Beyond Deep Learning Gen AI
With RGMs through Active Inference

Module 1: Introduction to RGMs and the Evolution of AI

This module introduces Renormalizing Generative Models (RGMs) as a transformative advancement in Active Inference AI. Operating within a scale-free framework, RGMs simplify complex data, require less training data, and adapt dynamically to changing environments. Rooted in the Free Energy Principle, they are energy-efficient and versatile, offering superior performance across tasks like perception, reasoning, and decision-making while addressing the limitations of deep learning.

Module 2: Key Innovations and The Science Behind RGMs

This module examines the core scientific principles of RGMs, explaining how RGMs use renormalization and multiscale learning to simplify complex systems and process data hierarchically. This approach allows RGMs to understand both fine details and broader patterns, making them versatile and able to adapt and generalize across tasks with unmatched efficiency.

Module 3: Active Inference and Conceptual Modeling

This module focuses on Active Inference, one of the foundational principles behind RGMs, and explains how RGMs go beyond pattern recognition by developing a conceptual understanding of data.  By modeling relationships and cause-and-effect dynamics across scales, RGMs integrate fine details with broader contexts. This enables them to adapt intelligently and make informed decisions in complex environments, outperforming traditional AI in accuracy and relevance.

Module 4: Advanced Learning and Decision-Making Processes in RGMs

This module explores the decision-making mechanisms of RGMs, such as discrete state-space representation, which simplify complex data into clear categories, making decision-making faster and more reliable. It also examines the use of Markov Decision Processes and highlights how RGMs integrate perception and planning into a unified model.

Module 5: Application of RGMs Across Various Domains

This module presents the diverse real-world applications of Renormalizing Generative Models (RGMs), showcasing their capabilities in image classification, video compression, audio processing, and strategic decision-making in game play. RGMs demonstrate superior performance in data efficiency, achieving high accuracy with significantly less training data compared to traditional deep learning models. The module highlights key innovations, such as recursive block transformations, dimensionality reduction, and hierarchical pattern recognition. By leveraging their ability to quantify uncertainty and adapt dynamically to real-time data, RGMs offer a transformative approach to solving complex challenges across industries, outperforming traditional AI systems in both efficiency and scalability.

Module 6: The Future of RGMs: The Spatial Web, Future Systems, and Society

This module focuses on the future potential of RGMs in conjunction with the Spatial Web, exploring how RGMs enhance autonomy, self-organization, and the larger implications for AI in society. Here we examine the integration of Renormalizing Generative Models (RGMs) with the Spatial Web to enable seamless operations across digital and physical environments. Using protocols like HSTP and HSML, RGMs support context-aware decision-making and real-time data exchange. Applications include smart cities, healthcare, and logistics, where RGMs optimize operations with scalability, adaptability, and interoperability. This module emphasizes RGMs’ scalability, adaptability, and ability to unify diverse AI systems, making them a cornerstone of decentralized and interoperable AI ecosystems.