Totally agree. I worked on a project that had to try and extract tables from PDFs. It is much harder that it would first appear.

Detecting where tables are is still an active research areas. Once we know where on the page, it’s easier to parse out their structure.

To be fair, that particular comic's 5 year task was solved shortly after it was posted, at least in part to prove it was wrong.

Without having heard of or tested the solution, I'll bet anyone $1M that I can produce an image that produces an incorrect answer. Which would mean it's not "solved".

If I can produce an image that you incorrectly label as Bird or No Bird, does that mean it's accurate to say you cannot tell me if pictures have birds in them? Or is that needlessly pedantic beyond any practical use case and clearly the intended context?

No, I don't think it's pedantic and impractical. There are many use cases where the possibility of adversarial input makes heuristics a non-starter.

Please tell me the practical concerns regarding adversarial inputs against a phone app that tries to tag pictures of birds in national parks.

Well of course if you define it narrowly that's true, but computer vision has many more safety-critical applications, most obviously self-driving.

"Doing better than me", or any other human, wasn't the problem proposed. Anything other than 100% accuracy means the problem isn't solved as there will always be room for a better solution.

The problem posed was "can my app tag photos with birds". The answer is now yes for all practical purposes.

You italicized the word 'solved' to emphasize that under technical scrutiny it is not true in response to a claim about being pedantic.

100% accuracy on identifying birds is, epistemologically, impossible.