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by Mikhail_K
726 days ago
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The article states: "However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally." This is the number one issue with using the so-called "deep learning": the results may be completely wrong and there is no known way to predict when they will be or detect when they are (relying on "deep learning" alone). The worse issue is that by "deep learning" we learn only the coefficients that give accurate predictions on a training set. Extrapolating the results is the hopeful leap of faith that is known to break down catastrophically on some inputs. The "neural nets" do not give us the new knowledge, but rather, an attractive nuisance of a tool. |
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