Artificial Intelligence

Updates and Lessons from AI Forecasting

Cross-posted from Bounded Regret.

Earlier this year, my research group commissioned 6 questions for professional forecasters to predict about AI. Broadly speaking, 2 were on geopolitical aspects of AI and 4 were on future capabilities:

Geopolitical: How much larger or smaller will the largest Chinese ML experiment be compared to the largest U.S. ML experiment, as measured by amount of compute used? How much computing power will have been used by the largest non-incumbent (OpenAI, Google, DeepMind, FB, Microsoft), non-Chinese organization? Future capabilities: What will SOTA (state-of-the-art accuracy) be on the MATH dataset? What will SOTA be on the Massive Multitask dataset (a broad measure of specialized subject knowledge, based on high school, college, and professional exams)? What will be the best adversarially robust accuracy on CIFAR-10? What will SOTA be on Something Something v2? (A video recognition dataset)

Forecasters output a probability distribution over outcomes for 2022, 2023, 2024, and 2025. They have financial incentives to produce accurate forecasts; the rewards total $5k per question ($30k total) and payoffs are (close to) a proper scoring rule, meaning forecasters are rewarded for outputting calibrated probabilities.

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