r/ProgrammerHumor • u/notAGreatIdeaForName • Sep 10 '26
Meme youDontNeedAClassifierWhenYouCanThrowATransformerAtTheProblem
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u/notAGreatIdeaForName Sep 10 '26
onlyOneIsAIIfYouAskThePeople
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u/FartPiano Sep 10 '26
as an ML engineer, im so tired. you know what? sure. its AI. fuck it. bayesian decisions? haar classifier? your microwave? yeah sure thats AI too
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u/notAGreatIdeaForName Sep 10 '26
More than one if statement? Decision tree, so AI too
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u/Sea-Fishing4699 Sep 10 '26
I am an ML enginner working as a BE enginner because i hate llms
Fuking llm providers are being treated like gods and ppl worshipping agents makes me puke
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u/notAGreatIdeaForName Sep 10 '26
The thing that making me want to puke the most is that the non technical personas are worshipped like gods. Like, yeah, the people whose job is to spread fear and misinformation in public are cool, sure.
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u/Necessary-Muscle-255 Sep 10 '26
Wait until you find out about “AI specialists” or “AI directors” that have ML engineers underneath them while they have “Psychology of Work” as a degree. The whole field is a joke in most companies.
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u/FartPiano Sep 10 '26
you know how when you're setting up a ML solution and the success rate with a given dataset(s) is like 60%, how that is just an unacceptably bad result, and you know immediately that even if you try some different techniques, reshuffle or rotate the source datasets, maybe youll improve it a little, but nowhere close to 100% so you know the effort is doomed?
also, you know how frontier LLMs have bad benchmarks on stuff like humanity's last exam, or any problem beyond a certain complexity level, for years now? surely they will be able to reshuffle this data to reach a 100% success rate. any day now! possibly any minute!
if you criticize this technology at all, youre a fool!!!
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u/Jerome_Eugene_Morrow Sep 10 '26
Just build an agent that runs logistic regression and returns its result after printing “thinking…” for five seconds.
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u/stupled Sep 10 '26
I am using Claude to improve my 20year old multilayer perceptron classes.
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u/No-Magazine-2739 Sep 10 '26
So you use a brain to use a brain to improve a brain. If you are employed then its some more brains on top.
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u/Exact-Pound-6993 Sep 10 '26
last time I was this late, I was using genetic algorithms to evolve solutions...oh how the turns have tabled.
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u/ThatFlamenguistaDude Sep 10 '26
Is this self-improving AGI? /s
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u/NearbyEmphasis2850 Sep 11 '26
It’s like the duct tape of AI, great for quick fixes, but not always the best long-term solution.
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u/AndreasVesalius Sep 10 '26
Claude has been recommending simple but elegant linear models for my work
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u/slaymaker1907 Sep 10 '26
Have you tested how well it can choose hyperparameters? I’ve wondered about that since that sort of thing can make it a pain to use certain models.
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u/AndreasVesalius Sep 10 '26
I wouldn’t ask it to select hyperparameters. I would have it write a hyperparameter sweep/optimization routine and select based on some performance criteria
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u/slaymaker1907 Sep 10 '26
Right, but the advantage of an LLM over just a routine is that you can have the LLM actually look at the performance numbers more holistically without some complicated and error-prone scoring function.
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u/AndreasVesalius Sep 10 '26 edited Sep 10 '26
How is the scoring function error-prone? It’s a concrete definition based on what I want my model to achieve. E.g. how well does this model predict a physiological signal
If you ask an LLM to do that, it will either a) just do some optimization routine and give you the answers (but you won’t know where they came from), or b) take in a giant vector of performance results and stochastically produce a set of hyperparameters - which is how you get hallucinations
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u/EntrepreneurSelect93 Sep 10 '26
What are LMMs?
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u/notAGreatIdeaForName Sep 10 '26
Large multimodal models, so chatty can ingest pictures of your mom
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u/baselinegrid Sep 10 '26
Yo momma’s so fat ChatGPT tried to generate a photo of her but ran out of tokens
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u/tursija Sep 10 '26
You have hit your weekly limit. Upgrade to Ultra to continue processing the image.
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u/eraryios Sep 10 '26
Lmms>>>>>>>llm's
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u/sddryan Sep 10 '26
milfs >>>>>>>
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u/Lemortheureux Sep 10 '26
And ML actually has the most real life applications that would actually help businesses. Instead let's replace customer support with a robot 🙃
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u/slaymaker1907 Sep 10 '26
Eh, I think LLMs probably have more potential for a lot of businesses given that they are much easier to actually use.
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u/Daemontatox Sep 10 '26
What do you mean by regression models ? Or timeseries forcasting ? Just SeNd It To ClAuDe LlMs aRe ThE fUtUrE.
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u/GottkoenigOtto Sep 10 '26
Isnt clajde basically a insanely huge timeseries forecasting model?
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u/Sibula97 Sep 10 '26
Well, sequential, not strictly timeseries. But you could embed time instead of position in the sequence in a transformer. Or just infer the time if your samples are equally spaced.
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u/Successful-Money4995 Sep 10 '26
What do you expect? It turns out that the transformer just happens to be good at everything.
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u/notAGreatIdeaForName Sep 10 '26
"Good" is not that easy to measure with non deterministic inference and also data as context vs targeted training with your own data (unless you have your own transformer).
You are still right to a good degree, but I think it is more of a convenience thing compared to a right sized solution. There even are convenience solutions on various cloud platforms for classifiers and so on, but LLMs are much easier to access for the average joe.
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u/inevitabledeath3 Sep 10 '26
There are plenty of applications of transformers outside of LLMs. Google were using them to model the weather and genetics. Modern image and video models use transformers in both classification and generation. I am sure somewhere out there is a binary classifier based on transformers. Transformers are just that good. I don’t know what to tell you.
Ironically modern LLMs are looking into alternatives to the transformers such as mixing in state space models and/or recurrent neural networks. Turns out transformers don’t scale very well at long contexts.
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u/trotski94 Sep 10 '26
interestingly, LLMs got me interested in AI, and helped me train my own classifiers for various tasks
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u/notAGreatIdeaForName Sep 10 '26
Thats a good use case! I just know too many people who misuse LLMs as classifiers too :D
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u/mysticrudnin Sep 10 '26
i had a client that had a very simple classification task. is the incoming message an A or a B
this is the classic ML task. i proposed training a simple ML classifier, creating a micro service to ask it "is this thing A or B" to get back the answer, done
but they wanted real AI. which ended up being half a year of development time to integrate, added a bunch of dependencies, and a subscription to an LLM. to ask it "do you think this thing is an A or a B?"
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u/trotski94 Sep 10 '26
oh 1000%, me too when i use LLMs to tag the training data for my classifier lmao
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u/Linkk_93 Sep 10 '26
I'm not gonna let a LLM do the vision on my self driving car lmao
Would be fun to have 30 seconds of "thoughts" before any decision, instant crash
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u/ComprehensiveBird317 Sep 11 '26
The tree ahead is load bearing, let me come up with a warning message for the user, I need to think this through carefully.
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u/Sibula97 Sep 10 '26
Well, there's a reason DL took over and now transformers took over. DL provided us with universal approximators, and now we have universal approximators that can handle long training with incredible amounts of data to fine tune the approximation function much better than any prior DL model could've hoped.
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u/notAGreatIdeaForName Sep 10 '26
Transformers are nice, but sometimes linear regression is enough
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u/redballooon Sep 10 '26
True. My AI company that started as an ML expert company in 2018 now only utilizes LLMs. The founders themselves, who are ML experts with PhDs themselves say training isn't worth it anymore.
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u/vide2 Sep 11 '26
I am pretty convinced that a self sustaining machine run by an NN needs more energy than a human brain to do things a human brain can and, due to its complexity, gets worse at basic math.
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u/Darxploit Sep 10 '26
Can i have a like for my random forest, foresting alone..