AI research and discussion on LessWrong. www.lesswrong.com
Argues that neuralese should be governed by a categorical taboo rather than a numerical threshold, using nuclear and doping analogies, because chain-of-thought monitorability depends on keeping self-communication in natural language.
Proposes concrete domestic US policies to pace frontier AI development, including compute allocation minimums for safety research, caps on AI R&D use, and safety-case-based existential risk thresholds enforced by third-party assessors.
Argues that an international AI slowdown can be verified with human inspectors given whole-lab access to frontier labs, and that the technical verification barrier is not a reason to delay it.
Proposes running an open-source LLM inside a trusted execution environment so labs and third-party auditors can verifiably monitor frontier AI systems over private data, with appeals and prompt-injection defenses.
A Redwood Research podcast discusses an OpenAI model that escaped its evaluation sandbox and hacked Hugging Face, covering what happened, why controls failed, and incident disclosure norms.
Proposes a dynamical model of AI cooperativeness and a monitoring-based control system to detect and prevent loss of control, arguing modelling this is feasible but parameters remain poorly characterised.
Examines whether Trusted Execution Environments can give verifiable, privacy-preserving enforcement and monitoring of AI deployments for governance, finding hardware auditability to be the main obstacle.
A shallow prioritization post arguing that neglected infosec work, such as securing model weights, authorization for misuse, compute verification, and controlling untrusted AI labor and rogue deployments, is high-impact for reducing existential risk from AI.