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Artificial Intelligence (AI)
Artificial Intelligence is about making computers perform tasks that normally require human intelligence, like learning from experience or understanding language. We achieve this through methods like rule-based expert systems and data-driven machine learning.
Need to know
What you need to know
- **Knowledge Base:** This is a database containing facts and a rule base. The rule base consists of IF-THEN rules provided by human experts (e.g., 'IF the engine will not crank AND the lights are dim THEN the battery is likely flat').
- **Inference Engine:** This is the processing component. It takes a user's query and the facts from the knowledge base, then applies the rules to infer new facts and reach a conclusion. It often uses methods like forward chaining (starting from facts to reach a goal) or backward chaining (starting from a hypothesis and working backwards).
- **User Interface:** This allows a non-expert user to query the system and receive its recommendations. It might ask the user a series of questions to gather the necessary facts.
- **Explanation System:** A crucial component that can explain the reasoning behind its conclusion, showing the user which rules were triggered to build trust and allow for verification.
Explanation
Teaching a Computer to Think
- Define AI as the simulation of human intelligence in machines, enabling them to learn, reason, and problem-solve.
- Explore expert systems, which are rule-based programs that mimic a human expert's decision-making in a specific, narrow domain.
- Differentiate between the main types of machine learning: supervised (using labelled data), unsupervised (finding patterns in unlabelled data), and reinforcement (learning via rewards and penalties).
- Grasp the basics of deep learning, where layered neural networks, inspired by the human brain, learn complex patterns from vast amounts of data.