There's some fascinating historical back-story and quotes on the talk page of that wikipedia entry, and also an interesting question about how machine learning is neat:

https://en.wikipedia.org/wiki/Talk:Neats_and_scruffies

>Roger Schank first used those terms "scruffy" and "neat" at an AI conference in the 1970s. He proudly called himself a scruffy. 71.183.59.144 (talk) 02:17, 26 October 2011 (UTC)

>The terminology is sourced to the late 1970s or early 1980s and originated by Schenk according to this:

>"In particular, certain personality traits go hand and hand with certain styles of research. Schank and Abelson hit upon one such phenomenon along these lines and dubbed it the neats vs. the scruffies. These terms moved into the mainstream AI community during the early 80s, shortly after Abelson presented the phenomenon in a keynote address at the Annual Meeting of the Cognitive Science Society in 1981. Here are some selected excerpts from the accompanying paper in the proceedings:"

>The article quotes a lengthy excerpt of this keynote address, some of which I include below

>“The study of the knowledge in a mental system tends toward both naturalism and phenomenology. The mind needs to represent what is out there in the real word, and it needs to manipulate it for particular purposes. But the world is messy, and purposes are manifold. Models of mind, therefore, can become garrulous and intractable as they become more and more realistic. If one’s emphasis is on science more than on cognition, however, the canons of hard science dictate a strategy of the isolation of idealized subsystems which can be modeled with elegant productive formalisms. Clarity and precision are highly prized, even at the expense of common sense realism. To caricature this tendency with a phrase from John Tukey (1969), the motto of the narrow hard scientist is, “Be exactly wrong, rather than approximately right”.

>The one tendency points inside the mind, to see what might be there. The other points outside the mind, to some formal system which can be logically manipulated [Kintsch et al., 1981]. Neither camp grants the other a legitimate claim on cognitive science.... an unnamed but easily guessed colleague of mine (Schenk?), who claims that the major clashes in human affairs are between the “neats” and the “scruffies”. The primary concern of the neat is that things should be orderly and predictable while the scruffy seeks the rough-and-tumble of life as it comes ... The fusion task is not easy. It is hard to neaten up a scruffy or scruffy up a neat. It is difficult to formalize aspects of human thought that which are variable, disorderly, and seemingly irrational, or to build tightly principled models of realistic language processing in messy natural domains.

>What are the difficulties in starting our from the scruffy side and moving toward the neat? The obvious advantage is that one has the option of letting the problem areas itself, rather than the available methodology, guide us about what is important. The obstacle, of course, is that we may not know how to attack the important problems. More likely, we may think we know how to proceed, but other people may find our methods sloppy. We may have to face accusations of being ad hoc, and scientifically unprincipled, and other awful things."

>Source is Chapter 5 of this book edited by Schenk and published in 1994, titled "Beliefs, Reasoning, and Decision Making: Psycho-logic in Honor of Bob Abelson". Article needs clean-up, which I am doing now.--FeralOink (talk) 13:58, 2 August 2021 (UTC)

https://books.google.com/books/about/Beliefs_Reasoning_and_D...

>How is machine learning neat?

>Machine learning is only provably correct for the known examples it was trained for. If that is not an adhoc approach to AI, then I don't know what is. Big data is the epitome of a scruffy. No model, just data, not formalism, besides fitting a curve/model to the given data. It is the exact same approach that scruffies follow: abstracting from examples for specific sub tasks.<unsigned>

>Just because some mathematical methods are employed, like optimization for a sub-problem, i.e. curve fitting, does not make the approach itself neat.

>Obviously, scruffies also use mathematically rigorous approaches, when employing provably correct algorithms, such as searching trees, or certain signal processing approaches.

>So far, the only valid "neats", are those doing GOFAI: they use a minimal model and deduce everything based on it, with no added assumptions or axioms along the way.

>Machine learning is only based on added assumptions/axioms: the training data. New for each problem, no general model.<unsigned>

>Yeah, I noticed that too. Not sure who introduced machine learning to the article. I'm trying to clean up, e.g. removing the jargon about scruffies just being casual hackers throwing stuff together in an ad hoc manner. I don't know enough about the people involved though. I know about the methods you mention (curve fitting, converging series, mathematical modeling) but not necessarily who did what. I don't even know whether most of these guys, the neats OR the scruffies, would be comfortable with "big data" (i.e. lots of specious results with very low cost of being wrong).--FeralOink (talk) 10:48, 3 August 2021 (UTC)