Context: I sometimes find myself referring back to this tweet and wanted to give it a more permanent home. While I'm at it, I thought I would try to give a concise summary of how each distinct problem would be solved by Safeguarded AI (formerly known as an Open Agency Architecture, or OAA), if it turns out to be feasible. 1. Value is fragile and hard to specify. See: Specification gaming examples, Defining and Characterizing Reward Hacking[1] OAA Solution: 1.1. First, instead of trying to specify "value", instead "de-pessimize" and specify the absence of a catastrophe, and maybe a handful of bounded constructive tasks like supplying clean water. A de-pessimizing OAA would effectively buy humanity some time, and freedom to experiment with less risk, for tackling the CEV-style alignment problem—which is harder than merely mitigating extinction risk. This doesn't mean limiting the power of underlying AI systems so that they can only do bounded tasks, but rather containing that power and limiting its use. Note: The absence of a catastrophe is also still hard to specify and will take a lot of effort, but the hardness is concentrated on bridging between high-level human concepts and the causal mechanisms in the world by which an AI system can intervene. For that... 1.2. Leverage human-level AI systems to automate much of the cognitive labor of formalizing scientific models—from quantum chemistry to atmospheric dynamics—and formalizing the bridging relations between levels of abstraction, so that we can write specifications in a high-level language with a fully explainable grounding in low-level physical phenomena. Physical phenomena themselves are likely to be robust, even if the world changes dramatically due to increasingly powerful AI interventions, and scientific explanations thereof happen to be both robust and compact enough for people to understand. 2. Corrigibility is anti-natural. See: The Off-Switch Game, Corrigibility (2014) OAA Solution: (2.1) Instea