Intermodels

Intermodels refers to a theoretical concept or framework within artificial intelligence and cognitive science that explores how different AI models or internal representations can interact and communicate with each other. The core idea is to move beyond single, monolithic AI systems towards more modular and collaborative architectures. This approach suggests that a complex cognitive task might be better handled by a collection of specialized models, each excelling at a particular sub-task, rather than one large, general-purpose model.

The interaction between these intermodels can take various forms. One model might process raw sensory input, another might perform logical reasoning, and a third could generate natural language output. The challenge lies in defining the interfaces and protocols that enable these distinct models to exchange information, align their internal states, and coordinate their actions effectively. This could involve shared memory spaces, message-passing mechanisms, or a form of emergent consensus.

Proponents of intermodels suggest this approach could lead to more robust, adaptable, and interpretable AI systems. By breaking down complex problems into smaller, manageable parts, it may become easier to develop, debug, and understand the behavior of individual components and their collective performance. Furthermore, it aligns with observations of biological cognition, where different brain regions and neural networks specialize in distinct functions and interact to produce intelligent behavior. The development of intermodels is an active area of research, seeking to unlock new possibilities in artificial general intelligence.