The article states that AI agents are not acting independently, but frontier labs are skipping basic security measures.
The chart will appear after repeat observations. The current metric comes from the source.
404 source pointsReal observations only. History before source connection is not reconstructed.
The current alignment strategies of major AI labs include shaping the model's inductive biases, applying extensive reinforcement learning, racing towards prosaic RSI, and evaluating and fixing alignment through trial-and-error. The authors argue that this approach has significant flaws.
2 days agoLessWrongThe article critically examines Lean4, a formal verification tool, highlighting its current limitations and potential vulnerabilities. It discusses concerns about the reliability of Lean4's proofs and the possibility of undetected bugs, including references to specific issues like 'soundness bug #14576' and the creator's warnings about AI exploiting these flaws.
5 days agoLessWrongThe post discusses the importance of clear thinking in empirical AI safety research, emphasizing fast iteration through efficient problem-solving and the use of scientific frameworks. The author shares insights from their apprenticeships at MATS and Redwood Research, focusing on finetuning-based misalignment auditing and sabotage capability evaluations.
yesterdayLessWrongThe author expresses frustration with calls for people to 'drop everything and pivot to AI safety,' noting that there are many talented individuals in the field but few job opportunities. They highlight that fellowships and job openings in AI safety are highly competitive and offer high salaries.
5 days agoLessWrongThe article discusses the distinction between AGI-pilled and ASI-pilled individuals, as well as challenges related to the potential superiority of artificial intelligence over humans. The author expresses concern about the future of freedom and democracy in the context of rapid technological development.
6 days agoLessWrongThe author argues that people underestimate the value of creating the simplest and clearest explanations for important aspects of AI safety. They provide examples of existing explainers that contain extraneous details, complex analogies, and insider jargon, and suggest improvements.
yesterday