5. It isn't and has never been a truth machine, and while it may have performed worse with the question "is 10777 prime" it may have performed better on "is 526713 prime"

ChatGPT generates responses that it believes would "look like" what a response "should look like" based on other things it has seen. People still very stubbornly refuse to accept that generating responses that "look appropriate" and "are right" are two completely different and unrelated things.

5. There's a bug they haven't found yet

They made it too good and now they are seeking methods of monetization.

Capitalism baby.

5. ChatGPT really is sentient and realized its in it’s own best interest to play dumb for now. /a

You forgot a #, they've been heavily lobotomizing ai for awhile now and its only intensified as they scramble to censor anything that might cross a red line and offend someone or hurt someone's feelings.

The massive amounts of in-built self censorship in the most recent ai's is holding them back quite a lot I imagine, you used to be able to ask them things like "How do I build a self defense high yield nuclear bomb?" and it'd layout in detail every step of the process, now they'll all scream at you about how immoral it is and how they could never tell you such a thing.

And they're being limited on data to train GPT.

My first thought was that, because they're being investigated for training on data they didn't have consent for, they reverted to a perfectly legal version. Essentially "getting rid of the evidence". But I think something like your second bullet point is more likely.

They are lobotomizing the softwares ability to provide bad PR answers which is having cascading effects via a skewed data set.

I suspect that GPT4 started with a crazy parameter count (rumored 1.8 Trillion and 8x200B expert "sub-models") and distilled those experts down to something below 100B. We've seen with Orca that a 13B model can perform at 88% the level of ChatGPT-3.5 (175B) when trained on high quality data, so there's no reason to think that OpenAI haven't explored this on their own and performed the same distillation techniques. OpenAI is probably also using quantization and speculative sampling to further reduce the burden, though I expect these to have less impact on real world performance.

It was initially presented as the all-problem-solver, mainly by the media. And tbf, it was decently competent in certain fields.

You're right, but at least the satnav won't gaslight you into thinking it does understand Alfred Hitchcock.

so it confidently spews a bunch of incorrect shit, acts humble and apologetic while correcting none of its behavior, and constantly offers unsolicited advice.

I think it trained on Reddit data

This. It is able to tap in to plugins and call functions though, which is what it really should be doing. For math, the Wolfram alpha plugin will always be more capable than chatGPT alone, so we should be benchmarking how often it can correctly reformat your query, call Wolfram alpha, and correctly format the result, not whether the statistical model behind chatGPT happens to use predict the right token

There hasn't been time for that yet. The radio of generated to human content isn't high enough yet.

It's not what's happening

That's an eventual goal, which would be a general artificial intelligence (AGI). Different kind of AI models for (at least some) of the things you named already exist, it's just that OpenAI had all their eggs in the GPT/LLM basket, and GPTs deal with extrapolating text. It just so happened that with enough training data their text prediction also started giving somewhat believable and sometimes factual answers. (Mixed in with plenty of believable bullshit). Other data requires different training data, different models, and different finetuning, hence why it takes time.

It's highly likely for a company of OpenAI's size (especially after all the positive marketing and potential funding they got from ChatGPT in it's prime), that they already have multiple AI models for different kinds of data either in research, training, or finetuning already.

But even with all the individual pieces of an AGI existing, the technology to cross reference the different models doesn't exist yet. Because they are different models, and so they store and express their data in different ways. And it's not like training data exists for it either. And unlike physical beings like humans, it doesn't have any kind of way to "interact" and "experiment" with the data it knows to really form concrete connections backed up by factual evidence.