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Deep learning'˜s reached the end of its rope. At least according to a group of MIT researchers who recently conducted an audit of more than 1,000 pre-print papers on arXiv. We've run out of compute, basically. The researchers claim we'll we'll soon reach a point where it's no longer economically or environmentally feasible to continue scaling deep learning systems. Per the team's paper: Progress along current lines is rapidly becoming economically, technically, and environmentally unsustainable. Thus, continued progress in these applications will require dramatically more computationally-efficient methods, which will either have to come from changes to deep learning or from… This story continues at The Next Web
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