Course 1 – Intro to Active Inference AI and Spatial Web Technologies: Master the Basics
This course explores the evolution of artificial intelligence, introducing students to concepts beyond traditional AI, including shared, distributed, and multi-scale models based on Active Inference.
Students will explore how the Free Energy Principle and real-time data integration create a foundation for more adaptable and context-aware AI systems. Through examining the Spatial Web and digital twin technologies, participants will learn how these new AI systems interact seamlessly within interconnected, intelligent environments.
This course equips students with the knowledge to understand how these advancements reshape industries by providing scalable, efficient, and decentralized solutions that adapt and respond to real-world contexts in real time.
COURSE OUTLINE:
Module 1: Introduction to Future AI and Spatial Web Fundamentals
This module introduces the limitations of traditional AI and highlights the transition to Active Inference AI. It explores the Free Energy Principle, shared intelligence in the Spatial Web, and the implications of multi-scale, distributed AI for enterprises and society.
Module 2: Protocols and Frameworks of the Spatial Web
Learn about the foundational protocols and frameworks of the Spatial Web, including HSTP and HSML, which enable interoperability, data sovereignty, and contextual intelligence. This module covers the importance of digital twin spaces in achieving intelligent, decentralized systems.
Module 3: Practical Applications of Spatial Technologies
This module delves into programmable spaces and the creation of digital twins and knowledge graphs, which provide a dynamic, interconnected representation of the physical and digital worlds. These tools enable intelligent agents to interact, adapt, and make decisions within nested ecosystems.
Module 4: Theoretical Foundations and Network Intelligence
Explore how the Spatial Web facilitates the creation of distributed networks of intelligent agents. This module explains how these agents collaborate in real-time using HSML, creating a scalable, self-evolving system that integrates human and AI intelligence.
Module 5: Deep Dive into Active Inference AI
This module introduces how Active Inference AI creates adaptive, real-time models of the world using the Free Energy Principle. Learn how this autonomous intelligence leverages data from sensors, IoT devices, and digital twins to minimize prediction errors and make precise, context-aware decisions. DIscover how Active Inference AI, unlike static models, acts dynamically like a biological organism, updating its understanding in response to real-world changes.
Module 6: AI Governance, Ethics, and Sustainability
Discover how Active Inference AI and the Spatial Web enable scalable, efficient, and ethical systems. By leveraging the Free Energy Principle, HSML, and HSTP, these technologies enable human governance, context-aware decision-making, and seamless human-AI collaboration while minimizing energy use. Interoperable intelligent agents foster innovation and scalability, creating a collaborative ecosystem that fosters real-time interaction and transforms industries through a smarter, more connected Internet.
Glossary of Terms
Course 1 - Active Inference AI and Spatial Web Technologies: Master the Basics
A
Active Inference – A framework for artificial intelligence (AI) based on the Free Energy Principle, where Intelligent Agents continuously update their beliefs and make decisions based on real-time data to minimize uncertainty and improve predictions.
Advanced General Intelligence (AGI) – A level of AI that can perform any intellectual task a human can do, exhibiting general reasoning and learning across domains.
D
Digital Twin – A virtual representation of a physical object, process, or environment that is continuously updated with real-time data to mirror its real-world counterpart in the Spatial Web.
F
Free Energy Principle – A theory from neuroscience, developed by Karl Friston, which states that all intelligent systems seek to minimize uncertainty (free energy) by continuously updating their internal models of the world to better predict sensory inputs.
H
Holarchies – Nested systems where each component (holon) operates autonomously while also contributing to the function of a larger system, often used in AI and complex networks to structure intelligence hierarchically.
Hyperspace Modeling Language (HSML) – The programming language of the Spatial Web, enabling the creation of digital twins and context-aware AI systems by encoding interactions between people, places, and things.
Hyperspace Transaction Protocol (HSTP) – The foundational protocol of the Spatial Web that assigns unique identifiers to every digital and physical entity, enabling seamless, secure, and decentralized transactions.
I
Intelligent Agent – A system that actively perceives, learns, and makes decisions based on real-time data, utilizing Active Inference to optimize its actions within the Spatial Web.
K
Knowledge Graph – A structured representation of data that interlinks different concepts, allowing AI and Intelligent Agents to build a dynamic understanding of relationships between people, places, and things.
M
Multi-Scale AI – An AI system that operates across different scales, from individual local interactions to large, interconnected global networks, enhancing adaptability and efficiency.
P
Prediction Error – The difference between an AI system’s expected outcome and actual sensory input; in Active Inference, minimizing prediction error leads to more accurate models and decisions.
Programmable Spaces – Digital environments in the Spatial Web where objects, interactions, and rules can be programmed, allowing for adaptive AI-driven ecosystems.
R
Real-Time Learning – The ability of AI systems to update their models and make decisions based on live data rather than relying solely on historical datasets.
S
Self-Organizing Systems – AI architectures that autonomously adapt, evolve, and refine their operations based on continuous interactions with their environment.
Shared Intelligence – A framework in which multiple AI systems collaborate and exchange insights in real time, forming a distributed and collective intelligence network.
Spatial Web (Web 3.0) – A next-generation web architecture that merges physical and digital realities, enabling seamless interactions between people, AI, and intelligent environments.
T
Trust and Reliability in AI – The ability of AI systems to function in a transparent, explainable, and secure manner, ensuring ethical and safe decision-making.
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Ratings and Reviews
Joanne Phillips
I am so appreciative of the work and energy Denise has put into the Learning Lab Central for those of us interested in Active Inference AI and Spatial Web Technologies. Her passion reverberates through the course materials making them both engaging and informative.
Christopher Baker
This course will take you by the hand and lead you into the world of Web 3.0. A gentle introduction and overview of the Spatial Web and what it has to offer. It is the perfect first step as you journey into Active Inference AI and the evolving internet.
Jon Wood
I remember having several questions about Internal Models; models in general. How and where understandings, surroundings, environments, and bayesian beliefs are kept. I believe I advanced after each subsequent Course. Many answers came later as I progressed through Courses 1-5. As I learned about HSTP and HSML.
This class is the first brick in the road to Active Inference AI, the Free Energy Principle, and Spatial Web technologies. It dives into adaptive, decentralized AI for real-world applications. There are white papers you can read and lectures you can listen to to gain this knowledge, but this is distilled and easier to swallow. She also hosts a once-a-month online group I pay for and recommend.