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In the philosophy of AI, “intelligence” is treated as a contested concept that shapes how we judge whether machines can be intelligent. One influential approach is Alan Turing’s Turing test: if a machine can converse such that an evaluator cannot reliably tell it apart from a human, then (by a “polite convention”) it is treated as intelligent. A different, more AI-research-oriented view defines intelligence in terms of goal-directed behavior: an agent is intelligent to the extent that it perceives and acts in an environment to maximize success according to a performance measure (e.g., John McCarthy’s idea of the computational ability to achieve goals). Arguments for machine general intelligence include the claim that the brain’s behavior could be reproduced by physical simulation (since it follows physical laws) and the claim that intelligence is essentially symbol processing (Newell and Simon’s physical symbol system hypothesis). Critics argue that human thinking is not solely high-level symbol manipulation: Dreyfus emphasizes the primacy of implicit, intuitive skills that may not be captured by explicit formal rules. Additional anti-mechanist arguments draw on Gödelian reasoning (Lucas and Penrose) to suggest human mathematical understanding might exceed what machines can compute, though the scientific community often views these arguments as not establishing a real limit on computation. Overall, the debate links definitions of intelligence to deeper questions about mind, consciousness, and what kinds of capacities matter for “general” intelligence.
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