Dr. David Blackwell was a mathematician and statistician of the first rank, whose contributions to statistical theory, game theory, and decision theory predated many of the algorithmic breakthroughs that define modern artificial intelligence. This survey examines three of his most consequential theoretical results the Rao Blackwell theorem, the Blackwell Approachability theorem, and the Blackwell Informativeness theorem (comparison of experiments) and traces their direct influence on contemporary AI and machine learning. We show that these results, developed primarily in the 1940s and 1950s, remain technically live across modern subfields including Markov Chain Monte Carlo inference, autonomous mobile robot navigation (SLAM), generative model training, no-regret online learning, reinforcement learning from human feedback (RLHF), large language model alignment, and information design. NVIDIAs 2024 decision to name their flagship GPU architecture (Blackwell) provides vivid testament to his enduring relevance. We also document an emerging frontier: explicit Rao Blackwellized variance reduction in LLM RLHF pipelines, recently proposed but not yet standard practice. Together, Blackwell theorems form a unified framework addressing information compression, sequential decision making under uncertainty, and the comparison of information sources precisely the problems at the core of modern AI.

翻译：大卫·布莱克威尔博士是一位杰出的数学家与统计学家，他在统计理论、博弈论和决策理论方面的贡献，早于定义现代人工智能的诸多算法突破。本综述考察了他最具影响力的三项理论成果——拉奥-布莱克威尔定理、布莱克威尔可逼近性定理以及布莱克威尔信息性定理（实验比较），并追溯了它们对当代人工智能与机器学习的直接影响。我们证明，这些主要形成于20世纪40年代和50年代的研究成果，至今仍活跃于现代子领域，包括马尔可夫链蒙特卡洛推理、自主移动机器人导航（SLAM）、生成模型训练、无遗憾在线学习、基于人类反馈的强化学习（RLHF）、大语言模型对齐以及信息设计。英伟达公司2024年决定以其旗舰GPU架构（Blackwell）命名，即为布莱克威尔持续影响力的生动见证。我们还记录了一个新兴前沿领域：在大语言模型RLHF管线中显式采用拉奥-布莱克威尔方差缩减技术，该方法虽已提出但尚未成为标准实践。综合而言，布莱克威尔定理构成了一个统一框架，用以应对信息压缩、不确定性下的序贯决策以及信息源比较——这些正是现代人工智能的核心问题所在。