Short, real facts about AI -- with sources.

The term "Artificial Intelligence" was coined by John McCarthy for the 1956 Dartmouth Workshop, an eight-week summer research project widely considered the founding event of AI as a field.

Alan Turing proposed what's now called the Turing Test in his 1950 paper "Computing Machinery and Intelligence," asking whether a machine's conversation could be indistinguishable from a human's.

ELIZA, created by Joseph Weizenbaum at MIT in 1966, was one of the first programs to simulate conversation -- some users became convinced they were talking to a real therapist.

IBM's Deep Blue became the first computer to defeat a reigning world chess champion in a full match, beating Garry Kasparov in 1997.

DeepMind's AlphaGo defeated world champion Lee Sedol at Go in 2016 -- a game long considered far harder for computers than chess because of its vastly larger space of possible positions.

Shakey the Robot, built at the Stanford Research Institute between 1966 and 1972, was the first mobile robot able to reason about and plan its own actions, rather than just follow fixed instructions.

The Transformer architecture, introduced in the 2017 paper "Attention Is All You Need," underpins nearly every modern large language model, including the GPT and BERT model families.

The Perceptron, an early neural network model capable of learning simple classifications, was invented by Frank Rosenblatt in 1958 -- decades before deep learning became computationally practical.

DeepMind's AlphaFold solved a roughly 50-year-old grand challenge in biology by predicting 3D protein structures from amino acid sequences with high accuracy, published in 2020-2021.

The first "AI winter" -- a period of sharply reduced funding and interest in AI research -- began in the mid-1970s after early, overly optimistic predictions failed to materialize on schedule.

IBM's Watson defeated Ken Jennings and Brad Rutter, the two most successful champions in the game show Jeopardy!'s history, in a televised match in 2011.

AI alignment -- the problem of ensuring an AI system's actual goals and behavior match what its designers and users intend -- is treated as a distinct research problem from simply making AI more capable.