Pack it all up, boys, programming's over. Hello, AI. Anyone want to hire me to teach your grandma how to use the internet?
Few days back, Sam Altman tweeted this "Prediction: AI will cause the price of work that can happen in front of a computer to decrease much faster than the price of work that happens in the physical world. This is the opposite of what most people (including me) expected, and will have strange effects" And I was like yeah I gotta start preparing for next decade.
I wonder does Sam Altman also believe that you can measure programmer productivity by lines-of-code?
In general: (1) training ML systems on public data is fair use (2) the output belongs to the operator, just like with a compiler. On the training question specifically, you can find OpenAI's position, as submitted to the USPTO here: https://www.uspto.gov/sites/default/files/documents/OpenAI_R... We expect that IP and AI will be an interesting policy discussion around the world in the coming years, and we're eager to…
How do you guarantee it doesn't copy a GPL-ed function line-by-line?
How do you know that when you write a simplish function for example, it is not identical to some GPL code somewhere? "Line by line" code does not exist anywhere in the neural network. It doesn't store or reference data in that way. Every character of code is in some sense "synthesized". If anything, this exposes the fragility of our concept of "copyright" in the realm of computer programs and source code. It has always been ridiculous. GPL is just another license that leverages the copyright framework (the enforcement of GPL cannot exist outside such a copyright framework after all) so in such weird "edge cases" GPL is bound to look stupid just like any other scheme. Remember that GPL also forbids "derivative" works to be relicensed (with a less "permissive" one). It is safe to say that you are writing code that is close enough to be considered "derivative" to some GPL code somewhere pretty much every day, and you can't possibly prove that you didn't cheat. So the whole framework collapses in the end anyways.
How is it different from the status quo of people just doing the wrong thing or copy pasting bad code? Yes there's the whole discussion below about float currency values, but I could very well see the opposite happening too, where this thing recommends better code that the person would've written otherwise.
> How is it different from the status quo of people just doing the wrong thing or copy pasting bad code? Well, yes, the wrong code would be used. However - the wrong code would then become more prevelant as an answer from gh, causing more people to blindly use it. It's a self-perpetuating cycle of finding and using bad and wrong code.
Hmm, not quite. My point was that if they aren't a good enough programmer to understand why the code is wrong, then chances are they would've written bad code or copy pasted bad code anyways. It just makes the cycle faster.
And again, I could argue that the opposite could happen too, people who would otherwise have written bad code could be given suggestions of better code that they would've written.
Automation has always produced an increase in jobs so far, although sometimes in a disruptive way. I consider this like the switch from instruction-level programming to compiled languages, a level of abstraction added that buys a large increase in productivity and makes projects affordable that weren’t affordable before. If anything this will probably lead to a boom in development work. But there’s a bunch of low ski…
> Automation has always produced an increase in jobs so far Do you have a source for this re the last 20 years? It seems to me automation has been shifting the demand recently towards more skilled cognitive work.
A global increase in jobs, a decrease in the west.
So if it was trained using "source code from publicly available sources, including code in public repositories on GitHub." was it also GPLv2? So everything generated also GPLv2?
This is a bit tricky, because at least in the U.S., I don't believe it's settled question in law yet. Some of the other posters on here have said that the resulting model isn't covered by GPL--that's partially true, but provenance of data, and the rights to it, definitely does matter. A good example of this was the Everalbum ruling, where the company was forced to delete both the data and the trained models used they…
until the legal position is clear it you'd have to be insane to allow output from this process to be incorporated into your codebases
imagine if the output was ruled as being GPLv2, then having to go through a proprietary codebase trying to rip out these bits of code
I'm amazed to see how positive the overall response is to this idea. Almost as if programmers think that writing programs is the worst part of the job and ready to be automated away. As someone more aligned with the Dijkstra perspective, this seems to me like one of the single worst ideas I've ever seen in this domain. We already have IDEs and other tools leading to an increase in boilerplate and the acceptance of it…
Cargo-cult programming has always been a problem, but now we're explicitly building tools for it.
I get what you're saying, but I'm not worried. At the end of the day, the programmer has to understand the code they're submitting, both the fine grain and the holistic context. If they don't know how to, or can't be bothered at least curate the suggestions the tool is making... then your organization has much bigger problems than can be helped by reading a Dijkstra paper or two.