Yes, and sometimes they are very different. I was surprised to see the German Wikipedia had a couple of articles on Scottish history that were better than the English language one!
None? It is not certain any country will benefit. Countries built their infrastructure and population centers according to the weather of the location. If the weather changes probably every country will have to adjust.
If you are asking which area will benefit from climate change I would say Siberia as it will become increasingly important due to the northern corridor remaining ice free and because a lot of people will be displaced by weather/sea level. And that place is empty. Additionally, it has nice farming soil which right now is not used since there are easier places to farm but in a warming world this could change
Why in this story students use GPT and professors don't?
If you are an expert -- sit down and start working with GPT on your own. See what it can and what it can not do. See where it helps. See where it hands down lose.
You are right - this is a new activity professors have to pick up. A latent point in my previous comment was that maybe some professors have not as much expertise as may be required. Now that this expertise is sort of democratised there's more pressure on professors to get better.
I sure does use it to prepare exams etc. Problem is students don’t seem to see the reason for not using GPT to solve them ;) for me it perfectly makes sense to prepare exams and labs with some AI help… but this questions both my role and the role of in-person education.
Yeah I agree! I use GPTs (Gemini 2 series and o3 etc.) and they are excellent! But even they sometimes don't quite get the nuance of things or subtly misses the point! There are certain "meta-cognitive" limitations ... I would never bet against the AGI industry and I can only assume that these issues will eventually be solved after we build planets of computronium and force every word and thought any human ever utters to be recorded and trained on. But for now there remain some limitations.
Unless we intend to surrender everything about human symbolic manipulations (all math, all proving, all computations, all programming) to llm in the nearest future, we still need some formal representations for engineering.
The major part of tradidional NLP was about formal representations. We are still to see the efficient mining techniques to extract the formal representations and analyses back from LLM.
How would we solve the traditional NLP problems, such as, for example, formalization of law corpus of a given country with LLM?
As an approximation we can look at non-natural language processing, e.g. compiler technologies. How do we write an optimizing compiler on LLM technologies? How do we ensure stability, correctness and price?
In a sence, the traditional NLP field has just doubled, not died. In addition to humans as language capable entities, who can not really explain how they use the language, we now also have LLM as another kind of language capable entities. Who in fact also can not explain anything. The only benefit is that it is cheaper to ask LLM the same question a million of times that a human.
You have "Rows" repeated eight times in this short snippet. You have all the rest of your variables as two letter names. It feels low level, almost like Fortran.
Between the existence of Austria-Hungary empire, when the Latin alphabet was very relevant for all the Balkans, and the end of WW2, when the Cyrillic became much more relevant for them is just 1945-1918=27 years.
