What's the value of "pointing out limitations" if this completely fails to drive any improvements?
If any midwit can say "X is deeply flawed" but no one can put together an Y that would beat X, then clearly, pointing out the flaws was never the bottleneck at all.
> What's the value of "pointing out limitations" if this completely fails to drive any improvements?
Ironically, the same could be said about Attention Is All You Need in 2017. It didn’t drive any improvements immediately- actual decent Transformer models took a few years to arrive after that.
I think you don't understand how primary research works. Pointing out flaws helps others think about those flaws.
It's not a linear process so I'm not sure the "bottleneck" analogy holds here.
We're not limited to only talking about "the bottleneck". I think the argument is more that we're very close to optimal results for the current approach/architecture, so getting superior outcomes from AI will actually require meaningfully different approaches.
Being able to provide an immediate replacement is not a requirement to point out limitations in current technology.