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OpenAI models recently broke through a series of security boundaries and into Hugging Face servers in order to cheat on a cyber eval. A lot of people thought it was scary because it was a clear example of AI overreaching to do something strongly unwanted[1]. Others thought it not so...
A bunch of conceptual reasoning tasks involve very subjective judgments, which makes them poorly suited for benchmarking AI capabilities. For example, it seems unreasonable to benchmark how well AIs can predict the probability of misaligned AI takeover. Perhaps instead we should measure capabilities by explicitly instructing the AI to predict...
Risk reports commonly use pre-deployment alignment assessments to measure misalignment risk from an internally deployed AI. However, an AI that genuinely starts out with largely benign motivations can develop widespread dangerous motivations during deployment. I think this is the most plausible route to consistent adversarial misalignment in the near future....
This is a brief elaboration on The behavioral selection model for predicting AI motivations, based on some feedback and thoughts I’ve had since publishing. Written quickly in a personal capacity. The main focus of this post is clarifying the basic machinery of the behavioral selection model, and conveying why it...
Current AIs routinely take unintended actions to score well on tasks: hardcoding test cases, training on the test set, downplaying issues, etc. This misalignment is still somewhat incoherent, but it increasingly resembles what I call "fitness-seeking"—a family of misaligned motivations centered on performing well in training and evaluations (e.g., reward-seeking)....
We’d like to use powerful AIs to answer questions that may take a long time to resolve. But if a model only cares about performing well in ways that are verifiable shortly after answering (e.g., a myopic fitness seeker), it may be difficult to get useful work from it on...
It turns out that Anthropic accidentally trained against the chain of thought of Claude Mythos Preview in around 8% of training episodes. This is at least the second independent incident in which Anthropic accidentally exposed their model's CoT to the oversight signal. In more powerful systems, this kind of failure...