I'm really late here, but a few problems:

• Time and space resolution might be too low to allow a meaningful estimate of air resistance, especially if the camera angle doesn't allow you to accurately determine the rock's 3D shape.
• Encoding the color in RGB eliminates spectra.
• If it didn't already have knowledge of the properties of minerals and elements, it would need to calculate them from first principles. Without looking into this specifically, I'd be surprised if it was computationally tractable, especially since the AI doesn't know beforehand our fundamental physics or the values of relevant constants.

And such experts are routinely denounced by people who know little about the subject in question. I leave examples as an exercise for the reader.

True, but that seems inconsistent with taking human experts but not algorithms as authorities. Maybe these tend to be different people, or they're just inconsistent about judging human experts.

Presentation will influence how people receive your ideas no matter what. If you present good ideas badly, you'll bias people away from the truth just as much as if you presented bad ideas cleverly.

I'm not sure that explains why they judge the algorithm's mistakes more harshly even after seeing the algorithm perform better. If you hadn't seen the algorithm perform and didn't know it had been rigorously tested, you could justify being skeptical about how it works, but seeing its performance should answer that. Besides, a human's "expert judgment" on a subject you know little about is just as much of a black box.