Maybe they realized that they were falling behind too much? To me it seems like user-feedback on bad descisions by the AI once it's trained to a basic level is among the most important signals in tuning the model to perform better.
Maybe they realized that they were falling behind too much? To me it seems like user-feedback on bad descisions by the AI once it's trained to a basic level is among the most important signals in tuning the model to perform better.
That's what I believe. Once you have scanned every passive source available, using the user conversations to e.g. find common paths to a solution and shortcut them would seem natural.
Paths and shortcuts isn't the most important part here I think, rather negative signals about unfitting choices is more important, do we use algorithm/library/etc XYZ in this situation or not, the developers using it would provide the context suitability of options on a more finegrained level than resulting (semi-)public artifacts provide.