Something that looks like higher order reasoning emerged from training larger networks. At the end of the day it’s still just spicy autocomplete. Theoretically you could give it a large enough dataset to “predict” almost anything with really high accuracy, but all it’s doing is pattern recognition. One could argue that that’s all humans do, but that’s getting more into philosophy and skipping a lot of nuance.
I’m not like, trying to argue with you by the way. Just having a fun time with this line of thought ^^
What makes the “spicy autocomplete” perspective incomplete is also what makes LLMs work. The “Attention is All You Need” paper that introduced attention transformers describes a type of self-awareness necessary to predict the next word. In the process of writing the next word of an essay, it navigates a 22,000-dimensional semantic space, And the similarity to the way humans experience language is more than philosophical - the advancements in LLMs have sparked a bunch of new research in neurology.
Something that looks like higher order reasoning emerged from training larger networks. At the end of the day it’s still just spicy autocomplete. Theoretically you could give it a large enough dataset to “predict” almost anything with really high accuracy, but all it’s doing is pattern recognition. One could argue that that’s all humans do, but that’s getting more into philosophy and skipping a lot of nuance.
I’m not like, trying to argue with you by the way. Just having a fun time with this line of thought ^^
What makes the “spicy autocomplete” perspective incomplete is also what makes LLMs work. The “Attention is All You Need” paper that introduced attention transformers describes a type of self-awareness necessary to predict the next word. In the process of writing the next word of an essay, it navigates a 22,000-dimensional semantic space, And the similarity to the way humans experience language is more than philosophical - the advancements in LLMs have sparked a bunch of new research in neurology.