Typical Methodologies
As might be guessed from the terms, neats use formal methods – such as logic or pure applied statistics – exclusively. Scruffies are hackers, who will cobble together a system built of anything – even logic. Neats care whether their reasoning is both provably sound and complete and that their machine learning systems can be shown to converge in a known length of time. Scruffies would like their learning to converge too, but they are happier if empirical experience shows their systems working than to have mere equations and proofs showing that they ought to.
To a neat, scruffy methods appear promiscuous, successful only by accident and unlikely to produce insights about how intelligence actually works. To a scruffy, neat methods appear to be hung up on formalism and to be too slow, fragile or boring to be applied to real systems.
Read more about this topic: Neats Vs. Scruffies
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