Nominated Posts for the 2019 Review
2019 Review Discussion
The generalized efficient markets (GEM) principle says, roughly, that things which would give you a big windfall of money and/or status, will not be easy. If such an opportunity were available, someone else would have already taken it. You will never find a $100 bill on the floor of Grand Central Station at rush hour, because someone would have picked it up already.
One way to circumvent GEM is to be the best in the world at some relevant skill. A superhuman with hawk-like eyesight and the speed of the Flash might very well be able to snag $100 bills off the floor of Grand Central. More realistically, even though financial markets are the ur-example of efficiency, a handful of firms do make impressive amounts of money by...
Thankyou to Sisi Cheng (of the Working as Intended comic) for the excellent drawings.
Suppose we have a gearbox. On one side is a crank, on the other side is a wheel which spins when the crank is turned. We want to predict the rotation of the wheel given the rotation of the crank, so we run a Kaggle competition.
We collect hundreds of thousands of data points on crank rotation and wheel rotation. 70% are used as training data, the other 30% set aside as test data and kept under lock and key in an old nuclear bunker. Hundreds of teams submit algorithms to predict wheel rotation from crank rotation. Several top teams combine their models into one gradient-boosted deep random neural support vector forest. The model...
I thinks its worth mentioning that there are two levels of black box models too. ML can memorize the expected value at each set of variables (at 1 rmp crank wheel rotates at 2 rpm) or it can 'generalize' and, for this example, tell us that the wheel rotates at 2x speed of crank. To some extent 'ML generalization' provides good 'out of distribution' predictions.
In regard to bullet 1, I would caution against relying on this. If you show up to many fields expecting to smash through it because you're smart, you'll be torn to bits in many many fields. This is because the fields that are useful are already being dominated by people who are good at things to the extent that they're economically or emotionally valuable.
The exact example of chess makes this clear. If a smart LWer thinks "Oh, I'll get to the chess leaderboards because I'm really smart", they are going to find out after some weeks of studying that… everyone else on the leaderboards is smart too!