Artificial Intelligence is now recognized as a general-purpose technology with ample impact on human life. This work aims at understanding the evolution of AI and, in particular Machine learning, from the perspective of researchers' contributions to the field. In order to do so, we present several measures allowing the analyses of AI and machine learning researchers' impact, influence, and leadership over the last decades. This work also contributes, to a certain extent, to shed new light on the history and evolution of AI by exploring the dynamics involved in the field's evolution by looking at papers published at the flagship AI and machine learning conferences since the first International Joint Conference on Artificial Intelligence (IJCAI) held in 1969. AI development and evolution have led to increasing research output, reflected in the number of articles published over the last sixty years. We construct comprehensive citation collaboration and paper-author datasets and compute corresponding centrality measures to carry out our analyses. These analyses allow a better understanding of how AI has reached its current state of affairs in research. Throughout the process, we correlate these datasets with the work of the ACM Turing Award winners and the so-called two AI winters the field has gone through. We also look at self-citation trends and new authors' behaviors. Finally, we present a novel way to infer the country of affiliation of a paper from its organization. Therefore, this work provides a deep analysis of Artificial Intelligence history from information gathered and analysed from large technical venues datasets and suggests novel insights that can contribute to understanding and measuring AI's evolution.

翻译：人工智能现已被公认为一种对人类生活具有广泛影响的通用技术。本研究旨在从研究人员对领域贡献的角度，理解人工智能特别是机器学习的演进过程。为此，我们提出了若干度量指标，用于分析过去几十年间人工智能与机器学习研究人员的影响力、作用及领导力。本研究在一定程度上还通过考察自1969年首届国际人工智能联合会议（IJCAI）以来，在旗舰人工智能与机器学习会议上发表的论文，探索领域演进中的动态机制，从而为人工智能的历史与发展提供新的视角。人工智能的发展与演进导致研究产出日益增长，这反映在过去六十年间发表的论文数量上。我们构建了全面的引文协作网络与论文-作者数据集，并计算相应的中心性度量以进行分析。这些分析有助于更深入地理解人工智能如何达到当前的研究状态。在此过程中，我们将这些数据集与ACM图灵奖得主的工作以及该领域经历过的两次所谓"人工智能寒冬"进行了关联。我们还审视了自引趋势与新作者的行为模式。最后，我们提出了一种从论文所属组织推断其国家归属的新方法。因此，本研究基于从大型技术会议数据集收集与分析的信息，对人工智能历史进行了深入剖析，并提出了有助于理解与测量人工智能演进的新见解。