numbas

Very related to differential privacy: https://research.googleblog.com/2015/08/the-reusable-holdout... (together with IBM and Samsung)

Actually Microsoft has been publishing a lot on privacy enhancing Machine Learning too.

For instance:

https://www.microsoft.com/en-us/research/publication/crypton...

https://www.microsoft.com/en-us/research/publication/private...

https://youtu.be/xsaXMUelOEA

As for Apple: seeing is believing.

Check their board of directors -- all very smart and established people.

A former tournament winner did well on both the public and private leaderboard. It is very difficult to do this by luck. He was also a student from Bangladesh who got the opportunity to play with hedge fund data with just the cost of an internet connection and zero risk for messing it up. Should he start working for a hedge fund now, without any finance experience? Could be a good bet. Numerai would still beat him, because they can aggregate all the top models into an ensemble. It is hard to beat 50 individuals, but near impossible to beat a team of 50 competitors. Compare the variance of a single decision tree with a Random Forest.

Because you don't have capital, risk-adversity, and access to the (expensive) dataset. You only have encrypted predictions, which are worthless for you.

You don't really need to know what the features mean to make a good model. Even if only you knew the meaning of the features you'd still get beaten by someone using a blackbox approach. This is exactly what happened to the owner. He worked as a quant making predictions on this expensive dataset, and in the first tournament he was beaten by around 50 other modelers using anonymized features.