# Designing Ecosystems of Intelligence from First Principles

arXiv: https://arxiv.org/abs/2212.01354 · HTML: https://mlsanity.com/paper/2212.01354/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 211 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 27 of 27)

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning](https://mlsanity.com/paper/2205.12548/) (2022)
- [Do Prompt-Based Models Really Understand the Meaning of their Prompts?](https://mlsanity.com/paper/2109.01247/) (2021)
- [Towards a Definition of Disentangled Representations](https://mlsanity.com/paper/1812.02230/) (2018)
- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [A free energy principle for a particular physics](https://mlsanity.com/paper/1906.10184/) (2019)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [The Free Energy Principle for Perception and Action: A Deep Learning Perspective](https://mlsanity.com/paper/2207.06415/) (2022)
- [Bayesian model reduction](https://mlsanity.com/paper/1805.07092/) (2018)
- [Learning Perception and Planning with Deep Active Inference](https://mlsanity.com/paper/2001.11841/) (2020)
- [A World unto Itself: Human Communication as Active Inference](https://mlsanity.com/paper/1906.10538/) (2019)
- [How Robust are Deep Neural Networks?](https://mlsanity.com/paper/1804.11313/) (2018)
- [Bayesian Mechanics for Stationary Processes](https://mlsanity.com/paper/2106.13830/) (2021)
- [A free energy principle for generic quantum systems](https://mlsanity.com/paper/2112.15242/) (2021)
- [The emergence of informative higher scales in complex networks](https://mlsanity.com/paper/1907.03902/) (2019)
- [Neural and phenotypic representation under the free-energy principle](https://mlsanity.com/paper/2008.03238/) (2020)
- [An active inference model of collective intelligence](https://mlsanity.com/paper/2104.01066/) (2021)
- [Disentangling What and Where for 3D Object-Centric Representations Through Active Inference](https://mlsanity.com/paper/2108.11762/) (2021)
- [Optimal design of experiments to identify latent behavioral types](https://mlsanity.com/paper/1807.07024/) (2018)
- [Spin glass systems as collective active inference](https://mlsanity.com/paper/2207.06970/) (2022)

## Built on by (most cited first, 23 of 23)

- [Brain-inspired Computational Intelligence via Predictive Coding](https://mlsanity.com/paper/2308.07870/) (2023)
- [Sources of Richness and Ineffability for Phenomenally Conscious States](https://mlsanity.com/paper/2302.06403/) (2023)
- [Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making](https://mlsanity.com/paper/2306.04025/) (2023)
- [Resolving uncertainty on the fly: Modeling adaptive driving behavior as active inference](https://mlsanity.com/paper/2311.06417/) (2023)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Toward Design of Synthetic Active Inference Agents by Mere Mortals](https://mlsanity.com/paper/2307.14145/) (2023)
- [Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learning](https://mlsanity.com/paper/2410.09099/) (2024)
- [If consciousness is dynamically relevant, artificial intelligence isn't conscious](https://mlsanity.com/paper/2304.05077/) (2023)
- [Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics](https://mlsanity.com/paper/2309.11981/) (2023)
- [Scalable Interactive Machine Learning for Future Command and Control](https://mlsanity.com/paper/2402.06501/) (2024)
- [Active Inference for Physical AI Agents -- An Engineering Perspective](https://mlsanity.com/paper/2603.20927/) (2026)
- [Belief sharing: a blessing or a curse](https://mlsanity.com/paper/2407.02465/) (2024)
- [An AI Theory of Mind Will Enhance Our Collective Intelligence](https://mlsanity.com/paper/2411.09168/) (2024)
- [Contemplative Artificial Intelligence](https://mlsanity.com/paper/2504.15125/) (2025)
- [Integrating large language models and active inference to understand eye movements in reading and dyslexia](https://mlsanity.com/paper/2308.04941/) (2023)
- [A Theory of Intelligences](https://mlsanity.com/paper/2308.12411/) (2023)
- [Online Pareto-Optimal Decision-Making for Complex Tasks using Active Inference](https://mlsanity.com/paper/2406.11984/) (2024)
- [CogNarr Ecosystem: Facilitating Group Cognition at Scale](https://mlsanity.com/paper/2407.18945/) (2024)
- [Let the Quantum Creep In: Designing Quantum Neural Network Models by Gradually Swapping Out Classical Components](https://mlsanity.com/paper/2409.17583/) (2024)
- [AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space](https://mlsanity.com/paper/2506.22487/) (2025)
- [The Generalist Brain Module: Module Repetition in Neural Networks in Light of the Minicolumn Hypothesis](https://mlsanity.com/paper/2507.12473/) (2025)
- [Intuition emerges in Maximum Caliber models at criticality](https://mlsanity.com/paper/2508.06477/) (2025)
- [Causal Foundations of Collective Agency](https://mlsanity.com/paper/2605.00248/) (2026)

## Abstract (arXiv)

This white paper lays out a vision of research and development in the field of artificial intelligence for the next decade (and beyond). Its denouement is a cyber-physical ecosystem of natural and synthetic sense-making, in which humans are integral participants -- what we call ''shared intelligence''. This vision is premised on active inference, a formulation of adaptive behavior that can be read as a physics of intelligence, and which inherits from the physics of self-organization. In this context, we understand intelligence as the capacity to accumulate evidence for a generative model of one's sensed world -- also known as self-evidencing. Formally, this corresponds to maximizing (Bayesian) model evidence, via belief updating over several scales: i.e., inference, learning, and model selection. Operationally, this self-evidencing can be realized via (variational) message passing or belief propagation on a factor graph. Crucially, active inference foregrounds an existential imperative of intelligent systems; namely, curiosity or the resolution of uncertainty. This same imperative underwrites belief sharing in ensembles of agents, in which certain aspects (i.e., factors) of each agent's generative world model provide a common ground or frame of reference. Active inference plays a foundational role in this ecology of belief sharing -- leading to a formal account of collective intelligence that rests on shared narratives and goals. We also consider the kinds of communication protocols that must be developed to enable such an ecosystem of intelligences and motivate the development of a shared hyper-spatial modeling language and transaction protocol, as a first -- and key -- step towards such an ecology.