[j-pb]

We are in the age of machine learning, whenever you search for something online, take a photo with your phone, drive in your (diver assisted) car, filter out spam, get a product or video recommendation, you are using a system that is founded on calculus.

Just because computer science started with file systems and regexes (discrete math) doesn't mean that the systems of today or tomorow don't have very different foundations.

[scrapheap]

I'm pretty certain that the systems of today have the same foundations as those of yesterday, we've just abstracted them away so we don't directly see it as often.

[j-pb]

That doesn't make a lot of sense given the novel hardware that runs in a lot of chips these days. TPUs, neural accellerators, perceptron branch prediction, flash based neural networks.

Thise are not abstractions over existing posix APIs or von neumann architectures, they are novel ways to do computation from a different branch of math.

Heck we don't even know if some of these are not breaking the church turing thesis by computing with true real numbers and not the computable subset that turing machines can handle.

[ghoshbishakh]

Everything is discrete. Computers cannot handle "true" real numbers.

[j-pb]

Digital computer cannot handle true real numbers.

Analog computers MIGHT.

It really depends on how quantized our universe is. If you have papers that show full quantization including tunneling probabilities e.t.c please share them! The physical existence of the reals has been a white whale of mine for some time ^^'.

[seanstrom]

Yeah these are good points. I guess we could also add AI to the list (AI before ML), since it also has use of calculus I think.

I’m now wondering whether to consider both of these subjects foundational to CS or a CS degree. My gut says yes since so much relies on ML and AI is used a bunch (e.g. games and stuff).

[j-pb]

AMR Ryzen uses perceptrons in it's branch predictor, I'd say that's pretty foundational.