Dialectics for Artificial Intelligence (arxiv.org)

Recent research introduces a novel approach to how artificial intelligence (AI) can discover and understand concepts independently, diving into the fluid nature of human-defined concepts. The study posits that human concepts are not fixed, as they can evolve over time (e.g., Pluto’s reclassification), which complicates how AI might learn them. By framing concepts as information objects defined through their structural relations to an agent's experience, the authors aim to create a dynamic model of concept formation that is consistent with evolving knowledge. This approach employs a "dialectical" framework where competing concepts optimize their explanations based on new information, promoting a process of systematic expansion and alteration. Key to the model is the idea of reversibility—ensuring that concepts cannot exist in isolation from experience—and the introduction of low-cost concept transmission methods that facilitate inter-agent communication. By formalizing these processes, the research aims to enhance multi-agent alignment, enabling AIs to share and reconstruct concepts efficiently. This work holds significant implications for the AI/ML community, as it provides a foundational structure for AI to autonomously navigate and adapt concepts, potentially leading to more sophisticated and human-like understanding in machine learning systems.