LLMs make too many mistakes when summarizing papers in their current state, I would never trust it to summarize a whole paper at the moment.

I only use it on a sentence or paragraph basis, otherwise it misses the point 90% of the time.

I would strongly advise against this use for the moment. The important part of reading a paper is not only to extract general rules, but to build your own internal model. Without it you cannot effectively do research. The main interesting points are often in the subtleties of the details deep in the paper.

Internal tought that come easily to mind when I read :

- 'oh they used that equation, but that could be also be interpreted totally differently, what happens if we change point of view, does it makes sense from this other perspective'

- 'I see they claim to achieve better results than sota, but actually, they compared with other methods that are not solving exactly the same problem, what shortcut or changes did they had to do to obtain a fair comparison, is it a fair comparison, can I trust those numbers? '

- 'oh, the authors didn't realize that they solved this other problem, or did they realize but there was a block somewhere preventing it?'

- 'I like this trick to achieve that result, but at the same time, it will prevent to solve a whole class of other problems, so their method will not work on those cases'

...

Also, notice that a paper IS a summary of multiple months/years of work, and researchers summarize it already to the maximum to stay within the page limit, by summarizing a summary you will always miss many things.

Sometimes you don't need a collaborator if you have the idea. If the other party is not at all working on the angle that you're interested in, it's probably not the correct collaboration to ask to.

Also, a collaborator is usually not a stranger over the internet, it's often someone who you know and you already worked with, so it is not that ackward to expose a new idea and propose to work together.

It takes time and social skills to make long lasting collaborations, the two parties must trust each other in order to collaborate. In this context, exchanging ideas is not really an issue.

I'm a little annoyed that 'modern AI' refers here only on LLMs, modern AI is way bigger than that.

Having said that, it's probably a good course, CMU courses are often great.

I was just expecting way more sota models in many fields due to the title.

If someone has this kind of ressource I would be extremely interested!

Very nice project, I see many use cases.

However, I use Manim for maths, for me having computational libraries (python) is a requirement. Most of the transformations that I do are found by using linear algebra, calculus and sometimes full neural networks. All my geometry is computed, not placed by hand.

I'm wondering if it would not be possible to have a 'canvas' backend for the web in the python version instead.

Btw, Manim is kind of easy to install in its own docker ;)

I really loved 'Track Down' (2000), if you want to try. The film depict maybe more 1980-1990 though