It’s really useful for programming. It’s not always right but it has good approaches and you can ask it to write tedious parts of your code like long switch statements. Most of my programming problems were solved because I just explained the problem like Rubber Duck Debugging.
If you mean it like the neighboring comment, who is probably a mathematician or physicist who just needs to feed it a science paper and run some models to verify the premise, but doesn’t care about the code itself, it’s a good tool. They aren’t programmers and learning programming or using a programmer would only delay them.
If you’re a professional programmer however your whole point is to create the most efficient specifications for the computer to do things. You cannot convey 100% of the spec to something like GPT so inevitably some is lost, so the end result is not the most efficient (or doesn’t even cover everything you needed).
You can of course use it to get a head start but there are also boilerplate and templating tools and frameworks that cover the same purpose.
Unlike the physicist, the code you make is the whole point, and it’s based in your knowledge of the subject matter, and you can’t replace it with GPT. Also, using GPT in this manner stunts your professional growth and damages you long term.
It would be somewhat worth it if at least it accelerated some part of your work, and it can find its way into the tooling, but straight out replacing your brain with it ain’t it.
For writing actual code and designing software it’s more trouble than it’s worth, it produces half-assed code that needs fixing.
TLDR figure out ASAP if you really mean to be a programmer or some other type of specialist that only deals with programming incidentally.
That level of condescension (rethink your life because you are making use of a tool I dont like) really isnt productive. You seem to be thinking that using AI as a tool to help you program is equivalent to turning your brain off and just copy and pasting code snippets, it isnt. It can be a good way to explore a language or framework you aren’t familiar with (when combined with the documentation) or to figure out general potential methods of solving a problem.
Not the person you’re replying to, but my main hangup is that LLMs are just statistical models, they don’t know anything. As such, they very often hallucinate language features and libraries that don’t exist. They suggest functions that aren’t real and they are effectively always going to produce average code - And average code is horrible code.
They can be useful for exploration and learning, sure. But lots of people are literally just copy-pasting code from LLMs - They just do it via an “accept copilot suggestion” button instead of actual copy paste.
I used Copilot for months and I eventually stopped because I found that the vast majority of the time its suggestions are garbage, and I was constantly pausing while I typed to await the suggestions, which broke flow state and tired me out more then it ever helped.
I’m still finding bugs it introduced months later. It’s great for unit tests, but that’s basically it in my case. I don’t let the AI write production code anymore
Even for unit tests it needs to be taken with a grain of salt because they should describe what should be there and at best Copilot can describe what is there.
The overlap may or may not be there but either way it’s a dicey proposition to allow Copilot to second guess the intent behind the code and make that guess the reference.
They can be useful for exploration and learning, sure. But lots of people are literally just copy-pasting code from LLMs - They just do it via an “accept copilot suggestion” button instead of actual copy paste.
Sure, people use all sorts of tools badly, that’s a problem with the user not the tool (generally, I would accept poor tool design can be a factor).
I really dislike the statement of “LLMs dont know anything they are just statistical models” it’s such a thought terminating cliche that is either vacuous or wrong depending on which way you mean it. If you mean they have no information content that’s just factually wrong, clearly they do. If you mean they dont understand concepts in the same way as a person does, well yes but neither does google search and we have no problem using that as the start point of finding out about things. If you mean they can get answers wrong, its not like people are infallible either (who I assume you agree do know things).
You can dislike the statement all you want, but they literally do not have a way to know things. They provide a convincing illusion of knowledge through statistical likelihood of the next token occurring, but they have no internal mechanism for looking up information.
They have no fact repositories to rely on.
They do not possess the ability to know what is and is not correct.
They cannot check documentation or verify that a function or library or API endpoint exists, even though they will confidently create calls to them.
They are statistical models, calculating how likely the next token is based on transformations in a many-dimensional space in which the relationships between existing tokens are treated as vectors in a process for determining the next token.
They have their uses, but relying on them for factual information (which includes knowledge of apis and libraries) is a bad idea. They are just as likely to provide realistic answers as they are to make up fake answers and present them as real.
They are good for inspiration or a jumping off point, but should always be fact checked and validated.
They’re fantastic at transforming data from one format to another, or extracting data from natural language written information. I’m even using one in a project to guess at filling in a form based on an incoming customer email.
They do not possess the ability to know what is and is not correct.
They cannot check documentation or verify that a function or library or API endpoint exists, even though they will confidently create calls to them.
These three are all just the same as asking a person about them, they might know or might not but they cant right there and then check. Yes LLMs due to their nature cannot access a region marked “C# methods” or whatever, but large models do have some of that information embedded in them, if they didnt they wouldnt get correct answers anywhere near as often as they do, which for large models and common languages/frameworks is most of the time. This is before getting into retrieval augmented generation where they do have access to repositories of fact.
This is what I was complaining about in the original post I replied to, no-where have I or anyone else I’ve seen in this thread say you should rely on these models, just that they are a useful input. Yet relying on them and using them without verification is the position you and the other poster are arguing against.
It’s really useful for programming. It’s not always right but it has good approaches and you can ask it to write tedious parts of your code like long switch statements. Most of my programming problems were solved because I just explained the problem like Rubber Duck Debugging.
Depends on what you mean by “programming”.
If you mean it like the neighboring comment, who is probably a mathematician or physicist who just needs to feed it a science paper and run some models to verify the premise, but doesn’t care about the code itself, it’s a good tool. They aren’t programmers and learning programming or using a programmer would only delay them.
If you’re a professional programmer however your whole point is to create the most efficient specifications for the computer to do things. You cannot convey 100% of the spec to something like GPT so inevitably some is lost, so the end result is not the most efficient (or doesn’t even cover everything you needed).
You can of course use it to get a head start but there are also boilerplate and templating tools and frameworks that cover the same purpose.
Unlike the physicist, the code you make is the whole point, and it’s based in your knowledge of the subject matter, and you can’t replace it with GPT. Also, using GPT in this manner stunts your professional growth and damages you long term.
It would be somewhat worth it if at least it accelerated some part of your work, and it can find its way into the tooling, but straight out replacing your brain with it ain’t it.
For writing actual code and designing software it’s more trouble than it’s worth, it produces half-assed code that needs fixing.
TLDR figure out ASAP if you really mean to be a programmer or some other type of specialist that only deals with programming incidentally.
That level of condescension (rethink your life because you are making use of a tool I dont like) really isnt productive. You seem to be thinking that using AI as a tool to help you program is equivalent to turning your brain off and just copy and pasting code snippets, it isnt. It can be a good way to explore a language or framework you aren’t familiar with (when combined with the documentation) or to figure out general potential methods of solving a problem.
Not the person you’re replying to, but my main hangup is that LLMs are just statistical models, they don’t know anything. As such, they very often hallucinate language features and libraries that don’t exist. They suggest functions that aren’t real and they are effectively always going to produce average code - And average code is horrible code.
They can be useful for exploration and learning, sure. But lots of people are literally just copy-pasting code from LLMs - They just do it via an “accept copilot suggestion” button instead of actual copy paste.
I used Copilot for months and I eventually stopped because I found that the vast majority of the time its suggestions are garbage, and I was constantly pausing while I typed to await the suggestions, which broke flow state and tired me out more then it ever helped.
I’m still finding bugs it introduced months later. It’s great for unit tests, but that’s basically it in my case. I don’t let the AI write production code anymore
Even for unit tests it needs to be taken with a grain of salt because they should describe what should be there and at best Copilot can describe what is there.
The overlap may or may not be there but either way it’s a dicey proposition to allow Copilot to second guess the intent behind the code and make that guess the reference.
Indeed. I stopped using it altogether a couple months ago.
Sure, people use all sorts of tools badly, that’s a problem with the user not the tool (generally, I would accept poor tool design can be a factor).
I really dislike the statement of “LLMs dont know anything they are just statistical models” it’s such a thought terminating cliche that is either vacuous or wrong depending on which way you mean it. If you mean they have no information content that’s just factually wrong, clearly they do. If you mean they dont understand concepts in the same way as a person does, well yes but neither does google search and we have no problem using that as the start point of finding out about things. If you mean they can get answers wrong, its not like people are infallible either (who I assume you agree do know things).
You can dislike the statement all you want, but they literally do not have a way to know things. They provide a convincing illusion of knowledge through statistical likelihood of the next token occurring, but they have no internal mechanism for looking up information.
They have no fact repositories to rely on.
They do not possess the ability to know what is and is not correct.
They cannot check documentation or verify that a function or library or API endpoint exists, even though they will confidently create calls to them.
They are statistical models, calculating how likely the next token is based on transformations in a many-dimensional space in which the relationships between existing tokens are treated as vectors in a process for determining the next token.
They have their uses, but relying on them for factual information (which includes knowledge of apis and libraries) is a bad idea. They are just as likely to provide realistic answers as they are to make up fake answers and present them as real.
They are good for inspiration or a jumping off point, but should always be fact checked and validated.
They’re fantastic at transforming data from one format to another, or extracting data from natural language written information. I’m even using one in a project to guess at filling in a form based on an incoming customer email.
These three are all just the same as asking a person about them, they might know or might not but they cant right there and then check. Yes LLMs due to their nature cannot access a region marked “C# methods” or whatever, but large models do have some of that information embedded in them, if they didnt they wouldnt get correct answers anywhere near as often as they do, which for large models and common languages/frameworks is most of the time. This is before getting into retrieval augmented generation where they do have access to repositories of fact.
This is what I was complaining about in the original post I replied to, no-where have I or anyone else I’ve seen in this thread say you should rely on these models, just that they are a useful input. Yet relying on them and using them without verification is the position you and the other poster are arguing against.