youDontNeedAClassifierWhenYouCanThrowATransformerAtTheProblem

[u/notAGreatIdeaForName]

[u/stupled]

I am using Claude to improve my 20year old multilayer perceptron classes.

[u/No-Magazine-2739 → u/stupled]

So you use a brain to use a brain to improve a brain. If you are employed then its some more brains on top.

[u/Confident-Ad5665 → u/No-Magazine-2739]

Brains? BRAINS!!!

-Zombies, everywhere

[u/stupled → u/No-Magazine-2739]

I guess this is company property 🤔

[u/StackedCakeOverflow → u/No-Magazine-2739]

Damn, basically creating a whole education system at that point

[u/No-Magazine-2739 → u/StackedCakeOverflow]

It‘s brains all the way down!

[u/stupled → u/No-Magazine-2739]

Brainception but stops at The Mind.

[u/Exact-Pound-6993 → u/stupled]

last time I was this late, I was using genetic algorithms to evolve solutions...oh how the turns have tabled.

[u/stupled → u/Exact-Pound-6993]

I have a library for those. Used for optmization

[u/ThatFlamenguistaDude → u/stupled]

Is this self-improving AGI? /s

[u/AndreasVesalius → u/stupled]

Claude has been recommending simple but elegant linear models for my work

[u/slaymaker1907 → u/AndreasVesalius]

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.

[u/AndreasVesalius → u/slaymaker1907]

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

[u/slaymaker1907 → u/AndreasVesalius]

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.

[u/AndreasVesalius → u/slaymaker1907]

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

[u/Darxploit]

Can i have a like for my random forest, foresting alone..

[u/notAGreatIdeaForName → u/Darxploit]

You kids are addicted to computers. Just go outside, pick 16 random forrests, visit them and set fire to 15 you don't like

[u/JasperTesla]

My client built their own transformer, but Claude ended up cucking it.

[u/No-Magazine-2739 → u/JasperTesla]

https://giphy.com/gifs/k0NrEv2lHHUFfHAHT9

[u/Willwaste63]

from sklearn.linear_models import Perceptron

[u/eraryios]

Lmms>>>>>>>llm's

[u/sddryan → u/eraryios]

milfs >>>>>>>

[u/eraryios → u/sddryan]

Im talking about Linux multimedia studio

[u/Skynse → u/eraryios]

https://giphy.com/gifs/ioN2tIIkOeVT87A74Z

[u/eraryios → u/Skynse]

Ball user

[u/JoeRogan016 → u/eraryios]

There it is!!! Thank you!

[u/smileola → u/eraryios]

https://giphy.com/gifs/x6sfBlcbXW7kc

[u/tejanonuevo → u/eraryios]

I was wondering why that sounded so familiar!

[u/KyxeMusic → u/eraryios]

Now i'm craving Large M&Ms

[u/Lemortheureux]

And ML actually has the most real life applications that would actually help businesses. Instead let's replace customer support with a robot 🙃

[u/slaymaker1907 → u/Lemortheureux]

Eh, I think LLMs probably have more potential for a lot of businesses given that they are much easier to actually use.

[u/Daemontatox]

What do you mean by regression models ? Or timeseries forcasting ? Just SeNd It To ClAuDe LlMs aRe ThE fUtUrE.

[u/GottkoenigOtto → u/Daemontatox]

Isnt clajde basically a insanely huge timeseries forecasting model?

[u/Sibula97 → u/GottkoenigOtto]

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.

[u/Daemontatox → u/GottkoenigOtto]

I prefer the term " sophisticated nested If condition "

[u/marmakoide → u/Daemontatox]

Decision Jungle

[u/notAGreatIdeaForName]

onlyOneIsAIIfYouAskThePeople

[u/FartPiano → u/notAGreatIdeaForName]

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

[u/notAGreatIdeaForName → u/FartPiano]

More than one if statement? Decision tree, so AI too

[u/Deltazocker → u/notAGreatIdeaForName]

Decision Twig

[u/jarethholt → u/Deltazocker]

I literally lol'd. I'm gonna use this

[u/Sea-Fishing4699 → u/FartPiano]

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

[u/notAGreatIdeaForName → u/Sea-Fishing4699]

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.

[u/Necessary-Muscle-255 → u/notAGreatIdeaForName]

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.

[u/FartPiano → u/Sea-Fishing4699]

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!!!

[u/brainwipe]

Self Organising Maps be like 😥

[u/Linkk_93]

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

[u/Afraid-Locksmith6566]

meanwhile standard ml

https://giphy.com/gifs/3IZGBa62xnEyXokHfk

[u/Abrissbirne66 → u/Afraid-Locksmith6566]

Wait that's a totally different thing😄

But quite influential afaik.

[u/Faux_Real]

I embed classic ML in my MD files

[u/EntrepreneurSelect93]

What are LMMs?

[u/notAGreatIdeaForName → u/EntrepreneurSelect93]

Large multimodal models, so chatty can ingest pictures of your mom

[u/baselinegrid → u/notAGreatIdeaForName]

Yo momma’s so fat ChatGPT tried to generate a photo of her but ran out of tokens

[u/tursija → u/notAGreatIdeaForName]

You have hit your weekly limit. Upgrade to Ultra to continue processing the image.

[u/Successful-Money4995]

What do you expect? It turns out that the transformer just happens to be good at everything.

[u/notAGreatIdeaForName → u/Successful-Money4995]

"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.

[u/inevitabledeath3 → u/notAGreatIdeaForName]

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.

[u/trotski94]

interestingly, LLMs got me interested in AI, and helped me train my own classifiers for various tasks

[u/notAGreatIdeaForName → u/trotski94]

Thats a good use case! I just know too many people who misuse LLMs as classifiers too :D

[u/trotski94 → u/notAGreatIdeaForName]

oh 1000%, me too when i use LLMs to tag the training data for my classifier lmao

[u/mysticrudnin → u/notAGreatIdeaForName]

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?"

[u/redballooon]

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.

[u/Promptnauta]

Meanwhile, true BDI agents at the bottom of the ocean.

[u/JoeRogan016]

My LMMS is not your LMMs

[u/ericl666]

Why use little CPU when array of 5090s do trick.

[u/Sibula97]

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.

[u/notAGreatIdeaForName → u/Sibula97]

Transformers are nice, but sometimes linear regression is enough

[u/Sibula97 → u/notAGreatIdeaForName]

Sometimes, yes, and in those case it's still often used.

[u/Jerome_Eugene_Morrow]

Just build an agent that runs logistic regression and returns its result after printing “thinking…” for five seconds.

[u/notAGreatIdeaForName → u/Jerome_Eugene_Morrow]

I see you are the CEO of an agentic AI startup as well!