Rubi J. Hudson — AI Alignment Forum

Defining Monitorable and Useful Goals

In my most recent post, I introduced a corrigibility transformation that could take an arbitrary goal over external environments and define a corrigible goal with no hit to performance. That post focused on corrigibility and deception in training, which are some of the biggest problems in AI alignment, but the...

Jul 15, 202515

Defining Corrigible and Useful Goals

This post became the paper Corrigibility Transformation: Constructing Goals That Accept Updates. The paper is more up to date, and after many rounds of edits for clarity, I believe a better introduction to the main idea. Ensuring that AI agents are corrigible, meaning they do not take actions to preserve...

Jun 25, 202544

Safe Predictive Agents with Joint Scoring Rules

Thanks to Evan Hubinger for funding this project and for introducing me to predictive models, Johannes Treutlein for many fruitful discussions on related topics, and Dan Valentine for providing valuable feedback on my code implementation. In September 2023, I received four months of funding through Manifund to extend my initial...

Oct 9, 202455

Simplifying Corrigibility – Subagent Corrigibility Is Not Anti-Natural

Max Harms recently published an interesting series of posts on corrigibility, which argue that corrigibility should be the sole objective we try to give to a potentially superintelligent AI. A large installment in the series is dedicated to cataloging the properties that make up such a goal, with open questions...

Jul 16, 202447

The Case for Predictive Models

Thanks to Johannes Treutlein and Paul Colognese for feedback on this post. Just over a year ago, the Conditioning Predictive Models paper was released. It laid out an argument and a plan for using powerful predictive models to reduce existential risk from AI, and outlined some foreseeable challenges to doing...

Apr 3, 202443

Searching for Searching for Search

Thanks to Leo Gao, Nicholas Dupuis, Paul Colognese, Janus, and Andrei Alexandru for their thoughts. This post was mostly written in 2022, and pulled out of my drafts after recent conversations on the topic. My main update from then is the framing. Rather that suggesting searching for substeps of search...

Feb 14, 202419

Conditional Prediction with Zero-Sum Training Solves Self-Fulfilling Prophecies

Thanks to Charlotte Siegmann, Caspar Oesterheld, Spencer Becker-Kahn, and Evan Hubinger for providing feedback on this post. The issue of self-fulfilling prophecies, also known as performative prediction, arises when the act of making a prediction can affect its own outcome. Systems aiming for accurate predictions are then incentivized not only...

May 26, 202388

Rubi Hudson

Rubi Hudson

Rubi Hudson

Conditioning Predictive Models: Large language models as predictors

Conditional Prediction with Zero-Sum Training Solves Self-Fulfilling Prophecies

Proper scoring rules don’t guarantee predicting fixed points

Conditioning Predictive Models: Outer alignment via careful conditioning

Rubi Hudson

Conditioning Predictive Models: Large language models as predictors

Conditional Prediction with Zero-Sum Training Solves Self-Fulfilling Prophecies

Proper scoring rules don’t guarantee predicting fixed points

Conditioning Predictive Models: Outer alignment via careful conditioning

Defining Monitorable and Useful Goals

Defining Corrigible and Useful Goals

Safe Predictive Agents with Joint Scoring Rules

Simplifying Corrigibility – Subagent Corrigibility Is Not Anti-Natural

The Case for Predictive Models

Searching for Searching for Search

Conditional Prediction with Zero-Sum Training Solves Self-Fulfilling Prophecies