You have no idea how many times you use it.
Maps? Recommendation systems (the algo)? Other optimizations?
BTW, to the best of my knowledge, dictation still has a high error rate.
There is only so much AI can do because it currently lacks in certain domain knowledge.
The worst one I ever had to fix was ESPN captions of commentary for some indoor motorcross thing with dirt bikes going around a track. First, the motorbike noise, but secondly, the commentators were using the (well known to fans) nicknames for all the riders, which the AI had no idea how to transcribe, no idea who they were, and were almost impossible for me to even Google.
Whisper has a feature where you can provide it with a text prompt before it starts transcribing to clue it up to additional vocabulary that it may need to know.
Like it or not "AI" has become synonymous with generative AI, when most people talk about how much they use AI they don't mean the YouTube algorithm or the neural network that handles autocorrect on their phone.
Whisper (OpenAI 2022) and other recent dictation models are rather good. Whisper automatically punctuates sentences and usually gets just a few words wrong in my experience.
Artificial Analysis says Whisper 3 has a 10% word error rate, although it typically does better than that in my experience. When I use Whisper 2 in the ChatGPT app it usually gets 2 or 3 words wrong per paragraph of prompt.
But that is really bad, no? I am not a user so please enlighten me on the ergonomics, but I understand that to mean I have to check and correct everything.
There is only so much AI can do because it currently lacks in certain domain knowledge.
The worst one I ever had to fix was ESPN captions of commentary for some indoor motorcross thing with dirt bikes going around a track. First, the motorbike noise, but secondly, the commentators were using the (well known to fans) nicknames for all the riders, which the AI had no idea how to transcribe, no idea who they were, and were almost impossible for me to even Google.