It’s great they start using these tools, but I hope they keep in mind that it needs much fine tuning before it’s reliable enough.
You need to use a product in practice to make it better. But you don’t need to rely on it from the start. They need to invest the time in implementation, and where it can be used and shouldn’t be used.
Nice progress towards something good, but we’re not there yet.
This shows that AI isn’t an infallible machine that gets everything right — instead, we can think of it as a person who can think quickly, but its output needs to be double-checked every time. AI is certainly a useful tool in many situations, but we can’t let it do the thinking for us, at least for now.
No, it’s not “like a person who can think.” Unless you mean it’s like an ADHD person who got distracted halfway through the transcript and started working on a different project in the same file.
How can it be that bad?
I’ve used zoom’s ai transcriptions, for far less mission critical stuff, and it’s generally fine, (I still wouldn’t trust it for medical purposes)
Zoom ai transcriptions also make things up.
That’s the point. They’re hallucination engines. They pattern match and fill holes by design. It doesn’t matter if the match isn’t perfect, it will patch it over with nonsense instead.
It’s not the transcripts that are the issue here. It’s that the transcripts are being interpreted by the model to give information.
