As far as DFT is concerned, ML can have a great impact from (at least) three perspectives: 1) improving DFAs, 2) accelerating DFT calculations, evaluation of potential-energy surfaces and (Born-Oppenheimer) molecular dynamics or, catastrophically for DFT, 3) obviating the need for DFT if ML-accelerated wave-function calculations become "infinitely" efficient (of course the advantages of a (Kohn-Sham) orbital picture would remain). I think the next few years will be very dynamic on all of these fronts, and others. And we, DFT aficionados, should also be paying attention to advances in quantum computers. . . (4.7.3) De Proft and Geerlings : ML may be a valuable tool to inspire researchers in conceptual DFT to look for possible relationships between reactivity and conceptual-DFT descriptors that are not obvious when a limited number of cases are explored "by hand". Care should of course be taken that, in the end, the researcher comes to a point where the link proposed by the ML ansatz provides him/her with a reasonable insight into the problem.