There’s a kind of grief that I feel when I think about how, over the last 70 years, engineers have tended to increasingly rely on tech that makes the math knowledge unnecessary.
The beauty of math has played a huge role in the career choices that I’ve made so far. I changed fields at one point because I realized that the math that practitioners used was almost exclusively all baked into CAD tools. I figured that if that was where the applied math lived, then I wanted to write software for a living.
With a lot of the AI tools now (which I use and appreciate daily FWIW), I’m beginning to see that even in math-heavy sub disciplines of software engineering (e.g. robotics), we’re not exempt from this trend.
I once had a mentor that was trying to help me write a CPU characterization and being a phd in math every suggestion he proposed involved derivatives. I was like, why would I describe I486 cache misses in terms of differential equations? To this day I still don’t know what he meant.
TBH, this might be a good time to figure out what he was talking about! A good continuous approximation of a discrete problem can radically simplify the situation. And current LLMs are extremely patient tutors.
The beauty of math has played a huge role in the career choices that I’ve made so far. I changed fields at one point because I realized that the math that practitioners used was almost exclusively all baked into CAD tools. I figured that if that was where the applied math lived, then I wanted to write software for a living.
With a lot of the AI tools now (which I use and appreciate daily FWIW), I’m beginning to see that even in math-heavy sub disciplines of software engineering (e.g. robotics), we’re not exempt from this trend.
See https://math.stackexchange.com/questions/4752749/how-could-f...