raghavgoyal14

> look over your code and understand and explain where you might have bugs.

This would certainly be interesting. I'm not aware of active research going on in this area (any pointers would be helpful!).

This would require an agent to have thorough understanding of the logic you're trying to implement, and locate the piece of code where it silently fails. For this you'd again need a training dataset where the input is a piece of code and the supervision signal (the output) is location of the bug. I could imagine some sort of self-supervision to tackle this initially where you'd intentionally introduce bugs in your code to generate training data. But not sure how far this can go!

Imagine all the Stackoverflow accepted answers funneled into your code just because the answers were repeatedly used multiple times in the training data.

I agree on the tab-completion part. Something like Gmail's smart-compose could have potentially huge benefits here.

But I'm not sure about the "deeply understand programming" part. Language modelling and "AI", in its current form, uncovers only statistical correlations and barely scratches the surface of what "understanding" is. This has restricted deployment of majority of academic research into the real-world and this, I believe, is no different and will work only in constrained settings.

Edit: typo

$350 for a modern Nokia?