How did we get to ChatGPT? The inventions, ideas and rules that made today’s AI, from the first electronic switches to reasoning models. Follow what each milestone built on and what it led to, right up to our news.
100 milestones · 1904 to today · each checked in at least 2 sources
When it happened
Each square is one decade. The brighter the square, the more milestones of that kind: progress was slow for decades, then sped up. Choose a theme to see only its milestones, or a decade to jump to it.
Milestones per theme per decade, from the 1900s to the 2020s
On 7 January 1954, Georgetown University and IBM showed a computer turning more than sixty Russian sentences into English in New York. The IBM 701 used a vocabulary of only 250 words and six grammar rules.
Why it matters It showed machine translation working on a real computer and won funding, but it made good translation seem closer than it was.
Note Widely described as the first public demonstration of machine translation. It was a showcase: the words and rules were chosen for a small set of sentences, so it could not translate general text.
In January 1966, Joseph Weizenbaum of MIT published a paper on ELIZA, a program that chats in everyday English. Its best-known script acted like a psychotherapist, reacting to keywords in what the user typed.
Why it matters ELIZA showed how easily people believe a program understands them, and Weizenbaum later warned against handing human decisions to machines.
Note MIT News calls ELIZA “perhaps the first” chatbot. January 1966 is the date of the paper; the program existed before then.
IBM’s Watson played the quiz show Jeopardy! against champions Ken Jennings and Brad Rutter. The episodes aired on 14–16 February 2011, and Watson won with $77,147, against $24,000 and $21,600.
Why it matters Watson showed that a computer could answer questions asked in everyday language, and IBM later sold the technology to businesses.
On 4 October 2011, Apple announced Siri, a voice assistant built into the new iPhone 4S. People could ask it to make calls, send messages, set reminders and find places.
Why it matters It put a voice assistant on millions of phones: Apple said it sold over four million iPhone 4S in the first three days.
Note Siri began as a separate company, a spin-off of SRI, and first came out as an iPhone app in February 2010. Apple bought the company in April 2010.
In 2013, Tomas Mikolov and colleagues at Google showed a fast way to learn a vector, a list of numbers, for every word. Trained on 1.6 billion words in under a day, the vectors captured links such as country and capital.
Why it matters Word vectors became easy to train on huge texts, and NeurIPS later said the work began a new era in language processing.
Note Word vectors were not new in 2013, as the paper itself says; word2vec made them fast to train. The first paper came in January 2013, the code later that year and the second paper in October.
In September 2014, Dzmitry Bahdanau, Kyunghyun Cho and Yoshua Bengio published a new design for translation by neural networks. While writing each word, the model looks back at the source words that matter most.
Why it matters Attention became a standard part of language models, and in 2017 the transformer was built on attention alone.
Eight researchers working at Google posted a paper that introduced the transformer, a neural network design built on attention alone. It set new best scores on two translation tests and needed far less training time.
Why it matters It became the base of later language models, including BERT and the GPT family.
OpenAI researchers trained a transformer on thousands of books, then fine-tuned it on each task. It beat the best earlier results on 9 of the 12 tests the authors studied.
Why it matters It started OpenAI’s GPT line: GPT-2 and GPT-3 kept its design and trained larger models on more text.
Google researchers introduced BERT, a language model that uses the words on both sides of a word to understand it. It set new best results on eleven language tests, and Google shared the code in November 2018.
Why it matters Open code let anyone build on it, and in October 2019 Google said it was bringing BERT to Search.
OpenAI introduced GPT-2, a language model with 1.5 billion parameters that writes coherent paragraphs. Citing fears of misuse, it first shared only a small version and released the full model in November 2019.
Why it matters It sparked a wide debate on when and how AI labs should release powerful models.
Note Experts disagreed: some saw the staged release as a sensible precaution, others called it a publicity stunt. OpenAI later said it had seen no strong evidence of misuse.
OpenAI researchers described GPT-3, a language model with 175 billion parameters, over 100 times more than GPT-2. Given only a few examples in the prompt, it did well on many language tests without extra training.
Why it matters OpenAI opened an API to developers in June 2020, and the paper won a NeurIPS 2020 best paper award.
Note The few-shot results come from OpenAI’s own tests. The paper lists tasks where GPT-3 does poorly, and MIT Technology Review stressed that its fluent text hides a lack of real understanding.
On 29 June 2021, GitHub opened a limited preview of Copilot, an AI tool that suggests whole lines or functions of code. It ran on OpenAI Codex, a GPT model fine-tuned on public code.
Why it matters It showed that AI could suggest code as programmers type, and by 2023 such tools were changing how software gets made.
Note Training on public code is contested: programmers have sued over it, MIT Technology Review reported in 2023.
On 30 November 2022, OpenAI released ChatGPT, a chatbot that answers questions in a back-and-forth conversation. OpenAI called it a research preview; its model was fine-tuned from GPT-3.5 with human feedback.
Why it matters By outside estimates it reached 100 million users in about two months, and rivals such as Google rushed out their own chatbots.
Note The 100 million users figure is an outside estimate, not an OpenAI count.
On 14 March 2023, OpenAI announced GPT-4, a model that accepts text and images and writes text. OpenAI said it scored around the top 10% of test takers on a simulated bar exam.
Why it matters Microsoft said Bing Chat already used a version of it, and OpenAI’s choice to withhold its size and training data drew criticism.
Note OpenAI did not publish the model’s size or training data, so size figures seen online are guesses. The exam results come from OpenAI’s own tests.
A milestone goes in when it changed what AI can do, the tools AI is built with, how many people use AI, or the rules for AI. It must be at least a year old: newer events are news.
We checked every milestone in at least two sources and read them: the original paper, patent, announcement or law where it exists, and a history source such as a museum, an encyclopedia or a report from the time. We never cite Wikipedia. Many “firsts” are disputed, so we say when they are, and dates are only as exact as the sources agree.
Last checked 4 October 2026. Found a mistake? Tell us, and we will correct it. How we report