Hard disagree on them being the same thing. LLMs are an entirely different beast from traditional machine learning models. The architecture and logic are worlds apart.
Machine Learning models are "just"statistics. Powerful, yes. And with tons of useful applications, but really just statistics, generally using just 1 to 10 variables in useful models to predict a handful of other variables.
LLMs are an entirely different thing, built using word vector matrices with hundreds or even thousands of variables, which are then fed into dozens or hundreds of layers of algorithms that each modify the matrix slightly, adding context and nudging the word vectors towards new outcomes.
Think of it like this: a word is given a massive chain of numbers to represent both the word and the “thoughts” associated with it, like the subject, tense, location, etc. This let’s the model do math like: Budapest + Rome = Constantinople.
The only thing they share in common is that the computer gives you new insights.
Hard disagree on them being the same thing. LLMs are an entirely different beast from traditional machine learning models. The architecture and logic are worlds apart.
Machine Learning models are "just"statistics. Powerful, yes. And with tons of useful applications, but really just statistics, generally using just 1 to 10 variables in useful models to predict a handful of other variables.
LLMs are an entirely different thing, built using word vector matrices with hundreds or even thousands of variables, which are then fed into dozens or hundreds of layers of algorithms that each modify the matrix slightly, adding context and nudging the word vectors towards new outcomes.
Think of it like this: a word is given a massive chain of numbers to represent both the word and the “thoughts” associated with it, like the subject, tense, location, etc. This let’s the model do math like: Budapest + Rome = Constantinople.
The only thing they share in common is that the computer gives you new insights.