Scott Alexander defends effective altruism by highlighting its major accomplishments and arguing that its occasional missteps are outweighed by its positive impact on the world. Longer summary

Scott Alexander defends effective altruism (EA) against recent criticisms, highlighting its accomplishments in global health, animal welfare, AI safety, and other areas. He argues that EA has saved around 200,000 lives, equivalent to ending gun violence, curing AIDS, and preventing a 9/11-scale attack in the US. Scott contends that EA's achievements are often overlooked because they focus on less publicized causes, and that the movement's occasional missteps are minor compared to its positive impact. He emphasizes that EA is a coalition of people who care about logically analyzing important causes, whether broadly popular or not, and encourages readers to investigate and support the most beneficial causes. Shorter summary

Scott Alexander reviews a debate on AI development pauses, discussing various strategies and their potential impacts on AI safety and progress. Longer summary

Scott Alexander summarizes a debate on pausing AI development, outlining five main strategies discussed: Simple Pause, Surgical Pause, Regulatory Pause, Total Stop, and No Pause. He explains the arguments for and against each approach, including considerations like compute overhang, international competition, and the potential for regulatory overreach. The post also covers additional perspectives from debate participants and Scott's own thoughts on the feasibility and implications of various pause strategies. Shorter summary

Scott Alexander critically examines OpenAI's 'Planning For AGI And Beyond' statement, discussing its implications for AI safety and development. Longer summary

Scott Alexander analyzes OpenAI's recent statement 'Planning For AGI And Beyond', comparing it to a hypothetical ExxonMobil statement on climate change. He discusses why AI doomers are critical of OpenAI's research, explores potential arguments for OpenAI's approach, and considers cynical interpretations of their motives. Despite skepticism, Scott acknowledges that OpenAI's statement represents a step in the right direction for AI safety, but urges for more concrete commitments and follow-through. Shorter summary

Scott Alexander examines the Eliciting Latent Knowledge (ELK) problem in AI alignment and various proposed solutions. Longer summary

Scott Alexander discusses the Eliciting Latent Knowledge (ELK) problem in AI alignment, which involves training an AI to truthfully report what it knows. He explains the challenges of distinguishing between an AI that genuinely tells the truth and one that simply tells humans what they want to hear. The post covers various strategies proposed by the Alignment Research Center (ARC) to solve this problem, including training on scenarios where humans are fooled, using complexity penalties, and testing the AI with different types of predictors. Scott also mentions the ELK prize contest and some criticisms of the approach from other AI safety researchers. Shorter summary