--- title: "Artificial Intelligence" description: "30 topics in Computing, in the order they build on each other." canonical: https://lightmysky.com/learn/computing/areas/artificial-intelligence source: https://lightmysky.com/learn/computing/areas/artificial-intelligence.md retrieved: 2026-10-19 --- > **Agent view.** This is the Markdown twin of the page, for tools and assistants. > When to use this site, and the call that answers each job: https://lightmysky.com/agent-instructions.md > API description (OpenAPI 3.1): https://lightmysky.com/openapi.json · Authentication: https://lightmysky.com/auth.md > Pricing: https://lightmysky.com/pricing.md · Catalog: https://lightmysky.com/llms.txt · Full catalog: https://lightmysky.com/llms-full.txt > Every machine-readable file on this domain: https://lightmysky.com/.well-known/ai-catalog.json > Ask for Markdown with `Accept: text/markdown`, a `.md` address, or `?mode=agent`. # Artificial Intelligence 30 topics in Computing, in the order they build on each other. Page: https://lightmysky.com/learn/computing/areas/artificial-intelligence - [Computers in Everyday Life](https://lightmysky.com/learn/computing/computers-in-everyday-life-mt_XbGfVhfiUz): Identifying computers in everyday life: not just laptops but phones, tablets, smart speakers, traffic lights, washing machines; what makes something a computer - [Smart Versus Not-Smart Devices](https://lightmysky.com/learn/computing/smart-versus-not-smart-devices-mt_WRRv1ABECC): Comparing ordinary objects with devices that have computer features, such as answering spoken questions. Sensors detect things and trigger responses, but sensing or using electricity alone does not prove that a device has smart computer features. - [Step-by-Step Instructions](https://lightmysky.com/learn/computing/step-by-step-instructions-mt__p5n8z5soJ): Step-by-step instructions for everyday tasks (making a sandwich, brushing teeth); that if instructions are wrong or missing, things go wrong; computers follow instructions exactly - [Real-World Robots](https://lightmysky.com/learn/computing/real-world-robots-mt_jvJ35MmCvK): Real robots use computers to control moving parts for physical jobs. Examples include robot vacuums, factory arms and doctor-controlled surgery arms. Robots use software and are not living things. - [Voice Assistants and How They Work](https://lightmysky.com/learn/computing/voice-assistants-and-how-they-work-mt_u6SYiVx7FX): How voice-assistant software handles spoken requests. A simple model follows listening, working out a request, finding an answer and speaking. Assistants can mishear, misinterpret requests or give mistaken answers. They are programs, not people with human feelings. - [AI in Daily Life](https://lightmysky.com/learn/computing/ai-in-daily-life-mt_AzTrT5ySCx): Recognise AI uses such as face unlock, learned word prediction, video recommendations, photo sorting and voice assistants; distinguish learned patterns from a fixed sensor rule - [Data and Information for Computers](https://lightmysky.com/learn/computing/data-and-information-for-computers-mt_kH1DzOPsXG): What data is: information that computers use, such as numbers, words, pictures, and sounds. Computers use data they receive and can make new results from that data. - [Patterns and Classification](https://lightmysky.com/learn/computing/patterns-and-classification-mt_ofOGCQ7FWj): Sorting items by a shared rule is classification. A computer can learn patterns from examples, and varied examples help us test whether its sorting works on new items. - [AI in Computer Games](https://lightmysky.com/learn/computing/ai-in-computer-games-mt_fT4G0QloX5): How computer opponents choose game actions using fixed rules, move search or learning from examples; difficulty adjustment and level generation; why changed behavior alone does not prove learning - [Machine Learning Basics](https://lightmysky.com/learn/computing/machine-learning-basics-mt_K6qtan847r): A basic explanation of machine learning: a computer studies many examples, finds patterns and makes predictions about new things. Includes hands-on practice with Teachable Machine or a similar tool. - [AI Mistakes and Limitations](https://lightmysky.com/learn/computing/ai-mistakes-and-limitations-mt_bPFToj0OhZ): Machines make mistakes; they only know what they've been shown; bad training data leads to bad results; AI is not magic — just maths on data; showing edge cases and failures - [Humans Versus Machines](https://lightmysky.com/learn/computing/humans-versus-machines-mt_oajUvqAiBJ): Compare people and computers on particular tasks, including calculation, storage, repetition, creativity and care. Introduce a simplified text-chat Turing Test and the limits of its conclusions. - [Recommendation Systems and Filter Bubbles](https://lightmysky.com/learn/computing/recommendation-systems-and-filter-bubbles-mt_EedcpioR0v): Recommendation systems use data, such as viewing history, to suggest things a person might like. Suggestions can save search time or introduce something new. Narrow suggestions can contribute to a filter bubble that limits exposure to different topics or views. - [Bias in AI Systems](https://lightmysky.com/learn/computing/bias-in-ai-systems-mt__BbOjiY5A5): If training data is biased, AI will be biased; examples: facial recognition working better for some skin tones, translation assuming gender; where bias comes from and whether we can fix it - [AI and Fairness in Decisions](https://lightmysky.com/learn/computing/ai-and-fairness-in-decisions-mt_cVp_nop-5L): Whether AI should make important decisions about people: jobs, loans, justice; who is responsible when AI makes unfair decisions; introduction to algorithmic fairness - [Deepfakes and AI-Generated Content](https://lightmysky.com/learn/computing/deepfakes-and-ai-generated-content-mt_tzMr83pS8v): AI-generated images, writing and deepfakes; why appearance and writing style cannot prove how content was made; checking original sources and evidence before believing or sharing - [Designing Fair AI Rules](https://lightmysky.com/learn/computing/designing-fair-ai-rules-mt_1z-gJBJFlM): Design thinking applied to AI ethics: if you were designing an AI system, what rules would you give it? Who should it help? What should it not be allowed to do? - [How a Chatbot Picks Its Next Token](https://lightmysky.com/learn/computing/how-a-chatbot-picks-its-next-token-mt_1Zbhamhzai): A language-model chatbot generates text in small pieces called tokens. It uses learned patterns and the available context to select a next token, adds it, and repeats. Some chatbots also retrieve information with tools. - [Weighted Models: Learned Parameters and Written Code](https://lightmysky.com/learn/computing/weighted-models-learned-parameters-and-written-code-mt_3_1toZUxak): In a weighted model, training fits numerical parameters to examples. Human-written code defines the calculation and training process. Small models can be traced directly; interpreting a large model can be harder. - [Training Data: Where It Came From and What It Leaves Out](https://lightmysky.com/learn/computing/training-data-where-it-came-from-and-what-it-leaves-out-mt_1Lw0TMHShk): Training-data coverage, collection dates and permitted use provide evidence about a model's risks. They do not establish suitability alone. Testing should reflect the people, conditions and task where the model will be used. - [Writing a Prompt That Says What You Actually Want](https://lightmysky.com/learn/computing/writing-a-prompt-that-says-what-you-actually-want-mt_dJ5-SDX3Ck): A chatbot uses the context available to it, not private intentions that were never supplied. Stating the task, audience, format and limits helps reduce unwanted assumptions and provides criteria for reviewing its answer. - [AI and the Future of Work](https://lightmysky.com/learn/computing/ai-and-the-future-of-work-mt__AWSThGJ0d): How AI can change tasks within jobs, create work around AI systems and reduce demand for some work; why human judgment, creativity, empathy and problem-solving remain useful - [AI Data Collection and Privacy](https://lightmysky.com/learn/computing/ai-data-collection-and-privacy-mt_HopZomN12L): Personal data that apps and AI services may collect; why companies use it; cookies and tracking; smart-speaker activation; checking permissions, storage and deletion controls with an adult - [Checking a Model's Claim Against a Source](https://lightmysky.com/learn/computing/checking-a-models-claim-against-a-source-mt_Qh4Pnnfqv2): Checking a model's claim means inspecting relevant external evidence, judging the source and comparing the exact claim with its original context. Record what is supported, contradicted or unconfirmed before using it. - [AI and the Environment](https://lightmysky.com/learn/computing/ai-and-the-environment-mt_ZpCcTU8j_o): Electricity used to train and run AI; water used by some data-centre cooling systems; possible environmental uses such as forest monitoring and renewable-energy forecasting; comparing costs and actual benefits - [The Future of AI](https://lightmysky.com/learn/computing/the-future-of-ai-mt__LiAEHt9nk): What AI might do in 10 years; what we want it to do and what we're worried about; children as future designers and decision-makers about AI; hopeful, empowered framing - [Why a Chatbot Can Be Confidently Wrong](https://lightmysky.com/learn/computing/why-a-chatbot-can-be-confidently-wrong-mt_TMHNkprrks): A language-model chatbot can generate fluent but false claims or references. Confident wording is not proof of accuracy. Retrieval can supply evidence, but claims and cited sources still need checking. - [Using an AI Tool on Schoolwork Without Cheating Yourself](https://lightmysky.com/learn/computing/using-an-ai-tool-on-schoolwork-without-cheating-yourself-mt_fjlXx-hkp_): Use AI within assignment rules while practicing the intended skill. Plan tool-free work, check suggestions, protect private information and describe assistance accurately. Disclosure does not make prohibited use acceptable. - [What You Owe a Reader When a Model Helped](https://lightmysky.com/learn/computing/what-you-owe-a-reader-when-a-model-helped-mt_VsUSovHzfI): Work a model helped produce still carries its author's name. What the reader is owed is a plain statement of what the tool did and what the author checked, in whatever form the setting asks for. - [Agents That Take Actions, Not Just Answer](https://lightmysky.com/learn/computing/agents-that-take-actions-not-just-answer-mt_D7KzIdIsjb): Once a model can send a message, spend money or change a file, a wrong answer stops being a wrong answer and becomes a wrong action. What the tool is allowed to reach sets how bad a mistake can get.