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 experiments with DALL-E 2 to create stained glass window designs, exploring the AI's capabilities and limitations in interpreting complex prompts. Longer summary

Scott Alexander explores the challenges and quirks of using DALL-E 2, an AI art generator, to create stained glass window designs depicting the Virtues of Rationality. He details his attempts to generate images for different virtues, discussing the AI's strengths, limitations, and unexpected behaviors. The post analyzes how DALL-E interprets prompts, handles historical figures and concepts, and struggles with combining specific subjects and styles. Scott concludes that while DALL-E is capable of impressive work, it currently has difficulties with unusual requests and maintaining consistent styles across multiple images. Shorter summary

Scott Alexander explores GPT-2's unexpected capabilities and argues that it demonstrates the potential for AI to develop abilities beyond its explicit programming, challenging skepticism about AGI. Longer summary

This post discusses GPT-2, a language model AI, and its implications for artificial general intelligence (AGI). Scott Alexander argues that while GPT-2 is not AGI, it demonstrates unexpected capabilities that arise from its training in language prediction. He compares GPT-2's learning process to human creativity and understanding, suggesting that both rely on pattern recognition and recombination of existing information. The post explores examples of GPT-2's abilities, such as rudimentary counting, acronym creation, and translation, which were not explicitly programmed. Alexander concludes that while GPT-2 is far from true AGI, it shows that AI can develop unexpected capabilities, challenging the notion that AGI is impossible or unrelated to current AI work. Shorter summary