Ask HN: What's the best paper you've read in 2020?

http://www.pnas.org/lookup/doi/10.1073/pnas.1915006117

You might think that it's possible to use machine learning to predict whether people will be successful using established socio-demographic, psychological, and educational metrics. It turns out that it's very hard and simple regression models outperform the fanciest machine learning ideas for this problem.

The way this study was done is also interesting and paves the way for new kinds of collaborative scientific projects that take on big questions. It draws on communities like Kaggle, but applies it to scientific questions not just pure prediction problems.

Gradualizing the Calculus of Inductive Constructions (https://hal.archives-ouvertes.fr/hal-02896776/)

I'm not sure if this is precisely the direction things should go in order to improve the utilisation of specification within software development but it's a very important contribution. As yet my favourite development style has been with F-star but F-star also leaves me a bit in a lurch when the automatic system isn't able to find the answer. Too much hinting in the case of hard proofs.

Eventually there will be a system that lets you turn the crank up on specification late in the game, allows lots of the assertions to be discharged automatically, and then finally saddles you with the remaining proof obligations in a powerful proof assistant.

"Estimating the number of unseen species: A bird in the hand is worth log(n) in the bush" https://arxiv.org/abs/1511.07428 https://www.pnas.org/content/113/47/13283

It deals with the classic, and wonderful, question of "If I go and catch 100 birds, and they're from 20 different species, how many species are left uncaught?" There's more one can say about that than it might first appear and it has plenty of applications. But mostly I just love the name. Apparently PNAS had them change it for the final publication, sadly.