I remember also having a massive wtf reaction to realizing that original ChatGPT was pretty good at decoding long random/unique base64 strings.

I think it's not that simple. A tuner is "hearing" the fundamental and all of the harmonic overtones combined. It has to guess at which frequency is the fundamental, even if the overtones are actually stronger than it amplitude-wise, and it does that by looking at the nature of the repeating overtone pattern and extrapolating back to the fundamental.

I think you can end up an octave too low (half the actual frequency) if the waveform repeats in a way that implies a different overtone repetition pattern, for example if there's an every-other-cycle artifact to the waveform.

More relevantly, you seem to me to be unduly confident about what this pilot's associative triggers might and might not be.

(I'm not strongly arguing against the murder scenario, just against the idea that it's impossible for it to be the confusion scenario.)

This is obviously an overstatement. Any two regularly performed actions can be confused. Sometimes (when tired or distracted) I've walked into my bathroom intending to shave, but mistakenly brushed my teeth and left. My toothbrush and razor are not similar in function or placement.

(Presumably delaying the amount of time before a raging engine fire stops receiving fuel would also have an impact on safety?)

But it's really just (more) indirect prompt injection, again. It affects every similar use of LLMs.

It could just be using speech to text (e.g. Whisper) on your input, and then using its text model on the text of your words. Or has OpenAI said that they aren't doing this?

I like this language quirk a lot. It almost feels subversive, pointing out through grammar that group entities are just people, responsible for their choices like everyone else.