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Artificial intelligence studies computational systems that perform tasks involving inference, learning, planning, perception or action under uncertainty. A classifier can learn to map measured features to labels; a planning system can search for a sequence of actions satisfying a goal. The capabilities depend on the model, training information and evaluation setting. Success on one benchmark does not by itself establish general competence or human-like understanding. Machine learning, symbolic reasoning and search algorithms offer different, sometimes combined, ways of building such systems.

Cognitive science investigates the mind and intelligence by combining methods from disciplines including psychology, neuroscience, linguistics, computer science and artificial intelligence. Its scientific questions concern mechanisms of perceiving, learning, reasoning and acting, rather than just naming mental abilities.^[ref]

A computational model of cognition makes an account precise enough to compare with observations. For example, consider two hypotheses about people performing synchronous speech. One predicts that a designated follower always trails a leader by a constant interval $d$. Another allows both people to adjust their timing. Recording corresponding speech landmarks produces a sequence of signed timing differences; this can test whether the proposed constant-follower account fits the task.

Fred Cummins's paired-reading experiments supply a concrete connection between linguistics and quantitative behavioural measurement: corresponding speech events can be closely aligned, and both speakers adjust their timing relative to individual readings.^[ref]

A successful behavioural prediction does not uniquely identify a neural mechanism. Combining task performance, explicit models and independent physiological measurements helps distinguish explanations that agree on one observable. The field's different methods constrain one another without requiring every question to be answered at the same descriptive level.

Peter Norvig is a computer scientist whose work combines artificial intelligence, computer programming and technical education. With Stuart J. Russell, he wrote Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, which connects search algorithms, logical reasoning, probabilistic reasoning and machine learning through the design of intelligent agents.^[ref]

His programming explanations also connect compact working examples with general ideas: his spelling-correction tutorial illustrates how candidate generation and a probability model can cooperate in selecting a correction.^[ref]

Stuart J. Russell is a computer scientist whose work concerns artificial intelligence and the design of systems that choose actions from available information. With Peter Norvig, he wrote Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig. Its agent perspective connects search algorithms, first-order logic, probabilistic reasoning, decision theory and machine learning.^[ref]

His research and lectures also examine uncertainty about an intelligent system's objectives. That question differs from uncertainty about its observations: even a system that predicts its environment accurately can need information about what outcome its user wants.^[ref]