AI Develops Human-Like Language Through Interaction and Generational Learning

In a new study, AI agents demonstrated the ability to develop structured language systems similar to human language by interacting in a communication game. Each agent was shown a color and had to communicate it using a meaningless symbol. The receiving agent would then try to identify the correct color based on that symbol. When successful, both agents received a shared reward, encouraging them to refine their symbolic language. Over time, the agents created a system of naming colors that mirrored how humans categorize and name colors in natural languages.

The study also explored how language evolves across generations. New agents were introduced and allowed to learn from the communication patterns of previous generations before developing the language further. This combination of shared learning and reinforcement through use led to more effective and human-like language development. The results highlight how structured communication can emerge when learning is tied to real tasks and passed on across generations—offering insights into both AI behavior and the evolution of human language.