Our Approach for AGI
Articles
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.
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.
"Catrgory Is All You Need."
Artificial Intelligence (AI) has experienced remarkable advancements recently, yet significant barriers remain on the path toward true Artificial General Intelligence (AGI). One promising candidate proposed to overcome these challenges is Category Theory, especially centered around the concept of Kan extensions. New York General Group argues that category theory represents the optimal framework for realizing AGI.
Core Technology
CN (Categorical Network)
Our AI models are based on category theory, which has higher performance and wider versatility than common AI models based on statistics. More details are available from the technical report below.
New York General Group
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