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Scott debunks the "all exponentials become sigmoids" argument against AI risk by showing how forecasters consistently predict premature flattening of exponential trends, and argues that without deep understanding of AI dynamics, we should expect current AI progress to continue for roughly as long as it's already been going.
Scott explains why Ajeya Cotra's influential 'Biological Anchors' report correctly predicted the AI scaling boom but got AGI timelines wrong by twenty years, due to severely underestimating the rate of algorithmic progress.
Scott Alexander examines GPT-3's capabilities, improvements over GPT-2, and potential implications for AI development through scaling.
Read Scott like a free app: tap then Add to Home Screen. Read Scott like a free app: tap then Add app to Home screen. Add Read Scott Alexander to your home screen to read it like an app.