Practical implementation of Free-Energy Principle/active inference in AI systems

Develop practical AI systems that realize learning based on the Free-Energy Principle and active inference, achieving energy-efficient learning without reliance on large training datasets, given the current lack of methods to implement this in contemporary AI systems.

Background

The paper contrasts the energy demands of current deep learning approaches with the potential efficiency of active inference under the Free-Energy Principle. It then cites Yann LeCun’s assessment that, despite this promise, the community currently lacks a way to build such systems.

This frames a concrete research gap: translating the theoretical benefits of active inference into practical AI architectures and training procedures.