I'm assuming that - by design - it has a feed-back-loop that allows it to tweak, learn and improve itself by feeding back the choices people make vs its own recommendations.
GitHub Copilot
231–240 of 1001 posts
Re: GitHub Copilot
#232So this is what happens when you're owned by Microsoft who has an exclusive contract with OpenAI. A couple weeks ago I ran a few experiments with AI-based code generation ( https://news.ycombinator.com/item?id=27621114 ) from a GPT model more suitable for code generation: it sounds like this new "Codex" model is something similar. If anyone from GitHub is reading this, please give me access to the Alpha so I can see…
I saw that post, neat stuff. We made an attempt to develop something similar 4 years ago and take it to YC, it simply wasn't good enough often enough because our training data (Stack Overflow posts) was garbage and models were weaker back then. I figured it would take about 5 years for it to really be useful given the technology trajectory, and here we are.
I'll note that we weren't trying to build "code auto-complete" but instead a automated "rubber duck debugger" which would function enough like another contextually-ignorant but intelligent programmer that you could explain your issues to and illuminate the solution yourself. But we did a poor job of cleaning the data and we found that English questions started returning Python code blocks, sometimes contextually relevant. It was neat. This GitHub/OpenAI project is neater.
I would be curious what the cost of developing and running this model is though.
Re: GitHub Copilot
#233For a second I got afraid that it is Copilot by Kite, with their infamous history (https://news.ycombinator.com/item?id=19018037).
Re: GitHub Copilot
#234Earlier quoted context omitted.
What do you think about this being overall detrimental to code quality as it allows people to just blindly accept completions without really understanding the generated code. Similar to copy-and-paste coding. The first example parse_expenses.py uses a float for currency - that seems to be a pretty big error that's being overlooked along with other minor issues around no error handling. I would say the quality of the…
Why would you say it's an error to use a float for currency? I would imagine it's better to use a float for calculations then round when you need to report a value rather than accumulate a bunch of rounding errors while doing computations.
Standard practice is to use a signed decimal number with an appropriate precision that you scale around.
Re: GitHub Copilot
#235This is very impressive! OpenAI’s tech opens an ethical Pandora’s box: 1. It’s clear that the raw inputs to all of OpenAI’s outputs originated with real, human creativity. 2. So, in a sense, OpenAI is laundering creativity. It reads in creative works, does complicated (and, yes, groundbreaking) transformations, and produces an output that is hard to trace to any particular source. 3. Yet, isn’t that effectively what…
> Yet, isn’t that effectively what human brains do too?
If I want to watch a bunch of movies, I have to pay the theater for each movie, or pay netflix, or whatever. The screenplay I write afterwards belongs to me, but the learning process involved me paying for access to others' work. That's what's often missing here. But at the same time, if you train on legally public data, there's no 'theater' to be paid.
(Often, people train on illegally public data though, like the eleuther folks. That's a whole extra can of worms I've ranted about plenty).
Maybe we'll start seeing licenses with a section saying "not for use as training data for commercial models."
Re: GitHub Copilot
#236Earlier quoted context omitted.
What do you think about this being overall detrimental to code quality as it allows people to just blindly accept completions without really understanding the generated code. Similar to copy-and-paste coding. The first example parse_expenses.py uses a float for currency - that seems to be a pretty big error that's being overlooked along with other minor issues around no error handling. I would say the quality of the…
Why would you say it's an error to use a float for currency? I would imagine it's better to use a float for calculations then round when you need to report a value rather than accumulate a bunch of rounding errors while doing computations.
It is widely accepted that using floats for money[1] is wrong because floating point numbers cannot guarantee precision.
The fact that you ask is a very good case in point though: Many programmers are not aware of this issue and would maybe not question the "wisdom" of the AI code generator. In that sense, it could have a similar effect to blindly copy-pasted answers from SO, just with even less friction.
[1] Exceptions may apply to e.g. finance mathematics where you need to work with statistics and you're not going to expect exact results anyway.
Re: GitHub Copilot
#237Hi HN, we've been building GitHub Copilot together with the incredibly talented team at OpenAI for the last year, and we're so excited to be able to show it off today. Hundreds of developers are using it every day internally, and the most common reaction has been the head exploding emoji. If the technical preview goes well, we'll plan to scale this up as a paid product at some point in the future.
I'm glad they find it head exploding but my concern is that it would be most head exploding to newbies who don't have the skill to discern if AI code is how it should be written.
For a seasoned veteran writing the code was never really the hard part in the first place.
Re: GitHub Copilot
#238Earlier quoted context omitted.
Might this end up putting GPL code into projects with an incompatible license?
It shouldn't do that, and we are taking steps to avoid reciting training data in the output: https://copilot.github.com/#faq-does-github-copilot-recite-c... https://docs.github.com/en/early-access/github/copilot/resea... In terms of the permissibility of training on public code, the jurisprudence here – broadly relied upon by the machine learning community – is that training ML models is fair use. We are certain this…
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Re: GitHub Copilot
#239Earlier quoted context omitted.
This is obviously controversial, since we are thinking about how this could displace a large portion of developers. How do you see Copilot being more augmentative than disruptive to the developer ecosystem? Also, how you see it different from regular code completion tools like tabnine.
How many jobs have developers helped displace in business and industry? I don't think it's controversial that we become fair game for that same automation process we've been leading.
> How many jobs have developers helped displace in business and industry?
How many?
> I don't think it's controversial that we become fair game for that same automation process we've been leading.
This is not correct. A human (developer) displacing another human (business person) is entirely different than a tool (AI bot) replacing a human (developer).
Regardless, this is the Lump of Labour fallacy (https://en.wikipedia.org/wiki/Lump_of_labour_fallacy).
In this case, it is assumed that the global amount of development work is fixed, so that, if AI takes a part of it, the equivalent workforce in terms of developers, will be out of job. Especially in the field of SWE, this is obviously false.
It also needs to be seen what this technology will actually do. SWE is a complex field, way more than typing a few routines. In best case (technologically speaking) this will be an augmentation.
Re: GitHub Copilot
#240Since most of the code written anywhere is crap (the tool was trained with "millions" of lines of code) I suspect it will repeat all the same anti-patterns,bad-structured,ill-thought code which fills Github.