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Testing GPT 4's code-writing capabilities with some real world problems

tylerglaiel.substack.com

31–40 of 677 posts

Re: Testing GPT 4's code-writing capabilities with some real world problems

#31
post #6

I don't think I have ever solved a truly new problem from scratch when programming... It's all been apply algorithm x to y problem or crud stuff. The most difficult problem that I have asked GPT-4 to solve was writing a parser for the Azure AD query language in a niche programming language and it did that just fine (I did have to copy paste some docs into the prompt).

Pathfinding with extra constraints isn't "a new problem" either. There are a bunch of papers on the topic, and I'm sure there are multiple different variations on github. It still didn't succeed (did get close though).

Re: Testing GPT 4's code-writing capabilities with some real world problems

#32
post #8

In before all the comments about how “most code is trivial” or “most programming is stuff that already exists” or “you’re missing the point look how it’s getting better”. I really am in awe of how much work people seem willing to do to justify this as revolutionary and programmers as infantile, and also why they do that. It’s fascinating. Thinking back to my first job out of college as a solid entry level programmer.…

Eh, it's still impressive that these systems can write such good code despite pretty much just predicting the next word. I guess it's a matter of perspective. You can either be astounded by how much it can do relative to your expectations from 2018 or you can be skeptical relative to the volume of excitement. I think some excitement is also people extrapolating to the future: if predicting the next word gets you this far, what happens when you actually try to make it good?

Re: Testing GPT 4's code-writing capabilities with some real world problems

#33
post #25

Earlier quoted context omitted.

I think most people see where the puck is going, and even where it is right now is very impressive. It's not hard to see that it will be likely less than 5 years before it will be able to do what you did on day 2, and much more, at a tiny fraction of the cost, with no downtime, no attitude problems, sick days, etc. The things you mentioned (taking on board feedback, testing across devices, iterating on solutions) doe…

That's what they said about Watson and Wolfram Alpha. You only hear this nonsense on HN.

I think everywhere except HN the expectations on Watson’s and Wolfram’s second seasons where even more inflated

Re: Testing GPT 4's code-writing capabilities with some real world problems

#34

So it continues to reaffirm what we’ve known: generative LLM does not have a model of the world, can not reason and can not plan. It generates text by mix-matching remembered texts and thus it can not generate truly new content. No surprise because GPT-4 is built upon the same model as GPT-3. Clever Engineering will bring us far, but breakthrough requires change of the fundamentals. Nevertheless, it’s useful and can…

I copied the opinion of Yann LeCun, one of the authorities on deep learning:

(Feb 13,2023)

My unwavering opinion on current (auto-regressive) LLMs 1. They are useful as writing aids.

2. They are "reactive" & don't plan nor reason.

3. They make stuff up or retrieve stuff approximately.

4. That can be mitigated but not fixed by human feedback.

5. Better systems will come.

6. Current LLMs should be used as writing aids, not much more.

7. Marrying them with tools such as search engines is highly non trivial.

8. There will be better systems that are factual, non toxic, and controllable. They just won't be auto-regressive LLMs.

9. have been consistent with the above while defending Galactica as a scientific writing aid.

10. Warning folks that AR-LLMs make stuff up and should not be used to get factual advice.

11. Warning that only a small superficial portion of human knowledge can ever be captured by LLMs.

12. Being clear that better system will be appearing, but they will be based on different principles. They will not be auto-regressive LLMs.

13. Why do LLMs appear much better at generating code than generating general text? Because, unlike the real world, the universe that a program manipulates (the state of the variables) is limited, discrete, deterministic, and fully observable. The real world is none of that.

14. Unlike what the most acerbic critics of Galactica have claimed - LLMs are being used as writing aids. - They will not destroy the fabric of society by causing the mindless masses to believe their made-up nonsense. - People will use them for what they are helpful with.

Re: Testing GPT 4's code-writing capabilities with some real world problems

#35
post #25

Earlier quoted context omitted.

I think most people see where the puck is going, and even where it is right now is very impressive. It's not hard to see that it will be likely less than 5 years before it will be able to do what you did on day 2, and much more, at a tiny fraction of the cost, with no downtime, no attitude problems, sick days, etc. The things you mentioned (taking on board feedback, testing across devices, iterating on solutions) doe…

That's what they said about Watson and Wolfram Alpha. You only hear this nonsense on HN.

Who? Crying wolf makes sense to discredit an individual but not a whole argument. Almost everything is being predicted by someone at any given time. If your criteria for predictions is “no one can have made a similar prediction and been wrong in the past”, then you’ll disbelieve all predictions.

And seriously, how many people were actually saying “WolframAlpha will destroy a huge amount of programming jobs”?

Re: Testing GPT 4's code-writing capabilities with some real world problems

#36
> The useful thing to do would be to just say “I do not know of an algorithm that does this.” But instead it’s overcompetent in its own capabilities, and just makes shit up.

I had recently very similar reaction. And then realized, that this is exactly same behavior as with many of my colleagues at work...

Re: Testing GPT 4's code-writing capabilities with some real world problems

#37
post #2

So, for a bit of fun, I signed up to GPT-4 thingy plus and I picked a fairly common web application and built it from scratch, only by talking to GPT-4 and copy pasting the code bits. I'm actually taken back by how well it's doing; including providing me some refreshers on stuff I forgot how it should work. I can see it failing at solving complex problems, but like the blog post mentions, most programming isn't new o…

I don't really have anyone to ask questions I get sometimes about building software, and chatGPT has been helping fill the gaps. Basically I'm thinking of it like a combination of a rubber duck and a dialogue-enabled google search. But it's been really helpful along those lines when I'm for example not sure a good way to change a bunch of stuff across a bunch of files, and am pretty sure it's something that can be automated somehow, and chat GPT will be like "have you considered using one tool to get the file names that need changing, then another tool to create an AST of the files, and then another tool to actually modify that AST?" And I'm like oh duh, yeah, I didn't know there's tools like that but I should have assumed there are, nice.

Basically that's how all my usage has gone. I've had it write some elisp and it has been ok, sometimes it invents made-up functions (that don't exist in org-mode for example) but I'll just tell it that a function doesn't exist and it'll come up with some other solution, until I get it to a point where all I need to do is change a couple things.

I remain highly skeptical the thing will replace me anytime soon (ever in my lifetime?) but I'm surprised at the possibilities of making my life less tedious.

Re: Testing GPT 4's code-writing capabilities with some real world problems

#38
Actually, if the state A* searches through is not "tile reached" but "tile reached + count of fires on path", then it just becomes regular A*. This solves the A to C doesn't always go through B, because it turns B into multiple distinct states, some with fires, one without.

There are a few issues with this. Search state is bigger (performance goes down), might not scale if other search features are needed in the game, you might need to be smart about when you stop the search and how you write your heuristic to not have to reach all combinations of fire counts before you end your search...

But the trick to "just use A*" is not in modifying the cost, but changing the search space.

PS. I see no reason why you should change your current code, obviously.

PPS. I don't think GPT could come up with that insight. It sure didn't in your case.

Re: Testing GPT 4's code-writing capabilities with some real world problems

#39
post #29

I had a similar experience earlier. Described a problem that isn't even that hard - very similar to something there are probably lots of examples of online but subtly different. I wanted to see if handled these subtly different requirements. It failed miserably, even with repeated instructions. It just assumed I wanted the more common problem. Every time I pointed out the problem it would say "sorry for the confusion…

I’ve found persistence is not a good strategy with GPT. Put effort into your prompt, maybe try clarifying once, and if it doesn’t work, do not keep trying. It will get closer to the solution at a diminishing rate, just enough to tease you along, never getting there.

Re: Testing GPT 4's code-writing capabilities with some real world problems

#40
post #8

In before all the comments about how “most code is trivial” or “most programming is stuff that already exists” or “you’re missing the point look how it’s getting better”. I really am in awe of how much work people seem willing to do to justify this as revolutionary and programmers as infantile, and also why they do that. It’s fascinating. Thinking back to my first job out of college as a solid entry level programmer.…

I think most people see where the puck is going, and even where it is right now is very impressive. It's not hard to see that it will be likely less than 5 years before it will be able to do what you did on day 2, and much more, at a tiny fraction of the cost, with no downtime, no attitude problems, sick days, etc. The things you mentioned (taking on board feedback, testing across devices, iterating on solutions) doe…

> I think most people see where the puck is going

Overinflated hype about “where the puck is going” being wrong is…not a new phenomenon. And the non-tech media (traditional and social, and particularly a whole lot of the elite commentariat that spans both more than the “hard” news side of traditional media, though that is influenced too) perspective on this is driven quite disproportionately by the marketing message of the narrow set of people with the most financial stake in promoting the hype. Even the cautionary notes being sounded there are exactly the ones that are being used by the same people to promote narrow control.

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