Active Inference AI for Scientific and Technical Computing:
A Physics-Grounded Alternative to Machine Learning

About the Session

AI is rapidly being adopted across scientific and technical computing, from hypothesis generation and autonomous experimentation to robotic control and computational optimization. Most of these systems are built on machine learning, relying on statistical training, pattern completion, and post-hoc control mechanisms.

This talk introduces Active Inference AI as a fundamentally different approach that is grounded in the Free Energy Principle and explicitly aligned with physical systems. We compare machine learning–based AI with Active Inference AI, highlighting key differences in representation, adaptability, uncertainty handling, and system stability. The discussion focuses on why Active Inference is particularly well suited for scientific and engineering domains that require real-time control, coherent reasoning under uncertainty, and tight coupling to physical constraints, with examples spanning advanced computing and emerging applications such as quantum systems.

Speaker