MRI uses RF wavelengths, which are measured in meters. Ultrasound waves are measured in fractions of a millimeter. Wavelength is not the relevant factor here.
The instinct of many people (myself included, a lot of the time) is "feed the AI the problems from the domains I'm least good at / familiar with" because those ones are the most frustrating and where I'm the most likely to spin my wheels. It's easy to imagine how this feeds into a cycle of "only put forth effort at things I'm already good at" and a consequent narrowing of professional / intellectual development.
For me it's less fear than an instant "I must kill it / get it out of here" feeling. A big spider or centipede gives me a more intense "creepy crawly" shiver but a cockroach is way higher on the disgust scale for some reason.
If you're outsourcing your writing to AI, I assume you're outsourcing your thinking to it as well. And I don't really care what some weighted average of all human text written on the topic "thinks."
Nice examples, I think I landed on that page quite a while ago. I love Processing and Perlin noise. One thing I enjoyed was using Perlin noise values to compute a "region" on the canvas and making the behavior of a particle depend on which region it was in. Examples:
I don't know, I was never one to "assign roles" to AI myself, but if it ends up working for some people in practice, then I guess it might be worth examining why.
I imagined it as kind of a shorthand for "you should be spending my tokens on looking for / addressing issues like X, Y, and Z," where X, Y, and Z are the sorts of things that an expert in [insert domain here] would be likely to care most about.
