This understates his criticality. The author list was randomized, but the critical idea was truly his. Wonder what this says about GDM …
Source for this? The notion of attention dates to a content-addressable lookup during sequence alignment (as well as, concurrently, memory lookups in neural Turing machines). Attention had been used in other models, like GRUs and LSTMs with attention. The Vaswani et. al. paper did not introduce attention, just removed everything _but_ attention (and FFW) from the network. Are you claiming the "critical idea" of removing the GRU and LSTM parts and just keeping attention was "truly" Noam's?
I don't know we can just say things now. Ah we're on the internet
That’s not true. Jakob, Ashish and Ilia for the core idea and initial implementation and Noam for several critical details on implementation.
Even more important, I wonder what it says about HBW...
The architecture was Shazeer's, but the rough idea came from Jakob Uszkoreit who initiated the project.
Uszkoreit wanted to build a more efficient/scalable language/seq2seq model that could take advantage of GPU parallelism (replacing RNNs which were the main approach to sequence modelling at that time).
Uszkoreit's insight was that although language appears sequential, it is in fact really part parallel part hierarchical, as can be seen by linguist's sentence parse trees where at each level there is parallelism/independence between the branches of the tree, with them getting combined at the next level up. This is what gave rise to the idea of a model that consisted of a stack of of parallel processing layers (transformer layers). I believe that attention was also part of the plan from day one, as this had already been proven to be valuable (Bahdanau) with RNN seq2seq modelling.
So, this is what Uszkoreit wanted to build, but by his own account he failed to come up with an implementation that matched or outperformed the prevailing RNN approach that he wanted to replace. At this point, Uszkoreit mentioned the idea to Shazeer, who got on board and eventually arrived at a performant architecture which was then pared back by an ablation process resulting in the initial encoder-decoder Transformer architecture. Shazeer later came up with the mixture-of-experts architecture, and also other optimizations after he left to found character.ai