Abstract
This paper presents a comprehensive synthesis of major breakthroughs in artificial intelligence (AI) over the past fifteen years, integrating historical, theoretical, and technological perspectives. It identifies key inflection points in AI' s evolution by tracing the convergence of computational resources, data access, and algorithmic innovation. The analysis highlights how researchers enabled GPU based model training, triggered a data centric shift with ImageNet, simplified architectures through the Transformer, and expanded modeling capabilities with the GPT series. Rather than treating these advances as isolated milestones, the paper frames them as indicators of deeper paradigm shifts. By applying concepts from statistical learning theory such as sample complexity and data efficiency, the paper explains how researchers translated breakthroughs into scalable solutions and why the field must now embrace data centric approaches. In response to rising privacy concerns and tightening regulations, the paper evaluates emerging solutions like federated learning, privacy enhancing technologies (PETs), and the data site paradigm, which reframe data access and security. In cases where real world data remains inaccessible, the paper also assesses the utility and constraints of mock and synthetic data generation. By aligning technical insights with evolving data infrastructure, this study offers strategic guidance for future AI research and policy development.
Abstract (translated)
本文综述了过去十五年人工智能（AI）领域的主要突破，从历史、理论和技术的角度进行了全面的整合。文章通过追踪计算资源、数据访问和算法创新之间的融合点，识别出人工智能演进过程中的关键转折点。分析强调研究人员如何利用基于GPU的模型训练、通过ImageNet推动以数据为中心的转变、采用Transformer简化架构，并借助GPT系列扩展建模能力。本文不仅将这些进展视为孤立的里程碑，还将其视作更深层次范式变化的标志。 论文运用统计学习理论中的样本复杂度和数据效率等概念，解释了研究人员如何将突破转化为可扩展解决方案以及为什么该领域现在必须接纳以数据为中心的方法。面对日益增长的隐私担忧和严格的监管环境，文章评估了联邦学习、隐私增强技术（PETs）和数据站点范式等新兴解决方案的有效性，这些方案重新定义了数据访问与安全。对于那些现实世界中的数据仍然不可获取的情况，论文也评估了模拟和合成数据生成的实用性和局限性。 通过将技术见解与不断发展的数据基础设施相结合，本研究为未来的人工智能研究和政策制定提供了战略指导。
URL
https://arxiv.org/abs/2505.16771
