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Digital Nature Fusion

Our Approach for AGI



The quest for Artificial General Intelligence (AGI) has been a central focus in the field of artificial intelligence. While recent advancements in machine learning, particularly in neural networks, have shown remarkable progress, it is becoming increasingly evident that achieving true AGI requires a more comprehensive approach. This article explores the necessity of simulating not only the neurons of the human brain but also the universe itself to create an authentic AGI system.

Space Supernova


The quest to understand and simulate the universe has been a central theme in scientific inquiry for centuries, driven by the fundamental human desire to comprehend the world around us and our place within it. However, traditional approaches to modeling the universe have often focused primarily on the physical aspects, treating it as a complex system governed by mathematical equations and physical laws. While these approaches have yielded significant insights and predictive power, they have largely neglected the metaphysical realm, which encompasses abstract concepts such as causality and the nature of reality itself.

Industrial Abstract Object

"Catrgory Is All You Need."

In the relentless pursuit of Artificial General Intelligence (AGI), New York General Group has made a groundbreaking discovery that could revolutionize the field of artificial intelligence. By incorporating category theory, a branch of mathematics that studies abstract structures and their relationships, into large-scale language models such as the Transformer architecture, our team has developed a novel approach that significantly enhances the model's ability to generalize and reason across diverse domains.

Abstract Background

Core Technology

CN (Categorical Network)


All of our AIs are based on category theory (we call it CN (Categorical Network)), which has higher performance and wider versatility than common AI models based on vector spaces. More details are available from the technical report below.

Technical Report: A Categorical Approach to Artificial Intelligence

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