I think these concerns are highly important - I will say the book he referenced, Technics and Civilization, covers this to a great extent. One point I think your comment discerns that Mumford really elicits in his book is the role of various political and socioeconomic institutions in shaping the unconscious norms we have in mind when confronting emerging technology. How different sectors should approach the language approach is a tricky question, and I suppose it walks a fine line between what language is "comfortable" for that area and what is genuinely accurate.

I've referenced this article previously, but I still think that one of the most interesting sources having to do with the future of AI/ML is Noam Chomsky's op-ed in the NYT (link). Whilst I am a strong believer in the potential of ChatGPT, I also recognize it is still too early to make sweeping claims about how it will impact society at large, and I think that reading this article is an excellent exercise in realizing why we shouldn't "over-predict" a phenomenon, even if it does pose large ramifications for society. Ironically enough, Chomsky's explanation of the failure of AI to reach what defines human intelligence mirrors strongly with the fundamentals of this course - indeed, his paragraph discussing of AI's inability to process with explanatory frameworks feels as though it was taken straight from a writeup on the Padua Rainbow. On the other hand, I will cast some mild criticism on Chomsky and suggests that his (rightful?) skepticism of grand claims runs so deeply he falls to the opposite form - a premature dismissal of AI/ML and some claims about its limitations I find slightly hasty. While he is certainly currently right to draw a firm line between ML methods and human language acquisition, it seems a bit unfair to compare the efficacy of a technology still in its relatively early stages with a gargantuan of a bioelectric processor with an evolutionary jump start of hundreds of millennia. As Chomsky claims, we have no idea how the miracle of human language acquisition under limited information works, but this seems to cut in his direction too - because we don't know what sparks actual language acquisition, we can't yet claim AI/ML is incapable of it. Likewise, while it is certainly true current neural network methods provide no clear explanation for produced answers, we have no way of determining that this will be a longstanding problem. My strongest objection is his stance that 1. moral intelligence is a prerequisite of intelligent thinking and 2. that AI has some sort of fundamental limitation on these questions. I think the (frankly optimistic) premise of 1 is sketchy at best, and that it is far too early to determine 2's accuracy. In short, recognize an unknown for an unknown - feel free to hedge what you think your likely expectations are, but don't claim they're the definitive reality until the event is over.

I was also intrigued by the fact that clouds (which seem relatively simple from an outsider's perspective) can be deeply complex. I appreciated Prof. Palmer's more intuitive explanation of looking out the window during an airport, and upon reflection it makes sense that clouds would have a lot of nonlinear behavior that makes them more complicated to model. The question of how one could vary the mesh sheets in a more adaptive way is an interesting one, but I would bet that Prof. Palmer would challenge it by diving into the complicated question of what standard(s) is best to model the mesh off of, and whether there is overlap between them or if they make radically different setups.