The number one thing to keep in mind about machine learning is that performance is evaluated on samples from one dataset, but the model is used in production on samples that may not necessarily follow the same characteristics...
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If significant uncertainty is involved, go with the human. They may have inferior pattern recognition capabilities (versus models trained on enormous amounts of data), but they understand what they do, they can reason about it, and they can improvise when faced with novelty
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If every possible situation is known and you want to prioritize scalability and cost-reduction, go with the model. Models exist to encode and operationalize human cognition in well-understood situations.
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("well understood" meaning either that it can be explicitly described by a programmer, or that you can amass a dataset that densely samples the distribution of possible situations -- which must be static)
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Hi, you can read this thread from
@fchollet here:https://threader.app/thread/1230611916701229064 …
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