Some authors have suggested in practice, that the definition of AI is vague and difficult to define, with
contention as to whether classical algorithms should be categorised as AI,[367] with many companies
during the early 2020s AI boom using the term as a marketing buzzword, often even if they did "not
actually use AI in a material way".[368]
No established unifying theory or paradigm has guided AI research for most of its history.[aa] The
unprecedented success of statistical machine learning in the 2010s eclipsed all other approaches (so much
so that some sources, especially in the business world, use the term "artificial intelligence" to mean
"machine learning with neural networks"). This approach is mostly sub-symbolic, soft and narrow. Critics
argue that these questions may have to be revisited by future generations of AI researchers.
Symbolic AI (or "GOFAI")[370] 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."[371]
However, the symbolic approach failed on many tasks that humans solve easily, such as learning,
recognizing an object or 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.[372]
Philosopher Hubert Dreyfus had 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.[373] Although his arguments had been ridiculed and ignored when they
were first presented, eventually, AI research came to agree with him.[ab][16]
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,[375][376] 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 artificial
intelligence attempts to bridge the two approaches.
"Neats" hope that intelligent behavior is described using simple, elegant principles (such as logic,
optimization, or neural networks). "Scruffies" expect that it necessarily requires solving a large number of
unrelated problems. Neats defend their programs with theoretical rigor, scruffies rely mainly on
incremental testing to see if they work. This issue was actively discussed in the 1970s and 1980s,[377] but
eventually was seen as irrelevant. Modern AI has elements of both.
Evaluating approaches to AI
Symbolic AI and its limits
Neat vs. scruffy
Soft vs. hard computing