Deep Learning is currently an empirical science guided by intuition of practitioners. A main principle in experimental sciences is that a theory without predictive power is not considered a full-fledged theory. As such, unless they are interesting predictions coming from their theory (rather than only barely justifying existing empirically observed phenomena), this is just speculative theory that I would not use the phrase "Foundations Built" for.
As an example of this general litmus test for a theory see e.g. Eddington's confirmation of GR: https://en.wikipedia.org/wiki/Tests_of_general_relativity#De... . If there are hitherto unknown phenomena in DL predicted by this theory then I'd stand corrected and concede that there may be something to these theories.
As an example of this general litmus test for a theory see e.g. Eddington's confirmation of GR: https://en.wikipedia.org/wiki/Tests_of_general_relativity#De... . If there are hitherto unknown phenomena in DL predicted by this theory then I'd stand corrected and concede that there may be something to these theories.