label
In plain wordsThe 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.
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Machine learning
The target value associated with an example in supervised learningsupervised learning, n.: Teaching 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.: a class name, a number, a bounding boxbounding box, n.: The rectangle a vision system draws around an object it has found, stored as a handful of numbers giving the rectangle's position and size., a text span or a preferred response that 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. is trained to predict from the example's input. Labels come from human labelersdata labeling, n.: The work of attaching correct answers to examples — often done by large paid workforces, sometimes by experts — so that AI systems can learn from them or be tested against them. or experts, from user behavior (clicks, purchases), from automated heuristics (weak supervisionweak supervision, n.: Training 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.) or from another model. A dataset whose examples carry labels is called labeled; most raw data is unlabeled, which is why self-supervised learningself-supervised learning, n.: A 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. — deriving targets from the data itself, such as the next tokentoken, n.: The 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. in a text — made web-scale training possible. Because labels are produced by fallible processes, they contain label noiselabel noise, n.: Mistakes in the answers attached to training or test examples — a dog photo marked 'cat' — which can mislead what a model learns and distort how good it seems. and encode the labelers' judgments: treating them as ground truthground truth, n.: The reference answers treated as correct when checking or training an AI system — for example, the confirmed diagnoses that a medical-imaging model's guesses are compared against. is a convention, not a guarantee.
About 4% of the labels in the validation split turned out to be wrong.
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Content disclosure
A visible notice or embedded metadata stating that an image, video, audio clip or text was generated or substantially altered by AI, required for some content in several jurisdictions; see AI-generated content labelingAI-generated content labeling, n.: Marking text, images, audio or video as made by AI — with a visible notice, hidden data in the file, or both — so that people and platforms can tell synthetic content from real. and Content CredentialsContent Credentials, prop. n.: A tamper-evident 'nutrition label' attached to a photo, video or document recording where it came from — which camera or AI tool made it and how it was edited — that anyone can inspect..
Origin
The supervised-learning sense grew out of statistics and pattern recognition, where training examples were labeled with their known class. 'Labeled data' was common by the 1990s; Avrim Blum and Tom Mitchell's 1998 paper on co-training, about combining labeled and unlabeled data, helped fix the contrast.
Usage note
'Label' and annotationannotation, n.: Adding information to raw data by hand or with software — marking objects in photos, tagging names in sentences, transcribing speech or ranking chatbot answers — so machines can learn from it. overlap: a label is usually the single target an example is scored against, while an annotation can be any added information, including rationales, relations or segment boundaries.
Related
- data labelingdata labeling, n.: The work of attaching correct answers to examples — often done by large paid workforces, sometimes by experts — so that AI systems can learn from them or be tested against them.
- annotationannotation, n.: Adding information to raw data by hand or with software — marking objects in photos, tagging names in sentences, transcribing speech or ranking chatbot answers — so machines can learn from it.
- ground truthground truth, n.: The reference answers treated as correct when checking or training an AI system — for example, the confirmed diagnoses that a medical-imaging model's guesses are compared against.
- label noiselabel noise, n.: Mistakes in the answers attached to training or test examples — a dog photo marked 'cat' — which can mislead what a model learns and distort how good it seems.
- supervised learningsupervised learning, n.: Teaching 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.
- weak supervisionweak supervision, n.: Training 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.
- self-supervised learningself-supervised learning, n.: A 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.
- featurefeature, n.: One 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.
Sources
- Blum, A. & Mitchell, T. (1998). Combining Labeled and Unlabeled Data with Co-Training. Proceedings of COLT 1998.
Mentioned in
datadata, n.: Recorded 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., 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., annotationannotation, n.: Adding information to raw data by hand or with software — marking objects in photos, tagging names in sentences, transcribing speech or ranking chatbot answers — so machines can learn from it., data labelingdata labeling, n.: The work of attaching correct answers to examples — often done by large paid workforces, sometimes by experts — so that AI systems can learn from them or be tested against them., exampleexample, n.: A 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., featurefeature, n.: One 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., ground truthground truth, n.: The reference answers treated as correct when checking or training an AI system — for example, the confirmed diagnoses that a medical-imaging model's guesses are compared against., semi-supervised learningsemi-supervised learning, n.: Machine 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., supervised learningsupervised learning, n.: Teaching 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., inter-annotator agreementinter-annotator agreement, n.: A measure of how often different people labeling the same data give the same answer. Low agreement warns that the task is unclear or the labels are unreliable., label noiselabel noise, n.: Mistakes in the answers attached to training or test examples — a dog photo marked 'cat' — which can mislead what a model learns and distort how good it seems.