In the history of artificial intelligence, neat and scruffy are two contrasting approaches to artificial intelligence (AI) research. The distinction was made in the 1970s and was a subject of discussion until the mid-1980s.[1][2][3]
"Neats" use algorithms based on a single formal paradigms, such as logic, mathematical optimization or neural networks. Neats verify their programs are correct with theorems and mathematical rigor. Neat researchers and analysts tend to express the hope that this single formal paradigm can be extended and improved to achieve general intelligence and superintelligence.
"Scruffies" use any number of different algorithms and methods to achieve intelligent behavior. Scruffies rely on incremental testing to verify their programs and scruffy programming requires large amounts of hand coding or knowledge engineering. Scruffies have argued that general intelligence can only be implemented by solving a large number of essentially unrelated problems, and that there is no silver bullet that will allow programs to develop general intelligence autonomously.
John Brockman compares the neat approach to physics, in that it uses simple mathematical models as its foundation. The scruffy approach is more like biology, where much of the work involves studying and categorizing diverse phenomena.[a]
Modern AI has elements of both scruffy and neat approaches. In the 1990s AI research applied mathematical rigor to their programs, as the neats did. [5][6] They also express the hope that there is a single paradigm (a "master algorithm") that will cause general intelligence and superintelligence to emerge.[7] But modern AI also resembles the scruffies:[8] modern machine learning applications require a great deal of hand-tuning and incremental testing; while the general algorithm is mathematically rigorous, accomplishing the specific goals of a particular application is not. Also, in the early 2000s, the field of software development embraced extreme programming, which is a modern version of the scruffy methodology -- try things and test them, without wasting time looking for more elegant or general solutions.
^McCorduck 2004, pp. 421–424, 486–489.
^Crevier 1993, p. 168.
^Nilsson 1983, pp. 10–11. sfn error: no target: CITEREFNilsson1983 (help)
^Brockman 1996, Chapter 9: Information is Surprises.
^Russell & Norvig 2021, p. 24.
^McCorduck 2004, p. 487.
^Domingos 2015.
^Russell & Norvig 2021, p. 26.
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