The problem is the part that I denoted as "hand waving". If you are asking it things about coding or other details it seems pretty good at, it can get pretty deep into it and thus that 'hand waving' can be resolved.

If you are asking about aerospace engineering concepts at the level of a PhD thesis... it just hand-waves away problems in ways that, when pressed, it cannot give detail on.

So, as far an a algorithm goes, the problem becomes determining the computational complexity of the 'hand waving' part. For writing reactJS frontend APIs it seems like it's pretty damn low and thus the AI can spit out code and even fix bugs for you. For developing something that actually took a human 7+ years of undergrad, graduate, and PhD work to ascertain and work out.... no chance.

But yes that general algorithm works for all walks of life essentially and I keep getting a format like that for everything setup I ask about, as if it was trained on 'How to answer a hard technical question in these easy steps...' and it is sticking to that.

I'm not well versed in this but IIUC the less context/data that exists, the less it should afford the creation of a succinct response, right? This would make sense for the gap between "frontend js api" responses, and "aerospace thesis" responses.

Would it be safe to assume the limiting factor with this or any algo of this type might always be availability of context/data, and these niche questions might always be an edge case?

Are there alglrithms that can reliably extrapolate "new" data/context that does not exist yet and is accurate IRL?

Sorry if my terminology or understanding is way off, I don't know much about this field.

No I think that is pretty much it- it is deep learning after all, the scope of the training data limits the model. It's so good with code because not only is there a TON of code in the data set, there is a TON of data of tutorials and examples and documentation EXPLAINING the code! I've long been amazed at the computer programming world at the amount of content programmers write about... programming. When it comes to other engineering fields, people don't write nearly as much on the internet (and, ofter, are not allowed to write as much, publicly) about the topics. So of course these large language models are simply not going to be able to produce super detailed analysis of those fields.

There is something a bit more needed - Meta did a deep model recently with training specifically more on scientific papers, and it is able to 'talk the talk' better but unfortunately the 'understanding' just isn't there.

I think at the end of the day, programming languages are languages after all, thus work well with these tools, but there is something harder about scientific reasoning and interdisciplinary planning that is going to take something a bit more. This is the main goal of various research into bring symbolic methods back into interface with deep learning models.