Here’s something to noodle on while you finalize your ICML submissions.

Have you ever heard of Max Martin? You probably haven’t, which is something considering he (currently) has 21 #1 hits in the United States. Lennon (26) and McCartney (2) have more, but Martin has the advantage of still being alive to catch up. A phenomenal genius, right? Well, yes, but if you look at his material he always has co-authors, usually several. His process is highly collaborative, as he manages a constellation of young songwriting talent which he nurtures like a good advisor does grad students and post-docs. In the increasingly winner-take-all dynamics of pop music, it’s better to write a #1 song with 5 people then to write a #20 song by yourself.

I think Machine Learning is headed in this direction. Already in Physics pushing the envelope experimentally involves an astonishing number of co-authors. Presumably Physics theory papers have fewer co-authors, but since the standard model is too damn good, in order to make real progress some amazingly difficult experimental work is required.

Now consider an historic recent achievement: conquering Go. That paper has 20 authors. Nature papers are a big deal, so presumably everybody is trying to attribute fairly and this leads to a long author list: nonetheless, there is no denying that this achievement required many people working together, with disparate skills. I think the days where Hastie and Tibshirani can just crush it by themselves, like Lennon and McCartney in their , are over. People with the right theoretical ideas to move something forward in, e.g., reinforcement learning are still going to need a small army of developers and systems experts to the tools necessary.

So here’s some advice to any young aspiring academics out there envisioning a Eureka moment alone at a white-board: if you want to be relevant, pair up with as many talented people as you can.

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