In the past, broad interventions would clearly have been more effective: for instance, there would have been little use in studying empirical alignment prior to deep learning. Even more recently than the advent of deep learning, many approaches to empirical alignment were highly deemphasized when large, pretrained language models arrived on the scene (refer to our discussion of creative destruction in the last post).
As discussed in the last post, a leading motivation for researchers is the interestingness or “coolness” of a problem. Getting more people to research relevant problems is highly dependent on finding interesting and well-defined subproblems for them to work on. This relies on concretizing problems and providing funding for solving them.
This seems be a conflicting advice to me. If you try to follow both you might end up having hard time finding direction for research.
Great post!
This seems be a conflicting advice to me. If you try to follow both you might end up having hard time finding direction for research.