From artificial intelligence to active inference: the key to true AI and the 6G world brain [Invited]
Bibliographic record
Abstract
In his opening OFC plenary talk back in 2021, Alibaba Group’s Yiqun Cai notably added in the follow-up Q&A that today’s complex networks are more than computer science—they grow, they are life . This entails that future networks may be better viewed as techno-social systems that resemble biological superorganisms with brain-like cognitive capabilities. Fast-forwarding, there is now growing awareness that we have to completely change our networks from being static to being a living entity that would act as an AI-powered network “brain,” as recently stated by Bruno Zerbib, Chief Technology and Innovation Officer of France’s Orange, at the Mobile World Congress (MWC) 2025. Even though AI was front and center at both MWC and OFC 2025 and has been widely studied in the context of optical networks, there are currently no publications on active inference in optical (and less so mobile) networks available. Active inference is an ideal methodology for developing more advanced AI systems by biomimicking the way living intelligent systems work while overcoming the limitations of today’s AI related to training, learning, and explainability. Active inference is considered the key to true AI: less artificial, more intelligent . It is a biomimetic mathematical framework that is premised on the first principles of statistical physics found in self-organizing/evolving complex adaptive systems, whether natural, artificial, or hybrid cyborganic ones. The goal of this paper is twofold. First, we aim at enabling optical network researchers to conceptualize new research lines for future optical networks with human-AI interaction capabilities by introducing them to the main mathematical concepts of the active inference framework. Second, we demonstrate how to move AI research beyond the human brain toward the 6G world brain by exploring the role of mycorrhizal networks, the largest living organism on planet Earth, in the AI vision and R&D roadmap for the next decade and beyond laid out by Karl Friston, the father of active inference.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".