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> Meaning you can apply it to any task as long as you convert it to the right embedding space This glosses over a massive issue which is that not everything ca
by uoaei 3y ago
> Meaning you can apply it to any task as long as you convert it to the right embedding space
This glosses over a massive issue which is that not everything can be efficiently represented as a vector space via embeddings. So your claim of "general purpose" rings hollow.
Not to mention that there is no feedback mechanism for the supposed "knowledge" advocates claim Transformer-based models have, so things like metacognition are literally impossible with this architecture. As it stands LLM outputs are isomorphic to psychotic stream-of-consciousness babble.
You've managed to find a tall tree, but from your response it seems like you haven't yet gotten to considering rockets.
- sigmoid10 3y agoThere is no basis to any of your arguments. Embedding spaces are not defined by humans, but learned. A priori you have zero idea in what way or how efficient things will be encoded. It all depends on the model and it's internally learned world representation. And things like "metacognition" are meaningless terms used by quacks. We don't know how the brain works, we only know what it can do on the outside. And it is mathematically proven that neural networks can do literally everything in principle as well, thanks to universal approximation.