Keynotes

Rethinking Efficiency in Machine Learning

Abstract

The success of Artificial Intelligence (AI) has so far relied on developing increasingly precise models. However, this has come at the cost of greater complexity, requiring a higher number of parameters to estimate. As a result, model transparency and explainability have diminished, while the energy demands for training and deployment have skyrocketed. It is estimated that by 2030, AI could account for more than 30% of the planet’s total energy consumption.

In this context, green and responsible AI has emerged as a promising alternative, characterized by lower carbon footprints, reduced model sizes, decreased computational complexity, and improved transparency. Various strategies can help achieve these goals, such as improving data quality, developing more energy-efficient execution models, and optimizing energy efficiency in model training and inference. These innovation approaches highlight the potential of green AI to challenge the prevailing paradigm of ever-growing models.

Evolutionary Computation as a Path to Safe, Trustworthy, and Responsible General-Purpose Artificial Intelligence

Abstract

As AI systems grow in capability and autonomy, concerns around safety, alignment, and trust have taken center stage. Issues such as goal misalignment, vulnerability to adversarial attacks, and the inability to generalize reliably in open-world settings are no longer theoretical: they are pressing challenges with real-world implications. At the same time, global regulatory efforts, including the EU AI Act and other emerging international frameworks, are setting strict expectations for transparency, robustness, and accountability in AI development. This keynote provides an accessible introduction to the key pillars of safe, trustworthy, responsible, and general-purpose AI, tailored for newcomers to the field. It highlights how evolutionary computation offers a powerful, underexplored toolkit for meeting safety and trustworthy requirements. With its emphasis on diversity, adaptability, and robustness, evolutionary computation can contribute to safer learning, better generalization, and more resilient systems. The talk will bridge technical concepts with regulatory perspectives, illustrating how evolutionary approaches can help meet both the ethical and legal requirements driving the future of responsible AI systems.

Evolutionary Computation: Back to the Future

Abstract

The evolution principles underlying Evolutionary Algorithms can be applied in any search space (i.e., to any representation), provided we are able to define meaningful variation operators with respect to the problem at hand. From the historical bitstring, continuous variables and Finite State Automata to advanced program or structure embeddings and beyond, EC has gradually, and sometimes painfully, earned its spurs, turning from confidential pocketknife to recognized Swiss Army Knife. I will try to illustrate this historical perspective with various examples gathered during my 35 (omg!) years of research in EC, and to demonstrate how a thorough exploitation of the past can provide useful hints for an efficient exploration of the future.