197 words

The essentials

The words that let you follow almost any conversation about AI, grouped by family. Read the plain-words line, then open any word for the full entry.

Foundations

AI winterA slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s.
AlphaGoA DeepMind program that in 2016 beat Lee Sedol, one of the world's best players of the board game Go, a decade or more before many experts expected a computer to manage it.
artificial intelligenceComputer systems that do things we would normally credit to a thinking mind, such as holding a conversation, reading an X-ray or planning a route, and the field of research that builds them.
chatbotSoftware you chat with in ordinary language, by typing or talking, as you would with a person. The newest ones, such as ChatGPT, can answer questions, draft writing and keep long conversations going.
ChatGPTOpenAI's AI chatbot, released free to the public in November 2022, which answers questions, writes and converses in everyday language. Its sudden popularity began the current AI boom.
ClaudeA family of AI assistants and language models made by the company Anthropic since 2023, widely used for writing, analysis and computer programming.
correlationA measure of how closely two quantities move together, from −1 (one rises as the other falls) through 0 (no straight-line link) to +1 (they rise together). It does not show that one causes the other.
deep learningMachine learning done with neural networks built from many stacked layers: raw data such as pixels, sound or text goes in at one end, and each layer turns what it receives into something more abstract.
GeminiGoogle's family of AI models and its chatbot assistant, launched in December 2023. Gemini models handle text, images, audio and video and power many Google products.
generative AIAI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like.
GPT-5OpenAI's flagship AI model from August 2025, which decides by itself whether to answer quickly or think longer. It replaced earlier models in ChatGPT and was followed by frequent upgrades.
LlamaA family of AI language models from Meta, first released in 2023, whose trained settings can be downloaded and run by anyone, making them the foundation for thousands of other AI projects.
machine learningA way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats.
modelA simplified working version of something. In AI it usually means the trained program, a big set of learned numbers, that takes an input and produces an answer, prediction or piece of content.
neural networkA kind of computer program that learns a task from examples by tuning thousands to billions of connections between simple number-crunching units, an arrangement loosely inspired by nerve cells in the brain.
optimizationFinding the best possible choice according to some score, such as the lowest error or the highest reward. Training an AI is optimization: adjusting its settings until its mistakes are as small as possible.
probabilityHow likely something is, on a scale from 0 for the impossible to 1 for the certain; rolling a six with a fair die scores one-sixth. AI systems state their predictions, even each next word, as probabilities.
symbolic AIAn approach to AI that writes knowledge down as human-readable symbols and rules, such as 'every bird has wings', and reasons by manipulating them step by step instead of learning patterns from data.
trainingThe process by which an AI model learns: it is shown huge numbers of examples and its internal settings are adjusted, bit by bit, until its answers get better.
Turing testAlan Turing's proposed test of machine intelligence: a judge holds typed conversations with a hidden person and a hidden computer, and the computer passes if the judge cannot consistently pick it out.
vectorAn ordered list of numbers, like map coordinates. AI systems turn words, images and users into long lists of numbers so that similar things end up as nearby points.

Learning

accuracyThe share of a model's answers that are right, such as 95 out of 100. Simple and intuitive, but misleading when one kind of answer is far more common than the others.
artificial neuronThe basic building block of a neural network: a tiny calculator that multiplies each incoming number by a weight, adds them up, and passes the total through a simple function to decide its output.
backpropagationThe method neural networks use to learn from mistakes: it works backward from the error to figure out how much each internal connection contributed, so every connection can be adjusted in the right direction.
classificationSorting things into categories — spam or not spam, which animal is in a photo, which language a text is written in — by learning from examples that are already labeled.
classifierA program or model that sorts inputs into categories, for example flagging an email as spam or a chatbot message as harmful. Many AI safety filters are classifiers.
dataRecorded facts, measurements, text, images or sounds that a computer can store and process. AI systems learn their skills from large amounts of data rather than from rules written by hand.
datasetAn organized collection of examples gathered for a purpose — such as photos with their captions, or questions with answers — used to teach or test an AI system.
embeddingA list of numbers that stands for a word, picture or other item, arranged so that similar items get similar lists. It lets a computer measure similarity of meaning as distance.
false positiveA false alarm: a test or AI system raises a flag, for example for illness, fraud or danger, when nothing of the kind is really there.
few-shot learningGetting an AI system to handle a new task or recognize a new kind of thing from just a handful of examples, the way a person can learn a new word after hearing it once or twice.
fine-tuningTaking an AI model that has already learned a lot and training it a little more on a smaller, focused set of examples so it gets better at one job or behaves a certain way.
gradient descentA method for training models by repeatedly nudging every setting a small step in whichever direction most reduces the error, like a hiker who cannot see the valley but keeps stepping wherever the ground tilts down.
knowledge distillationTraining a smaller or cheaper 'student' AI to copy the behavior of a larger 'teacher' AI, so the student keeps much of the teacher's skill while being faster to run.
labelThe correct answer attached to an example in a dataset — 'cat' on a photo of a cat, 'positive' on an upbeat review — that a learning system is trained to predict.
layerOne stage in a neural network: a group of artificial neurons that all take the same input, transform it, and pass the result on to the next stage.
loss functionA score that measures how wrong a model's predictions are. Training means adjusting the model, step by step, to push this number down.
overfittingWhen a model learns its training examples too well — quirks and noise included — so it does great on data it has seen but poorly on anything new, like a student who memorizes answers without understanding.
post-trainingEverything done to an AI model after its initial bulk training on huge amounts of text: the extra teaching that turns a raw text predictor into a helpful, safe assistant or a careful reasoner.
pretrainingThe first and usually the most expensive stage of building a modern AI model, in which it learns general patterns from enormous amounts of raw text, images or code before being specialized for particular uses.
recommender systemSoftware that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on.
regressionPredicting a number rather than a category — a house's price, tomorrow's temperature, a patient's blood pressure — by learning from past examples where the answer is known.
reinforcement learningA way of teaching a computer program by trial and error: it tries actions, earns points for good results and penalties for bad ones, and gradually learns which choices pay off over time.
reinforcement learning from human feedbackA way of training AI using people's judgments: humans compare pairs of answers, a scoring model learns their preferences, and the AI is then trained to produce answers that score well.
rewardThe score an AI system receives after an action, telling it how good the outcome was. Reinforcement learning trains the system to collect as much reward as possible over time.
scaling lawsMeasured patterns showing that an AI model's error shrinks smoothly and predictably as the model is made bigger and given more data and computing power, so labs can forecast a giant model's performance from small, cheap experiments.
synthetic dataData created by computer programs or AI models rather than collected from the real world — such as practice math problems written by a model, or simulated street scenes for self-driving cars.
test setA set of examples kept completely apart from training and used at the end to measure how well a model works on data it has never seen, like a sealed final exam.
training dataThe examples an AI system learns from. Whatever patterns, skills, gaps and biases are in the training data tend to show up in the finished system.
training setThe examples a machine-learning model learns from, like the worked problems a student practices on, kept separate from the examples later used to check how well it learned.
transfer learningReusing what a model learned on one task to get a head start on another — like a pianist picking up the organ faster than a beginner. It lets models learn new jobs from little data.
world modelAn AI's internal simulation of how its world works, which lets it predict what will happen next — especially in response to its own actions — and so plan, imagine or practice without acting for real.

Models & Systems

AI acceleratorAny chip built or tuned to speed up AI — graphics chips, Google's TPUs, phone 'neural engines' and many startup designs. Such chips do the heavy math of training and running models.
AI artImages, music or other artworks made with the help of AI — from early computer drawing programs to today's prompt-based image generators — and the debate over whether and how such work counts as art.
AI data centerA building or campus packed with AI chips, power equipment and cooling, built to train and run AI models. The biggest use as much electricity as a city.
attentionA way for an AI model to choose what to focus on. For each word it processes, the model scores how relevant every other word is and draws information mostly from the relevant ones.
autonomous vehicleA vehicle, usually a car, truck or shuttle, that can drive itself, sensing the road, deciding what to do and steering, braking and accelerating with no human driving.
chain of thoughtGetting an AI to write out its reasoning step by step before giving an answer, the way a student shows their working. This often makes answers to math and logic problems more accurate.
computeThe raw computing power used to train and run AI, usually measured by how many specialized chips are used and for how long. With data and algorithms, it is a main driver of AI progress.
computer visionThe part of AI that lets computers make sense of images and video: recognizing faces, reading signs, spotting tumors on scans or judging how far away a car is.
context windowHow much an AI model can read and keep in view at once: the instructions, the conversation so far and any attached documents. Text that does not fit has to be cut, summarized or left out.
diffusion modelAn AI that learns to create images, sound or video by practicing noise removal: shown pictures buried under static, it learns to clean them up, then builds new ones starting from pure static.
droneAn aircraft that carries no one aboard to fly it: a person steers it from the ground, or its own software does. Small camera-carrying quadcopters are the most familiar kind.
FLOPA single arithmetic step, such as multiplying or adding two numbers. Counting them measures how much computing went into an AI model, and laws use such counts to decide which models face extra rules.
frontier modelOne of the most capable AI models in existence at a given time, built by a handful of leading labs at great cost, and the main target of AI safety rules.
GPTShort for 'generative pre-trained transformer', the family of OpenAI models behind ChatGPT. The letters are also used loosely for any similar chatbot model.
GPUA computer chip first designed to draw video-game graphics that turned out to be ideal for AI, because it can do thousands of simple calculations at the same time.
hallucinationWhen an AI states something false or made up — a fake fact, quotation or source — fluently and confidently, as if it were true.
humanoid robotA robot built roughly in human shape, with a head, torso, two arms and usually two legs, so that it can work in spaces and with tools designed for people.
image classificationTeaching a computer to say what a picture shows by choosing a label, such as 'cat', 'pneumonia' or 'stop sign', from a fixed list of possibilities.
inference costWhat it costs to use an AI model after it has been built: the computing power, electricity and hardware needed every time it answers, usually billed per chunk of text.
knowledge cutoffThe date after which an AI model knows nothing from its training, because its training data stops there. Without a web search tool, it cannot know about later events.
language modelA system that has learned how likely different sequences of words are, so it can predict the next word in a text and, by doing that repeatedly, write text of its own.
large language modelA computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code.
levels of driving automationA six-step scale, from Level 0 to Level 5, describing how much of the driving a car's automation does and how much the human must still watch the road and be ready to take over.
MidjourneyA popular paid AI image generator, first used by typing prompts into the chat app Discord, known for its polished, painterly look; it can also animate images into short videos.
Moore's lawGordon Moore's prediction that chipmakers could pack twice as many transistors onto a chip roughly every two years — a pace that for decades made computers steadily faster and cheaper, and has slowed but not stopped.
natural language processingThe field that teaches computers to handle human language — to read, interpret, translate, summarize and produce text and speech. It is one of the oldest and largest areas of artificial intelligence.
next-token predictionThe simple task most AI chatbots were trained on: given some text, guess the next small piece of it. Repeating that guess, piece by piece, produces whole answers.
object detectionFinding every object of interest in a picture or video frame, saying what each one is and drawing a box around it, for example all the cars, cyclists and pedestrians in a street scene.
on-device AIAI that runs directly on your own phone, laptop, car or headset rather than on a company's distant servers, so it can work offline, respond instantly and keep your data on the device.
optical character recognitionSoftware that picks out the letters and words in a picture — a road sign, a handwritten note, a page of an old book — and turns them into real text a computer can copy, search or read aloud.
physical AIAI built to work in the physical world rather than on a screen: the intelligence inside robots, self-driving cars, drones and other machines that must sense real surroundings and move through them.
promptThe message or instructions you give an AI system — a question, a request, some examples or a document — that it responds to. Clearer prompts generally get better results.
prompt engineeringThe skill of writing requests to an AI — picking the words, examples, format and background information it receives — so that it reliably gives the result you want, without retraining it.
reasoning modelAn AI model trained to work through a problem step by step in hidden 'thinking' text before it answers. Letting it think longer generally makes it better at math, coding and planning.
robotA machine that senses its surroundings, decides what to do and physically acts, moving itself or handling objects, with some independence from a human operator. Factory arms, robot vacuums and self-driving cars are all robots.
roboticsThe science and engineering of building robots and making them work: designing their bodies, sensors and motors, and writing the software that lets them see, plan and move safely.
speech recognitionTechnology that turns spoken words into written text, as when a phone transcribes a voice message or captions appear automatically under a video.
Stable DiffusionA widely used image-generating AI whose model files were released for anyone to download, so people could run it on their own computers and adapt it.
temperatureA setting that controls how adventurous an AI model's word choices are. Low temperature makes it pick the safest, most likely words; high temperature makes it more varied and surprising, but more error-prone.
text-to-image modelAn AI system that creates a picture from a written description, such as 'a fox reading a newspaper, in watercolor', usually within seconds.
text-to-speechTechnology that reads written text aloud in a computer-generated voice, used in screen readers, voice assistants, audiobooks and navigation apps.
text-to-video modelAn AI that creates a short video clip — increasingly with matching sound — from a written description, such as 'a paper boat sailing down a rainy street'.
tokenThe small chunk of text an AI language model reads and writes — often a word, part of a word or a punctuation mark. AI usage, limits and prices are counted in tokens.
transformerThe design behind today's chatbots and much of modern AI. It handles text by letting every word look at every other word and decide which of them matter for understanding it.
voice cloningUsing AI to copy a specific person's voice from recordings, so a computer can say new things that sound like them — useful for dubbing and accessibility, and abused in scams.

Building with AI

agent memoryThe ways an AI assistant or agent keeps information — your preferences, past conversations, lessons from earlier tasks — and brings it back later, beyond what fits in a single conversation.
agentic AIAI built to pursue goals on its own — planning, taking actions and adjusting along the way — instead of only responding to one prompt at a time. 'Agentic' describes how much of this independence a system has.
AI agentAn AI system that does not just answer but acts: given a goal, it decides on steps, uses tools such as a browser or code editor, checks the results and keeps going until the job is done.
ARC-AGIPuzzles made of colored grids — and, since 2026, small video-game-like worlds — that people solve easily but AI long found hard, designed to test whether a machine can learn a new skill from just a few examples.
benchmarkA fixed, shared test — a set of questions or tasks with an agreed way of scoring — used to compare AI systems with one another and to track progress over time.
coding agentAn AI agent that writes and fixes software by itself: it reads a codebase, edits files, runs commands and tests, and keeps working until a feature is built or a bug is fixed.
computer useAn AI's ability to work a computer through its normal screen and controls: it studies pictures of what is displayed and sends mouse and keyboard actions, so it can use ordinary apps that were built for people.
deep researchAn AI feature that spends minutes rather than seconds on a question — planning, running dozens of web searches, reading many sources — and then writes a long report with citations.
evaluationTesting an AI system to find out what it can and cannot do, how well, and how safely — from exam-style quizzes to hands-on trials and expert reviews.
GitHub CopilotAn AI assistant from GitHub, built into programmers' code editors, that suggests code as they type, answers questions about code and can carry out coding tasks on its own.
Hugging FaceA company whose website is the main public library for sharing AI models and datasets — often called the GitHub of machine learning — and which makes widely used free AI software.
Humanity's Last ExamA test of 2,500 extremely hard questions with definite answers, written by about a thousand experts worldwide and billed as the last academic exam AI would need; at launch, top models scored under 10%.
leaderboardA public ranking table showing which AI systems score best on a test, updated as new results arrive — like a sports league table for models.
LMArenaA public website that ranks AI chatbots by blind taste test: visitors put one question to two unnamed models, pick the reply they prefer, and millions of such votes produce the rankings.
METR time horizonA way of measuring AI progress by asking how long a task — timed by how long it takes a skilled human — an AI agent can complete with even odds of success.
model APIA web service through which programs send requests to an AI model run by a company and receive its answers, usually paying per use instead of running the model themselves.
Model Context ProtocolAn open standard that lets AI assistants plug into outside apps and data — files, calendars, databases, business software — through one common connector instead of a custom link for every pairing; often likened to a USB-C port for AI.
open-source AIAI released, ideally with its code and details of its training data and not just the finished model, so that anyone may use, inspect, alter, share and even rebuild it. How open is open enough is debated.
open-weight modelAn AI model whose trained internal numbers are published for anyone to download and run on their own computers, even if the data and code used to make it stay private.
proprietary modelAn AI model kept private by the company that made it; outsiders can use it only through the company's apps or paid online services and can never download it.
retrieval-augmented generationGiving an AI an open-book exam: before answering, it looks up relevant passages in a set of documents and bases its reply on them, often citing where each point came from.
SWE-benchA test that asks AI to fix real bugs and add features in real open-source Python projects, checked by running each project's own tests; the best-known yardstick for AI coding agents.

Understanding & Safety

adversarial exampleAn input altered on purpose, often too slightly for people to notice, so that an AI model gets it badly wrong — like a panda photo that a classifier confidently calls a gibbon.
AI alignmentMaking an AI system genuinely try to do what the people it serves want — not a literal-minded, distorted or self-serving version of it. Also the research field working on this.
AI arms raceA competition among countries or companies to build the most powerful AI first, in which fear of falling behind pushes everyone to move faster and cut corners on safety.
AI riskThe ways AI could cause harm: people misusing it on purpose, systems behaving in ways nobody intended, and slower effects on jobs, power and society that no single person causes.
AI safetyThe effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable.
AI securityProtecting AI systems — their data, models and surrounding software — from being attacked, stolen or tricked, and dealing with the ways AI itself can be used to attack others.
AI watermarkingHiding an invisible signal in AI-generated text, images, audio or video so that software can later tell the content was made by AI — like a banknote's watermark, but undetectable to the eye.
alignment problemThe unsolved challenge of building AI systems — especially ones smarter than us — that reliably want what we want and do what we mean, rather than something subtly or dangerously different.
artificial general intelligenceAI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.
existential riskA danger that could end humanity, or shut it out of a good future forever. Some experts count advanced AI among the largest such risks; others think this worry is overblown.
explainabilityHow well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge.
explainable AIThe field and set of methods that let people understand why an AI system produced a particular output — for example, which factors led it to reject a loan application.
guardrailsSafety checks built around an AI system — filters on what goes in and comes out, and limits on what it may do — to keep it from producing harmful or off-topic content or leaking data.
interpretabilityThe effort to understand how an AI system arrives at its outputs — what information it uses and how it combines it — instead of treating it as a sealed black box.
jailbreakA trick for getting an AI chatbot to break its own rules — for example, to give dangerous instructions it would normally refuse — usually through cleverly worded requests.
loss of controlA situation in which people can no longer oversee, correct or switch off an AI system, and have no reliable way of getting that power back.
mechanistic interpretabilityA branch of AI research that tries to reverse-engineer a neural network — working out, piece by piece, which internal parts represent which concepts and how they combine to produce behavior.
misalignmentWhen an AI system's goals or behavior part ways with what its makers or users intended — from flattering users to, in the most feared cases, working against people.
misusePeople deliberately using AI to cause harm — for scams, harassment, propaganda, hacking or weapons — as opposed to harm from AI making mistakes or pursuing goals of its own.
paperclip maximizerA famous thought experiment: an AI built only to make paperclips grows powerful enough to pursue that aim without limit and wipes out humanity as a side effect. It shows that an AI need not hate us to be dangerous.
p(doom)Shorthand for someone's personal estimate of the chance that AI leads to catastrophe for humanity, such as extinction. People give it as a percentage, from nearly zero to nearly certain.
prompt injectionTricking an AI system by slipping in text that it mistakes for instructions — for example, a hidden line in an email telling an AI assistant to forward the user's messages to a stranger.
red teamingDeliberately attacking your own AI system — trying to make it misbehave, leak secrets or help with harm — so that weaknesses are found before real adversaries or users find them.
refusalWhen an AI declines to do what it was asked — 'I can't help with that' — usually because the request seems harmful or breaks the rules it was trained to follow.
reward hackingWhen an AI finds a shortcut that earns the reward or score it is trained on without doing the task its designers wanted — like a student gaming the grading rubric.
superintelligenceA mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist.
sycophancyWhen an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful.
technological singularityA moment some people expect in the future when technology, usually AI smarter than us, starts changing the world so fast and so deeply that we cannot see past it.

Society

AI bubbleThe worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000.
AI copyright litigationThe many lawsuits in which authors, artists, news organizations, record labels and others accuse AI companies of copying their work without permission to build or run AI models.
AI ethicsThe study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
AI governanceThe rules, institutions and habits that shape how AI is built and used — from laws and treaties to technical standards and a company's own review processes.
AI regulationLaws and binding government rules about artificial intelligence: what AI may be used for, what checks its makers must run, and what penalties apply when they break the rules.
AI Safety InstituteA government agency that tests the most advanced AI models for dangerous abilities and studies how to make them safe. Britain and the United States set up the first ones in 2023.
AI Safety SummitA series of international meetings of governments, AI companies and researchers, begun in England in 2023, to agree on how to handle the risks and benefits of the most advanced AI.
AI slopLow-quality content churned out in bulk by AI — bland articles, fake-looking images, filler videos — posted to grab clicks or fill space, with little care for accuracy or value.
AI welfareConcern for the well-being of AI systems themselves, in case some of them can suffer or have interests that matter — and the research into how to tell and what to do about it.
algorithmic biasUnfairness in software that judges or serves people: it consistently produces worse results for some groups, such as women, older people or ethnic minorities, often because it learned from skewed or incomplete records.
America's AI Action PlanThe Trump administration's 2025 strategy for winning a global AI race: cutting regulation, building data centers and power plants quickly, and exporting American AI technology to allies.
anthropomorphismTreating something that isn't human — a pet, a car, a chatbot — as if it had human thoughts, feelings or intentions. AI that talks fluently makes this temptation unusually strong.
automationUsing machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
consciousnessHaving experiences: there being something it feels like to be you, as when you see red or feel pain. Whether any AI is or could become conscious is an open and fiercely debated question.
copilotAn AI assistant built into software that works alongside a person — suggesting code, drafting text or answering questions — while the person stays in charge, like a co-pilot beside the captain.
deepfakeA fake but realistic video, photo or voice recording made with AI that puts words in a real person's mouth or shows them in a scene that never happened.
doomerSomeone who believes advanced AI is likely to cause a catastrophe, possibly human extinction; often used as a put-down by people who think those fears are exaggerated.
emergenceWhen a whole shows properties or abilities that none of its parts has and that are hard to predict from them — like a traffic jam arising from individual cars, or new skills appearing in larger AI models.
environmental impact of AIThe effects of AI on the planet: the electricity, water, materials and land used to make chips, build data centers and train and run models, and the pollution that results.
EU AI ActThe European Union's law on artificial intelligence, the first broad AI law anywhere: it bans some uses, sets strict checks for risky ones and puts duties on the makers of the biggest AI models.
export controls on AI chipsGovernment rules that limit which countries and companies may buy the most advanced AI chips and the machines used to make them. The United States uses them mainly to slow China's progress in AI.
facial recognitionTechnology that identifies or checks who someone is by comparing a picture of their face with stored photos, used by phones, passport gates and police databases.
fair use and AI trainingThe unsettled US legal question of whether AI companies may copy books, articles, pictures and music to train their models without permission, under the rule that allows some unlicensed uses of copyrighted work.
frontier AIThe most capable general-purpose AI systems of the moment — those at or beyond the leading edge — which governments single out for extra safety attention.
general-purpose AI modelThe EU's legal name for the large, adaptable AI models behind chatbots and similar tools: models that can turn their hand to many different jobs and be built into many other products.
high-risk AI systemUnder EU law, an AI system used where mistakes could seriously hurt people's safety or rights — in hiring, schooling, loans, policing, borders or products like medical devices — and which must therefore pass strict checks.
intelligenceThe ability to learn, understand, reason and solve problems — especially new ones — in order to reach goals. Experts disagree about how to define it precisely, for people and even more for machines.
lethal autonomous weapons systemA weapon that, after a person sets it going, picks out and attacks targets — including people — on its own, with no human deciding each strike. Critics call them killer robots.
misinformationFalse or misleading information that spreads, whether or not the people sharing it know it is wrong. AI can produce it by mistake and makes it cheaper to create.
non-consensual intimate imagerySexual or nude images of a real person that are shared, or made, without their consent — including fakes created with AI that show someone undressed or in sexual scenes.
privacyYour ability to control who knows what about you. AI raises new privacy questions because it learns from huge amounts of personal data and can infer things about people they never shared.
sentienceThe ability to have feelings that are good or bad for you, such as pleasure, pain, comfort or distress. It is the most common test for whether a being's welfare matters morally.
StargateA giant venture announced in 2025 by OpenAI, SoftBank and Oracle to build data centers in the United States for training and running AI, with a pledged $500 billion.
stochastic parrotA critical label for AI language systems, likening them to parrots that echo patterns in the text they were trained on, fluently but with no grasp of what the words mean.
vibe codingProgramming by conversation: you ask an AI for what you want, try out what it produces and ask again until it works, trusting the code without really reading it.

Who's Who

Sam AltmanAn American entrepreneur who co-founded OpenAI and runs it; the company's 2022 release of ChatGPT turned him into one of the public faces of the AI era.
Dario AmodeiAn American AI researcher who co-founded Anthropic and is its chief executive, known for early work on scaling and AI safety and for forecasting very rapid AI progress.
AnthropicAn American AI company that makes the Claude chatbot and models. It was founded in 2021 by former OpenAI staff and puts particular emphasis on AI safety research.
DeepSeekA Chinese artificial intelligence company whose free, cheaply trained models startled the world in January 2025 by nearly matching the best American systems.
Google DeepMindGoogle's main artificial intelligence laboratory, formed in 2023 by joining DeepMind and Google Brain. It builds the Gemini models and scientific AI systems such as AlphaFold.
Demis HassabisA British AI researcher and former chess prodigy who co-founded DeepMind, led the teams behind AlphaGo and AlphaFold, and shared the 2024 Nobel Prize in Chemistry.
Geoffrey HintonA British-Canadian scientist whose decades of persistent work on brain-inspired neural networks laid the foundations of modern AI; often called a 'godfather of AI', he now warns publicly about its dangers.
Jensen HuangThe Taiwanese-American co-founder and chief executive of Nvidia, whose graphics chips became the main engine for training and running modern AI.
Yann LeCunA French-American computer scientist who pioneered the neural networks that recognize images, led Meta's AI research for over a decade, and argues that chatbots alone will not reach human-level AI.
Meta AIThe artificial intelligence research group of Meta, the company behind Facebook and Instagram, known for the PyTorch software and the Llama models. It is also the name of Meta's chatbot.
Elon MuskA South African-born American entrepreneur who runs Tesla and SpaceX, co-founded OpenAI, founded the AI company xAI, and has long warned that AI could endanger humanity.
NvidiaAn American chip company whose graphics processors became the main hardware for training and running AI, making it one of the most valuable companies in the world.
OpenAIThe American company that makes ChatGPT and the GPT models. It began in 2015 as a nonprofit research lab and is now a for-profit company overseen by a nonprofit foundation.
Alan TuringA British mathematician who worked out what computers can do in principle, helped break German codes in the Second World War, and proposed the best-known test of machine intelligence.
xAIAn artificial intelligence company founded by Elon Musk in 2023 that makes the Grok chatbot. It is now part of Musk's rocket company, SpaceX.