[internet_points]

> You have to go read it yourself afterwards

^ this is important.

Otherwise you may very well be missing anything really surprising or novel.

See for example https://www.programmablemutter.com/p/after-software-eats-the... , an experience report of NotebookLM where

> It was remarkable to see how many errors could be stuffed into 5 minutes of vacuous conversation. What was even more striking was that the errors systematically pointed in a particular direction. In every instance, the model took an argument that was at least notionally surprising, and yanked it hard in the direction of banality.

[WarmWash]

On one hand 2024 in AI time was a decade ago.

On the other, Google might not have done much to upgrade the podcast feature since them.

[internet_points]

This regression towards the mean is still very much a feature of the newer models, in my experience. I don't see how a model that predicts the most likely word based on previous context + corpus data could possibly not have some bias towards non-novelty / banality.

[mathattack]

It’s gotten somewhat better over time though clearly not their top priority.