dira3

10+ years ago, "AI" would likely refer to work in RL, evolutionary/genetic algorithms, etc.

Nowadays most of the spotlight seems centered on LLMs, CNNs, and other methods that are either human-labeled or at least reliant on human-created data, and have a static separation between "learning" and "inference".

I know that there are still non-LLM, non-CNN, non-anthropocentric topics of AI development currently, in RL and in other areas. Which would you say are the most prominent or promising today, or likeliest to come to fruition?

If I am trying to read/explore/join/ETL deeply nested tables in PSQL or Spark, Cursor doesn't seem to work very well for me.

Which LLM-assistant tools have worked for you to expedite data exploration? Whether in a notebook format or otherwise.

Or is there simply a better way to use Cursor for this purpose that I haven't thought of?

About 10 years ago, multi-armed bandits (and especially contextual bandits) were really important for optimization and business use cases.

But nowadays you hardly hear about it, despite optimization continuing to be a need. The only major open source library continues to be Vowpal Wabbit - and even that is not very robustly maintained or documented.

What are people doing for bandits or optimization needs nowadays? And what are some active, Python-centric, open-source libraries with a strong user community and stable code?