用蛋糕比喻AI全生命周期，揭示技术与社会的深层关联。
The Cake that is Intelligence and Who Gets to Bake it: An AI Analogy and its Implications for Participation
- 将数据、训练、评估等环节比作蛋糕的原料、烘焙与品鉴过程。
- 指出每个阶段的技术假设都影响社会结果，如公平性与可及性。
- 为从业者、用户和研究者提供参与AI讨论的具体行动建议。
Yann LeCun提出的机器智能如蛋糕的比喻——无监督学习为基底，有监督学习为糖霜，强化学习为顶部樱桃——被扩展为人工智能系统全生命周期的框架。该框架涵盖原料（数据）、食谱（指令）、烘焙（训练）以及品尝与销售（评估与分发）。通过这一重构，论文分析了各环节背后的社会影响及其受机器学习统计假设的制约。技术基础与社会后果紧密交织，却常被孤立研究，导致参与壁垒。本文提出跨学科对话路径，明确技术与社会的交互点，并在每个阶段给出具体行动建议，提升未来从业者、使用者和研究者对人工智能话语的参与意识与能力。
原文摘要 · Abstract (English)
In a widely popular analogy by Turing Award Laureate Yann LeCun, machine intelligence has been compared to cake - where unsupervised learning forms the base, supervised learning adds the icing, and reinforcement learning is the cherry on top. We expand this 'cake that is intelligence' analogy from a simple structural metaphor to the full life-cycle of AI systems, extending it to sourcing of ingredients (data), conception of recipes (instructions), the baking process (training), and the tasting and selling of the cake (evaluation and distribution). Leveraging our re-conceptualization, we describe each step's entailed social ramifications and how they are bounded by statistical assumptions within machine learning. Whereas these technical foundations and social impacts are deeply intertwined, they are often studied in isolation, creating barriers that restrict meaningful participation. Our re-conceptualization paves the way to bridge this gap by mapping where technical foundations interact with social outcomes, highlighting opportunities for cross-disciplinary dialogue. Finally, we conclude with actionable recommendations at each stage of the metaphorical AI cake's life-cycle, empowering prospective AI practitioners, users, and researchers, with increased awareness and ability to engage in broader AI discourse.
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