Artificial intelligence编辑历史

# 1 History

## 1.1 Early foundations (1940s–1960s)
The conceptual roots of artificial intelligence trace to the 1940s, when mathematicians and logicians such as Norbert Wiener (cybernetics) and Alan Turing (computability and the Turing Machine) laid theoretical groundwork. Turing’s 1950 paper “Computing Machinery and Intelligence” proposed the famous imitation game (later called the Turing test) as a criterion for machine intelligence. In 1956, the Dartmouth Summer Research Project on Artificial Intelligence—organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon—officially coined the term “artificial intelligence.” Early successes included the development of programs that could prove theorems in symbolic logic (e.g., the Logic Theorist) and play checkers. These efforts demonstrated that machines could perform tasks once thought exclusive to human cognition.

## 1.2 Symbolic AI and expert systems (1960s–1980s)
During the 1960s and 1970s, AI research focused on symbolic reasoning and the manipulation of abstract concepts. Researchers created programs for problem-solving, natural language understanding, and game playing. The “Lisp” programming language, developed by McCarthy, became a dominant tool. By the late 1970s, the paradigm of expert systems emerged—rule-based programs that encoded human expertise for specialized domains such as medical diagnosis (MYCIN) and mineral exploration (PROSPECTOR). These systems achieved commercial success, prompting a boom in AI investment. However, they relied on handcrafted knowledge bases and faced limitations in handling uncertainty and common-sense reasoning.

## 1.3 AI winters and resurgence (1980s–2000s)
Following initial enthusiasm, two major “AI winters” occurred (mid‑1970s and late‑1980s) when funding and interest plummeted due to unmet expectations and technical hurdles. The collapse of the Lisp machine market and the failure of expert systems to scale contribut