Active Inference Project, Free Energy Principle, Artificial Intelligence
Active Inference
Active inference is a theoretical framework that relates pragmatics and epistemics. It models the freedom that we have when our cognitive models do not match our perceived reality: We can either update our models or adjust our reality. The free energy principle states that we behave so as to reduce uncertainty, which is to say, reduce surprise.
Active inference is a robust conceptual modeling language which allows us to connect with the perspectives of all manner of frameworks. Look for Active Inference concepts in our Theory Translator based on Wondrous Wisdom.
Active inference is of special importance to Econet because both are embraced by Daniel Friedman, President of the Active Inference Institute, and Andrius Kulikauskas, a student of Active Inference. Daniel has set up the Math 4 Wisdom Coda where they explore connections between Active Inference and Wondrous Wisdom.
- Currently, Daniel is developing Case-Enabled Reasoning Engine with Bayesian Representations for Unified Modeling (CEREBRUM)
- Daniel Ari Friedman youtube videos
- Andrius applied to be an Active Inference Institute Research Fellow
- Andrius contributed to a proposal for enhancing ActiveInference.jl, which is a software package in the Julia programming language for scientific computation.
- Andrius thought of proposing an Active Inference Ecosystem Project called Levels of Awareness.
- Andrius is investigating language by studying triangle centers and making use of Active Inference.
- Andrius is attending the Theoretical Neurobiology group which meets Mondays and Tuesdays, 2:30 pm UK time. Here are videos of some of the meetings.
- Andrius studied Active Inference as part of Cohorts 7 and Cohort 8. We spent two weeks on each chapter, two chapters in parallel, starting with theoretical Chapter 1 and application Chapter 2.
Learning Active Inference
Here are some references for learning Active Inference.
A comprehensive foundation is provided by the Active Inference Textbook.
The video by Shamil Chandaria has a mathematical exposition of the free energy priciple?.
Activities Grounded in Active Inference
Examples from chapter 7
- Perceptual processing (listening to an amateur musician) - deducing (intended) signal
- Decision-making and planning (rat navigating a T-maze by reading info) - epistemic vs. pragmatic
- Information seeking (eye saccade) seeking precision where you can get it, yielding streetlight effect
- Learning and novelty (synthetic worm exploring its environment) changing the generative model
- Hierarchical or deep inference (words and sentences) separate time scales
What I want to model
- How issues come to matter (to the continuous, procedural mind) discovering critical points and navigating with regard to them
- How meaning arises (to the discrete, declarative mind) for a language of critical points
How does prediction and resulting error come into play?
Notes
https://arxiv.org/abs/2504.14898
Briefing Document: Modeling Meta-Awareness and Attentional Control with Deep Parametric Active Inference Source: Excerpts from "Towards a computational phenomenology of mental action: modelling meta-awareness and attentional control with deep parametric active inference" by Lars Sandved-Smith , Casper Hesp , Jérémie Mattout , Karl Friston , Antoine Lutz , Maxwell J D Ramstead Neuroscience of Consciousness, Volume 2021, Issue 1, 2021, niab018, https://doi.org/10.1093/nc/niab018
https://arxiv.org/abs/2504.15125 Contemplative Wisdom for Superalignment
Mind and Supermind