英文【Epoch AI】2030年人工智能：基于当前趋势的外推分析

AI in 2030 Extrapolating current trends This Epoch AI report was commissioned by Google DeepMind. All points of views and conclusions expressed are those of the authors and do not necessarily reflect the position or endorsement of Google DeepMind. AI in 2030 | Epoch AI 1 Table of Contents Executive summary 3 Introduction 6 Scaling and capabilities 19 Scale 25 Compute 28 Investment 33 Data 39 Hardware 45 Energy and the environment 50 Interlude: From scale to capabilities 57 Capabilities 66 How capabilities are deployed 67 Software engineering 72 Mathematics 77 Molecular biology 83 Weather predictions 89 Discussion and conclusion 92 Appendix: AIʼs potential to reduce GHG emissions 94 Appendix: benchmark extrapolation details 99 AI in 2030 | Epoch AI 2 Executive summary How will advanced AI be developed, and what will its effects be in the world at large? What will happen if current trends in scaling up AI development persist all the way to 2030? This report examines what this scale-up would involve in terms of compute, investment, data, hardware, and energy. We explore the role of compute across inference and training, the promise of economic value that would be necessary to justify such investment, and potential challenges in data availability and energy. Based on these predictions for how AI will be developed, we turn to predict future AI capabilities, and the impacts they will have in scientific R&D. AI for science is the explicit goal of several leading AI developers, and is likely to be among the top priorities for AI deployment. Scientific R&D provides a valuable lens for understanding what advanced AI will achieve. Compute scaling has played a key role in AI development, and will likely continue to do so. Compute for training and inference drives improvements in AI capabilities, and much progress in AI research has come from developing general-purpose methods to enable the use of more compute. The trajectory of AI development can be forecasted based on continued compute scaling. Scaling has significant implications across many areas of AI development: training and inference compute, investment, data, hardware, and energy. When we predict that compute scaling will continue, we can then examine the consequences within each of these — and how they need to scale accordingly to allow compute scaling trends to continue. Exponential growth will likely continue to 2030 across all key trends. Across training and inference compute, investment, data, hardware, and energy, we argue that a continuation of existing trends is feasible. We explore each factor in detail, showing how growth could continue to 2030, and discussing the most credible reasons for slowdown or acceleration before then. We argue the most credible reasons for a deviation from trend are changes in societal coordination of AI development (e.g. investor sentiment or tight regulation), supply bottlenecks for AI clusters (e.g. chips AI in 2030 | Epoch AI 3 or energy), or paradi