[Forgeties79]

Dude I see hilariously absurd ideas for AI implementation in both the film and podcast worlds. There is definitely potential - such as transcriptions - but people are way overselling the efficacy and trying to “disrupt” with AI in ways no industry professional is asking for.

[nikanj]

Most people say they want "speech recognition", but actually mean they want "speech understanding". Think computer from Star Trek, not speech-to-text.

[usrusr]

Even transcription is hard when it's used in the way manual transcriptions are used. Whenever there is sufficient value in transcription to bring the manual process close to worthwhile the cost of mistakes will also be high. But lower value applications can have much lower quality requirements, e.g. imperfect transcription could still be used to generate a high level topic log of a conversation that might be useful e.g. for backtracking after a digression.

The pinnacle of the low failure cost principle must be ad targeting, it costs nothing besides opportunity to display the wrong ad. And the success metric can even be inverted if the mistake is sufficiently surprising: I'd probably be more likely to deliberately click an ad that is entirely off my beaten path, out of curiosity, than something that aligns with my actual interests. Who wouldn't click on an ad for e.g. curling brooms? Curlers.

[TomMarius]

Isn't that a given when you're disrupting the industry itself? Of course not every idea is good.

[Forgeties79]

It’s only disruptive when it actually disrupts something I guess is my point. I’m not so curmudgeonly as to think “this stuff will never work,” I just see so many folks selling it as if it already does.