Scott tests OpenAI's o3 model's ability to identify locations from photos, finding it has remarkable success even with minimal visual information, raising questions about AI capabilities. Longer summary

Scott tests OpenAI's o3 model on increasingly difficult GeoGuessr-style location guessing challenges using his own photos. Starting with a Google Street View image of a featureless plain, progressing through personal photos of Nepal mountains, a dorm room, and extremely zoomed-in shots of grass and river water, Scott finds that o3 shows remarkable ability to identify locations from minimal visual cues. While it fails on some challenges like identifying a specific house address, its success rate and reasoning process on most images is impressive enough to make Scott question whether this represents a qualitatively different level of AI capability. Shorter summary

Scott Alexander analyzes the astronomical costs and resources needed for future AI models, sparked by Sam Altman's reported $7 trillion fundraising goal. Longer summary

Scott Alexander discusses Sam Altman's reported plan to raise $7 trillion for AI development. He breaks down the potential costs of future GPT models, explaining how each generation requires exponentially more computing power, energy, and training data. The post explores the challenges of scaling AI, including the need for vast amounts of computing power, energy infrastructure, and training data that may not exist yet. Scott also considers the implications for AI safety and OpenAI's stance on responsible AI development. Shorter summary

The post explores AI forecasting capabilities, compares prediction market performances, and provides updates on various ongoing predictions in technology and politics. Longer summary

This post discusses recent developments in AI forecasting and prediction markets. It covers a study testing GPT-2's ability to predict past events, reports on Metaculus' accuracy compared to low-information priors and Manifold Markets, and updates on various prediction markets including those related to AI development, abortion medication, and Elon Musk's role at Twitter. The author also mentions new features in prediction platforms and research on forecasting methodologies. Shorter summary

Scott Alexander examines Redwood Research's attempt to create an AI that avoids generating violent content, using Alex Rider fanfiction as training data. Longer summary

Scott Alexander reviews Redwood Research's project to create an AI that can classify and avoid violent content in text completions, using Alex Rider fanfiction as training data. The project aimed to test whether AI alignment through reinforcement learning could work, but ultimately failed to create an unbeatable violence classifier. The article explores the challenges faced, the methods used, and the implications for broader AI alignment efforts. Shorter summary

Scott Alexander argues against Gary Marcus's critique of AI scaling, discussing the potential for future AI capabilities and the nature of human intelligence. Longer summary

Scott Alexander responds to Gary Marcus's critique of AI scaling, arguing that current AI limitations don't necessarily prove statistical AI is a dead end. He discusses the scaling hypothesis, compares AI development to human cognitive development, and suggests that 'world-modeling' may emerge from pattern-matching abilities rather than being a distinct, hard-coded function. Alexander also considers the potential capabilities of future AI systems, even if they don't achieve human-like general intelligence. Shorter summary

Scott Alexander bets that AI models will quickly overcome current limitations, based on how GPT-3 improved on GPT-2's shortcomings identified by Gary Marcus. Longer summary

Scott Alexander discusses his prediction that AI models will quickly overcome current limitations, using examples of how GPT-3 improved on GPT-2's shortcomings. He analyzes Gary Marcus's critiques of AI capabilities, showing how many issues Marcus pointed out with GPT-2 and GPT-3 were resolved in subsequent versions. While acknowledging Marcus's expertise, Scott argues that the pattern of AI rapidly improving suggests current flaws will likely be fixed soon, though this doesn't necessarily disprove Marcus's deeper concerns about AI's true intelligence. Shorter summary