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Natural language processing studies computational methods for analyzing, transforming and generating human language. Tasks include translation, extracting information from text, and predicting a missing or subsequent word. A system must resolve ambiguity from context: “bank” in a sentence about a river and in a sentence about a loan has different meanings.

A neural language model tackles one precise part of the problem by assigning probabilities to word sequences. For example, after “the cat sat on the”, its distribution can rank “mat” above “democracy”, without thereby proving that it understands the scene. Evaluating a specific task and its errors is more informative than treating fluent text as a universal test of language competence.^[ref]

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]

A state-space search solves a problem by exploring a transition system: a state records the current situation, a transition relation specifies legal moves, and a goal state specifies success. The answer is usually a path in a transition system, rather than just the final configuration. This separates modeling the problem from choosing the search algorithm.

For a sliding-tile puzzle, the state is the arrangement of tiles and the empty square; one move slides a neighboring tile into that square. Breadth-first search finds a solution using the fewest moves when all moves have equal cost. Depth-first search can use less working storage but need not find a shortest solution. Remembering visited configurations prevents repeatedly exploring a cycle. The same formulation applies to route finding and automated planning.^[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]