this post was submitted on 08 Feb 2024
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the comic is about using a machine learning algorithm instead of a hand-coded algorithm. not about using chatGPT to write a trivial program that no doubt exists a thousand times in the data it was trained on.
GPT is ML
But not all ML is GPT
The strengths of Machine Learning are in the extremely complex programs.
Programs no junior dev would be able to accomplish.
So if the post can misrepresent the issue, then the commenter can do so too.
The biggest high level challenge in any tech org is security and there's no way you can convince me that ML can successfully counter these challenges
"oh but it will but it will!"
when
"in the future"
how long in the future
"When it can do it"
how will we know it can do it
"When it can do it"
cool.
You probably wreck in chess. :)
I strongly disagree. ML is perfect for small bullshit like "What's the area of a rectangle" - it falls on its face when asked:
I got interested and asked ChatGPT. It gave a middle-management answer.
Guess we know who'll be the first to go.
Lol, no. ML is not capable of writing extremely complex code.
It's basically like having a bunch of junior devs cranking out code that they don't really understand.
ML for coding is only really good at providing basic bitch code that is more time intensive than complex. And even that you have to check for hallucinations.
ML is not good for coding, it is good for approximately solving very complex problems.
LLM are not good for that, but Machine Learning as a whole does not have that limitation.
To reiterate what the parent comment of the one you replied to said, this isn't about chat GPT generating code, it's about using ML to create a indeterministic algorithm, that's why in the comic it's only very close to 12 and not 12 exactly.
I think the exact opposite, ML is good for automating away the trivial, repetitive tasks that take time away from development but they have a harder time with making a coherent, maintainable architecture of interconnected modules.
It is also good for data analysis, for example when the dynamics of a system are complex but you have a lot of data. In that context, the algorithm doesn't have to infer a model that matches reality completely, just one that is close enough for the region of interest.
Yes that is what they are good at. But not as good as a deterministic algorithm that can do the same thing. You use machine learning when the problem is too complex to solve deterministically, and an approximate result is acceptable.
But can machine learning teach nerds not to ruin jokes? ;)
I think it's the nerds' job to teach the ML to ruin jokes.
But like why would you use ML to do basic maths? Whoever did that is dumber than a junior dev 😝
if it did, they wouldn't be nerds anymore.