Artificial intelligence
Introduced| English | Meaning |
|---|---|
| artificial intelligence/ˌɑːtɪˈfɪʃl ɪnˈtelɪdʒəns/ | Computer methods designed to perform tasks involving abilities such as learning, reasoning, perception or language processing. |
| machine learning/məˈʃiːn ˈlɜːnɪŋ/ | Methods that use data to fit models for making predictions or identifying patterns. |
| training data/ˈtreɪnɪŋ ˈdeɪtə/ | The examples used to fit a machine-learning model's parameters or decision rules. |
| deep learning/diːp ˈlɜːnɪŋ/ | Machine learning using neural networks with multiple processing layers to learn representations from data. |
| neural networks/ˈnjuːrəl ˈnetwɜːks/ | Computational models made of interconnected units that learn relationships by adjusting connection weights. |
| speech recognition/spiːtʃ ˌrekəɡˈnɪʃn/ | Computer processing that identifies spoken words from an audio signal. |
| image recognition/ˈɪmɪdʒ ˌrekəɡˈnɪʃn/ | Computer processing that identifies or classifies objects, patterns or other features in images. |
| machine translation/məˈʃiːn trænˈsleɪʃn/ | Automatic conversion of text or speech from one human language into another. |
| recommendation systems/ˌrekəmenˈdeɪʃn ˈsɪstəmz/ | recommendation systems |
| autonomous vehicles/ɔːˈtɒnəməs ˈvɪəklz/ | Vehicles that use sensing and control systems to perform driving tasks with reduced or no direct human control in their designed conditions. |
| optical character recognition/ˈɒptɪkl ˈkærɪktə ˌrekəɡˈnɪʃn/ | Converting images of printed or handwritten characters into machine-readable text. |
| text-to-speech/tekst tə spiːtʃ/ | Converting written text into synthesised spoken output. |
| bias/ˈbaɪəs/ | A systematic preference or prejudice that can distort judgement, decisions or the representation of information. |
| accountability/əˌkaʊntəˈbɪlɪti/ | Responsibility for explaining decisions and actions and being answerable for their consequences. |
The move no human would have played
- In March 2016 a program called AlphaGo beat Lee Sedol, one of the strongest Go players alive, four games to one. In the second game it played a move that commentators called a mistake; it turned out to be the winning idea.
- Nobody had programmed that move. The system had learned Go by studying millions of positions and playing itself, finding patterns its makers could not have written down.
- Two years later Amazon scrapped an AI recruiting tool after finding it had learned from ten years of CVs to mark down any that mentioned "women's".
- Both are artificial intelligence 人工智能. This lesson is how it learns, what it is used for, and its social, economic and environmental impact.
What AI is and how modern AI learns
- Artificial intelligence (AI) means computer systems that perform tasks which normally need human intelligence: recognising speech and images, translating, playing games, driving.
- Most modern AI uses machine learning 机器学习: instead of being programmed step by step, the system improves at a task by learning patterns from large amounts of training data 训练数据.
- Deep learning 深度学习 uses neural networks 神经网络 with many layers of connected nodes, and is the leading approach today. Better and larger data gives a better model; bad data gives bad results.
A model is trained on examples, not written as rules
Machine learning differs from traditional programming because it:
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ML algorithms improve at a task by learning from large amounts of data, rather than following hand-written rules.
Match each AI term to what it means.
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ML learns from data; deep learning uses many layers; bias and opacity (black box) are key ethical concerns.
Applications
- Understanding input: speech recognition 语音识别 turns spoken words into text; image recognition 图像识别 finds objects, faces or text in pictures.
- Producing output or decisions: machine translation 机器翻译 between languages; recommendation systems 推荐系统 suggesting videos, products or music; autonomous vehicles 自动驾驶汽车 and robots; medical diagnosis and fraud detection.
- The exam usually gives a scenario and asks which kinds of AI it uses, or how it works.
Computing concept lab
Classify concrete examples by the computing idea they demonstrate.
Which is an everyday application of AI?
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Recommendation systems (and speech/image recognition, translation, self-driving) are common AI applications.
Worked example: reading a foreign label aloud
- A phone app photographs a product label, translates it and reads it aloud. Explain how AI is used. [4]
- Optical character recognition 光学字符识别 analyses the pixels of the photograph to locate the characters, and the patterns of pixels are converted into individual characters and words.
- Machine translation converts the words into the user's language.
- Text-to-speech 文本转语音 produces the spoken output. Each stage is a mark, in pipeline order.
Recognise, translate, speak
Put the stages of the label-reading app in order.
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Recognise, read, translate, speak. Each stage is a mark in a four-mark answer.
Benefits
- Accessibility: speech and image AI helps users with impairments; translation helps people who do not speak the language.
- Productivity: repetitive tasks are automated, freeing people for creative work.
- Decision support: AI finds patterns in datasets too large for a person, in medical scans or in transactions.
- It is available at any hour and can be personalised to each user.
A benefit of AI is:
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AI excels at finding patterns in large data (decision support). Fairness and explainability are concerns, not guarantees.
Social impact
- Benefits: a label reader helps people with a visual impairment, people who cannot read the language, and people with reading difficulties; facial recognition at an airport speeds identity checks and can stop wanted people entering.
- Harms: facial recognition misidentifies some people and tracks everyone without consent, so privacy is lost; people who rely on AI may lose skills; recommendation systems can trap users in narrow views.
- A social-impact answer names the group affected and what changes for them.
Economic impact
- An AI fault-diagnosis module in a repair garage diagnoses faults faster and more accurately, so more vehicles are repaired a day and costs fall.
- But fewer skilled mechanics may be needed, so jobs are lost, and the module must be bought, trained and maintained.
- More generally, AI raises productivity and creates new jobs in some fields while removing routine jobs in others.
Environmental impact
- Training and running large models uses a great deal of electricity, and data centres use water for cooling; the hardware they run on becomes electronic waste.
- On the other side, AI cuts energy use in buildings, optimises transport routes and monitors the environment.
- Both directions score, and the exam expects you to know they exist.
The hardware behind AI has a lifetime, and an afterlife
Match each example to the heading it belongs under.
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Jobs and costs are economic, people and privacy are social, energy and waste are environmental.
Training and running large AI models has no significant environmental cost.
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Large models consume a great deal of electricity and cooling water, and their hardware becomes e-waste. AI also helps cut energy use elsewhere, so both directions belong in an answer.
Worked example: answering an "impact" question
- Explain the economic impact of a garage installing an AI fault-diagnosis system. [4]
- Give the impact and its consequence, and give both directions.
- Faults are diagnosed faster and more accurately, so more vehicles are repaired per day and revenue rises. Fewer mechanics' hours are needed per car, so costs fall.
- Skilled mechanics may lose their jobs, so there is unemployment and a loss of skills; and the system must be bought and maintained, so there is an initial and ongoing cost.
A full-mark impact point states the impact and its ____, usually joined by "so".
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"Fewer mechanics are needed, so skilled workers lose their jobs" is a mark; "fewer mechanics" alone is not.
Concerns
- Bias 偏见: unfair patterns in the training data become unfair decisions, as in Amazon's recruiting tool. Job displacement: routine roles disappear.
- Privacy: training uses large amounts of personal data. Transparency: large models are black boxes whose decisions are hard to explain.
- Accountability 问责: when the AI is wrong, is the developer, the operator or the user responsible? Misuse: deepfakes, misinformation, surveillance.
- Professionals must understand the limits of what they build, inform users, and reduce harm.
Bias goes in with the data and comes out in the decisions
AI "bias" usually arises because:
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If the training data reflects unfair patterns, the model learns and repeats them — e.g. in hiring or lending.
Select all genuine concerns about AI.
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Bias, opacity and misuse are real concerns. AI certainly can be wrong — accountability for that is itself a concern.
Marks that slip away
- AI is not "a robot" or "a computer that thinks". Define it as systems performing tasks that normally need human intelligence.
- An impact without a consequence is half a mark. "Jobs are lost" needs "so skilled workers are unemployed".
- Social, economic and environmental are three headings. Answer under the one the question names.
- Bias comes from the data the model learned from, not from the computer having opinions.
You've got it
- AI performs tasks that normally need human intelligence; machine learning learns patterns from training data rather than following written rules; deep learning uses layered neural networks
- applications: speech and image recognition understand input; translation, recommendation and autonomous vehicles produce output; OCR → translation → text-to-speech reads a label aloud
- impact under three headings, each with a consequence and both directions: social, economic, environmental
- concerns: bias from data, job displacement, privacy, transparency, accountability, misuse