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I wish I was as bold as these authors.
(discuss.tchncs.de)
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this post was submitted on 28 Jun 2024
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You’ve been speaking with your chest this whole time and now that we’re into the nitty gritty you really just said “The ai does... something!” It’s so general a description that by your measure automated thermostats are engaging in human reasoning when they make it a little bit cooler on a hot day.
You might’ve been oversimplifying on purpose. I just can’t help but think you have no idea how LLMs work outside of this inherently flawed comparison to human thought.
Not OP, but speaking from a fairly deep layman understanding of how LLMs work - all anyone really knows is that capabilities of fundamentally higher orders (like deception, which requires theory of mind) emerged by simply training larger networks. Since we don't have a great understanding of how our own intelligence emerges from our wetware, we're only guessing.
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.