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AI and web data terms describe different layers of technology. Artificial intelligence (AI) is the broad field; machine learning (ML) is commonly treated as one part of it. A dataset is the collected material, while a model is the learned or programmed mechanism that turns inputs into outputs. On the web, cookies, browser storage, pixel tags, referrer URLs and server logs are separate ways of storing, sending or recording information.
This glossary groups the terms by what they describe and where they operate. Definitions attributed to Google, MDN or ITU-T reflect those publications’ scope; they are not automatically legal definitions or universal terminology.
AI foundations
Artificial intelligence (AI)
Artificial intelligence is the broad area of computing concerned with systems that perform tasks associated with human intelligence, such as recognizing patterns, understanding language, planning or making decisions. AI can use rules written by people, statistical methods, machine learning, or combinations of these approaches. Calling a feature “AI” does not by itself reveal how it works or how reliable it is.
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Machine learning (ML)
Machine learning is commonly described as a subfield of AI in Google’s Machine Learning Glossary. An ML system derives patterns or parameters from examples instead of requiring a programmer to specify every rule. Some organizations use “AI” and “ML” interchangeably, but the narrower ML meaning is useful when a system learns from data.
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| Term | What it describes | Typical output |
|---|---|---|
| AI | The broad field of intelligent-computing methods | A decision, action, generated text, image or other result |
| ML | A family of AI methods that learn patterns from data | A prediction, classification, ranking or generated result |
| Rule-based program | Explicit instructions written by people | The result specified by those rules |
Algorithm
An algorithm is a procedure for solving a problem or completing a task. Google’s Privacy & Terms key-terms glossary defines it as “A process or set of rules followed by a computer in performing problem-solving operations.” An algorithm can be simple, such as sorting names alphabetically, or part of a complex ML pipeline.
Automation
Automation means software performs a task with limited human intervention. It does not necessarily involve AI: a scheduled backup is automated but need not learn or interpret anything. AI may be used inside an automated workflow, such as routing support requests by topic.
Data, models and learning
Data
Data is recorded information that a computer can store or process. It may be numbers, text, images, audio, sensor readings, web events or database records. Data can be raw, cleaned, transformed or labeled; those stages affect what an ML system can learn.
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Dataset
A dataset is a collection of raw or prepared data. A spreadsheet of transactions, a folder of photographs or a table of website events can each be a dataset. Google’s ML terminology distinguishes the collected material (the dataset) from the model that processes it.
Feature
A feature is an input variable used by a model. In a fraud detector, transaction amount, location and time might be features. Text and images are often transformed into numerical features before many algorithms can process them.
Label
A label is the target answer attached to an example. “Spam” or “not spam” can label an email; a house’s sale price can label a property record. Labeled examples support supervised learning. Incorrect, inconsistent or ambiguous labels can limit the model even when the dataset is large.
Model
A model is a structure and set of parameters used to process inputs and produce outputs such as predictions. During training, those parameters are adjusted using data. After training, the model can be used for inference on new inputs. A model is not the same thing as the dataset that produced it.
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| Concept | Role | Example |
|---|---|---|
| Dataset | Collected examples or records | Past support messages and their categories |
| Feature | An input signal extracted from an example | Words, language or message length |
| Label | The desired answer for an example | “Billing” or “Technical support” |
| Model | Parameters and structure that map inputs to outputs | A classifier assigning a category to a new message |
Training
Training is the process of adjusting a model with data so that its outputs better match a chosen objective. The training data may be split into separate portions for fitting, tuning and final evaluation. Training does not guarantee that a model will perform well on situations absent from its examples.
Inference
Inference is a trained model’s use of new input to produce an output. Asking a language model to complete a sentence, scoring a transaction or detecting an object in a photograph are inference operations. Inference can run on a device, a server or a combination of both.
Supervised, unsupervised and reinforcement learning
- Supervised learning: learns from examples paired with labels, such as photographs marked “cat” or “dog.”
- Unsupervised learning: looks for structure in data without supplied target labels, such as grouping similar records.
- Reinforcement learning: learns through actions, feedback and a reward objective. The system seeks choices that improve its longer-term reward.
Validation and test data
A validation set helps select settings or compare model versions during development. A test set is held back for a final estimate of performance. Keeping evaluation data separate reduces the risk of tuning a model to the same examples used to judge it.
Overfitting and underfitting
Overfitting occurs when a model matches training examples or their noise too closely and performs worse on new data. Underfitting occurs when the model is too limited to capture useful patterns, even in its training examples. More data is not always the cure; better labels, a suitable model and representative examples may matter more.
Bias and fairness
In ML discussions, bias can mean systematic error or an uneven relationship between outputs and different groups. It can enter through unrepresentative data, labels, measurement choices or deployment conditions. A model can be accurate on average while producing poorer results for a subgroup, so evaluation should examine the populations and uses that matter.
Modern AI terms
Neural network
A neural network is a model made of connected computational layers whose parameters are learned from data. The connections transform an input through successive calculations. Neural networks are used for language, images, speech and many other tasks.
Deep learning
Deep learning generally refers to neural networks with multiple processing layers. “Deep” describes the layered structure, not a guarantee of quality. A deep model still depends on suitable data, objectives and evaluation.
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Generative AI
Generative AI produces new content such as text, code, images, audio or video in response to an input. The output is generated from patterns learned during training; it is not automatically a verified quotation or factual report.
Large language model (LLM)
An LLM is a language-focused model trained to calculate likely sequences of text. It can summarize, translate, answer questions or generate code, but fluent wording does not prove that a statement is correct. The model’s training, prompt and operating safeguards all affect the result.
Prompt
A prompt is the instruction or input supplied to a generative model. It can include a question, context, examples, constraints and a requested format. Clear requirements and relevant context usually make an output easier to evaluate, but a prompt cannot guarantee accuracy.
Token
A token is a unit a language model processes. A token may be a whole word, part of a word, punctuation or another text fragment, depending on the model’s tokenizer. Token limits affect how much input and output can fit in one request; they are not identical to word or character counts.
Embedding
An embedding is a numerical representation of an item such as text, an image or a user action. Items with related patterns may occupy nearby regions in the representation space. Embeddings are commonly used for search, recommendations and clustering rather than displayed directly to readers.
Web technology terms
MDN’s web glossary is designed to explain jargon, acronyms and abbreviations encountered in web documentation and related contexts. Its page lists a last modification date of July 11, 2025. The following terms describe different parts of a browser-server interaction.
Browser
A browser is software that requests web resources and presents them as pages or applications. It stores some information locally, executes permitted code and sends network requests to servers. Different browsers can implement standards or privacy controls differently.
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Website, web page and web application
A website is a collection of related web resources under a domain or service. A web page is an individual document or view. A web application is an interactive site that performs tasks in the browser, such as editing a document or managing an account.
URL
A URL (Uniform Resource Locator) identifies where a resource can be retrieved and may include a scheme such as HTTPS, a host, a path and query parameters. A referrer URL can reveal the page that led to a request, subject to browser and site policies.
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HTTP is the protocol used for exchanging web requests and responses. HTTPS is HTTP protected by encryption and server authentication through TLS. Encryption protects data in transit; it does not make a site trustworthy or prevent the site itself from recording information.
HTML, CSS and JavaScript
- HTML supplies a page’s structure and content.
- CSS controls presentation, layout and visual styling.
- JavaScript adds programmable behavior, such as responding to clicks or updating content without a full page reload.
API
An application programming interface (API) is a defined way for software components to request data or actions from one another. A web API commonly receives an HTTP request and returns structured data, often JSON. Authentication, permissions, rate limits and the API’s documentation determine what a caller can do.
Server
A server is a computer or software service that receives requests and returns resources or performs operations. A single website may use multiple servers for pages, databases, media, authentication and analytics.
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Cookie
Google’s Privacy & Terms key-terms glossary defines a cookie as: “A cookie is a small file containing a string of characters that is sent to your computer when you visit a website.” A site can send a cookie to a browser, which may return it on a later request so the site can recognize a browser or preserve a setting. Cookies can support sessions, preferences, security controls or measurement. Google’s wording is an accessible published definition, not a complete legal definition for every jurisdiction.
First-party and third-party cookie
A first-party cookie is associated with the site the browser is visiting. A third-party cookie is associated with a different site or service whose content is included in the page. Browser policies increasingly restrict third-party cookies, and the exact behavior depends on browser settings and applicable rules.
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Session and persistent cookie
A session cookie is generally removed when its browsing session ends, while a persistent cookie has an expiry or duration and can remain afterward. A cookie’s actual lifetime and security attributes are set by the site and interpreted by the browser.
Browser web storage
Browser storage lets a site keep key-value data on a device through browser APIs. localStorage normally persists until site data is cleared, while sessionStorage is scoped to a browsing session or tab context. Storage is separate from cookies: stored values are not automatically sent with every HTTP request.
Pixel tag
A pixel tag is a tiny, often invisible resource or code element used to signal that a page, message or feature was accessed. The request can record information such as time, browser characteristics or the referring page, depending on the implementation. A pixel tag is not the same mechanism as a cookie, although a service may use both.
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A referrer URL is the address of the page that led a browser to request another resource. Browsers can omit, reduce or alter referrer information through policy and privacy settings. A referrer may contain sensitive details if a site places them in a URL, so sites should avoid putting secrets in query strings.
Server log
A server log is a record created by a server while handling requests. It may include a timestamp, requested path, response status, network address, user-agent details and other operational fields. Logs support troubleshooting, security and traffic analysis, but retention and access practices vary by service.
| Mechanism or record | Where it exists | What it commonly does |
|---|---|---|
| Cookie | Browser and site response/request headers | Remembers an identifier, session or preference |
localStorage |
Browser-managed site storage | Persists site data without attaching it to every request |
sessionStorage |
Browser-managed session or tab storage | Keeps temporary site data for a session context |
| Pixel tag | Page, message or embedded resource request | Signals that content was viewed or loaded |
| Referrer URL | Request metadata | Indicates the preceding page when the browser sends it |
| Server log | Server-side records | Records requests and responses for operations or analysis |
Privacy and data-use language
Personal data
Personal data generally means information that relates to an identified or identifiable person, but the exact legal meaning depends on the jurisdiction and rule set. An identifier, location record or combination of seemingly ordinary fields may become personal data when it can be linked to a person.
Consent and choice
Consent is a person’s permission for a stated use of data. A banner, setting or opt-out control may implement a choice, but its legal effect depends on the service, purpose and applicable law. Reading a site’s privacy notice is necessary to understand what a particular control covers.
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Anonymization aims to remove the ability to link data back to a person, while pseudonymization replaces direct identifiers with a code that may still be linked using additional information. These words have specialized legal and technical meanings in some standards; do not treat a random identifier as proof that data is anonymous.
How the terms connect in a real workflow
- A website collects records such as page requests, form submissions or sensor events. Those records form a dataset.
- Features and, when available, labels are prepared from the dataset.
- An ML algorithm adjusts a model during training.
- Validation and test data help evaluate whether the model generalizes beyond its training examples.
- During inference, an application sends new input to the model through code or an API.
- The web application may use cookies or browser storage to preserve a session, while pixel requests and server logs record separate events.
- People interpret the output, check its limitations and decide whether an automated action is appropriate.
This sequence explains why terms should not be swapped casually: the dataset is not the model, a cookie is not browser storage, and a server log is not a pixel tag. Each names a different object, location or operation.
Standards and source context
Definitions can vary between everyday speech, engineering documentation and formal standards. ITU-T Y Supplement 97, published in November 2025, compiles machine-learning terminology and points to definitions from external standards, including ISO/IEC. When a project requires contractual, regulatory or standards compliance, use the cited standard’s exact scope rather than relying on a general glossary meaning.
Google’s machine-learning and privacy glossaries are practical explanatory references. MDN focuses on web terminology, and web.dev describes its AI glossary as complementary to Google’s ML glossary. These resources help decode documentation, but a product’s own technical specification, privacy notice or API contract controls how that product actually behaves.
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