Robotics and Intelligent Technology
FoundationA robot senses its surroundings, decides and acts — built from sensors, a controller and actuators; artificial intelligence lets computers learn patterns from data. Together they bring autonomous driving, surgical robots, warehouse sorting and home helpers. Technology delivers efficiency and convenience along with job shifts and ethical questions; using tools well while keeping human judgement and empathy is the way to live with intelligent machines.
- A robot unites sensors, a controller and actuators, looping through sense, decide, act.
- Machine learning finds patterns from data; voice assistants, image recognition and recommendations are all AI.
- Robots and AI are reshaping making, medicine, logistics and rescue, taking the dangerous, tiring, precise jobs.
文體小知識：科技科普說明文解釋新科技的原理和應用，同時引導讀者思考科技與人的關係。
§Text Passage
1Robots are nearer than we think: the robot vacuum at home, the delivery cart in restaurants, the arms on factory lines. A robot has three parts. Sensors are its eyes and ears — cameras to 'see', ultrasound to measure distance, touch sensors to feel. The controller is its brain — a program deciding what to do from what is sensed. Actuators are its hands and legs — motors turning wheels, arms gripping objects. A robot vacuum, for instance, senses walls and stair edges, plans a cleaning route in its program, and drives and vacuums by motor — 'sense, decide, act' looping forever.
2Artificial intelligence is what makes computers 'clever'. Traditional programs hard-code every rule; machine learning instead lets computers find patterns from many examples: show it tens of thousands of cat and dog photos and it learns to tell them apart. The voice assistants, face-grouped albums, online translation and shopping suggestions you meet daily all run on machine learning, and the recent generative AI can write, draw and converse. Yet AI is not almighty: it leans on training data, so biased data bring biased results, and it can err with total confidence — important decisions still need human review.
3Robots plus AI are reshaping every trade: factory arms weld and spray day and night; warehouse sorters handle over a thousand parcels an hour; the da Vinci surgical robot steadies a surgeon's minimally invasive work; drones patrol and spray farm fields; hotel robots deliver to guest rooms; in earthquake ruins robots enter where rescuers dare not. Hong Kong has its own uses — airport baggage systems, hospital dispensing robots, street-washing machines — and Bay Area factories lead in smart manufacturing.
4Intelligent technology brings efficiency and new questions. Jobs: repetitive work yields to machines, yet new roles arise — robot maintenance technicians, data labellers, AI trainers — the key is lifelong learning. Ethics: who is liable when an autonomous car crashes? How do cameras and data collection guard privacy? Are AI decisions fair and transparent? Society must debate and legislate. For pupils the crucial insight: AI is a powerful tool, not a substitute for thought; creativity, critical thinking and empathy are humanity's irreplaceable edge. Learning to code and build robots is equipping your own future.
1Summary 重點摘要
A robot senses its surroundings, decides and acts — built from sensors, a controller and actuators; artificial intelligence lets computers learn patterns from data. Together they bring autonomous driving, surgical robots, warehouse sorting and home helpers. Technology delivers efficiency and convenience along with job shifts and ethical questions; using tools well while keeping human judgement and empathy is the way to live with intelligent machines.
2Vocabulary cards Vocabulary
An automated machine that senses its surroundings, decides and acts.
A robot's senses — cameras, ultrasound and touch — collecting data about surroundings.
A robot's hands and legs — motors and arms that turn commands into motion.
Using machines to run work processes in place of human hands.
Technology letting computers mimic intelligent behaviour such as learning, reasoning and judging.
An AI method where computers find patterns themselves from large amounts of data.
A clear step-by-step sequence for solving a problem — the basis of every program.
Vehicles using sensors and AI to sense traffic, plan routes and drive themselves.
Unfair or systematically skewed AI results caused by unbalanced training data.
Principles of right and wrong; tech ethics asks how technology ought to be used.
3Step by step Steps
- Step 1: Break a robot into its three partsWhatever a robot looks like, it breaks into three parts — sensing, deciding, acting — like our senses, brain and limbs. Example: a vacuum robot measures distance by ultrasound (sense), computes left or right next (decide), and drives and sucks by motor (act) — a loop of sense, decide, act. With this frame, any robot can be analysed from a clear starting point.
- Step 2: Tell machine learning from classic programsClassic programs follow rules written line by line by people, while machine learning lets computers find the patterns themselves from many examples — the core principle of AI. Example: telling cats from dogs — an engineer writes every rule by hand, while machine learning studies tens of thousands of labelled photos and sums up the features itself, often more accurately. Yet biased training data bring biased results — a limit of machine learning.
- Step 3: Spot AI in daily lifeAI is already woven into daily life; learning to spot it is the first step to understanding its powers and limits. Example: saying 'tomorrow's weather' (speech recognition), albums grouping classmates automatically (image recognition), 'you may also like' online (recommendation) — behind all of these AI is at work. Recent generative AI can write, draw and converse, widening the uses further still.
- Step 4: See robots across industriesRobots excel at dangerous, dull and ultra-precise work, sparing humans much toil. Example: factory arms weld within a millimetre; surgical robots cut wounds a few centimetres long; disaster robots enter heat and toxic zones — robots take the dangerous, tiring, precise jobs. Hong Kong's airport baggage systems, hospital dispensing robots and street-washing machines are examples close at hand.
- Step 5: Test AI's limitsAI is not omnipotent: it depends entirely on its training data and can be confidently wrong. Example: trained only on daylight photos, AI fails to read road signs at night; generative AI can invent plausible wrong answers — verify sources for anything important, and never swallow it whole. AI is a powerful tool, not a substitute for thought, and key decisions still need human review.
- Step 6: Weigh jobs and ethicsAI reshapes jobs and raises new ethical puzzles that society must debate and answer with law. Example: cashier posts shrink while robot technicians and data labellers grow; who answers for a self-driving crash, and how face data is protected, need public debate and legislation. The more advanced the technology, the more ethics matters; the key is lifelong learning, renewing one's skills through the transition.
- Step 7: Build your human advantagesIn the age of AI the safest strategy is to grow the abilities machines find hardest to replace. Example: design a maze-solving program in coding class, join robot contests — and practise critical thinking and teamwork; the stronger the tool, the more its user's judgement matters. Creativity, critical thinking and empathy are humanity's irreplaceable edge; learning by making is the best learning.
4Diagrams Diagrams
5Worked example Example
Example: the school library considers a book-sorting robot. List its three parts and their functions, and give one benefit and one possible problem to weigh before purchase.
- Part one, sensors: a camera reads spine barcodes and titles; distance sensors find the shelves.
- Part two, controller: a built-in program matches the library catalogue and decides each book's class and shelf.
- Part three, actuators: an arm grips the book, wheels carry it to the right shelf, and it is placed correctly.
- Benefit: librarians are freed from repetitive shelving to lead reading promotion, and mis-shelving falls.
- Possible problems: purchase and upkeep cost; worn barcodes defeat recognition; arrangements must train librarians in new roles rather than cut them.
- Answer: The robot combines sensors (reading titles and codes), a controller (matching the catalogue) and actuators (gripping and shelving); the benefit is freeing people for better service, the risks are cost and exceptions — training and fallbacks must come first.
6Common mistakes Mistakes
7Quick check Quick check
Q1What are the three parts of a robot?
Q2How does machine learning differ from a traditional program?
Q3Give two industrial uses of robots or AI.
Q4Why must important decisions never be left wholly to AI?
Q5What abilities should pupils build for the age of intelligent technology?
8Recap Recap
- A robot unites sensors, a controller and actuators, looping through sense, decide, act.
- Machine learning finds patterns from data; voice assistants, image recognition and recommendations are all AI.
- Robots and AI are reshaping making, medicine, logistics and rescue, taking the dangerous, tiring, precise jobs.
- AI risks bias and error — humans must review what matters, with privacy protected and ethics legislated.
- AI is a tool, not a substitute for thought: build creativity, critical thinking and empathy, and learn coding hands-on.