text-davinci-002 is often extremely confident about its "predictions" for no
apparent good reason (e.g. when generating "open-ended" text being ~99%
confident about the exact phrasing)
This is almost certainly due to the RLHF "Instruct" tuning text-davinci-002 has
been subjected to. To whatever extent probabilities output by models trained
with pure SSL can be assigned an epistemic interpretation (the model's credence
for the next token in a hypothetical training sample), that interpretation no
longer holds for models modified by RLHF.
Isn't price of energy typically measured in kW hours. Energy = Power x Time.
If a space solar system can output more energy since it stays on for longer, wouldn't this mean that the cost per watt hour would naturally decrease? This would be because the price of a watt hour I imagine would be Energy / price. So, if our launch cost is a fixed cost, then we would find that E / price decreases.
* Very good point: I think the website I linked to refers to peak power, so the
Kilowatthours would be lower. (not sure on this, sorry)
* If the panels on orbit last double the time and produce double the energy
that is only a factor of 4, while the system is about 300 times more
expensive. (but again you have transmission losses that I did not consider)
Should the role of a distiller include spotting mistakes? I assume that you'd only want distillers to get to work once you have some confidence that the original claims are correct.
I'm always happy to be cited :-)
Sample size is one major issue, the other is who/what gets to be in the sample.
Psychology has its issues with using WEIRD subjects.
Software engineering has issues with the use of student subjects, because most
of them have relatively little experience.
It all revolves around convenience sampling.
I love this post.