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I work in water management in the Netherlands and it feels the same. A human domain expert knows what kind of thing to look for. Custom tools can help to get a
by Scarblac 6y ago
I work in water management in the Netherlands and it feels the same.
A human domain expert knows what kind of thing to look for. Custom tools can help to get an overview (eg we visualize water velocities of a whole regional system on a map), but generic machine learning can't add much even though we record basically everything everywhere at five minute intervals.
The value of ML is perhaps in things that give little value in the individual case but can be used a huge number of times, like image classification.
- pbhjpbhj 6y agoHuman directed machine learning then? Presumably a domain expert who can also expertly leverage ML/AI could make some useful tools?
- moksly 6y agoThere are no such people though.
- bostik 6y agoI can give one example where the ML/AI toolkit is useful, even if only the largest providers in the world may need to work with real Big Data[tm] volumes. Fraud detection. And you don't need to be a particularly big operator to get sufficiently large & varied data sets. In a way I think it helps that fraud is an ever-evolving, fiercely adversarial domain. New avenues are explored all the time. Occasionally old tricks are revived for a while, because they may work in the margins but become distinguishing features as soon as they see more use. And even if your ML is based on nothing more than a random forest, you can still get surprisingly useful feature combinations out of it. (Or as our data scientists said: individually meaningless features may become a valuable signal when enough of them occur at the same time.)