Why not? Isn't that basically what humans do? Sit there and think for a while before answering, going down different branches/chains of thought?
1) The "bitter lesson" may not be true, and there is a fundamental limit to transformer intelligence.
2) The "bitter lesson" is true, and there just isn't enough data/compute/energy to train AGI.
All the cognition should be happening inside the transformer. Attention is all you need. The possible cognition and reasoning occurring "inside" in high dimensions is much more advanced than any possible cognition that you output into text tokens.
This feels like a sidequest/hack on what was otherwise a promising path to AGI.
It's the exact same thing here.
At best it maybe vaguely resembles thinking
I've never found this sort of argument convincing. it's very Chalmers.
In a sense, maybe yeah. Of course if one were to really be absolute about that statement it would be absurd, it would greatly overfit the reality.
But it is interesting to assume this statement as true. Oftentimes when we think of ideas "off the top of our heads" they are not as profound as ideas that "come to us" in the shower. The subconscious may be doing 'more' 'computation' in a sense. Lakoff said the subconscious was 98% of the brain, and that the conscious mind is the tip of the iceberg of thought.
This chain of thought / reflection method allows you to make better use of the hardware as the hardware itself scales. If a given transformer is N billion parameters, and to solve a harder problem we estimate we need 10N billion parameters, one way to do it is to build a GPU cluster 10x larger.
This method shows that there might be another way: instead train the N billion model differently so that we can use 10x of it at inference time. Say hardware gets 2x better in 2 years -- then this method will be 20x better than now!
Even the attention-token approach is on the grand scale of things a simple line outwards from the centre; we have not even explored around the centre (with the same compute spend) for things like non-token generation, different layers/different activation functions and norming / query/key/value set up (why do we only use the 3 inherent to contextualising tokens, why not add a 4th matrix for something else?), character, sentence, whole thought, paragraph one-shot generation, positional embeddings which could work differently.
The bitter lesson says there is almost a work completely untouched by our findings for us to explore. The temporary work of non-data approaches can piggy back off a point on the line; it cannot expand it like we can as we exude out from the circle..
Everybody seems to be so focused on how to get ahead in race to profitability, that they don't consider the shortcut they are taking might be leading to a cliff.
Human thinking is much more nuanced than this mechanical process. We rely on actually understanding the meaning of what the text represents. We use deduction, intuition and reasoning that involves semantic relationships between ideas. Our understanding of the world doesn't require "reinforcement learning" and being trained on all the text that's ever been written.
Of course, this isn't to say that machine learning methods can't be useful, or that we can't keep improving them to yield better results. But these are still methods that mimic human intelligence, and I think it's disingenuous to label them as such.