Glossary of artificial intelligence – List of definitions of terms and concepts commonly used
in the study of artificial intelligence
Intelligence amplification – Use of information technology to augment human intelligence
Intelligent agent – Software agent which acts autonomously
Mind uploading – Hypothetical process of digitally emulating a brain
Organoid intelligence – Use of brain cells and brain organoids for intelligent computing
Robotic process automation – Form of business process automation technology
Wetware computer – Computer composed of organic material
a. This list of intelligent traits is based on the topics covered by the major AI textbooks,
including: Russell & Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel
(1998) and Nilsson (1998)
b. This list of tools is based on the topics covered by the major AI textbooks, including: Russell
& Norvig (2021), Luger & Stubblefield (2004), Poole, Mackworth & Goebel (1998) and
Nilsson (1998)
c. It is among the reasons that expert systems proved to be inefficient for capturing
knowledge.[30][31]
d. "Rational agent" is general term used in economics, philosophy and theoretical artificial
intelligence. It can refer to anything that directs its behavior to accomplish goals, such as a
person, an animal, a corporation, a nation, or in the case of AI, a computer program.
e. Alan Turing discussed the centrality of learning as early as 1950, in his classic paper
"Computing Machinery and Intelligence".[42] In 1956, at the original Dartmouth AI summer
conference, Ray Solomonoff wrote a report on unsupervised probabilistic machine learning:
"An Inductive Inference Machine".[43]
f. See AI winter § Machine translation and the ALPAC report of 1966
g. Compared with symbolic logic, formal Bayesian inference is computationally expensive. For
inference to be tractable, most observations must be conditionally independent of one
another. AdSense uses a Bayesian network with over 300 million edges to learn which ads
to serve.[93]
h. Expectation–maximization, one of the most popular algorithms in machine learning, allows
clustering in the presence of unknown latent variables.[95]
i. Some form of deep neural networks (without a specific learning algorithm) were described
by: Warren S. McCulloch and Walter Pitts (1943)[115] Alan Turing (1948);[116] Karl Steinbuch
and Roger David Joseph (1961).[117] Deep or recurrent networks that learned (or used
gradient descent) were developed by: Frank Rosenblatt(1957);[116] Oliver Selfridge
(1959);[117] Alexey Ivakhnenko and Valentin Lapa (1965);[118] Kaoru Nakano (1971);[119]
Shun-Ichi Amari (1972);[119] John Joseph Hopfield (1982).[119] Precursors to
backpropagation were developed by: Henry J. Kelley (1960);[116] Arthur E. Bryson
(1962);[116] Stuart Dreyfus (1962);[116] Arthur E. Bryson and Yu-Chi Ho (1969);[116]
Backpropagation was independently developed by: Seppo Linnainmaa (1970);[120] Paul
Werbos (1974).[116]
j. Geoffrey Hinton said, of his work on neural networks in the 1990s, "our labeled datasets
were thousands of times too small. [And] our computers were millions of times too slow."[121]
Explanatory notes