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This is true for AI research too -- the attention of the community is constantly being captured by shiny objects, but these are not the ideas and results that will be retrospectively understood as having shaped the field
The worst thing you can do as a researcher is to be constantly moving from one shiny object to the next, and never stop to consider the important questions
This is perhaps exacerbated by social media, which not only directs our attention to the wrong things, but also creates an incentive system where you get rewarded for "contributing" to the latest fad or controversy
I feel like I've heard anecdotes about someone who considered themselves slow compared to their colleagues, and the need for them to spend extra time understanding, while their colleagues seemed to understand and move on, is what led them to their biggest discoveries. #WhoWasThat
Recent advances in transfer learning on NLP are a great example for such attitude. Still many open questions for BERT (why it works? what can be improved?), but people release other jewels (GPT-2, XLNet, etc.) by just "guessing" better variations or exploiting bigger resources.
What would you say are the 3 most important questions in AI research now? (Great book btw)
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