Time as the protagonist of learning

Sensory information is characterized by a natural temporal development of the data that is commonly neglected by current technologies. In nature, we do not learn from a huge dataset of "shuffled images", and we do not store our entire visual life, stochastically sampling from it. Interacting over time allows multiple agents to share information at different stages of their development, to grow their own skills in an appropriate manner, or to help other agents improve: we educate children in function of their skills at the current age...

Is there a problem with current AI technologies?

The growing ubiquity of Large Language Models (LLM) has recently opened strong debates on scenarios giving rise to potentially rogue AIs involving social and political aspects. The source of these debates is deeply connected with the exploitation of increasingly large data collections, which requires huge financial resources, thus leading to the centralization of information. This aspect produces undeniable privacy problems as well as very controversial geopolitical effects. In a nutshell: data centralization issues; privacy and geopolitical issues;  energy efficiency issues; limited control, customizability, and causality.

Look forward!

The outstanding results of current Machine Learning-based models should be still massively leveraged by the current technologies. However, they are all based on previously collected datasets and networks trained by stochastically sampling from them, without any dynamic human intervention: humans are only bare data labelers, then they are out of the game.