101–110 of 158 posts
Creating flashy AI demos relatively easy. Creating important AI products that actually operate in the real world is the difficulty.
When is OpenAI planning to actually solve a hard problem? They have spent a huge amount of money and time creating useless demos so far. Creating flashy AI demos relatively easy. Creating important AI products that actually operate in the real world is the difficulty.
Earlier quoted context omitted.
I think you are underselling the potential of a model which deeply understand programming. Imagine combining such a model with something like AutoML-Zero: https://arxiv.org/abs/2003.03384 It may not be 'creative', but used as tab-completion, it's not being rewarded or incentivized or used in any way which would expose its abilities towards creating a new sort algorithm.
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 t…
Earlier quoted context omitted.
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 t…
It would be nice to have an AI that could write unit tests, or 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!
So that's basically program synthesis from natural language (ish) specifications (i.e. the comments). I can see this being a useful tool [1]. However, I don't expect any ability for innovation. At best this is like having an exceptionally smart autocomplete function that can look up code snippets on SO for you (provided those code snippets are no longer than one line). That's not to say that it can't write new code,…
Earlier quoted context omitted.
It would be nice to have an AI that could write unit tests, or look over your code and understand and explain where you might have bugs.
> 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…
2. Generate test cases from fuzz results.
3. Run tests and walk outward from symbols around relevant stacktrace frames (line numbers,).
4. Mutate and run the test again.
...
Model-based Testing (MBT) https://en.wikipedia.org/wiki/Model-based_testing
> Models can also be constructed from completed systems