Unfortunately, removing Harris from the ticket doesn't have the best optics in a lot of scenarios.
Exactly. The difference between a cached response and a live one even for non-AI queries is an OOM difference.
At this point, a lot of people just care about the 'feel' of anti-AI articles even if the substance is BS though.
And then people just feed whatever gets clicks and shares.
It's right in the research I was mentioning:
https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html
Find the section on the model's representation of self and then the ranked feature activations.
I misremembered the top feature slightly, which was: responding "I'm fine" or gives a positive but insincere response when asked how they are doing.
This comic would slap harder if not for the Supreme Court under christofascist influence from the belief in the divine right of kings having today ruled that Presidents are immune from prosecution for official acts.
That whole divine king thing isn't nearly as dead as the last panel would like to portray it.
But you also don't have Alfred as the one suiting up to fight the Joker either.
This is incorrect as was shown last year with the Skill-Mix research:
Furthermore, simple probability calculations indicate that GPT-4's reasonable performance on k=5 is suggestive of going beyond "stochastic parrot" behavior (Bender et al., 2021), i.e., it combines skills in ways that it had not seen during training.
The problem is that they are prone to making up why they are correct too.
There's various techniques to try and identify and correct hallucinations, but they all increase the cost and none are a silver bullet.
But the rate at which it occurs decreased with the jump in pretrained models, and will likely decrease further with the next jump too.
Here you are: https://www.nature.com/articles/s41562-024-01882-z
The other interesting thing is how they get it to end up correct on the faux pas questions asking for less certainty to get it to go from refusal to near perfect accuracy.
Even with early GPT-4 it would also cite real citations that weren't actually about the topic. So you may be doing a lot of work double checking as opposed to just looking into an answer yourself from the start.
Part of the problem is fine tuning is very shallow, and that a contributing issue for claiming to be right when it isn't is the pretraining on a bunch of training data of people online claiming to be right when they aren't.
"Shhh honey, I'm about to kill God."