I tested GPT a lot in other domains, what I found that as long the information explicitly exists (connection between facts) then the responses are fine. I assume that if GPT will reach the state where it can infer new facts, we will be flooded with discoveries that require cross domain knowledge. Nothing like that happened yet.
Feels like we're only one paper away now that the context window has absolutely ballooned.
> Thinking step by step, if King Charles III and his son William abdicate who will be head of state of Australia.
My response (with chatGPT) is:
Firstly, it's important to note that Australia is a constitutional monarchy, which means that the monarch of the United Kingdom is also the monarch of Australia.
If King Charles III were to abdicate, the next in line to the throne is his eldest son, Prince William, who would become the King of the United Kingdom and the Commonwealth Realms, including Australia.
However, if both King Charles III and Prince William were to abdicate, the next in line to the throne is Prince William's eldest child, Prince George. If Prince George is still a minor at the time of the abdication, a regent would be appointed to serve as head of state until he comes of age.
It's worth noting that abdications of this nature are rare and would likely only occur in exceptional circumstances. Additionally, any decision regarding abdication would be made by the monarch themselves and would be subject to the laws and customs of the country in question.
Humans continually update their foundational understanding by assimilating vital information from their "context window" and dumping irrelevant noise. If LLMs could emulate this, it would be a huge win.
Overall, very exciting area of research!
Inapplicable why exactly? Because you say so? Logic isn't magic. Nor is learning. No (external) grounding is required either: iteratively eliminating inconsistent world models is all you need to converge toward a model of the real world. Nothing especially human or inhuman about it. LLM architecture may not be able to represent a fully recursive backtracking truth maintenance system, but it evidently managed to learn a pretty decent approximation anyway.
Chill my friend, no need to get personal. We are talking about ideas. It’s OK to disagree. I am simply dismissing your initial claim. This usually happens when you present a scientific argument based on personal beliefs. If it’s not magic, then we should be able to doubt and examine it and it should eventually pass scientific muster.
> No grounding is required… It evidently managed to learn a pretty decent approximation.
Well, last time I used an LLM it suggested that I should lift the chair I am sitting in. I guess OpenAI has a lot of work to do. They have to eliminate this inconsistent world model for chairs, tables, floor, My dog, my cat and all the cats living on Mars…
edit: added a missing word.
I'm seeing an emergent capability of encoding higher order logic, and the whole point of such abstractions is to not need to hardcode your weights with the minutiae of cats on Mars. LLMs today are only trained to predict text, so it's hardly surprising that they have some gaps in their understanding of Newtonian physics. But that doesn't mean the innate capability of grasping such logic isn't there, waiting for the right training regime to expose it to its own falling apples, so to speak.