Previously, I've argued that future ML systems might exhibit unfamiliar, emergent capabilities [https://bounded-regret.ghost.io/p/1527e9dd-c48d-4941-9b14-4f7293318d5c/], and that thought experiments provide one approach [https://bounded-regret.ghost.io/p/a2d733a7-108a-4587-97fb-db90f66ce030/

In the previous post [https://bounded-regret.ghost.io/thought-experiments-provide-a-third-anchor/], I talked about several "anchors" that we could use to think about future ML systems, including current ML systems, humans, ideal optimizers,

Previously, I argued [https://bounded-regret.ghost.io/future-ml-systems-will-be-qualitatively-different/] that we should expect future ML systems to often exhibit "emergent" behavior, where they acquire new capabilities that were not explicitly designed or

In 1972, the Nobel prize-winning physicist Philip Anderson wrote the essay " More Is Different [https://science.sciencemag.org/content/177/4047/393]". In it, he argues that quantitative changes can lead

Machine learning is touching increasingly many aspects of our society, and its effect will only continue to grow. Given this, I and many others care about risks from future ML systems and how