this post was submitted on 26 Feb 2025
689 points (96.4% liked)
Technology
63297 readers
5502 users here now
This is a most excellent place for technology news and articles.
Our Rules
- Follow the lemmy.world rules.
- Only tech related content.
- Be excellent to each other!
- Mod approved content bots can post up to 10 articles per day.
- Threads asking for personal tech support may be deleted.
- Politics threads may be removed.
- No memes allowed as posts, OK to post as comments.
- Only approved bots from the list below, to ask if your bot can be added please contact us.
- Check for duplicates before posting, duplicates may be removed
- Accounts 7 days and younger will have their posts automatically removed.
Approved Bots
founded 2 years ago
MODERATORS
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
A Large Language Model is not a Machine Learning program.
An LLM is a program that translates human speech into sentiment instead of trying to acheive literal translations. It's a layer that sits on other tech to make it easier for a program to talk with a person. It is not intelligent, an LLM does not learn.
You really don't know what you are talking about. A perfect example of how obfuscating tech to make it sound cool invites any random person to have an opinion on "AI"
When people say AI is not real or intelligent they are speaking from a computer scientist perspective instead of trying to make sense of something they don't understand from scratch.
LLMs are deep learning models that were developed off of multi-head attention/transformer layers. They are absolutely Machine Learning as they use a blend of supervised and unsupervised training (plus some reinforcement learning with some recent developments like DeepSeek).
LLMs are a type of machine learning. Input is broken into tokens, which are then fed through a type of neural network called a transformer model.
The models are trained with a process known as deep learning, which involves the probabilistic analysis of unstructured data, which eventually enables the model to recognize distinctions between pieces of content.
That's like textbook machine learning. What you said about interpreting sentiment isn't wrong, but it does so with machine learning algorithms.
I'm a researcher in ML and LLMs absolutely fall under ML. Learning in the term "Machine Learning" just means fitting the parameters of a model, hence just an optimization problem. In the case of an LLM this means fitting parameters of the transformer.
A model doesn't have to be intelligent to fall under the umbrella of ML. Linear least squares is considered ML; in fact, it's probably the first thing you'll do if you take an ML course at a university. Decision trees, nearest neighbor classifiers, and linear models all are machine learning models, despite the fact that nobody would consider them to be intelligent.