Neural Network Models of Autonomous Adaptive Intelligence and Artificial General Intelligence: How Our Brains Learn Large Language Models and Their Meanings

Stephen Grossberg^1,2,3*

• ¹Center for Adaptive Systems; Departments of Mathematics and Statistics, Psychological and Brain Sciences, and Biomedical Engineering, Boston University, Boston, Massachusetts, United States
• ²Boston University, Boston, Massachusetts, United States
• ³Boston Universit, Boston, Massachusetts, United States

This article describes a biological neural network model that explains how humans learn to understand large language models and their meanings. This kind of learning typically occurs when a student learns from a teacher about events that they experience together. Multiple types of self-organizing brain processes are involved, including content-addressable memory; conscious visual perception; joint attention; object learning, categorization, and cognition; conscious recognition; cognitive working memory; cognitive planning; neural-symbolic computing; emotion; cognitive-emotional interactions and reinforcement learning; volition; and goal-oriented actions. The article advances results in Grossberg ( 2023) showing how small language models are learned that have perceptual and affective meanings. The current article explains how humans, and neural network models thereof, learn to consciously see and recognize an unlimited number of visual scenes. Then bi-directional associative links can be learned and stably remembered between these scenes, the emotions that they evoke, and descriptive language utterances of them. Adaptive Resonance Theory circuits control model learning and self-stabilizing memory. These human capabilities are not found in AI models like ChatGPT. The current model is called ChatSOME, where SOME abbreviates Self-Organizing MEaning. The article summarizes neural network highlights since the 1950s and of leading models, including Adaptive Resonance, Deep Learning, LLMs, and Transformers.