Augmenting Statistical Models with Natural Language Parameters
This is a guest post by my student Ruiqi Zhong, who has some very exciting work defining new families of statistical models that can take natural language explanations as parameters. The motivation is
Analyzing the Historical Rate of Catastrophes
To communicate risks, we often turn to stories. Nuclear weapons conjure stories of mutually assured destruction, briefcases with red buttons, and nuclear winter. Climate change conjures stories of extreme weather, cities overtaken by
Forecasting AI (Overview)
This is a landing page for various posts I’ve written, and plan to write, about forecasting future developments in AI. I draw on the field of human judgmental forecasting, sometimes colloquially referred
GPT-2030 and Catastrophic Drives: Four Vignettes
I previously discussed the capabilities we might expect from future AI systems, illustrated through GPT2030, a hypothetical successor of GPT-4 trained in 2030. GPT2030 had a number of advanced capabilities, including superhuman programming,
Intrinsic Drives and Extrinsic Misuse: Two Intertwined Risks of AI
Given their advanced capabilities, future AI systems could pose significant risks to society. Some of this risk stems from humans using AI systems for bad ends (misuse), while some stems from the difficulty