Artificial Intelligence
Artificial intelligence (AI) is the project of building computer systems that perform tasks associated with intelligent beings — systems that infer, from the input they receive, how to generate outputs such as predictions, content, recommendations or decisions. The term was coined for a 1956 summer workshop at Dartmouth College, proposed in 1955 by John McCarthy, Marvin Minsky, Nathaniel Rochester and Claude Shannon on the conjecture that every feature of intelligence can be described precisely enough for a machine to simulate it. The field's first test of machine thinking was Alan Turing's 1950 imitation game. Early work on symbolic programs, robots such as SRI's Shakey and expert systems such as DENDRAL (1965) gave way to a first funding winter after the Lighthill report of 1973; the learning turn arrived with the back-propagation paper by Rumelhart, Hinton and Williams in Nature in 1986, and accelerated in 2012 when Krizhevsky, Sutskever and Hinton's AlexNet cut the ImageNet contest error to 15.3% against 26.2% for the runner-up. Deep Blue beat Garry Kasparov in 1997, AlphaGo beat Lee Sedol 4-1 in March 2016, and the Transformer architecture of 2017 led to large language models: GPT-3 (175 billion parameters, 2020), ChatGPT (November 2022) and GPT-4 (2023), which scored around the top 10% of test takers on a simulated bar exam. Governance moved in parallel — the OECD AI Principles (2019, updated 2024), the NIST AI RMF (January 2023) and the EU AI Act (Regulation (EU) 2024/1689), in force since 1 August 2024. The systems remain narrow and fallible: NIST's Generative AI Profile names confabulation — confidently presented false content — as a core risk, and Stanford's 2025 AI Index recorded record investment, 78% organizational adoption and sharply rising incidents.