Artificial Intelligence
1. What is AI?
Artificial Intelligence is the simulation of human intelligence by computer systems. It involves the ability of a machine to learn from data, reason (make decisions), and self-correct.
2. Key AI Technologies
Machine Learning
A subset of AI where the system improves its performance over time by analyzing large amounts of data without being explicitly programmed for every scenario.
Example: A streaming service learning your music taste based on what you skip.
Expert Systems
A computer program that mimics the decision-making ability of a human expert in a specific field (like medicine or law).
Example: A system that diagnoses a disease based on a list of symptoms.
3. Components of an Expert System
To act like an "expert," the system needs these three core parts:
- Knowledge Base: A massive database of facts and rules provided by human experts.
- Inference Engine: The "brain" that applies logical rules to the knowledge base to find answers.
- User Interface: The screen where the user enters data and receives the system's advice.
4. AI Applications in the Real World
- Autonomous Vehicles: AI processes sensor data instantly to navigate roads safely.
- Healthcare: Analyzing X-rays to spot tumors that might be missed by the human eye.
- Search Engines: Predicting what you want to find before you finish typing.
- Game Playing: AI (like AlphaGo) beating world champions by calculating millions of possible moves.
5. The Ethics of AI
As AI becomes more advanced, it raises important questions:
- Bias: If the data used to train the AI is biased, the AI's decisions will also be biased.
- Accountability: If a self-driving car crashes, who is responsible? The owner or the programmer?
- Job Displacement: AI can perform cognitive tasks (like accounting or coding) faster than humans.
⚠️ Exam Tip: When describing an Expert System, you must mention the Inference Engine. It is the component that does the actual "reasoning" by searching the Knowledge Base.