I think NN=kNN is reductive to the point of near uselessness as a statement. You can treat human object recognition as a sort of kNN as well in that we 'detect'

I think NN=kNN is reductive to the point of near uselessness as a statement. You can treat human object recognition as a sort of kNN as well in that we 'detect' a large number of features, the grouping of which ultimately allows us to recognize something. But of course that doesn't actually tell us much about how we recognize things, since that explanation just changes the question to "how do we detect useful features?". Similarly, I think most ML people recognize that NN classifiers are just learning to look for groups of features, yet that doesn't really count as an analysis because what we want to understand is why it arrives at the features it does. >The magic of ML is a sleight-of-hand trick -- we dont really need to know how its averaging of our data does anything useful -- it almost never does. I strongly disagree, we do need to know how the 'averaging' does what it does so we can improve our models further. A lot of big steps in NN architectures have come in part from applying theories and insights regarding the properties of the training process, so a deeper understanding of the process may allow us to make better models, ones that have better accuracy as well as ones that train faster with less data. >Rather, it is our "experimental design" which produces the usefulness of the system. ML algorithms are just interpolations and averages through data prepared to produce useful averages by (literally millenia) of human ingenuity. >It takes actual intelligence to do this because the world isnt data, and almost any measurement one cares to make (with eyes, even) produces endless ambiguities and coincidences that you have to "be in the world" to resolve; and resolution is a dynamic process which you "have to be here for". I think most of the industry agrees that current ML models are sort of like being able to make the icing but not the cake (i.e AGI), so I'm not really sure what you're trying to say. Of course the models need a general intelligence to filter general data down to a specific problem and set things up for the model to learn?