Position Paper: Collectionless Artificial Intelligence

Storing and handling huge data collections has become the fundamental player in the progress of Machine Learning and of its spectacular results. However, learning from such data collections introduces risks related to data centralization, privacy, energy efficiency, limited customizability, and control. This paper sustains the position that the time has come for thinking of new learning protocols where machines conquer cognitive skills by online learning from potentially lifelong streams of sensory data, without the privilege of recording the temporal stream. The perspective of what we refer to as “Collectionless AI” pushes towards interactions with the environment, including humans and other artificial agents, to favor dynamic adaptation, customizability, control. At each time instant, data acquired from the environment is only processed with the purpose of contributing to update the current agent-internal representation of the environment, promoting the development of self-organized memorization skills. The goal of this paper is not to introduce new algorithms, but to present an extreme perspective which recovers largely known notions out of the current mainstream, with the goal of stimulating the development of new foundations on computational processes of learning and reasoning. This might open the doors to a truly orthogonal competitive track on AI technologies that avoid data accumulation by design, thus offering a framework which is better suited concerning privacy issues, control and customizability. Pushing towards massively distributed computation, the collectionless approach to AI might reduce the concentration of power in companies and governments, better facing geopolitical issues.