Rapid technological advancements in Artificial Intelligence (AI) as well as the growing deployment of intelligent technologies in new application domains has generated serious anxiety, and a fear of missing out among different stake-holders, fostering a racing narrative. Whether real or not, the belief in such an AI race, can make it real simply from its consequences, as put forward by the Thomas theorem. These consequences may be negative, as racing for technological supremacy creates a complex ecology of choices that could push stake-holders to underestimate or even ignore ethical and safety procedures.
As a consequence, different actors are urging to consider both the normative and social impact of these technological advancements, contemplating the use of the precautionary principle in AI innovation and research. Yet, given the breadth and depth of the field of AI, it is difficult to assess which technology needs regulation and when. As there is no easy access to data describing this alleged AI race, theoretical models are necessary to understand its potential dynamics, allowing for the identification of when, how and which procedures need to be put in place to favour outcomes beneficial for all.
This talk introduces our reflections about how the time-scale, in which AI supremacy can be achieved, plays a crucial role. Thus, when defining the codes of conduct and the regulatory policies for AI, a clear understanding about the time-scale of the race is required, as this may induce important non-trivial effects.
Tom Lenaerts is Professor at the Université Libre de Bruxelles (ULB) where he is co-heading the Machine Learning group in the Department of Computer Science. He is currently the ULB director of the Interuniversity Institute of Bioinformatics in Brussels (IB2) and Vice-president of the ULB Computer Science Department. He holds also a partial affiliation as Research Professor with the Artificial intelligence lab (AI-lab) of the Vrije Universiteit Brussel (VUB). His research encompasses interdisciplinary topics ranging from AI, machine learning and behavioural informatics (evolution, network and group formation, emotions, anticipation, …) to computational biology and medicine (metastasis dynamics, molecular communication, oligogenic diseases, …).
Francisco C. Santos is an Associate Professor of the Department of Computer Science and Engineering of Instituto Superior Técnico, University of Lisbon (Portugal). He is interested in the development and application of scientific computing and modelling tools to understand collective dynamics in social and life sciences. He has been working on problems related to the evolution of cooperation, the origins of social norms, network science, and environmental governance, urban planning, among other topics. He obtained a PhD in computer science from the Université Libre de Bruxelles in 2007.