As I use copilot to write software, I have a hard time seeing how it’ll get better than it already is. The fundamental problem of all machine learning is that the training data has to be good enough to solve the problem. So the problems I run into make sense, like:
Copilot can’t read my mind and figure out what I’m trying to do.
I’m working on an uncommon problem where the typical solutions don’t work
Copilot is unable to tell when it doesn’t “know” the answer, because of course it’s just simulating communication and doesn’t really know anything.
2 and 3 could be alleviated, but probably not solved completely with more and better data or engineering changes - but obviously AI developers started by training the models on the most useful data and strategies that they think work best. 1 seems fundamentally unsolvable.
I think there could be some more advances in finding more and better use cases, but I’m a pessimist when it comes to any serious advances in the underlying technology.
Ahh right, so when I use copilot to autocomplete the creation of more tests in exactly the same style of the tests I manually created with my own conscious thought, you’re saying that it’s really just copying what someone else wrote? If you really believe that, then you clearly don’t understand how LLMs work.
I know both LLM mechanisms better than you, it would appear, and my point is not so weak that I would have to fabricate a strawman that I then claim is what you said, to proceed to argue the strawman.
Using LLMs trained on other people’s source code is parasitic behaviour and violates copyrights and licenses.
Ah, I guess I’ll have to question why I am lying to myself then.
Don’t be a douchebag. Don’t use open source without respecting copyrights & licenses. The authors are already providing their work for free. Don’t shit on that legacy.
Not copilot, but I run into a fourth problem:
4. The LLM gets hung up on insisting that a newer feature of the language I’m using is wrong and keeps focusing on “fixing” it, even though it has access to the newest correct specifications where the feature is explicitly defined and explained.
Oh god yes, ran into this asking for a shell.nix file with a handful of tricky dependencies. It kept trying to do this insanely complicated temporary pull and build from git instead of just a 6 line file asking for the right packages.
Yeah, once you have to question its answer, it’s all over. It got stuck and gave you the next best answer in it’s weights which was absolutely wrong.
You can always restart the convo, re-insert the code and say what’s wrong in a slightly different way and hope the random noise generator leads it down a better path :)
I’m doing some stuff with translation now, and I’m finding you can restart the session, run the same prompt and get better or worse versions of a translation. After a few runs, you can take all the output and ask it to rank each translation on correctness and critique them. I’m still not completely happy with the output, but it does seem that sometime if you MUST get AI to answer the question, there can be value in making it answer it across more than one session.
As I use copilot to write software, I have a hard time seeing how it’ll get better than it already is. The fundamental problem of all machine learning is that the training data has to be good enough to solve the problem. So the problems I run into make sense, like:
2 and 3 could be alleviated, but probably not solved completely with more and better data or engineering changes - but obviously AI developers started by training the models on the most useful data and strategies that they think work best. 1 seems fundamentally unsolvable.
I think there could be some more advances in finding more and better use cases, but I’m a pessimist when it comes to any serious advances in the underlying technology.
So you use other people’s open source code without crediting the authors or respecting their license conditions? Good for you, parasite.
Ahh right, so when I use copilot to autocomplete the creation of more tests in exactly the same style of the tests I manually created with my own conscious thought, you’re saying that it’s really just copying what someone else wrote? If you really believe that, then you clearly don’t understand how LLMs work.
I know both LLM mechanisms better than you, it would appear, and my point is not so weak that I would have to fabricate a strawman that I then claim is what you said, to proceed to argue the strawman.
Using LLMs trained on other people’s source code is parasitic behaviour and violates copyrights and licenses.
Programmers don’t have the luxury of using inferior toolsets.
That statement is as dumb as it is non-sensical.
Very frequently, yes. As well as closed source code and intellectual property of all kinds. Anyone who tells you otherwise is a liar.
Ah, I guess I’ll have to question why I am lying to myself then. Don’t be a douchebag. Don’t use open source without respecting copyrights & licenses. The authors are already providing their work for free. Don’t shit on that legacy.
Not copilot, but I run into a fourth problem:
4. The LLM gets hung up on insisting that a newer feature of the language I’m using is wrong and keeps focusing on “fixing” it, even though it has access to the newest correct specifications where the feature is explicitly defined and explained.
Oh god yes, ran into this asking for a shell.nix file with a handful of tricky dependencies. It kept trying to do this insanely complicated temporary pull and build from git instead of just a 6 line file asking for the right packages.
“This code is giving me a return value of X instead of Y”
“Ah the reason you’re having trouble is because you initialized this list with brackets instead of
new().”“How would a syntax error give me an incorrect return”
“You’re right, thanks for correcting me!”
“Ok so like… The problem though.”
Yeah, once you have to question its answer, it’s all over. It got stuck and gave you the next best answer in it’s weights which was absolutely wrong.
You can always restart the convo, re-insert the code and say what’s wrong in a slightly different way and hope the random noise generator leads it down a better path :)
I’m doing some stuff with translation now, and I’m finding you can restart the session, run the same prompt and get better or worse versions of a translation. After a few runs, you can take all the output and ask it to rank each translation on correctness and critique them. I’m still not completely happy with the output, but it does seem that sometime if you MUST get AI to answer the question, there can be value in making it answer it across more than one session.