That’s because it doesn’t have an actual understanding of the geography of the globe, because the training texts werent sufficient to give it that. It can explain latitude, but doesn’t actually know how to reason about it, even though it can explain how to reason about it. That’s because explaining something and doing it are completely different kinds of tasks.
If it does this with the globe and simple stuff like latitudes, what are the chances it will mess up basic relationships between organs, symptoms, treatments, etc for the human body? Im not going to trust medical advice from these things without an awful lot of very strong evidence.
I think you mean that it can only intelligently converse in domains for which it's seen training data. Obviously the corpus of natural language it was trained on does not give it enough information to infer the spatial relationships of latitude and longitude.
I think this is important to clarify, because people might confuse your statement to mean that LLMs cannot process non-textual content, which is incorrect. In fact, adding multimodal training improves LLMs by orders of magnitude because the richer structure enables them to infer better relationships even in textual data:
Multimodal Chain-of-Thought Reasoning in Language Models, https://arxiv.org/abs/2302.00923
As I said in my comment, even if the model 'knows' and tells you that town A is at 64' North latitude and town B is at 53', it will sometimes tell you town B is the furthest north.
That's because it's training set includes texts where people talk about one town being further north that the other, and their latitudes, but the neural net wasn't able to infer the significance of the numbers in the latitude values. There wasn't enough correlation in the text for it to infer their significance, or generate a model for accurately doing calculations on them.
Meanwhile the training text must have contained many explanations of what latitude and longitude are and how to do calculations on them. As a result the model can splurge out texts explaining latitude and longitude. That only helps it splurge out that kind of text though. It doesn't do anything towards actually teaching it what these concepts are, how they relate to a spherical geographic model, or to actually do the calculations.
It's the same way GPT-3 could reliably generate texts explaining mathematics and how to do arithmetic in lots of very accurate detail, because it was trained on many texts that gave such explanations, but couldn't actually do maths.
It is possible to overcome these issues with a huge amount of domain relevant training text to help the LLM build a model of the specific problem domain. So these problems can be overcome. But the point stands that just because a model can explain in detail how to do something, that doesn't mean it can actually do it itself at all. They're completely different things that require radically different training approaches.
I think it's really cute how defensive and dismissive humans get (including those who profess zero supernatural beliefs) when they're trying so valiantly to write off all AI as a cheap parlor trick.
I'm guessing it is fewer than Microsoft.
