Editing Artificial intelligence (section)

====Symbolic AI and its limits====
[[Symbolic AI]] (or "[[GOFAI]]"){{Sfnp|Haugeland|1985|pp=112–117}} simulated the high-level conscious reasoning that people use when they solve puzzles, express legal reasoning and do mathematics. They were highly successful at "intelligent" tasks such as algebra or IQ tests. In the 1960s, Newell and Simon proposed the [[physical symbol systems hypothesis]]: "A physical symbol system has the necessary and sufficient means of general intelligent action."<ref>Physical symbol system hypothesis: {{Harvtxt|Newell|Simon|1976|p=116}}
Historical significance: {{Harvtxt|McCorduck|2004|p=153}}, {{Harvtxt|Russell|Norvig|2021|p=19}}</ref>

However, the symbolic approach failed on many tasks that humans solve easily, such as learning, recognizing an object or [[Commonsense reasoning|commonsense reasoning]]. [[Moravec's paradox]] is the discovery that high-level "intelligent" tasks were easy for AI, but low level "instinctive" tasks were extremely difficult.<ref>[[Moravec's paradox]]: {{Harvtxt|Moravec|1988|pp=15–16}}, {{Harvtxt|Minsky|1986|p=29}}, {{Harvtxt|Pinker|2007|pp=190–191}}</ref> Philosopher [[Hubert Dreyfus]] had [[Dreyfus' critique of AI|argued]] since the 1960s that human expertise depends on unconscious instinct rather than conscious symbol manipulation, and on having a "feel" for the situation, rather than explicit symbolic knowledge.<ref>[[Dreyfus' critique of AI]]: {{Harvtxt|Dreyfus|1972}}, {{Harvtxt|Dreyfus|Dreyfus|1986}}
Historical significance and philosophical implications: {{Harvtxt|Crevier|1993|pp=120–132}}, {{Harvtxt|McCorduck|2004|pp=211–239}}, {{Harvtxt|Russell|Norvig|2021|pp=981–982}}, {{Harvtxt|Fearn|2007|loc=chpt. 3}}</ref> Although his arguments had been ridiculed and ignored when they were first presented, eventually, AI research came to agree with him.{{Efn|
Daniel Crevier wrote that "time has proven the accuracy and perceptiveness of some of Dreyfus's comments. Had he formulated them less aggressively, constructive actions they suggested might have been taken much earlier."{{Sfnp|Crevier|1993|p=125}}
}}<ref name="Psychological evidence of the prevalence of sub"/>

The issue is not resolved: [[sub-symbolic]] reasoning can make many of the same inscrutable mistakes that human intuition does, such as [[algorithmic bias]]. Critics such as [[Noam Chomsky]] argue continuing research into symbolic AI will still be necessary to attain general intelligence,{{Sfnp|Langley|2011}}{{Sfnp|Katz|2012}} in part because sub-symbolic AI is a move away from [[explainable AI]]: it can be difficult or impossible to understand why a modern statistical AI program made a particular decision. The emerging field of [[Neuro-symbolic AI|neuro-symbolic artificial intelligence]] attempts to bridge the two approaches.