When we output a forecast, we're either explicitly or implicitly outputting a probability distribution. For example, if we forecast the AQI in Berkeley tomorrow to be "around" 30, plus

Part of lecture notes for the upcoming Stat157 [http://www.stat157.com/] class on Forecasting. Let's start by considering the following question: > What is the probability that Joe Biden is

Part of lecture notes for the upcoming Stat157 [https://www.stat157.com/] class on Forecasting. Let's say you are trying to predict how long it will take to finish your homework

As part of my work for Open Philanthropy, I recently wrote a call for grant proposals on measuring and forecasting risks [https://docs.google.com/document/d/1cPwcUSl0Y8TyZxCumGPBhdVUN0Yyyw9AR1QshlRI3gc/edit] from future AI systems.

Cross-posted from the BAIR Blog [https://bair.berkeley.edu/blog/2021/11/08/similarity/]. To understand neural networks, researchers often use similarity metrics to measure how similar or different two neural networks are