Maybe not yet, but...

• Spez will turn Reddit into a bot farm and sell this as training data
• Musk turns Twitter into a bigoted cesspool and will sell this as training data, which will subsequently be flagged for low quality (also: a botfarm)
• Threads is a corporate ad dashboard (and we already know how easy it is to GPT copy) and Zuck will sell this as training data
• Facebook is either dead or only good for boomers and Poles
• blogs are dead
• Fediverse is out there waiting to be scraped but possibly too small to sustain a big model

We'te getting there, hopefully.

I suspect future models are going to have to put some more focus on learning using techniques more like what humans use, and on cognition.

Like, compared to a human these language models need very large quantities of text input. When humans are first learning language they get lots of visual input along with language input, and can test their understanding with trial-and-error feedback from other intelligent actors. I wonder if perhaps those factors greatly increase the rate at which understanding develops.

Also, humans tend to cogitate on inputs while ingesting them during learning. So if the information in new inputs disagrees with current understanding, those inputs are less likely to affect current understanding (there's a whole 'how to change your mind' thing here that is necessary for people to use, but if we're training a model on curated data that's probably less important for early model training).

I don't know details of how model training works, but it would be interesting to know if anyone is using a progressive learning technique where the model that is being trained is used to judge new training data before it is used as a training input to update the model's weights. That would be kind of like how children learn by starting with very simple words and syntax and building up conceptual understanding gradually. I'd assume so, since it's an obvious idea, but I haven't heard about it.

That hasn't happened yet. Most likely they quantized GPT-4 more. It's still based on the same training data.

The chatgpt people are really paranoid. Gpt-3 is so good at not halucinating that it often cant, even if it needs to do so to accomplish a task. Fearing the ai will confidently give the wrong answer.

Honestly as a daily user I think it's a combination of it getting worse at understanding vague prompts and people bumbing up against edge cases more. I would suspect the former is due to things like prompt hardening but can only speculate, while the latter isn't hard to imagine just from frequent use.

You mean "I was right* or "i wrote*"?