Learning Lab LIVE: REVIEW 1 – Intro to Active Inference AI and Spatial Web Technologies

This Learning Lab LIVE session provides a comprehensive introduction to Active Inference AI and the Spatial Web as the next foundational shift in computing and intelligent automation. The session explains why traditional machine learning and large language models struggle with real-world complexity, uncertainty, and governance—and how Active Inference offers a fundamentally different approach grounded in biology, physics, and causal reasoning. Viewers are introduced to Active Inference as a first-principles framework for building adaptive Intelligent Agents that learn continuously, reason about cause and effect, and operate efficiently without massive datasets or retraining cycles.

The presentation then explores how the Spatial Web Protocol expands the internet from content delivery to real-time, programmable spatial domains. Through HSTP and HSML, context becomes computable across people, places, systems, and devices, enabling secure interoperability and digital twins of everything. When combined with Active Inference, this infrastructure supports distributed intelligence at the edge, shared world models, and coordinated decision-making across complex environments.

By the end of this session, viewers will understand how Active Inference and the Spatial Web together form the foundation for trusted, explainable, and scalable intelligent systems. This session is designed for enterprise leaders, technologists, and innovators preparing for the transition from centralized, static AI toward adaptive, self-organizing systems capable of operating safely in mission-critical contexts.

Key Topics Covered

• Why AI is entering a new phase
  Explains the limitations of current machine learning and generative AI systems, including static learning, high energy demands, lack of explainability, and poor performance in uncertain real-world environments.
• Active Inference as a first-principles AI framework
  Introduces Active Inference as a physics-grounded approach to intelligence based on the Free Energy Principle, enabling continuous learning, adaptation, and causal reasoning similar to biological systems.
• The Free Energy Principle and how learning actually works
  Explains how prediction, surprise, and uncertainty drive learning in the brain and how Active Inference agents minimize uncertainty through perception and action.
• Perceptual inference and active inference
  Breaks down Bayesian belief updating, active learning, and active selection, showing how perception and decision-making operate as a unified process rather than separate systems.
• Real-world examples of Active Inference in action
  Uses intuitive examples such as touching a surface, crossing the street, medical diagnosis, logistics planning, and airline operations to demonstrate how Active Inference works in real time.
• Generative models and predictive simulation
  Explains how Active Inference agents simulate future scenarios, predict outcomes, and update their internal models based on success or failure without retraining.
• Introduction to the Spatial Web Protocol
  Describes how HSTP and HSML extend the internet beyond web pages into programmable spatial domains, enabling persistent identity, digital twins, and universal domain graphs.
• HSML as a common language for context
  Explains how HSML encodes semantic, spatial, and temporal context, allowing heterogeneous systems and Intelligent Agents to interoperate across networks.
• Distributed intelligence and edge processing
  Covers how decentralized processing improves resilience, speed, privacy, and energy efficiency by moving intelligence to the edge rather than relying on centralized databases.
• Explainable AI, governance, and compliance
  Explains why Active Inference is inherently explainable, how decision logic is explicit, and how this supports regulatory compliance, ethical governance, and human oversight.
• Renormalizing Generative Models (RGMs)
  Introduces RGMs as a scale-free extension of Active Inference that unifies perception and planning while efficiently modeling complex systems across multiple scales.
• Agency, causality, and why LLM agents fall short
  Clarifies what true agency requires, why causal reasoning matters, and why correlation-based systems cannot reliably operate in novel or high-stakes environments.
• Decision-making under uncertainty
  Explains how Active Inference agents use partially observable Markov decision processes to make optimal decisions with incomplete information.
• Mission-critical operational challenges
  Addresses delayed responsiveness, system failures, coordination at scale, and high energy costs—and how Active Inference AI addresses each of these challenges.
• Strategic implications for enterprises
  Outlines why early adoption creates competitive advantage and how organizations can begin preparing today through phased adoption and education.