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From my limited experience, ML is good at massively multi-factor problems. If a human can understand the input, normal code will usually suffice. This is why M
by rpmisms 5y ago
From my limited experience, ML is good at massively multi-factor problems. If a human can understand the input, normal code will usually suffice.
This is why ML is pretty much the only option for autonomous driving, but not for calculating credit scores.
- GeneralMayhem 5y agoCredit scores aren't a great point of comparison because they have specific explainability requirements. If your goal is to predict defaults - for instance, if you work for a bank or a hedge fund dealing in bonds - then more sophisticated ML techniques might be appropriate. But credit scores are optimized for consistency, not accuracy. I know that was probably an offhand example, but it's illustrative of the kinds of non-functional requirements that can make ML solutions more or less viable as soon as the technology has contact with human society.
- thinkharderdev 5y agoWas going to say the same. In any decision where the outcome affects a human being, "because the algorithm said so" is usually not a satisfactory answer either to the human being affected or to any regulators who have an interest.