Definitions and a Short History

Seventy years of ideas

Artificial intelligence (AI) is the field that builds systems able to perform tasks we associate with intelligence: reasoning, planning, learning, perceiving, understanding language. The term dates from the 1956 Dartmouth workshop. Early AI focused on symbolic methods: search, logic and hand-written rules, leading to expert systems in the 1980s. Periods of over-promising were followed by funding cuts ("AI winters"). From the 1990s, probabilistic methods and machine learning took over; since the 2010s, deep learning on large data and compute has driven breakthroughs in vision, speech, games and, recently, large language models.

Machines that act sensibly

AI studies how to build systems that perceive, reason, learn and act to achieve goals.

Milestones

A compressed timeline.

1950  Turing asks "Can machines think?" and proposes the imitation game
1956  Dartmouth workshop names the field "artificial intelligence"
1960s-80s  search, logic, expert systems; AI winters follow over-promising
1997  Deep Blue defeats the world chess champion (search + evaluation)
2012  deep neural networks win ImageNet by a large margin
2016  AlphaGo beats a top Go professional (deep learning + search + RL)
2020s large language models and generative AI reach mainstream use

Old ideas still matter

Search, logic and probability are inside modern systems too: game engines, planners, route finders and LLM agents all use them.

Quick check: Which approach dominated early AI research?

• Deep convolutional networks
• Large language models
• Symbolic methods: search, logic and hand-written rules
• GPU clusters