data
In plain wordsRecorded 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.
Information represented in a form that machines can store, transmit and process: numbers, text, images, audio, sensor readings, logs and records of human activity. In machine learningmachine learning, n.: A 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., data take over the role that hand-written rules play in conventional programming: a modelmodel, n.: A 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. acquires its behavior by fitting its parametersparameter, n.: One 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. to examples, so the quantity, quality, diversity and legal status of the data shape what a system can do and how it fails. Practitioners distinguish structured data (tables with fixed fields; see tabular datatabular data, n.: Data arranged as a table, like a spreadsheet: each row is a case — a customer, a loan, a patient — and each column a measured property. Much business and scientific data looks like this.) from unstructured data (free text, images, audio), and labeled from unlabeled data (see labellabel, n.: The 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.). Data used to fit a model are training datatraining data, n.: The 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.; data held back to tune or measure it form the validation setvalidation set, n.: A set of examples kept out of training and used during development to compare model versions and settings, like a practice exam taken before the real one. and test settest set, n.: A 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.; data supplied when the system runs are its inputs.
Most of the gains came from better data, not a bigger model.
Origin
From Latin data, 'things given', plural of datum. English used the word from the 1640s for facts granted as a basis for reasoning, from the 1890s for collected numerical facts, and from 1946 for information stored and operated on by computers; 'data processing' (1954) and 'database' (by 1962) followed.
Usage note
Originally a plural. In computing and AI, 'data' is usually a mass noun ('the data is noisy'), while many scientific journals keep the plural ('the data are'); both are standard. The singular 'datum' survives mainly in formal and technical writing.
Related
- datasetdataset, n.: An 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.
- training datatraining data, n.: The 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.
- big databig data, n.: Data 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.
- data qualitydata quality, n.: How well a set of data does its job: whether it is accurate, complete, up to date, consistent and representative — and, for AI training, informative enough to teach useful skills.
- labellabel, n.: The 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.
- corpuscorpus, n.: A large collection of written or spoken language, gathered so that people or computers can study it or learn from it — like a library assembled for a language model.
- data sciencedata science, n.: The practice of collecting, cleaning, analyzing and interpreting data, using statistics, programming and machine learning, to answer questions and guide decisions.
- modelmodel, n.: A 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.
Mentioned in
datasetdataset, n.: An 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.