{"id":70332,"date":"2026-11-11T23:21:49","date_gmt":"2026-11-11T17:51:49","guid":{"rendered":"https:\/\/www.quickonlinetips.com\/archives\/?page_id=70332"},"modified":"2026-11-11T23:21:49","modified_gmt":"2026-11-11T17:51:49","slug":"ai-terms","status":"publish","type":"page","link":"https:\/\/www.quickonlinetips.com\/archives\/ai-terms\/","title":{"rendered":"AI Terms: 180+ Artificial Intelligence Terms Explained"},"content":{"rendered":"

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Home<\/a> ›<\/span> AI terms<\/p>\n

AI Terms: 180+ Artificial Intelligence Terms Explained<\/h1>\n

Every AI term you keep hearing, explained in plain English. This AI glossary covers 183 artificial intelligence terms, from the everyday words in chatbots and phone apps to the AI terminology developers use. Search for a word or pick a topic, then test yourself with the free quiz.<\/p>\n

183<\/b> AI terms<\/span>8<\/b> topics<\/span>Free quiz + printable cheat sheet<\/span>Updated November 2026<\/span><\/p>\n<\/div>\n

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AI basics 22<\/span><\/h2>\n
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Artificial intelligence♥<\/span><\/h3>\n

Also called: AI<\/p>\n

Computer systems that do tasks we normally link with human thinking, such as understanding language, recognising images, making decisions or solving problems.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Chatbot♥<\/span><\/h3>\n

A program you talk to in everyday language by typing or speaking. Modern AI chatbots are built on large language models.<\/p>\n

Everyday<\/span>Free chatbots worth trying<\/a><\/p>\n<\/article>\n

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Generative AI♥<\/span><\/h3>\n

Also called: GenAI<\/p>\n

AI that creates new content, such as text, images, music, voice, video or code, instead of only sorting or analysing existing data.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Machine learning♥<\/span><\/h3>\n

Also called: ML<\/p>\n

A branch of AI where software learns patterns from examples (data) instead of following rules a programmer wrote by hand. The more good examples it sees, the better it usually gets.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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AI winter<\/h3>\n

A period when funding and interest in AI dropped sharply after early hype failed to deliver. The field went through AI winters in the 1970s and late 1980s.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Algorithm<\/h3>\n

A set of step-by-step instructions a computer follows to solve a problem or complete a task.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Artificial general intelligence<\/h3>\n

Also called: AGI<\/p>\n

A hypothetical AI that could learn and perform almost any intellectual task a human can, across many fields. It does not exist yet, and experts disagree on when or whether it will.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Computer vision<\/h3>\n

The part of AI that lets computers understand images and video, for example spotting faces, reading text in photos or finding objects in a scene.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Conversational AI<\/h3>\n

AI designed to hold a back-and-forth conversation in natural language, by text or voice. Chatbots and voice assistants are examples.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Deep learning<\/h3>\n

A kind of machine learning that uses neural networks with many layers. It powers modern speech recognition, image recognition and chatbots.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Expert system<\/h3>\n

An older style of AI built from hand-written if-then rules supplied by human experts, popular in the 1980s for tasks like diagnosis and troubleshooting.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Model<\/h3>\n

Also called: AI model<\/p>\n

The trained result of machine learning: a file of learned patterns that takes an input (a question, a photo) and produces an output (an answer, a label, an image).<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Narrow AI<\/h3>\n

Also called: weak AI<\/p>\n

AI built for one specific job, such as recommending films or detecting spam. Almost all AI in use today is narrow AI.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Natural language processing<\/h3>\n

Also called: NLP<\/p>\n

The part of AI that deals with human language: reading, understanding, translating, summarising and writing text.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Neural network<\/h3>\n

A computer model loosely inspired by the brain: layers of connected ‘neurons’ (simple maths units) that pass numbers to each other and adjust their connections as they learn.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Recommendation system<\/h3>\n

Also called: recommender<\/p>\n

AI that suggests what you might like next, such as videos, songs, products or posts, based on your past behaviour and people like you.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Robotics<\/h3>\n

The field of building machines that sense and move in the physical world. AI gives robots the ability to see, plan and adapt.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Speech recognition<\/h3>\n

Also called: speech-to-text<\/p>\n

AI that turns spoken words into written text, used in voice typing, captions and voice assistants.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Superintelligence<\/h3>\n

Also called: ASI<\/p>\n

A hypothetical AI far smarter than the best humans in nearly every area. It is a subject of research and debate, not something that exists today.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Text-to-speech<\/h3>\n

Also called: TTS<\/p>\n

Technology that reads text aloud in a synthetic voice. AI voices now sound much more natural than the old robotic ones.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Turing test<\/h3>\n

A test proposed by Alan Turing in 1950: if a person chatting by text cannot reliably tell a machine from a human, the machine passes.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Virtual assistant<\/h3>\n

Also called: voice assistant<\/p>\n

An AI helper on your phone, speaker or computer that answers questions and does simple tasks, like setting timers or sending messages, by voice or text.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

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How AI models work 31<\/span><\/h2>\n
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Context window♥<\/span><\/h3>\n

Also called: context length<\/p>\n

How much text (counted in tokens) a model can take into account at once, including your messages, files and its own replies. Beyond that, older parts are dropped or forgotten.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Large language model♥<\/span><\/h3>\n

Also called: LLM<\/p>\n

An AI model trained on huge amounts of text so it can understand and write language. It works by predicting the next piece of text, one token at a time. Most modern chatbots run on LLMs.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Multimodal AI♥<\/span><\/h3>\n

AI that can work with more than one type of input or output, such as text, images, audio and video, in the same conversation.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Reasoning model♥<\/span><\/h3>\n

Also called: thinking model<\/p>\n

A model that works through a problem step by step before it answers, often showing a ‘thinking’ stage. It is better at maths, logic and coding but slower.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Token♥<\/span><\/h3>\n

The small chunk of text an AI model reads and writes, often a word or part of a word. AI limits, speed and prices are usually counted in tokens.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Attention<\/h3>\n

Also called: self-attention<\/p>\n

The mechanism inside a transformer that decides which parts of the input matter most for each word it is processing.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Benchmark<\/h3>\n

A standard test used to compare AI models, for example sets of maths problems, coding tasks or exam questions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Chain of thought<\/h3>\n

Also called: CoT<\/p>\n

Step-by-step reasoning written out before the final answer. Asking a model to think step by step often improves results on hard questions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Closed model<\/h3>\n

Also called: proprietary model<\/p>\n

A model you can only use through the maker’s app or API. Its weights and training details are kept private.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Diffusion model<\/h3>\n

The kind of model behind many AI image and video generators. It learns to start from random noise and remove it step by step until a clear picture appears.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Distillation<\/h3>\n

Also called: knowledge distillation<\/p>\n

Training a smaller ‘student’ model to copy the behaviour of a larger ‘teacher’ model, giving a faster model that keeps much of the skill.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Embedding<\/h3>\n

A list of numbers that represents the meaning of a word, sentence or image, so that similar meanings sit close together. Used for search and recommendations.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Foundation model<\/h3>\n

A large, general-purpose model trained on broad data that other apps and models are built on top of, often after fine-tuning for a specific job.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Frontier model<\/h3>\n

A term for the most advanced and capable AI models available at any given time, usually from the biggest AI labs.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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GPT<\/h3>\n

Also called: Generative Pre-trained Transformer<\/p>\n

Short for Generative Pre-trained Transformer: a language model that is first pre-trained on lots of text, then generates new text. The letters appear in some product names, but the idea is a general one.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Inference<\/h3>\n

Using a trained model to get an answer. Training is learning; inference is the model doing its job each time you send a prompt.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Knowledge cutoff<\/h3>\n

Also called: training cutoff<\/p>\n

The date after which a model has no training data. It will not know about later events unless it can search the web or read files you give it.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Latency<\/h3>\n

The delay between asking an AI something and getting the response. Lower latency feels faster.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Mixture of experts<\/h3>\n

Also called: MoE<\/p>\n

A model design where only some specialist sub-networks (‘experts’) switch on for each token. It lets very large models run more cheaply.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Model card<\/h3>\n

A document from a model’s maker describing what the model is for, how it was trained and tested, its limits and known risks.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Natural language understanding<\/h3>\n

Also called: NLU<\/p>\n

The part of language AI focused on working out what text means, such as intent, sentiment and who did what.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Open source AI<\/h3>\n

AI whose code and, ideally, weights and training details are freely available to use, study, change and share. Definitions vary on how much must be open.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Open-weight model<\/h3>\n

Also called: open model<\/p>\n

A model whose trained weights are published so anyone can download and run it on their own hardware, usually under a licence with conditions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Parameters<\/h3>\n

Also called: weights<\/p>\n

The internal numbers a model learns during training. Model size is often given in parameters, such as 7 billion; more parameters usually means more capability but more computing power.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Quantization<\/h3>\n

Shrinking a model by storing its numbers with less precision, so it uses less memory and runs faster, with a small loss in quality.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Small language model<\/h3>\n

Also called: SLM<\/p>\n

A smaller, cheaper language model that can run on a phone or laptop. It is less capable than the biggest models but faster and more private.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Temperature<\/h3>\n

A setting that controls how random an AI’s output is. Low temperature gives predictable, focused answers; high temperature gives more varied and creative ones.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Tokenization<\/h3>\n

Splitting text into tokens before a model processes it. For example, ‘unbelievable’ may become ‘un’, ‘believ’ and ‘able’.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Transformer<\/h3>\n

The neural network design behind modern language models. Introduced in 2017, it uses ‘attention’ to weigh how every word in a passage relates to every other word.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Vector<\/h3>\n

A list of numbers. In AI, text and images are turned into vectors so a computer can measure how similar they are.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

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Vector database<\/h3>\n

A database built to store embeddings and quickly find the ones most similar to a query. It is often used to give chatbots access to your documents.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

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Prompts and chatting with AI 19<\/span><\/h2>\n
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Prompt♥<\/span><\/h3>\n

The instruction or question you give an AI. Clear prompts with context and an example usually get better answers.<\/p>\n

Everyday<\/span>Ready-to-use prompts<\/a><\/p>\n<\/article>\n

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AI search<\/h3>\n

Also called: answer engine<\/p>\n

Search that gives a written answer drawn from several web pages, usually with source links, instead of only a list of results.<\/p>\n

Everyday<\/span>AI search engines compared<\/a><\/p>\n<\/article>\n

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Canvas<\/h3>\n

Also called: side-panel editor<\/p>\n

A side panel in some chatbots where a document, code or app is shown and edited separately from the chat.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Citation<\/h3>\n

Also called: source link<\/p>\n

A link or reference an AI shows for where a fact came from, so you can check it yourself.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Custom instructions<\/h3>\n

Saved preferences you give a chatbot once (who you are, how you like answers) so it applies them in every chat.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Deep research<\/h3>\n

An AI mode that runs many searches, reads dozens of sources and writes a long report with citations. It takes minutes rather than seconds.<\/p>\n

Everyday<\/span>AI research tools with real sources<\/a><\/p>\n<\/article>\n

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Few-shot prompting<\/h3>\n

Giving an AI a few worked examples in your prompt so it copies the pattern for your real request.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Grounding<\/h3>\n

Tying an AI’s answer to trusted sources, such as search results or your documents, so it is less likely to make things up.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Memory<\/h3>\n

Also called: AI memory<\/p>\n

A chatbot feature that remembers facts and preferences from past chats so you do not have to repeat them. You can usually view or delete what it stores.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Output<\/h3>\n

Also called: completion, response<\/p>\n

What the AI produces in reply to your prompt: text, code, an image, audio and so on.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Prompt engineering<\/h3>\n

The skill of writing and refining prompts to get better, more reliable results from an AI model.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Prompt library<\/h3>\n

A saved collection of prompts that work well, kept for reuse by a person or a team.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Prompt template<\/h3>\n

A reusable prompt with blanks to fill in, such as a topic, audience or tone, so you get consistent results.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

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Regenerate<\/h3>\n

Asking the AI to produce a fresh answer to the same prompt. Because AI output varies, a second try can be better.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Role prompting<\/h3>\n

Also called: persona prompt<\/p>\n

Asking the AI to act as a particular expert or character, such as ‘act as a patient maths tutor’, to shape its answers.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Summarization<\/h3>\n

Using AI to shorten a long text, video or meeting into the main points.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

System prompt<\/h3>\n

Also called: system message<\/p>\n

Hidden or background instructions that set an AI’s role, rules and tone before the conversation starts.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Tone<\/h3>\n

The style of an AI’s writing, such as formal, friendly or funny. You can ask for a tone in your prompt.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

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Zero-shot prompting<\/h3>\n

Asking an AI to do a task with no examples, relying only on what it already knows.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

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Images, voice and video AI 20<\/span><\/h2>\n
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Deepfake♥<\/span><\/h3>\n

Fake video, audio or images made with AI to show a real person saying or doing something they never did.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI avatar<\/h3>\n

A digital character or portrait created with AI, sometimes animated to speak in a video.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI music generation<\/h3>\n

AI that composes songs or background music from a text description, sometimes with vocals.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI voice cloning<\/h3>\n

Also called: voice cloning<\/p>\n

Creating a synthetic copy of a real person’s voice from a recording. Useful for accessibility and dubbing, but also abused in scam calls.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Aspect ratio<\/h3>\n

The width-to-height shape of an image or video, such as 16:9 for widescreen or 9:16 for phone stories.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Content credentials<\/h3>\n

Also called: provenance<\/p>\n

Tamper-evident information attached to a file that records how it was made and edited, including whether AI was used.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Image recognition<\/h3>\n

AI that identifies what is in a picture, such as objects, plants, landmarks or text.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Image-to-image<\/h3>\n

AI that changes an existing picture based on your instructions, such as changing its style, background or lighting.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Inpainting<\/h3>\n

Editing part of an image with AI: you mark an area and describe what should replace it.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Lip sync<\/h3>\n

AI that moves a face’s mouth in a video to match new speech, used in dubbing and talking avatars.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Negative prompt<\/h3>\n

A list of things you do not want in an AI image, such as ‘blurry, extra fingers, text’.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Optical character recognition<\/h3>\n

Also called: OCR<\/p>\n

Technology that reads printed or handwritten text from images and turns it into editable text.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Outpainting<\/h3>\n

Also called: generative expand<\/p>\n

Extending an image beyond its original edges with AI, filling in new scenery that matches.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Seed<\/h3>\n

A number that sets the starting randomness of an AI image. Using the same seed and prompt usually gives a similar picture again.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Style transfer<\/h3>\n

Applying the look of one image, such as a watercolour or sketch style, to another image.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Synthetic media<\/h3>\n

Also called: AI-generated media<\/p>\n

Any image, audio, video or text created or heavily changed by AI rather than captured from real life.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Text-to-image<\/h3>\n

AI that creates a picture from a written description, called a prompt.<\/p>\n

Everyday<\/span>Free AI art generators<\/a><\/p>\n<\/article>\n

\n

Text-to-video<\/h3>\n

AI that generates a short video clip from a written description.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Upscaling<\/h3>\n

Also called: AI upscaler<\/p>\n

Using AI to enlarge an image or video and add detail so it looks sharper at a bigger size.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Watermark<\/h3>\n

Also called: AI watermark<\/p>\n

A hidden or visible mark added to AI-generated content so it can later be identified as made by AI.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

\n

Training and data 24<\/span><\/h2>\n
\n
\n

Fine-tuning♥<\/span><\/h3>\n

Extra training of an existing model on a smaller, focused dataset so it gets better at a specific task or style.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Training data♥<\/span><\/h3>\n

Also called: dataset<\/p>\n

The examples a model learns from, such as text, images or audio. Its quality and variety shape what the model can do and its biases.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Backpropagation<\/h3>\n

The method a neural network uses to work out how much each weight contributed to an error, so each can be adjusted.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Classification<\/h3>\n

A task where AI sorts inputs into categories, such as spam or not spam.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Clustering<\/h3>\n

Grouping similar items together without labels, such as grouping customers by shopping habits.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Data labeling<\/h3>\n

Also called: data annotation<\/p>\n

The work of tagging data with the right answers, often done by people, to train and check AI models.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Data scraping<\/h3>\n

Also called: web scraping<\/p>\n

Collecting large amounts of content from websites automatically. AI companies scrape the web for training data, which raises copyright and privacy questions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Epoch<\/h3>\n

One complete pass through the whole training dataset during training.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Federated learning<\/h3>\n

Training a shared model across many devices, such as phones, without sending the raw data to a central server.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Gradient descent<\/h3>\n

The step-by-step method used to adjust a model’s weights to reduce its errors during training.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Labeled data<\/h3>\n

Also called: annotation<\/p>\n

Data that people have tagged with the correct answer, used to train and test supervised models.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Loss function<\/h3>\n

The formula that measures how wrong a model’s predictions are during training. Training tries to make the loss smaller.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Model collapse<\/h3>\n

A drop in quality that can happen when AI models are trained mostly on AI-generated content instead of human-made data.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Overfitting<\/h3>\n

When a model memorises its training examples so closely that it performs badly on new data it has not seen.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Pre-training<\/h3>\n

The first, biggest stage of training a language model, where it learns general language and knowledge from a huge amount of text.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Regression<\/h3>\n

A task where AI predicts a number, such as a house price or tomorrow’s temperature.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Reinforcement learning<\/h3>\n

Also called: RL<\/p>\n

Learning by trial and error: the AI takes actions and gets rewards or penalties, and gradually learns what works.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Reinforcement learning from human feedback<\/h3>\n

Also called: RLHF<\/p>\n

A training step where people rate a model’s answers, and the model is tuned toward the kinds of answers people preferred.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Self-supervised learning<\/h3>\n

Training where the data supplies its own answers, such as hiding a word in a sentence and asking the model to predict it. It is how language models learn from raw text.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Supervised learning<\/h3>\n

Training with labelled examples, where each example comes with the right answer, such as photos tagged ‘cat’ or ‘dog’.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Synthetic data<\/h3>\n

Artificially generated data, often made by AI, used to train or test models when real data is scarce, costly or private.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Training<\/h3>\n

The process of feeding a model lots of examples so it learns patterns. Big models train for weeks on thousands of chips.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Underfitting<\/h3>\n

When a model is too simple or undertrained to capture the real patterns, so it performs badly even on training data.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Unsupervised learning<\/h3>\n

Training on data without labels, where the model finds patterns or groups on its own.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

\n

AI agents and tools 20<\/span><\/h2>\n
\n
\n

Agentic AI♥<\/span><\/h3>\n

AI that acts with some independence toward a goal: it breaks the goal into steps, uses tools and checks its own progress.<\/p>\n

Everyday<\/span>Our plain-English guide to agentic AI<\/a><\/p>\n<\/article>\n

\n

AI agent♥<\/span><\/h3>\n

Also called: agent<\/p>\n

An AI system that can plan and carry out multi-step tasks for you, such as browsing websites, filling forms or editing files, rather than only answering questions.<\/p>\n

Everyday<\/span>AI agents you can try<\/a><\/p>\n<\/article>\n

\n

Retrieval-augmented generation♥<\/span><\/h3>\n

Also called: RAG<\/p>\n

A method where the AI first looks up relevant information in documents or the web, then writes its answer using what it found. It reduces made-up answers and keeps responses current.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

AI coding assistant<\/h3>\n

An AI tool that suggests, writes, explains or fixes code inside a code editor or terminal.<\/p>\n

Everyday<\/span>The best AI tools for coding<\/a><\/p>\n<\/article>\n

\n

AI copilot<\/h3>\n

Also called: AI assistant<\/p>\n

A general term for an AI helper built into an app that works alongside you, suggesting text, code or actions as you go.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI note taker<\/h3>\n

Also called: meeting assistant<\/p>\n

An app that joins or records meetings, writes a transcript and summarises decisions and action items.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

API<\/h3>\n

Also called: application programming interface<\/p>\n

A way for one program to talk to another. Developers use AI APIs to add AI features to their own apps.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Autonomous<\/h3>\n

Able to act without step-by-step human direction. Fully autonomous AI is still limited, and most products ask you to confirm important actions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Computer use<\/h3>\n

Also called: browser agent<\/p>\n

An AI ability to operate a computer or web browser like a person: looking at the screen, clicking, typing and scrolling.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Custom chatbot<\/h3>\n

Also called: custom assistant, custom bot<\/p>\n

A version of a chatbot you set up with your own instructions and files for a specific job, such as a recipe helper or a study tutor.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Human in the loop<\/h3>\n

Also called: HITL<\/p>\n

A setup where a person reviews or approves an AI’s work at key steps before it acts, such as before sending an email or making a payment.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Knowledge base<\/h3>\n

A collection of documents, such as manuals, FAQs or notes, that an AI assistant can search to answer questions accurately.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Model Context Protocol<\/h3>\n

Also called: MCP<\/p>\n

An open standard for connecting AI assistants to outside apps and data, such as files, calendars and databases, through a common plug-in style interface.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Multi-agent system<\/h3>\n

Several AI agents working together, each with its own role, such as one researching, one writing and one checking.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Orchestration<\/h3>\n

Coordinating several AI models, tools or agents so they work together on a larger task in the right order.<\/p>\n

Advanced<\/span><\/p>\n<\/article>\n

\n

Plugin<\/h3>\n

Also called: connector, extension<\/p>\n

An add-on that connects an AI assistant to another service, such as email, a calendar or a shopping site, so it can use that service’s data.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Semantic search<\/h3>\n

Search that matches meaning rather than exact words, so ‘cheap flights’ can find a page about ‘low-cost airfares’.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Tool use<\/h3>\n

Also called: function calling<\/p>\n

When an AI model calls outside tools, such as a calculator, web search, calendar or database, to get information or take actions.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

Vibe coding<\/h3>\n

Building software mainly by describing what you want to an AI in plain language and accepting the code it writes, with little manual coding.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Workflow automation<\/h3>\n

Using software, now often with AI, to run a series of tasks automatically, such as filing emails and updating a spreadsheet.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n<\/div>\n<\/section>\n

\n

Safety, ethics and risks 24<\/span><\/h2>\n
\n
\n

AI bias♥<\/span><\/h3>\n

Also called: algorithmic bias<\/p>\n

When an AI system produces unfair or skewed results, often because its training data was unbalanced or reflected human prejudice.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Hallucination♥<\/span><\/h3>\n

Also called: confabulation<\/p>\n

When an AI states something false or made up, such as a fake fact, quote or source, as if it were true. Always check important facts.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI alignment<\/h3>\n

Also called: alignment<\/p>\n

The work of making sure AI systems pursue the goals and values their makers and users intend, and do not cause harm.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

AI detector<\/h3>\n

Also called: AI content detector<\/p>\n

A tool that estimates whether text or images were made by AI. Results are often unreliable and should not be the only evidence.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI ethics<\/h3>\n

The study of moral questions raised by AI, such as fairness, privacy, jobs, consent and who is responsible when AI causes harm.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI regulation<\/h3>\n

Also called: AI law<\/p>\n

Laws and rules that govern how AI is built and used, such as transparency, safety testing and banned uses.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n

\n

AI safety<\/h3>\n

The field focused on preventing AI from causing accidents or harm, from everyday mistakes to large-scale risks.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

AI scam<\/h3>\n

Fraud that uses AI, such as cloned voices of relatives, deepfake videos of celebrities or AI-written phishing emails.<\/p>\n

Everyday<\/span><\/p>\n<\/article>\n

\n

Black box<\/h3>\n

A system whose inner workings are hard to see or understand, even for its makers. Large neural networks are often described this way.<\/p>\n

Intermediate<\/span><\/p>\n<\/article>\n