In the context of AI systems, what do "Tasks and Output" refer to?

The term "Tasks and Output" in the context of AI systems refers primarily to the specific functions that the AI is designed to perform (tasks) and the results or information it generates through these functions (output). When discussing AI, understanding the capabilities of the system involves assessing what tasks it can undertake, such as classification, prediction, or recommendation. The output is the result of those tasks, which could range from numerical data to insights, visualizations, or decisions. This relationship between tasks and output is crucial for evaluating the effectiveness and utility of an AI system in real-world applications.

In contrast, the other options focus on different aspects of AI. Theories of AI Ethics examines the moral considerations surrounding the development and deployment of AI, while Training Methods pertain to the approaches used to teach AI systems through data. Active Learning Techniques involve specific strategies in machine learning where the algorithm selects the data from which it learns, often aimed at improving efficiency in data usage. These are important concepts within AI but not directly related to the core definition of "Tasks and Output."