What type of AI facilitates improvements over time based on its algorithms and past experiences?

Limited memory AI is characterized by its ability to learn from historical data and past experiences to enhance its performance over time. This type of AI uses information it collects over a period to make better decisions and predictions. In practice, this might involve analyzing previous interactions, outcomes of decisions, or user behaviors to refine its algorithms.

For instance, in applications such as recommendation systems or autonomous vehicles, limited memory AI systems can adjust their responses based on new data gathered from their experiences, thus improving accuracy and efficiency. This capacity for learning differentiates limited memory AI from other types that do not possess the same level of adaptive capability.

Reactive AI, on the other hand, typically responds to specific inputs with fixed responses, lacking the ability to learn from experience. Self-aware AI is a theoretical concept concerning machines that possess consciousness and self-awareness, which is not yet realized in practical applications. Theory of mind AI is still in the research phase and involves understanding human emotions and thoughts, and it does not specifically focus on learning through past experiences.