Cursive big f: "integration", which can be interpreted in two ways. One is "area under the curve" for some part of the curve. Other is "average value of a part of the curve multiplied by the size of that part of the curve". Curve being the function, the graph, f(x), however you wanna call it.
Normal d: "differentiation" (from difference), infinitely small change. Usually used in ratios: df/dx means how much does f(x) change relative to x when you change x a little bit.
Cursive d: "partial", same as normal d but used when working with higher dimensional data like 3D. Can also mean "boundary" of something. Example: boundary of a volume in 3D, like wrapping paper around a box. Or, boundary of such wrapping paper itself, if it's not perfectly connecting.
Omega: just a Greek letter used as a variable, in this case there's a history of it being used as a sort of "density" variable in the field of differential geometry. The college row in the meme is kind of translating the high school row from a function to a 3D volume.
That's specifically LLMs. Image recognition like OP has nothing to do with language processing. Then there's generative AI which needs some kind of mapping between prompts and weights, but is also a completely different type of "AI"
That doesn't mean any of these "AI" products can think, but don't conflate LLMs and AI as being the same
Tbh that's right wing rhetoric. "It doesn't matter that it's factually false, it's enough that it could be true"