Indeed, and this has been the case for quite a while now. You can always improve on some general algorithm by taking advantage of knowledge of the data but that never generalizes and usually leads to either worse performance on other data and/or new pathological cases that result in results that are unusable.
>Indeed, and this has been the case for quite a while now. You can always improve on some general algorithm by taking advantage of knowledge of the data but that never generalizes and usually leads to either worse performance on other data and/or new pathological cases that result in results that are unusable.
Deepmind did the exact same thing with AlphaTensor. While they do some geniunely incredible things, there's always a massive caveat that the media ignores. Still, I think it's great that they figured out a way to search a massive space where most of the solutions are wrong, and with only 16 TPUs running for 2 days max. Hopefully this can be repurposed into a more useful program, like one that finds proofs for theorems.
Ship the optimization framework in with the application, sample from the user data, and optimize for that? It isn’t overfitting if you overfit on the data you care about, right?
Data tends to change over time, and once a hash function is in use you can't really replace it easily without a lot of overhead, possibly quite a bit more overhead than what you saved in the first place. There are some examples of this in the sorting arena too, such as 'Timsort', personally I haven't found any that gave a substantial boost, but probably there are some cases where they do. Unless sorting or hashing (and lookup) are the main bottleneck for an application I would spend my time on other aspects of it.
It's an instance of overfitting.