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AI Researcher Warns Data Science Could Face a Reproducibility Crisis
Long-time Slashdot reader theodp shared this warning from a long-time AI researcher arguing that data science "is due" for a reckoning over whether results can be reproduced. "Few technological revolutions came with such a low barrier of entry as Machine Learning..." Unlike Machine Learning, Data S...
The tooling infrastructure is still very immature and the non-standard combination of data and software creates unforeseen challenges for engineering teams. - Weak software engineering knowledge and practices compounded by the tools themselves; - Knowledge gap in mathematical, statistical, and computational methods, encouraged black boxing API; - Ill-defined range of competence for the role of data scientist, reinforced by a pool of candidates with an unusually wide range of backgrounds; - A tendency to follow the hype rather than the science. - At a minimum, any AI/ML project should include an Exploratory Data Analysis, whose results directly support the design choices for feature engineering and model selection.
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