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.
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.
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.
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.
I strongly disagree. ML is perfect for small bullshit like “What’s the area of a rectangle” - it falls on its face when asked:
Can we build a website for our security paranoid client that wants the server to completely refuse to communicate with users that aren’t authenticated as being employees… Oh, and our CEO requested a password recovery option on the login prompt.
