I think the most interesting takeaway I had was that despite the fact that Kotlin interfaces with Java pretty much seamlessly, you can’t let them mingle too much or you’ll give up the null-safety you would otherwise get from Kotlin, and you definitely don’t want to give it up.
As I was walking home from work a few nights ago I was thinking about privilege. I was also thinking about the AI Google built that beat a human champion at Go. Most algorithms that play Go use some form of the Monte Carlo tree search (MCTS) algorithm, the Google algorithm is no exception, though MCTS is only a relatively small part of it.
I know a little bit about MCTS, having read some of the papers on it and implemented it in school (my AI played Connect Four). MCTS is generally most applicable on game trees with a finite depth. In other words, the games must definitely end at some point. This is not true of games like chess or checkers where, in theory, the game could go on forever if the players repeatedly make neutral moves (like moving two pieces back and forth forever).
The reason for this is that MCTS works by choosing moves at random for both players until the game ends, then recording who won. This process is repeated many times (usually until a computational or time budget is exhausted), at which point the move with the best simulated results is chosen. Obviously, the random games must be guaranteed to come to an end or the algorithm wouldn’t work very well.
MCTS operates based on the density of winning outcomes on a particular branch of the game tree. If move A at a particular point in the game results in a win (when random moves are chosen) 70% of the time, and move B results in a win 45% of the time, the algorithm will choose branch A (in reality it is a little more complicated than this, but the idea is the same).
This is pretty much how “privilege” works in real life. At any given node in the tree representing all the decisions each of us makes in our lives, there is some probability that a particular choice will lead to a good outcome. In other words, each branch has a particular density of good outcomes.
Privilege, then, is when a person has a higher density of good outcomes on all of his or her branches.
For example, I read recently that a child with wealthy parents who does not attend college is over twice as likely to end up wealthy than a child with poor parents who does attend college. So when young people decide whether or not to attend college, those with rich parents face better probable outcomes across the whole range of choices. That, in a nutshell, is privilege.