Computers increasingly run systems on their own. This mini-lesson covers automated systems and sensors, robotics, and artificial intelligence (including the basics of machine learning), with their advantages and limitations.
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An automated system uses sensors and a microprocessor to monitor and control a process without constant human input. The typical loop is:
Examples: central heating, automatic greenhouses, self-parking cars, factory production lines.
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Robots are programmable machines that can carry out tasks, often using sensors, a control program and actuators/end-effectors (grippers, arms).
Characteristics: a physical body, sensors to gather data, and programmability. Advantages: can work 24/7, are consistent and accurate, and can operate in dangerous places. Disadvantages: high set-up cost, can replace human jobs, and lack human judgement/flexibility.
Artificial intelligence is computer systems that simulate aspects of human intelligence — such as learning, reasoning and decision-making. An AI system usually has a knowledge base (data/facts) and a rule/inference engine that draws conclusions.
Examples: chatbots, expert systems (e.g. medical diagnosis), self-driving cars, voice assistants, recommendation systems.
Machine learning is a type of AI where a system learns from data rather than being explicitly programmed with every rule. It is given lots of example data, spots patterns, builds a model, and improves as it processes more data.
Example: a spam filter is shown thousands of emails labelled ‘spam’ or ‘not spam’. It learns the patterns, then classifies new emails — and gets better as more examples arrive.
Tap a description on the left, then its matching term on the right.
Automated system loop: sensor → microprocessor (compare to pre-set) → actuator
Robotics: physical body + sensors + programmable; work 24/7 & in danger, but costly & job loss
AI: systems that simulate human intelligence (knowledge base + inference)
Machine learning: learns patterns from data, builds a model, improves with more data
Limitations: cost, bias/poor data, lack of human judgement, job impact
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