the imitation game. The words ‘artificial intelligence’ do not yet exist"},{"when":"1956","what":"The Dartmouth workshop names the field in a funding proposal. First boom begins"},{"when":"1973","what":"A British government report finds the promises unmet. First winter"},{"when":"1980s","what":"Expert systems: hand-written rules, real revenue, then ten thousand rules to maintain"},{"when":"1987","what":"The specialised-hardware market collapses in a year. Second winter"},{"when":"2012","what":"A network trained on two gaming cards halves the error on a labelled-image competition"},{"when":"2017","what":"The transformer design makes training parallel, and therefore purchasable"},{"when":"2022","what":"A chatbot on a public web page. Access, not capability, is what changed that November"}],"caption":"Each boom began with a demonstration, attracted money on a promise, and cooled when the promise met the cases the demonstration had not covered. The method for training multi-layer networks was published in the middle of the second winter and waited twenty-five years for the data and the chips."},"alt":"Figure: Three booms, two winters, one repeating shape. Timeline: 1950, Turing proposes the imitation game. The words ‘artificial intelligence’ do not yet exist; 1956, The Dartmouth workshop names the field in a funding proposal. First boom begins; 1973, A British government report finds the promises unmet. First winter; 1980s, Expert systems: hand-written rules, real revenue, then ten thousand rules to maintain; 1987, The specialised-hardware market collapses in a year. Second winter; 2012, A network trained on two gaming cards halves the error on a labelled-image competition; 2017, The transformer design makes training parallel, and therefore purchasable; 2022, A chatbot on a public web page. Access, not capability, is what changed that November. Each boom began with a demonstration, attracted money on a promise, and cooled when the promise met the cases the demonstration had not covered. The method for training multi-layer networks was published in the middle of the second winter and waited twenty-five years for the data and the chips."}},"blocks":[{"t":"h2","id":"the-name-is-older-than-the-transistor-radio","runs":[{"text":"The name is older than the transistor radio"}]},{"t":"p","runs":[{"text":"In 1950 Alan Turing published a paper asking whether a machine could think, and proposed a test: could a person, typing questions, tell the machine from a human? He called it the imitation game. He did not use the words “artificial intelligence”. Nobody had yet."}]},{"t":"p","runs":[{"text":"Those words were written in 1955, in a funding proposal for a summer workshop at Dartmouth College in the United States. 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