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Problems that involve interacting with humans, such as natural language understanding, have not proven to be solvable by concise, neat formulas like F = ma. Instead, the best approach appears to be to embrace the complexity of the domain and address it by harnessing the power of data: if other humans engage in the tasks and generate large amounts of unlabeled, noisy data, new algorithms can be used to build high-quality models from the data.
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Halevy et al. (Sun,) studied this question.
www.synapsesocial.com/papers/6a00270f4716aad0cc8599e2 — DOI: https://doi.org/10.1109/mis.2009.36
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:
Alon Halevy
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IEEE Intelligent Systems
Google (United States)
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