AI-related lessons can introduce machine learning fundamentals, generative AI, prompt engineering, and intelligent applications. Students can explore these topics through guided activities and suitable projects.
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Artificial intelligence studies computational systems that perform tasks involving inference, learning, planning, perception or action under uncertainty. A classifier can learn to map measured features to labels; a planning system can search for a sequence of actions satisfying a goal. The capabilities depend on the model, training information and evaluation setting. Success on one benchmark does not by itself establish general competence or human-like understanding. Machine learning, symbolic reasoning and search algorithms offer different, sometimes combined, ways of building such systems.
Artificial Intelligence (AI) refers to the simulation of human intelligence processes by machines, particularly computer systems. These processes include learning (the acquisition of information and rules for using it), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI can be categorized into different types and subfields: 1. **Narrow AI (Weak AI)**: This form of AI is designed and trained for a specific task, such as facial recognition, language translation, or playing chess.
