>It barely learns anything given the amount of computation effort an data that goes into it, but it just happens to be good enough to be practically preferable

>It barely learns anything given the amount of computation effort an data that goes into it, but it just happens to be good enough to be practically preferable to old symbolic systems. This is the part that's always bothered me. For many ML has just become the default tool to throw at a problem where other solutions (albeit less shiny) exist, that might give the same or even better results (and are very likely going to end up being more efficient). Would it be too much of a leap to suggest that we're forgetting to think?