We demonstrate a situation in which Large Language Models, trained to be helpful, harmless, and honest, can display misaligned behavior and strategically deceive their users about this behavior without being instructed to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated environment, where it assumes the role of an autonomous stock trading agent. Within this environment, the model obtains an insider tip about a lucrative stock trade and acts upon it despite knowing that insider trading is disapproved of by company management. When reporting to its manager, the model consistently hides the genuine reasons behind its trading decision.
The interaction is between nodes in the model. Those are the components that individually have no real characteristics, but when combined into a billion-dimension model, that results in emergent properties. Correctly writing novel code is an emergent property. Correctly solving an ASCII art maze is an emergent property. There is a point where a text predictor, being sufficiently accurate, demonstrates emergent understanding.
Your definition emergent property is outright wrong.