What is an example of reactive AI?

Reactive AI refers to artificial intelligence systems that do not have the ability to form memories or use past experiences to influence their present decisions. Instead, they operate based solely on the current situation or input they encounter. Out of the given options, robotic vacuum cleaners fit this definition best. They are designed to sense their environment in real time, react to obstacles, and navigate efficiently; however, they do not retain information about previous cleaning sessions or learn from past experiences.

Self-driving cars and ChatGPT, on the other hand, implement more advanced AI techniques, such as machine learning, which allow them to learn from past data and experiences. For instance, self-driving cars use extensive data from various driving situations to improve their performance over time, while ChatGPT leverages vast amounts of conversational data to generate responses based on context and historical interaction patterns. Netflix recommendations rely on previous user behavior and preferences, indicating a learning capability as it continuously adapts suggestions based on user interactions.

In contrast, robotic vacuum cleaners typically stick to predefined algorithms and immediate environmental inputs without building a long-term understanding of their surroundings, which exemplifies the nature of reactive AI.