I remember one of the initial transformer people saying in an interview that they didn't think this was the "one true architecture" but a lot of the performance came from people rallying around it and pushing in the one direction.
On the other hand, while "As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime." is true, a sufficiently expressive mechanism may not be computationally or memory efficient. As both are constraints on what you can actually build, it's not whether the architecture can produce the result, but whether a feasible/practical instantiation of that architecture can produce the result.
> I remember one of the initial transformer people saying in an interview that they didn't think this was the "one true architecture" but a lot of the performance came from people rallying around it and pushing in the one direction.
You may be referring to Aidan Gomez (CEO of Cohere and contributor to the transformer architecture) during his Machine Learning Street Talk podcast interview. I agree, if as much attention had been put towards the RNN during the initial transformer hype, we may have very well seen these advancements earlier.
On the other hand, while "As long as your curve is sufficiently expressive all architectures will converge to the same performance in the large-data regime." is true, a sufficiently expressive mechanism may not be computationally or memory efficient. As both are constraints on what you can actually build, it's not whether the architecture can produce the result, but whether a feasible/practical instantiation of that architecture can produce the result.