> 1. NOT have any representation about the meaning of the prompt.

This one is bizarre, if true (I'm not convinced it is).

The entire purpose of the attention mechanism in the transformer architecture is to build this representation, in many layers (conceptually: in many layers of abstraction).

> 2. NOT have any representation about what they were going to say.

The only place for this to go is in the model weights. More parameters means "more places to remember things", so clearly that's at least a representation.

Again: who was pushing this belief? Presumably not researchers, these are fundamental properties of the transformer architecture. To the best of my knowledge, they are not disputed.

> I believe [...] it is not impossible they get us to AGI even without fundamentally new paradigms appearing.

Same, at least for the OpenAI AGI definition: "An AI system that is at least as intelligent as a normal human, and is able to do any economically valuable work."

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zahlman 2 days ago | parent [-]

> This one is bizarre, if true (I'm not convinced it is).

> The entire purpose of the attention mechanism in the transformer architecture is to build this representation, in many layers (conceptually: in many layers of abstraction).

I think this is really about a hidden (i.e. not readily communicated) difference in what the word "meaning" means to different people.

	▲	erichocean 2 days ago \| parent [-]
		Could be, by "meaning" I mean (heh) that transformers are able to distinguish tokens (and prompts) in a consequential ("causal") way, and that they do so at various levels of detail ("abstractions"). I think that's the usual understanding of how transformer architectures work, at the level of math.