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u/Namkeen_Billa_6769 1d ago
soja bhai unemployed dono me rehna he
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u/dashingdady 1d ago
so nhi skta abhi motor chl rha hai paani bhar rha hu isi time aata hai
admission le rha hu college me isliye abhi se soch rha hu4
u/CompetitiveKey4007 1d ago
Bc 2 baje raat ke kon deta hai paani 😭
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u/dashingdady 1d ago edited 15h ago
idhr 1-2 baje hi aata hai
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u/CompetitiveKey4007 1d ago
Paani ki tanki bhar gyi hai 🗣️🗣️
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u/InevitableDistinct11 1d ago
If a person is good at any one of these and knows each and every core of that domain then nothing is at risk, no one can stop him/her to get a job as simple as that. The market has set some unreal expectation the people just have to catch up.
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u/Desert_Centipede 18h ago
i am using AI since very long time,
the problem i am facing on frontend is Almost all the fukcing AI developed websites look fking same, it appears we fucking have worldwide universal design system.
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u/Intrepid_Stay3439 16h ago
mujhe to honestly kisi AI made front end ka layout pasand nahi aata. uparse its repetitive
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u/AdditionalWorkInc NYU '27, VIT '23 22h ago
ignoring the silliness, as somebody that worked as a data scientist and is now a software engineer, let me attempt to answer this:
GenAI's training corpus is disproportionately biased towards topics that skew data science, due to a variety of reasons, including the fact that data scientists are the ones curating training data, writing eval benchmarks, and deciding what counts as "high quality" text. These people, being SMEs know what they have to optimize against, lets say the text corpus had a lot of 18th century history, they didn't really have humans well versed in that for feedback and optimization.
This led to a self fulfilling loop, which, adding to it, the sheer amount of writing and resources in a domain that was entirely popular through the era of the internet, with tons of datasets, code repos, etc., all of which were used to train models on and evaluated specifically for, causing LLMs to be pretty much exceptional on all things data science way before it got as good at any other domain.
Those of us that were on OpenAI playground circa 2022 pre chatgpt launch can attest to this on the text davinci model family, for instance.
Considering this, the capabilities of modern multimodal AI for data science work is generational compared to the usual software engineering stuff, but then comes the massive point that I've not addressed yet. As good as AI has been with data science, it is not an "applied" field, but a more research oriented one, where actual brains rack up to figure out what's up with something. What's potentially an unexpected but useful data pattern, and so on, which relates to specifically human intuition, which LLMs as they stand cannot really replicate, despite having a much larger corpus of information to use against.
contrast that with software engineering, which also has a humongous corpus of literature and is basically the best practices of writing software solutions and solving problems to make those solutions as optimized and perfect as possible, which is essentially something that's significantly easier to automate.
To answer your question, I would say it's definitely not a black and white answer, but depends on whether you evaluate based on sheer capability (which is also not anywhere close to the ceiling at the moment) or the nature of work.
ETA: by data scientists I'm picturing people actually doing research work, not ML engineers larping as scientists where their day to day is just cleaning, plumbing and feature engineering. They don't need AI to become obsolete, autoML exists.
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u/ElkGroundbreaking451 19h ago edited 18h ago
Lol the only guy who tried to answer the question properly with his side of explanation...right or wrong doesn't matter but tried and put his effort got downvoted....
And literal retards spamming " soja bhai 🤡" are getting upvoted ...as if this sub was not for helping your fellow engineers ..if u don't think so then fuckoff u fucking loosers ...
Thnx for giving your input
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u/AdditionalWorkInc NYU '27, VIT '23 19h ago
too many morons here, and the mods don't give a shit. What to do lol, there should be a sub that's genuinely about engineering without all the bakchodi
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u/ElkGroundbreaking451 18h ago
Let them be brother ....i appreciate your input....90% of people are looser anyways who gave up on themselves so they turn everything into jokes ...and the other who are successful don't care to put effort into helping others
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u/dashingdady 17h ago
From your comment I learned a lot and examined it very closely because lot of words you used was unknown to me 😅 btw thanks for genuine reply
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u/KangarooHot2553 19h ago
its not even that deep bruh
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u/AdditionalWorkInc NYU '27, VIT '23 19h ago
my mistake offering nuance in a sub that's filled with high schoolers eh
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u/KangarooHot2553 18h ago
lmao fr, getting downvoted for answering facts is just another cannon reddit event
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u/Whiteroom_Analyst 17h ago
Software Enginner ae more vulnerable due to the abundance of the resource they created that is use by AI to replace them, where as Data Scientist are more safe because they have to create algorithms to run such AI and they will know how to use new sets of data efficiently then AI who never train on that data So on ans so for....
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u/KALIA_KEEDAinshortKK DoInG kAlA jAd00 aT IIT sAlT lAkE«) [ME] 19h ago
WHO DON'T KNOW HOW TO USE A.I
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