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Is traditional ML relevant anymore? Any active research going on in ML methods?

2•mitml•1h ago
Everything I see today is only about LLMs. I personally use LLMs in my daily activities and they are doing great job. But I wonder what happened to traditional machine learning methods all of a sudden! Those hamspam classifiers, sentiment analysis models, word2vecs, RNNs, CNNs, LSTMs, FFNNs, where are they now? What is a typical data scientist or a ML engineer of late 2010s doing now? The decision trees they trained, the neural nets they architected, the accuracy evaluations, hyperparameter tunings where are they all now? It feels like it is all about LLMs suddenly. The traditional ML models which served specific problem with light weight and easily deployable loads are all obsolate now?

Comments

al_borland•1h ago
AI is the hot term, so everyone is using that, even for a lot of things that are actually ML.

Apple used to exclusively say ML for a ton of things they do in iOS, but they caved and are mostly focusing on where they can say AI now. ML never caught on as a marketing buzz word with the public.

Cal Newport recently made a video where he was trying to distill the real-world benefits LLMs have given us, and he got his big list from people who replied online (I think). Half the suggestions he threw out as ML, as they weren’t related to the generative AI, which is the main focus of the current trend. But these things are all getting lumped under the larger AI umbrella once they get to the marketing department.

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