In Future ML Systems Will Be Qualitatively Different, I argued that we should expect ML systems to exhibit emergent capabilities. My main support for this was four historical examples of emergence in ML.

Previously, I argued that emergent phenomena in machine learning mean that we can't rely on current trends to predict what the future of ML will be like. In this post, I will argue

Previously, I've argued that future ML systems might exhibit unfamiliar, emergent capabilities, and that thought experiments provide one approach towards predicting these capabilities and their consequences. In this post I’ll describe a

In the previous post, I talked about several "anchors" that we could use to think about future ML systems, including current ML systems, humans, ideal optimizers, and complex systems. In fact,

Previously, I argued that we should expect future ML systems to often exhibit "emergent" behavior, where they acquire new capabilities that were not explicitly designed or intended, simply as a result