  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.

Artificial Intelligence;Computers and Society;Machine Learning