Well I am not defending the paper thesis but no it's time to realize that we are in a new AI winter where progress has stopped.
Sure we can make accuracy progress on tasks that were underesearched before, moreover we do make extremely slow (and with increasingly diminishing returns) accuracy gains on core tasks.
But the diminishing returns are diminishing fast to the point that progress in terms of applications has stopped for core AI tasks such as NLU.
However there is still some hope as the vast majority of papers bring an innovation but almost never attempt to merge/synergize with other papers innovations.
If human resources where allocated at merging the top 10 papers on a given task, I'm sure it would lead to a major accuracy improvement.
However there is still some hope as the vast majority of papers bring an innovation but almost never attempt to merge/synergize with other papers innovations. If human resources where allocated at merging the top 10 papers on a given task, I'm sure it would lead to a major accuracy improvement.
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