By “good” I mean code that is written professionally and concisely (and obviously works as intended). Apart from personal interest and understanding what the machine spits out, is there any legit reason anyone should learn advanced coding techniques? Specifically in an engineering perspective?
If not, learning how to write code seems a tad trivial now.
understanding what the machine spits out
This is exactly why people will still need to learn to code. It might write good code, but until it can write perfect code every time, people should still know enough to check and correct the mistakes.
I very much agree, thank you for indulging my question.
I used an LLM to write some code I knew I could write, but was a little lazy to do. Coding is not my trade, but I did learn Python during the pandemic. Had I not known to code, I would not have been able to direct the LLM to make the required corrections.
In the end, I got decent code that worked for the purpose I needed.
I still didn’t write any docstrings or comments.
For a very long time people will also still need to understand what they are asking the machine to do. If you tell it to write code for an impossible concept, it can’t make it. If you ask it to write code to do something incredibly inefficiently, it’s going to give you code that is incredibly inefficient.
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I’ve even seen human engineers’ code thrown out because no one else could understand it. Back in the day, one webdev took it upon himself to whip up a mobile version of our company’s very complex website. He did it as a side project. It worked. It was complete. It looked good. It was very fast. The code was completely unreadable by anyone else. We didn’t use it.
I worry for the future generations of people who can use chatgpt to write code but have absolutely no idea what said code is doing.
My CTO thoroughly believes that within 4-6 years we will no longer need to know how to read or write code, just how to ask an AI to do it. Coincidentally, he also doesn’t code anymore and hasn’t for over 15 years.
From a business perspective, no shareholder cares at how good an employee is at personally achieving a high degree of skill. They only care about selling and earning, and to a lesser degree an enduring reputation for longer term earnings.
Economics could very well drive this forward. But I don’t think the craft will be lost. People will need to supervise this progress as well as collaborate with the machines to extend its capabilities and dictate its purposes.
I couldn’t tell you if we’re talking on a time scale of months or decades, but I do think “we” will get there.
Hackers and hobbiests will persist despite any economics. Much of what they do I don’t see AI replacing, as AI creates based off of what it “knows”, which is mostly things it has previously ingested.
We are not (yet?) at the point where LLM does anything other than put together code snippets it’s seen or derived. If you ask it to find a new attack vector or code dissimilar to something it’s seen before the results are poor.
But the counterpoint every developer needs to keep in mind: AI will only get better. It’s not going to lose any of the current capabilities to generate code, and very likely will continue to expand on what it can accomplish. It’d be naive to assume it can never achieve these new capabilities… The question is just when & how much it costs (in terms of processing and storage).
Agree, and the point I always want to make is that any LLM or neural net or any other AI tech is going to be a mere component in a powerful product, rather than the entirety of the product.
The way I think of it is that my brain is of little value without my body, and my person is of little value without my team at work. I don’t exist in a vacuum but I can be highly productive within my environment.
Don’t be, there will come a time when nearly all code is AI created, and not human readable.
You need to worry for the future when big data sites are running code they literally don’t know how it works and have no way to verify because of how cheap and relatively effective it is.
Then after that LLMs will get better at coding than any human can achieve, but will still be black box human unreadable code but there will be no chain of discipline left to teach new programmers.
Hardly anyone is taking this seriously because corporations stand to make a fucktonne of money and normal people are in general clueless about complex subjects that require a nuanced understanding, yet strangely arrogant about their ignorant opinions based on movies and their drinking buddies malformed opinions.
I’m my experience they do a decent job of whipping out mindless minutea and things that are well known patterns in very popular languages.
They do not solve problems.
I think for an “AI” product to be truly useful at writing code it would need to incorporate the LLM as a mere component, with something facilitating checks through static analysis and maybe some other technologies, maybe even mulling the result through a loop over the components until they’re all satisfied before finally delivering it to the user as a proposal.
No, because that would require it being trained on good code. Which is rather rare.
If it is trained on Stack Overflow there is no chance.
The answers on stack overflow are often borderline genius.
It’s training on practically all code that exists. It has plenty of good examples, and plenty of junk examples.
A broken clock is right twice a day.
Yes … and it doesn’t know when it is on time.
Also, machines are getting better and they can help us with inspiration.
I think your wording is something to consider. If you want something that’s written professionally, by definition it needs to be written by a professional. So that’s clearly not what you’re asking for, but that’s what you wrote. And that kind of detail does matter, because LLMs are very good at getting part of the format correct and then messing up small details in random places, which makes them precisely useless on their own. But if you want to use them to produce templates that you’re later going to modify, of course you can do that.
I’m not clear what you think an advanced coding technique would be. But if your system breaks and you don’t understand it well enough to fix it, then I sure hope a competent programmer is on staff who can help you.
Finally, if you rely on automation to write your programs for you and somehow they magically seem to work most of the time, how do you know that they actually work all of the time? If they’re giving you numbers, can you believe the numbers? When? Why? Who is guaranteeing you quality in product? Of course nobody is.
This seems like the most sane take.
A computer can do a lot. But if you give the computer to a regular fish instead of a regular human, that’s just a regular fish next to a computer. Not very useful.
That all depends on where the data set comes from. The code you’ll get out of an LLM is the average code of the data set. If it’s scraped from the internet (which is very likely) the code you’ll get will be an amalgam of concise examples from one website, incorrect examples from another, bits from blogs with all the typos and all the gunk and garbage that’s out there.
Getting LLM code to work well takes an understanding of what the code it gives you actually does and why it’s bad. It will always be bad because it cannot be better than the dataset and in order for a dataset to be big enough to train an LLM it’ll have to have everything they can get including all the trash. But it can be good for providing you a framework to start with. It is however never going to replace actual programming and understanding of programming. The talk of LLMs completely replacing programers is mostly coming from people who do not understand coding or LLMs at all.
Can’t LLM’s eventually gain some form of “sentience”, and be able to self correct? A sort of thinking before speaking kind of situation.
This question right here perfectly encapsulates everything wrong with LLMs right now. They could be good tools but the people pushing them have no idea what they even are. LLMs do not make decisions. All the decisions an LLM appears to make were made in the dataset. All those things that an LLM does that make it seem intelligent were done or said by a human somewhere on the internet. It is a statistical model that determines what output is mostly likely to come next. That is it. It is nothing else. It is not smart. It does not and cannot make decisions. It is an algorithm that searches a dataset and when it can’t find something it’ll provide convincing-looking gibberish instead.
Listen think of it like this; a man decides to take exams to become a doctor in France, but for some reason he doesn’t learn either french or medicine. No, no instead he studies every former exam and all the answers to them. He gets very good at regurgitating those answers so much so that he can even pass the exam. But at no point does he understand what any of it means and when asked new and novel questions he provides utter nonsense answers. No matter how good he gets at memorising those answers he will never get any better at medicine. LLMs are as likely to gain sentience as my excel spreadsheets are.
It is an algorithm that searches a dataset and when it can’t find something it’ll provide convincing-looking gibberish instead.
This is very misleading. An LLM doesn’t have access to its training dataset in order to “search” it. Producing convincing looking gibberish is what it always does, that’s its only mode of operation. The key is that the gibberish that comes out of today’s models is so convincing that it actually becomes broadly useful.
That also means that no, not everything an LLM produces has to have been in its training dataset, they can absolutely output things that have never been said before. There’s even research showing that LLMs are capable of creating actual internal models of real world concepts, which suggests a deeper kind of understanding than what the “stochastic parrot” moniker wants you to believe.
LLMs do not make decisions.
What do you mean by “decisions”? LLMs constantly make decisions about which token comes next, that’s all they do really. And in doing so, on a higher, emergent level they can make any kind of decision that you ask them to, the only question is how good those decisions are going be, which in turn entirely depends on the training data, how good the model is, and how good your prompt is.
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For basic boiler plate like routes for an API, an etl script from sample data to DB tables, or other similar basics, yeah, it’s perfectly acceptable. You’ll need to swap out dummy addresses, and maybe change a choice or two, but it’s fine.
But when you’re trying to organize more complicated business logic or debug complicated dependencies it falls over
In my experience, not at all. But sometimes they help with creativity when you hit a wall or challenge you can’t resolve.
They have been trained off internet examples where everyone has a different style/method of coding, like writing style. It’s all very messy and very unreliable. It will be years for LLMs to code “good” and will require a lot of training that isn’t scraping.
No LLM is trust worthy.
Unless you understand the code and can double check what it’s doing I wouldn’t risk running it.
And if you do understand it any benefit of time saved is likely going to be offset by debugging and verifying what it actually does.
Yes, in small bits, after several tries, with human supervision. For now.
No in large amounts, too hard to human review, they’re still doing it anyway.
Technically it’s possible, but it’s neither probable nor likely, and it’s especially not effective. From what I understand, a lot of devs who do try to use something like ChatGPT to write code end up spending as much or more time debugging it, and just generally trying to get it to work, than they would have if they’d just written it themselves. Additionally, you have to know how to code to be able to figure out why it’s not working, and even when all of that is done, it’s almost impossible to get it to integrate with a larger project without just rewriting the whole thing anyway.
So to answer the question you intend to ask, no, LLMs will not be replacing programmers any time soon. They may serve as a tool of dubious value, but the idea that programmers will be replaced is only taken seriously by by people who manage programmers, and not the programmers themselves.
Its the most ok’est coder with the attention span of a 5 year old.
Yes and no. GPT usually gives me clever solutions I wouldn’t have thought of. Very often GPT also screws up, and I need to fine tune variable names, function parameters and such.
I think the best thing about GPTis that it knows the documentation of every function, so I can ask technical questions. For example, can this function really handle dataframes, or will it internally convert the variable into a matrix and then spit out a dataframe as if nothing happened? Such conversions tend to screw up the data, which explains some strange errors I bump into. You could read all of the documentation to find out, or you could just ask GPT about it. Alternatively, you could show how badly the data got screwed up after a particular function, and GPT would tell that it’s because this function uses matrices internally, even though it looks like it works with dataframes.
I think of GPT as an assistant painter some famous artists had. The artist tells the assistant to paint the boring trees in the background and the rough shape of the main subject. Once that’s done, the artist can work on the fine details, sign the painting, send it to the local king and charge a thousand gold coins.
Absolutely, but they need a lot of guidance. GitHub CoPilot often writes cleaner code than I do. I’ll write the code and then ask it to clean it up for me and DRYify it.
