Keynotes

Abstract

The number and variety of intelligent systems being used for real-world problem-solving in all domains is growing rapidly. In this context, modelling the measurement of machine/system intelligence becomes of utmost importance. Modelled machine intelligence metrics presented in the scientific literature are based on different philosophies, which hinders their effective comparison. There is no standardization of what machine intelligence is and what should be measured to quantify it. This study examines the measurement of the intelligence of artificial complex computing systems from the perspective of their ability to solve real-world difficult problems and highlights the importance of being able to make accurate and robust comparisons of intelligence within multiple intelligent complex systems. The most important property of an intelligence metric must be universality, based on the very large diversity of intelligent complex systems. In this sense, an important approach consists of modeling black-box-based intelligence metrics that should be able to handle aspects like variability in intelligence and extreme intelligence (rare very low, and high intelligence manifestations in different situations). Modelled universal black-box-based machine intelligence metrics represent a useful tool for intelligent system developers to measure the intelligence of their systems with the intent to compare it with the intelligence of other systems regardless of the diversity of their architectures.

The lecture will discuss selected issues related to machine unlearning, i.e., how to remove the influence of a specified subset of training data from a machine learning model.

Curriculum Vitae

L.B. Iantovics obtained a BSc and MSc in Mathematics and Informatics from the Transilvania University of Brasov; a Ph.D. in Artificial Intelligence (AI) from the Babes-Bolyai University of Cluj-Napoca and completed a Postdoctoral study in AI at the Alexandru Ioan Cuza University of Iasi. In 2020 he attained a habilitation degree at the George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures (UMFST). Dr. Iantovics since 2020 is Ph.D. supervisor at the Doctoral School of UMFST and Full Professor since 2021. Since 2018 is the Director of the Research Center on Artificial Intelligence, Data Science, and Smart Engineering (ARTEMIS) at UMFST. His main research directions are related to Artificial Intelligence, Data Science, and Applied Statistics with particular topics approached like: intelligent systems, measurement of machine intelligence, in-depth performance analysis of algorithms, and data analytics intelligence applied mostly in health sciences.

He published dozens of papers and book chapters on these topics and contributed to research projects as project leader or researcher. Contributions to international research project coordination include: Hybrid Medical Complex Systems (ComplexMediSys); Electronic Health Records for the Next Generation Medical Decision Support in Romanian and Bulgarian National Healthcare Systems (NextGenElectroMedSupport) and Social Network of Machines (SOON) among others. Dr. Iantovics has acted as a committee member at numerous prestigious conferences, has reviewed papers in several prestigious journals, has been a member of the editorial board of multiple journals, and has organized conferences as General Chair and Principal Scientific Organizer such as BICS, CANS, UICS, etc. He has edited valuable books and journal special issues, such as "Advanced Intelligent Computational Technologies and Decision Support Systems"; "Advances in Intelligent Analysis of Medical Data and Decision Support Systems", published in the Computational Intelligence series; "Advanced Computational Technologies", published by the Romanian Academy Publishing House, and others.