1950: Turing asks if machines can think

Alan Turing published "Computing Machinery and Intelligence" and asked whether machines can think. His proposal, known as the Turing Test or the imitation game, was to see whether a person talking to a machine could tell it apart from a human. Lawrence Livermore National Laboratory has a short history piece on how AI research began, and it is a nice read. Today's chatbots pass versions of that test so routinely that we hardly notice.

1956: the word "AI" is born

The Dartmouth workshop gave the field its name. The organisers believed a summer of work could crack human-level language. It took roughly 66 years to get something close to that, which is a good reminder to be careful with confident timelines, including mine.

1997: Deep Blue beats Kasparov

IBM's Deep Blue beat world chess champion Garry Kasparov 3.5 to 2.5, searching around 200 million positions per second. It could play chess and nothing else. It was a good achievement, and ChessBase called it a significant step forward for AI.

2011: Watson wins Jeopardy

Fourteen years later, another IBM system, described by ChessBase as about 100 times faster than Deep Blue, beat the best human players at Jeopardy. Ken Jennings wrote on his answer screen: "I for one welcome our new computer overlords." Watson was better at language than Deep Blue, but still built for one game.

2012: AlexNet teaches machines to see

A deep neural network trained on GPUs, AlexNet, won an image recognition contest by a wide margin. That result is why your phone can get with your face and find every photo of your dog.

2016: AlphaGo and Move 37

DeepMind's AlphaGo beat Lee Sedol 4 to 1 in Seoul. The British Go Association's match report is a good record of the games. In game two, AlphaGo played a move that professional commentators first thought was a mistake. It was not. It was a creative idea no human had tried. If you have never seen the documentary AlphaGo: The Movie, I recommend it. It is free on YouTube.

2017: the Transformer

A team of researchers published "Attention Is All You Need" on arXiv in June 2017. The architecture it introduced is the T in GPT. Almost every large language model you use today descends from it.

The idea to remember

Old AI was narrow: one task, one machine. The Transformer made AI general purpose, so one model can handle many language tasks. That is the shift that matters for your career. You no longer need a different system for translation, summaries and code. One system, many jobs, with a quality that varies from job to job.

Sources

Part of the talk "The AI Era" by Faisal Adnan, Riphah International University, 6 November 2026.

For Decades AI Could Do One Thing: A Short History From Turing to the Transformer