Core Concepts

The big ideas: AI, models, training, inference, and the words with many meanings. 58 entries live here, and 61 from other domains touch it.

Essential

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.
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.
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.
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.
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.

All other entries

agentSomething that acts. In AI, a program that observes its situation and takes actions toward a goal: a game-playing learner, a software helper, or a chatbot that uses tools to complete tasks.
AI applicationA program or product that uses AI to do something useful for people, such as a translation app, a medical-scan checker or a coding assistant.
AI assistantAn AI program, such as ChatGPT, Claude or Gemini, that helps a person through conversation: answering questions, writing, summarizing, coding and, increasingly, taking actions on their behalf.
AI modelThe trained core of an AI: a large file of learned numbers plus the recipe for using them, which turns an input such as a question or a photo into an output.
AI systemA complete piece of technology that uses AI to do something, such as screening loan applications, driving a car or chatting with customers, including the AI model and the software around it.
AI-poweredDescribed as using artificial intelligence to work. The label appears on everything from search engines to toothbrushes and often says little about how much AI is really involved.
algorithmA precise, step-by-step procedure for solving a problem or doing a task, like a recipe a computer can follow. In the news, 'the algorithm' often means software that ranks what you see online.
architectureThe overall design of an AI model, meaning what kinds of parts it has and how they are connected, as opposed to the specific numbers it learns during training.
autonomyHow much an AI system can act on its own without a person approving each step, from merely making suggestions to carrying out long tasks with no supervision at all.
biasA word with several AI meanings: an adjustable number added inside a model; a systematic error in estimates; or unfair treatment of some groups of people by an AI system.
big dataData that piles up too fast, in too many forms or in too great a quantity for one ordinary computer to store and study, and the methods invented to cope with it.
black boxA system whose inner workings are hidden or too complex to understand: we can see what goes in and what comes out, but not why it gave a particular answer.
bounded rationalityThe idea that real decision-makers, whether people or machines, cannot consider every option or compute the perfect choice, so they make reasonable decisions within limits of time, information and thinking power.
capabilitySomething an AI system is able to do, such as writing working code or solving math problems, and how well. Measuring capabilities is how researchers track progress and spot dangerous skills.
computational intelligenceA family of AI methods inspired by nature, such as neural networks, fuzzy logic and evolutionary algorithms, that learn or adapt from numbers rather than following explicit logical rules.
conversational AIThe technology that lets computers hold conversations with people in everyday language, by text or voice. It powers chatbots, voice assistants and automated customer-service lines.
data miningSearching large collections of data with computers to discover useful patterns, such as products often bought together or signs that a transaction may be fraudulent.
data scienceThe practice of collecting, cleaning, analyzing and interpreting data, using statistics, programming and machine learning, to answer questions and guide decisions.
end-to-end learningTraining one AI system to go straight from raw input, such as camera images, to the final output, such as steering commands, instead of chaining hand-designed steps.
exampleA single item of data used to train or test an AI model, such as one photo with its label, one email marked spam or not, or one question with its answer.
featureOne piece of information about each example that a model uses, like a house's size or a pixel's color, or a pattern a model has learned to detect inside itself.
feedback loopA cycle in which what a system produces comes back to shape its own future input, like a microphone howling near its loudspeaker. In AI, a model's predictions can shape the very data it later learns from.
foundation modelA very large AI model, built once at great cost from vast amounts of text, images or code gathered mostly from the internet, that many products then reuse as their starting point, from chatbots to coding tools.
frontier labOne of the handful of companies building the most advanced AI models of the moment, spending billions of dollars on computing power to train them.
generalizationHow well a model copes with new examples it has never seen, rather than just memorizing its training data, like a student who understands the method, not just the answers.
general-purpose AIAI that can do a wide range of different tasks, like writing, coding, translating and analyzing images, rather than the one job it was built for. Today's chatbots are the main example.
human-in-the-loopDescribes AI systems in which a person checks, corrects or approves the machine's work at key points, rather than letting it act entirely on its own.
inferenceDrawing a conclusion from evidence. In today's AI it most often means running an already-trained model to get an answer, as opposed to training it.
intelligent agentIn AI theory, a decision-maker, such as a robot, a self-driving car, a chess program or a helpful piece of software, that notices what is going on around it and responds in ways that serve its goals.
kernelA word with several AI meanings: a similarity measure used by some learning methods; a small grid of numbers that scans an image inside a neural network; or a small program that runs on a GPU.
learningGetting better at something through experience. For a machine, it means changing its internal settings based on data so that it performs a task better than before.
machine intelligenceIntelligence shown by machines; another name for artificial intelligence, preferred by some because it does not suggest that such intelligence is fake.
multimodal AIAI that can take in or produce more than one kind of information, such as text, images, sound and video, so you can show it a photo and ask questions about it.
narrow AIAI built to do one specific job, or a small set of related jobs, such as playing chess or spotting spam, without being able to turn its skill to unrelated problems.
parameterOne of the adjustable numbers inside an AI model that are tuned during training. Large language models have billions of them, and together they store what the model has learned.
pattern recognitionThe ability of a computer, or a brain, to spot regularities in data and sort things into categories, such as recognizing handwritten letters, faces or spoken words.
predictionA model's output for a given input: its best guess at an unknown answer. In AI it need not be about the future; labeling a photo 'cat' counts as a prediction.
predictive AIAI used to forecast outcomes about people or events, such as who will repay a loan, which patient may get sicker or which machine will fail, in order to guide decisions.
reasoningWorking out conclusions step by step from facts, rules or evidence, as in solving a math problem or planning a trip. Whether today's AI truly reasons, or imitates reasoning, is debated.
satisficingChoosing the first option that is good enough instead of searching for the very best one — a sensible strategy when time, information or brainpower are limited.
self-supervised learningA way for AI to learn from huge amounts of unlabeled data by setting itself fill-in-the-blank puzzles, such as guessing a hidden word or a missing patch of an image.
semi-supervised learningMachine learning that combines a small set of labeled examples with a much larger pile of unlabeled ones, which are cheaper to collect, to learn better than from the labeled data alone.
soft computingA group of computing methods, including fuzzy logic, neural networks and evolutionary algorithms, that accept imprecise, approximate answers in exchange for being practical on messy real-world problems.
state of the artThe best performance or most advanced technique currently known in a field. In AI papers, a state-of-the-art result is one that beats every earlier published result on a test.
supervised learningTeaching a computer by example: you give it many inputs paired with the right answers, such as photos labeled 'cat' or 'dog', and it learns to answer correctly for new inputs.
task (machine learning)The job an AI model is meant to do, such as translating sentences, spotting spam or answering questions, defined by its inputs, its expected outputs and how success is scored.
unsupervised learningMachine learning without answer keys: the computer looks at unlabeled data and finds structure on its own, such as groups of similar customers or the main ways in which data vary.
virtual assistantSoftware you speak or type to, like Siri, Alexa or Google Assistant, that answers questions and does simple jobs such as setting timers, playing music or controlling smart-home devices.
weak supervisionTraining AI with labels that are cheap but imperfect, such as rough rules of thumb, partial tags or noisy guesses, instead of carefully hand-checked answers.
weightsThe learned numbers inside a neural network that set how strongly each part influences the next. The finished set of weights is, in effect, the trained model itself.

Also relevant

abductive reasoningReasoning from an observation to its most likely explanation — the grass is wet, so it probably rained — the kind of educated guess that doctors and detectives make.
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.
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.
algorithm aversionLosing faith in a computer's predictions after watching it slip up once or twice, and turning back to human judgment even though the machine remains the better forecaster.
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.
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.
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.
augmentationUsing AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own.
automated decision-makingWhen a computer system, rather than a person, makes or effectively makes a decision about someone — such as approving credit, screening job applicants or flagging possible welfare fraud.
automationUsing machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
automation biasThe habit of trusting a computer's suggestion too much: following it even when it is wrong, or missing problems because the machine did not flag them.
Bayes' theoremA formula for updating how much you believe something after seeing new evidence: weigh your earlier belief by how well that explanation predicts what you actually observed.
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.
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.
cognitive scienceThe scientific study of how minds work — how we reason, use language, remember and see — combining psychology, neuroscience, linguistics, philosophy, anthropology and artificial intelligence.
computational linguisticsThe science of human language studied with computers: using computational models to understand how language works, and building the language technology that grows out of that understanding.
computational theory of mindThe idea that thinking is a kind of computation: the mind processes information by following rules, much as a computer runs a program, and the brain is the machine doing it.
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.
Computing Machinery and IntelligenceAlan Turing's 1950 essay asking whether machines can think. It proposed a conversation test in place of that question, answered objections to thinking machines and suggested that machines might learn like children.
CPUThe main general-purpose chip in a computer, good at running many different programs step by step. In AI systems it usually coordinates the work while specialized chips do the heavy math.
cyberneticsThe study of how animals, machines and organizations steer themselves using feedback: sensing the result of an action and correcting course. It was both a parent and an early rival of AI.
Dartmouth workshopA summer meeting of scientists at Dartmouth College in 1956, planned in a 1955 funding proposal that first used the phrase 'artificial intelligence'. Most histories treat it as the moment AI became a research field of its own.
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.
deductive reasoningReasoning in which true starting points guarantee a true conclusion: if every square is a rectangle and this shape is a square, then it must be a rectangle.
embodied AIAI that has a body, usually a robot and sometimes a simulated character, and learns or acts by sensing and moving in a world rather than only processing text or images.
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.
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.
feedforward neural networkA neural network laid out like an assembly line: each stage hands its result only to later stages, never back, so every answer depends on the current input alone and nothing is remembered between inputs.
functionalismThe view that minds are defined by how they work, not what they are made of: pain, for example, is whatever inner state plays pain's part, linking injury, other thoughts and behavior.
heuristicA rule of thumb: a quick, usually good-enough way to make a decision or to estimate how close you are to a goal, without any guarantee of finding the best answer.
homogenizationThe tendency of AI to make things more alike: products built on the same few models share the same flaws, and people who write or create with AI produce more similar work.
human-level AIAI that can match an ordinary adult across the whole range of thinking tasks, such as talking, learning, reasoning and planning. An older, more intuitive name for the goal now usually called general AI.
information retrievalThe study and engineering of search: how to organize huge piles of documents so that the few that matter to someone's question can be found and ranked quickly. AI systems rely on it to look things up.
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.
intelligence augmentationUsing computers to make people smarter and more capable rather than building machines that think on their own: AI as a tool for human minds.
ISO/IEC 22989An international standard that fixes agreed meanings for basic AI words — such as AI system, machine learning and trustworthiness — so that other standards, contracts and laws can use the same vocabulary.
knowledge representationThe study of how to record knowledge about objects, categories, rules and events in a precise format, so that a program can reason with it and reach conclusions nobody typed in.
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.
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.
McCulloch–Pitts neuronThe first mathematical model of a brain cell as a simple on–off switch: it fires if enough of its inputs are on and no blocking input is. Networks of them can carry out logic.
Moravec's paradoxThe observation that what is hard for people, like chess or algebra, is often easy for computers, while what toddlers do effortlessly, such as seeing, walking and picking things up, is very hard for machines.
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.
philosophy of artificial intelligenceThe branch of philosophy that asks what AI is and could be: whether machines can think, understand or be conscious, what intelligence is, and what we owe to and should expect from intelligent machines.
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.
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.
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.
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.
random seedA number that fixes the 'random' choices a computer makes; reuse the same seed and settings, and an image generator produces the same picture again.
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.
representation learningLetting a computer work out for itself how best to describe its data, such as the edges and shapes in images or the meanings of words, instead of relying on descriptions people design by hand.
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.
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.
robot foundation modelOne large AI model trained on huge amounts of robot and internet data so that it can control many robots on many tasks, and be adapted quickly to new ones.
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.
state space searchSolving a problem by treating every situation it could be in as a point on a map, with each possible move as a road between points, and searching for a route from the start to a goal.
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.
the bitter lessonThe argument, made by AI pioneer Rich Sutton in 2019, that AI has advanced mostly through general methods that use ever more computing power, not by programming in human knowledge.
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.
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.