The research from Purdue University, first spotted by news outlet Futurism, was presented earlier this month at the Computer-Human Interaction Conference in Hawaii and looked at 517 programming questions on Stack Overflow that were then fed to ChatGPT.
“Our analysis shows that 52% of ChatGPT answers contain incorrect information and 77% are verbose,” the new study explained. “Nonetheless, our user study participants still preferred ChatGPT answers 35% of the time due to their comprehensiveness and well-articulated language style.”
Disturbingly, programmers in the study didn’t always catch the mistakes being produced by the AI chatbot.
“However, they also overlooked the misinformation in the ChatGPT answers 39% of the time,” according to the study. “This implies the need to counter misinformation in ChatGPT answers to programming questions and raise awareness of the risks associated with seemingly correct answers.”
Hey! I can keep my job for at least a few more years
They’ve done studies: 48% of the time, it works every time.
Yeah it’s wrong a lot but as a developer, damn it’s useful. I use Gemini for asking questions and Copilot in my IDE personally, and it’s really good at doing mundane text editing bullshit quickly and writing boilerplate, which is a massive time saver. Gemini has at least pointed me in the right direction with quite obscure issues or helped pinpoint the cause of hidden bugs many times. I treat it like an intelligent rubber duck rather than expecting it to just solve everything for me outright.
I tend to agree, but I’ve found that most LLMs are worse than I am with regex, and that’s quite the achievement considering how bad I am with them.
Hey, at least we can rest easy knowing that human devs will be needed to write regex for quite a while longer.
… Wait, I’m horrible at Regex. Oh well.
That’s a good way to use it. Like every technological evolution it comes with risks and downsides. But if you are aware of that and know how to use it, it can be a useful tool.
And as always, it only gets better over time. One day we will probably rely more heavily on such AI tools, so it’s a good idea to adapt quickly.Same here. It’s good for writing your basic unit tests, and the explain feature is useful getting for getting your head wrapped around complex syntax, especially as bad as searching for useful documentation has gotten on Google and ddg.
Actually the 4o version feels worse than the 4. Im getting tons of wrong answers now…
Yeah, it’s not supposed to be better than 4 for logic/reason/coding, etc… its strong points are it’s natural voice interaction, ability to react to streaming video, and its fast and efficient inference. The good voice and video are not available to many people yet. It is so efficient that it is going to be available to free users. If you want good reasoning, then you need to stick with 4 for now, or better yet, switch to something like Claude Opus. If you really want strong reasoning abilities, then at this point, you need a setup using agents, but that requires some research and understanding.
So it is incorrect and verbose, but also comprehensive and using a well-articulated language style at the same time?
Also “study participants still preferred ChatGPT answers 35% of the time”, meaning that the overwhelming majority (two-thirds) did not prefer the bot answers over the human(e), correct ones, that maybe were not phrased as confidently as they could have been.
Just say it out loud: ChatGPT is style over substance, aka Fox News. 🦊
ChatGPT is not good enough to use as a substitute for (whatever), but can be a useful tool for someone who can quantify its output.
Still good enough for my needs.
It does but when you input error logs it does pretty good job at finding issues. I tried it out first by making game of snake that plays itself. Took some prompting to get all features I wanted but in the end it worked great in no time. After that I decided to try to make distortion VST3 plugin similar to ZVEX Fuzz Factory guitar pedal. It took lot’s of prompting to get out something that actually builds without error I was quickly able to fix those when I copied the error log to the prompt. After that I kept prompting it further eg. “great, now it works but Gate knob doesn’t seem to do anything and knobs are not centered”.
In the end I got perfectly functional distortion plugin. Haven’t compared it to an actual pedal version yet. Not that AI will just replace us all but it can be truly powerful once you go beyond initial answer.
My experience with an AI coding tool today.
Me: Can you optimize this method.
AI: Okay, here’s an optimized method.
Me seeing the AI completely removed a critical conditional check.
Me: Hey, you completely removed this check with variable xyz
Ai: oops you’re right, here you go I fixed it.
It did this 3 times on 3 different optimization requests.
It was 0 for 3
Although there was some good suggestions in the suggestions once you get past the blatant first error
My favorite is when I ask for something and it gets stuck in a loop, pasting the same comment over and over
That’s been my experience with GPT - every answer Is a hallucination to some extent, so nearly every answer I receive is inaccurate in some ways. However, the same applies if I was asking a human colleague unfamiliar with a particular system to help me debug something - their answers will be quite inaccurate too, but I’m not expecting them to be accurate, just to have helpful suggestions of things to try.
I still prefer the human colleague in most situations, but if that’s not possible or convenient GPT sometimes at least gets me on the right path.
And ya, it did provide some useful info, so it’s not like it was all wrong.
I’m more just surprised that it was wrong in that way.
I’m curious about what percentage of programmers would give error free answers to these questions in seconds.
Probably less than the same amount of developers whose code runs on the first try.
Don’t mean to victim blame but i don’t understand why you would use ChatGPT for hard problems like optimization. And i say this as a heavy ChatGPT/Copilot user.
From my observation, the angle of LLMs on code is linked to the linguistic / syntactic aspects, not to the technical effects of it.
Because I had some methods I thought were too complex and I wanted to see what it’d come up with?
In one case part of the method was checking if a value was within one of 4 ranges and it just dropped 2 of the ranges in the output.
I don’t think that’s asking too much of it.
I don’t think that’s asking too much of it.
Apparently it was :D i mean the confines of the tool are very limited, despite what the Devin.ai cult would like to believe.
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Who would have thought that an artificial intelligence trained on human intelligence would be just as dumb
I actually had the opposite the other day where the code only broke on my machine…
In the end I just commented it out. I don’t see why everybody else should have working code and not me.
Hm. This is what I got.
I think about 90% of the screenshots we see of LLMs failing hilariously are doctored.
Edit:
Or you missed the point that this was a joke?
I’ve had lots of great experiences with ChatGPT, and I’ve also had it hallucinate things.
I saw someone post an image of a simplified riddle, where ChatGPT tried to solve it as if it were the entire riddle, but it added extra restrictions and have a confusing response. I tried it for myself and got an even better answer.
Prompt (no prior context except saying I have a riddle for it):
A man and a goat are on one side of the river. They have a boat. How can they go across?
Response:
The man takes the goat across the river first, then he returns alone and takes the boat across again. Finally, he brings the goat’s friend, Mr. Cabbage, across the river.
I wish I was witty enough to make this up.
I reproduced that one and so I believe that one is true.
I looked up the whole riddle and see how it got confused.
It happened on 3.5 but not 4.
Interesting! What did 4 say?
Evidently I didn’t save the conversation but I went ahead and entered the exact prompt above into GPT-4. It responded with:
The man can take the goat across the river in the boat. After reaching the other side, he can leave the goat and return alone to the starting side if needed. This solution assumes the boat is capable of carrying at least the man and the goat at the same time. If there are no further constraints like a need to transport additional items or animals, this straightforward approach should work just fine!
Thanks for sharing!
Yesterday, someone posted a doctored one on here saying everyone eats it up even if you use a ridiculous font in your poorly doctored photo. People who want to believe are quite easy to fool.
Holy fuck did it just pass the Turing test?
So this issue for me is this:
If these technologies still require large amounts of human intervention to make them usable then why are we expending so much energy on solutions that still require human intervention to make them usable?
Why not skip the burning the planet to a crisp for half-formed technology that can’t give consistent results and instead just pay people a living fucking wage to do the job in the first place?
Seriously, one of the biggest jokes in computer science is that debugging other people’s code gives you worse headaches than migraines.
So now we’re supposed to dump insane amounts of money and energy (as in burning fossil fuels and needing so much energy they’re pushing for a nuclear resurgence) into a tool that results in… having to debug other people’s code?
They’ve literally turned all of programming into the worst aspect of programming for barely any fucking improvement over just letting humans do it.
Why do we think it’s important to burn the planet to a crisp in pursuit of this when humans can already fucking make art and code? Especially when we still need humans to fix the fucking AIs work to make it functionally usable. That’s still a lot of fucking work expected of humans for a “tool” that’s demanding more energy sources than currently exists.
There is a good chance that it is instrumental in discoveries that lead to efficient clean energy. It’s not as if we were at some super clean, unabused planet before language models came along. We have needed help for quite some time. Almost nobody wants to change their own habits(meat, cars, planes, constant AC and heat…), so we need something. Maybe AI will help in this endevour like it has at so many other things.
There is a good chance that it is instrumental in discoveries that lead to efficient clean energy
There is exactly zero chance… LLMs don’t discover anything, they just remix already existing information. That is how it works.
This is a common misunderstanding of what it means to discover new things. New things are just remixing old things. For example, AI has discovered new matrix multiplications, protein foldings, drugs, chess/go/poker strategies, and much more that are all far superior to anything humans have ever come up with in these fields. In all these cases, the AI was just combining old things in new ways. Even Einstein was just combining old things into new ways. There is exactly zero chance that AI will all of a sudden quit making new discoveries all of a sudden.
It’s also “discovered” multitudes more that are complete nonsense.
Yeah, that’s the nature of discovery. Humans also “discovery” tons of things like chess strategies that complete nonsense. Over time, we discard the most nonsense ones and keep the good ones as best as we can. It just turns out that this process is done way faster and efficiently by machines. That’s why nobody thinks humans are going to surpass AI at chess, go, poker, protein folding, matrix multiplation algorithm creation, and a whole bunch of other things.
Can you provide a source for the claim that all these discoveries are “far superior” than what humans have discovered? I struggle to see how a discovery can be ‘superior’- isn’t how the discovery is classified and dealt with, the crucial aspect?
Just a slight correction. ML/AI has aided in all sorts of discoveries, GenAI is a “remixing of existing concepts”. I don’t believe I’ve read, nor does the underlying principles really enable, anything regarding GenAI and discovering new ways to do things.
Yes, ML/AI has, you are correct. So far as the capabilities of GenAI goes, we have not even begun to scratch the surface of understanding how all the emergent abilities of GenAI are happening, and nobody has any idea where they will max out at. All we know is that it is finding some patterns that humans find over time as well as many patterns that humans have not been able to find. The chances that it continues to find more and more complex patterns that we have not found are much higher than the chances that we are currently at the max of its ability.
Maybe it won’t be transformers that leads to breakthroughs, it may be some completely different architecture such as Mamba/state space, but there is a good chance that transformers are a step in the direction of discovering something better.
For example, AI has discovered
no, people have discovered. llms were just a tool used to manipulate large sets of data (instructed and trained by people for the specific task) which is something in which computers are obviously better than people. but same as we don’t say “keyboard made a discovery”, the llm didn’t make a discovery either.
that is just intentionally misleading, as is calling the technology “artificial intelligence”, because there is absolutely no intelligence whatsoever.
and comparing that to einstein is just laughable. einstein understood the broad context and principles and applied them creatively. llm doesn’t understand anything. it is more like a toddler watching its father shave and then moving a lego piece accross its face pretending to shave as well, without really understaning what is shaving.
I didn’t say LLMs made these discoveries. They didn’t. AI made those discoveries. Yes, it is true that humans made AI, so in a way, humans made the discoveries, but if that is your take, then it is impossible for AI to ever make any discovery. Really, if we take this way of thinking to its natural conclusion, then even humans can never make discoveries, only the universe can make discoveries, since humans are a result of the universe “universing”. It is arbitrary to try to credit humans with anything that happens further down their evolution.
Humans tried for a long time to get good at chess, and AI came along and made the absolute best chess players utterly irrelevant even if we give a team of the worlds best chessplayers an endless clock and thr AI a single minute for the entire game. That was 20 years ago. This is happening in more and more fields and showing no sign of stopping. We don’t know yet if discoveries will come from future LLMs like theybm have from other forms of AI, but we do know that with each generation more and more complex patterns are being identified and utilized by LLMs. 3 years ago the best LLMs would have scored single digits on IQ test, now they are triple digits, it is laughable to think that anyone knows where the current rapid trajectory will stop for this new technology, and much more laughable to think we are already at the end.
AI made those discoveries. Yes, it is true that humans made AI, so in a way, humans made the discoveries, but if that is your take, then it is impossible for AI to ever make any discovery.
if this is your take, then lot of keyboard made a lot of discovery.
AI could make a discovery if there was one (ai). there is none at the moment, and there won’t be any for any foreseeable future.
tool that can generate statistically probable text without really understanding meaning of the words is not an intelligence in any sense of the word.
your other examples, like playing chess, is just applying the computers to brute-force through specific mundane task, which is obviously something computers are good at and being used since we have them, but again, does not constitute a thinking, or intelligence, in any way.
it is laughable to think that anyone knows where the current rapid trajectory will stop for this new technology, and much more laughable to think we are already at the end.
it is also laughable to assume it will just continue indefinitely, because “there is a trajectory”. lot of technology have some kind of limit.
and just to clarify, i am not some anti-computer get back to trees type. i am eager to see what machine learning models will bring in the field of evidence based medicine, for example, which is something where humans notoriously suck. but i will still not call it “intelligence” or “thinking”, or “making a discovery”. i will call it synthetizing so much data that would be humanly impossible and finding a pattern in it, and i will consider it cool result, no matter what we call it.
if this is your take, then lot of keyboard made a lot of discovery.
This is literally my point. It is arbitrary to choose that all the good ideas came from “humans”. If we are going to give all credit for anything AI produces to humans, then it only seems fair to give all credit for human things to our common ancestors with chimpanzees, because if it were not for their clever ideas, we would never have been here. But wait, we can’t stop there, because we have to give credit to the original single-celled life forms, and eventually, back to the universe itself(like I mentioned before).
Look, I totally get the desire to want to glorify humans and think that we have something special that machines don’t/can’t have. It kinda sucks to think that we are not so special, and potentially extememly inferior to what is right around the corner. We can’t let that primal ego desire cloud our judgement, though. Our brains are physical machines doing calculations. There is not some magical difference between our calculations that make it so we can make discoveries and machines cannot.
Imagine you teach your little brother how to play chess, and then your brother thinks about it a bunch and comes up with a bunch of new strategies and starts to kick your butt every time, and eventually atatts crushing tournaments. Sure, you can cling to the fact that you taught him how to play, and you can go around telling everyone how “you” are winning all these tournaments because your brother is actually winning them, but it doesn’t change the fact that your brother is the one with the secret sauce that you simply are unable to comprehend.
Your whole point is that if people do it, then it is some special discovery thing, but if computers do it, then it is just computational brute force. There is actually no difference between the two, it is just two different ways of wording the same process. We made programs that could understand the rules, and then it went further and in the same direction that we were trying to go.
So far as continuing indefinitely because we are on a trajectory goes, sure, we will eventually hit some intelligence plateaus, but we are nowhere near this point. Why can I say this with such certainty? Because we have things that we know will work that we haven’t gotten around to combining yet. Some of this gets a bit technical, but a nice way to think of it is this. Right now, we are mainly using hardware designed to generate general graphics that we have hijacked to use for machine learning. The usual speedup when we go from using generalized hardware to specialized is about 5 orders of magnitude(10,000x). That kind of a gain has huge implications in the AI/ML world. This is just one out of many known improvements on the horizon, but it is one of the simplest to wrap your head around. I don’t know how familiar you are with things like crewAI or autogen, but they are phenomenal, they absolutely crush all of the greatest base LLMs, but they are still a bit slow due to how many LLM calls they take. When we have a 10,000x speedup(which is pretty much guarenteed), then everyone will be able to instantly use enormous agent frameworks like this in an instant.
I understand wanting to see humans as having a monopoly on “intelligence”, but quite frankly that era is coming to an end. It may be a bumpy ride, but the sooner humans learn to adjust to this new world, the better. I don’t think it is something that someone can really make someone else see, but once you do see it, it is very obvious. I suggest you check out the cutting-edge agent stuff out there and then imagine that the most impressive stuff will be routinely done from a single prompt in an instant. Then, on top of that, consider that the base LLMs that we have now are the worst there will ever be. We are in for a very wild ride.
It will just be exploited by megacorporations and distorted in unimaginable ways to push profit to new heights. Just like every glimmer of hope for the future.
Check out the open source AI world. There are incredible things happening, it isn’t all as doom and gloom as pesemistic losers want everyone to believe. The open source community is a thriving ecosystem. Linux is a product of the open source world, it is completely free for anyone to use. It is superior to anything that private corporations have ever created in many ways and this can be plainly seen in the fact that nearly all important computing networks are run on linux.
Linux is a product of the open source world
And has nothing to do with AI
Yeah, I was responding to someone saying that big corporations were going to take over AI, I was just pointing out that this isn’t a given since there are other massively successful tech projects that are open source community-driven projects. Sorry if I wasn’t clear enough.
The only downside of Free Open Source Software is that it has been unintentionally the biggest transfer of wealth created by labor from volunteer labor to the capitalist class in history.
Way better software, so much so that capitalists use the hell out of it to make tons of money.
The main limiting factor of the open source AI world is hardware. Hard for individual enthusiasts to compete with corporations who have billions of GPUs worth of processing power. I just have one GPU, and its an AMD, so it’s even more limited because nVidia is the brand majorly used for AI projects.
The decentralized AI hardware movement is also rapidly growing to deal with this issue.
Counter arguments
- technology develops exponentially, while humans are … static
- even now single line of code LLM generates faster and cheaper
- the replacement is not programmer->LLM but programmer -> (programmer +LLM). LLM is just a tool.
technology develops exponentially, while humans are … static
I have yet to see a self-improving technology that does not require adaptive human intelligence as an input.
So refreshing when the voice of reason pops up in here. Thankyou!
I honestly don’t know how well AI is going to scale when it comes to power consumption vs performance. If it’s like most of the progress we’ve seen in hardware and software over the years, it could be very promising. On the other hand, past performance is no guarantee for future performance. And your concerns are quite valid. It uses an absurd amount of resources.
The usual AI squad may jump in here with their usual unbridled enthusiasm and copium that other jobs are under threat, but my job is safe, because I’m special.
Eye roll.
Meanwhile, thousands have been laid off already, and executives and shareholders are drooling at the possibility of thinning the workforce even more. Those who think AI will create as many jobs as it destroys are thinking wishfully. Assuming it scales well, it could spell massive layoffs. Some experts predict tens of millions of jobs lost to AI by 2030.
To try and answer the other part of your question…at my job (which is very technical and related to healthcare) we have found AI to be extremely useful. Using Google to search for answers to problems pales by comparison. AI has saved us a lot of time and effort. I can easily imagine us cutting staff eventually, and we’re a small shop.
The future will be a fascinating mix of good and bad when it comes to AI. Some things are quite predictable. Like the loss of creative jobs in art, music, animation, etc. And canned response type jobs like help desk chat, etc. The future of other things like software development, healthcare, accounting, and so on are a lot murkier. But no job is 100% safe. Especially in sectors with high demand and low supply of workers. Some of these models understand incredibly complex things like drug interactions. It’s going to be a wild ride.
They don’t require as much human intervention to make the results usable as would be required if the tool didn’t exist at all.
Compilers produce machine code, but require human intervention to write the programs that they compile to machine code. Are compilers useless wastes of energy?
Compilers are deterministic and you can reason about how they came to their results, and because of that they are useful.
No, they’re useful because they produce useful machine code.
That’s a distinction without a difference. The code is useful because we can reason how it was made and we can then make deterministic changes. Try using a compiler that gives you a qualitatively different result each time it runs even though the inputs are the same.
It’s useful because it does the stuff we want it to do.
You’re focusing on a very high level philosophical meaning of “usefulness.” I’m focusing on what actually does what I need it to do.
I’m providing explicit examples of compilers doing “the stuff we want it to do”. LLMs do what the want 50% of the time and it still needs modifications afterwards. Imagine having to correct a compiler output and calling that compiler “useful”.
So if something isn’t perfect it’s not “useful?”
I use LLMs when programming. Despite their imperfection they save me an enormous amount of time. I can confidently confirm that LLMs are useful from personal direct experience.
Well fuckin’ said. Preach!
Just like answers on the Internet, you have to read the output and not just paste it blindly. I find the answers are usually useful, even if they aren’t completely accurate. Figuring out the last bit is why we are paid as programmers.
Not to mention that if the code fails you can often tell ChatGPT “here’s what happened” and it can debug its own code correctly much of the time.
Me too \s
Sure does, but even when wrong it still gives a good start. Meaning in writing less syntax.
Particularly for boring stuff.
Example: My boss is a fan of useMemo in react, not bothered about the overhead, so I just write a comment for the repetitive stuff like sorting easier to write
// Sort members by last name ascendingAnd then pressing return a few times. Plus with integration in to Visual Studio Professional it will learn from your other files so if you have coding standards it’s great for that.
Is it perfect? No. Does it same time and allow us to actually solve complex problems? Yes.
Agreed and i have the exact same approach. It’s like having a colleague next to you who’s not very good but who’s super patient and always willing to help. It’s like having a rubber duck on Adderall who has read all the documentation that exists.
It seems people are in such a hurry to reject this technology that they fall into the age old trap of forming completely unrealistic expectations then being disappointed when they don’t pan out.
Exactly. I suspect many of the people that complain about its inadequacies don’t really work in an industry that can leverage the potential of this tool.
You’re spot on about the documentation aspect. I can install a package and rely on the LLM to know the methods and such and if it doesn’t, then I can spend some time to read it.
Also, I suck at regex but writing a comment about what the regex will do will make the LLM do it for me. Then I’ll test it.
Honestly i started at a new job 2 weeks ago and i’ve been breezing through subjects (notably thanks to ChatGPT) at an alarming rate. I’m happy, the boss is happy, OpenAI get their 20 bucks a month. It’s fascinating to read all the posts from people who claim it cannot generate any good code - sounds like a skill issue to me.
ChatGPT and github copilot are great tools, but they’re like a chainsaw: if you apply them incorrectly or become too casual and careless with them, they will kickback at you and fuck your day up.
