A/B Testing (Models)
- Definition
- A/B testing of models is a controlled experiment that splits live traffic between two model versions and compares their business outcomes under identical conditions before one version is adopted for everyone.
Showing 20 of 65 terms
Accountability (AI)
- Definition
- Accountability in AI means that a named person or organization is answerable for an AI system's decisions and effects, with the authority and the records needed to act on that responsibility.
Accuracy
- Definition
- Accuracy is the share of a classifier's predictions that were correct, counting all classes together, which makes it easy to understand and misleading whenever one class is rare.
Activation Function
- Definition
- An activation function is the nonlinear operation applied to each neuron's output in a neural network, which lets the network learn patterns that a straight line cannot describe.
Active Learning
- Definition
- Active learning is a training approach in which the model selects the unlabeled examples it is least sure about and asks a human to label those first.
Adaptable AI
- Definition
- Adaptable AI is an AI system that adjusts its behavior, strategy, or underlying models in response to changing conditions without being reprogrammed, using feedback from its own results. It depends on monitoring,…
Adversarial Example
- Definition
- An adversarial example is an input that has been deliberately altered, often in ways a person cannot notice, so that a machine learning model gets it wrong, frequently with high confidence.
Adversarial Machine Learning
- Definition
- Adversarial machine learning is the study of attacks that manipulate machine learning systems through their training data, inputs, or outputs, and of the defenses that make those systems harder to fool, corrupt,…
Affective AI (Emotion AI)
- Definition
- Affective AI, also called emotion AI, is technology that infers a person's emotional state from signals such as voice tone, facial expression, and word choice. Its use carries privacy and consent obligations.
Agent Assist
- Definition
- Agent assist is AI software that supports a human customer-service representative during a live contact by transcribing the conversation, surfacing answers, and drafting notes. Here the agent is the human representative.
Agent Memory
- Definition
- Agent memory is the stored information an AI agent carries beyond a single prompt, from the current conversation to facts, preferences, and past outcomes it retrieves in later sessions.
Agent Observability
- Definition
- Agent observability is the practice of recording and inspecting what an AI agent did during a task, including each model call, tool call, intermediate result, cost, and failure, so teams can debug…
Agent Runtime
- Definition
- An agent runtime is the software environment that executes an AI agent: it runs the reasoning loop, calls the model, dispatches tool calls, manages state and memory, and enforces limits and permissions.
Agent Skills
- Definition
- Agent skills are packaged instructions, examples, reference files, and tools that an AI agent loads on demand when a task calls for them, so it can perform a specific kind of work…
Agentic AI
- Definition
- Agentic AI is AI that pursues a goal by planning steps, calling tools, observing results, and adjusting until the task is done, with limited human input. It extends generative AI from answering…
Agentic Process Automation
- Definition
- Agentic process automation applies AI agents to business processes, so the automation can read unstructured inputs, choose the next step, act across systems, and work the exceptions that rule-based tools send to…
Agentic RAG
- Definition
- Agentic RAG is a form of retrieval-augmented generation in which an AI agent decides what to search, runs several retrieval steps across sources, checks the results, and retries until it can answer.
Agentic Workflow
- Definition
- An agentic workflow is a business process in which an AI agent carries out some steps on its own, choosing actions and calling tools, while fixed rules and human checkpoints govern the…
AI Accelerator (NPU, VPU)
- Definition
- An AI accelerator is a specialized chip or chip section built to run neural network inference efficiently, such as the neural processing units (NPUs) in laptops and phones and the vision processing…
AI Acceptable Use Policy
- Definition
- An AI acceptable use policy is the written rule set that tells staff which AI tools they may use, with which data, for which tasks, and who approves exceptions.
AI Adoption
- Definition
- AI adoption is the extent to which people and processes in a company actually use AI systems in daily work, measured by who uses them, how often, for which tasks, and with…
AI Agent Framework
- Definition
- An AI agent framework is a software library or platform that supplies the building blocks for agents, including the reasoning loop, tool definitions, memory, and connections to models, so teams do not…
AI Agents
- Definition
- AI agents are software systems that pursue a goal by planning steps, calling tools, observing results, and adjusting until the task is done or a human steps in. Unlike a chatbot, an…
AI Alignment
- Definition
- AI alignment is the effort to make an AI system's goals and behavior match what its operators and users actually intend, including their values and constraints, rather than a literal or convenient…
AI Audit
- Definition
- An AI audit is a structured, evidence-based review of an AI system that checks whether it does what was claimed, follows policy and law, and has controls that actually work.
AI Audit Log
- Definition
- An AI audit log is a tamper-resistant record of every action an AI system took, including prompts, model responses, tool calls, data accessed, and human approvals, kept so the activity can be…
AI Center of Excellence
- Definition
- An AI center of excellence is a small central team that sets AI standards, runs shared tools and infrastructure, and helps business units deliver AI projects without each one rebuilding the same…
AI Component of a System
- Definition
- In SOC 1 and SOC 2 reporting, the AI component of a system is the part of a service organization's system that uses AI, including its models, data, configuration, and the controls…
AI Content Watermarking
- Definition
- AI content watermarking embeds a hidden, machine-readable signal in generated text, images, audio, or video so that the output can later be identified as AI-made.
AI Gateway
- Definition
- An AI gateway is a proxy that sits between applications and model endpoints to enforce authentication, routing, rate limits, logging, and data policies for every request to an AI model.
AI Governance
- Definition
- AI governance is the set of policies, roles, controls, and records a company uses to decide which AI systems it runs, how they are built or bought, and who answers for their…
AI in Customer Service
- Definition
- AI in customer service is the use of chatbots, voice assistants, agent assist tools, and AI agents to answer customer questions, resolve routine requests, and support human representatives on harder ones.
AI in Finance
- Definition
- AI in finance is the use of machine learning and language models in banking, lending, insurance, collections, and corporate finance to score risk, detect crime, and automate document-heavy work.
AI in Healthcare
- Definition
- AI in healthcare is the use of machine learning and language models in clinical, administrative, and revenue cycle work, under privacy rules that govern where patient data may be processed.
AI in Legal Services
- Definition
- AI in legal services is the use of language models and document analysis in law firms and corporate legal departments for research, contract review, discovery, drafting, and matter management under strict confidentiality.
AI in Manufacturing
- Definition
- AI in manufacturing is the use of machine learning, computer vision, and language models on the plant floor and in planning to improve quality, uptime, scheduling, and the use of materials and…
AI Incident
- Definition
- An AI incident is an event in which an AI system's use or malfunction causes harm, such as an unfair decision about a person, a leak of confidential data, or an unauthorized…
AI Inventory
- Definition
- An AI inventory is a maintained register of every AI system a company builds, buys, or uses, recording each one's owner, purpose, data, connected systems, and risk tier.
AI Literacy
- Definition
- AI literacy is the knowledge and judgment people need to use AI systems well in their role. The EU AI Act requires providers and deployers to take measures that support it among…
AI Maturity Model
- Definition
- An AI maturity model is a framework that describes the stages a company moves through as it learns to use AI and scores the company on dimensions such as data, skills, governance,…
AI Model
- Definition
- An AI model is a mathematical function, learned from data, that turns inputs into predictions, classifications, or generated content. Its learned parameters are the stored result of training and the part that…
AI Ownership
- Definition
- AI ownership means running AI on model weights, infrastructure, and data paths a company controls, instead of renting a provider's model per token. It suits companies with sensitive data or steady volume.
AI Readiness
- Definition
- AI readiness is the degree to which a company has the data, infrastructure, security controls, skills, and governance needed to put a specific AI use case into production and keep it running.
AI Red Teaming
- Definition
- AI red teaming is the structured practice of attacking an AI system before and after deployment to find the prompts, inputs, and workflows that make it fail, leak data, or cause harm.
AI Risk Classification (EU AI Act)
- Definition
- AI risk classification under the EU AI Act is the process of assigning an AI system to one of four tiers, prohibited, high, limited, or minimal risk, which determines the legal duties…
AI Risk Management
- Definition
- AI risk management is the ongoing process of identifying what could go wrong with an AI system, judging how likely and how serious each harm is, and deciding what to do about…
AI Safety
- Definition
- AI safety is the practice of preventing AI systems from causing harm, whether through errors, misuse, unintended behavior, or loss of human control, using testing, constraints, monitoring, and oversight throughout the system's…
AI Security
- Definition
- AI security is the practice of protecting AI systems, including models, data, prompts, and agents, from attack and misuse, and of preventing a manipulated AI system from harming the business that runs…
AI Strategy
- Definition
- An AI strategy is a company's plan for where AI will create value, which use cases come first, how models will be sourced and governed, and what data and skills the plan…
AI System
- Definition
- An AI system is a deployed product made of one or more AI models plus the interface, data pipelines, integrations, and controls around them. Regulation such as the EU AI Act applies…
AI Value Tracking
- Definition
- AI value tracking is the ongoing measurement of whether a deployed AI system delivers the benefits in its business case, comparing outcomes, usage, and running costs against a baseline taken before launch.
Air-Gapped AI Deployment
- Definition
- An air-gapped AI deployment runs models on a network with no connection to the internet or to other networks, so data cannot leave and updates arrive only through controlled physical transfer.
Algorithm
- Definition
- An algorithm is a precise, finite sequence of steps that turns an input into an output. In AI, algorithms both train models from data and use trained models to make predictions or…
Anchor Box
- Definition
- An anchor box is a preset rectangle of a fixed size and shape that an object detection model uses as a starting point, adjusting it to fit the objects it finds.
Annotation Format
- Definition
- An annotation format is the file structure used to store image labels, such as boxes, masks, and classes, and to link each label to its image, typically in JSON, XML, or CSV.
Anomaly Detection
- Definition
- Anomaly detection is the use of statistics and machine learning to find records, events, or readings that differ markedly from normal patterns, such as a fraudulent payment or a machine that is…
Anti-Money Laundering (AML)
- Definition
- Anti-money laundering (AML) is the set of laws, controls, and monitoring systems that financial institutions use to detect and report attempts to disguise the origin of illicit funds. AI now does much…
Application Programming Interface (API)
- Definition
- An application programming interface (API) is a defined set of requests and responses that lets one program use another's functions or data without access to its internal code.
Approval Workflow
- Definition
- An approval workflow is a control that pauses an AI agent before a consequential action, presents the proposed change to a named person, and proceeds only when that person approves it.
Artificial General Intelligence (AGI)
- Definition
- Artificial general intelligence (AGI) is a hypothetical AI that could learn and perform any intellectual task a person can, across domains, without being built for each one. No deployed system meets that…
Artificial Intelligence (AI)
- Definition
- Artificial intelligence (AI) is software that performs tasks which normally require human judgment, such as recognizing patterns, understanding language, and choosing actions. In business it ranges from fraud scoring to agents working…
Asset Performance Management
- Definition
- Asset performance management (APM) is the practice of combining equipment condition data, maintenance history, and risk analysis to decide what to maintain, when, and how. The term also covers the software that…
Asset Reliability
- Definition
- Asset reliability is the probability that a piece of equipment performs its required function without failure for a stated period under stated conditions, and the engineering work that keeps that probability high.
Attention Mechanism
- Definition
- An attention mechanism lets a neural network decide how much weight to give each part of its input when producing each part of its output, and it is the central operation inside…
AUC (Area Under the Curve)
- Definition
- AUC, the area under the ROC curve, is a number from 0 to 1 that measures how well a classifier ranks positive cases above negative ones across all thresholds, where 0.5 means…
Auditable AI
- Definition
- Auditable AI is an AI system built so that an independent reviewer can reconstruct what it did, why, with which data and model version, and who approved the result. It rests on…
Augmented Reality (AR)
- Definition
- Augmented reality (AR) overlays digital information, such as labels, instructions, or 3D models, on a live view of the physical world through a phone, tablet, smart glasses, or headset.
Autoencoder
- Definition
- An autoencoder is a neural network trained to compress its input into a compact representation and then reconstruct it, which makes it useful for anomaly detection, denoising, and dimensionality reduction.
Automated Decision-Making
- Definition
- Automated decision-making is the use of software to reach decisions about people without meaningful human involvement. GDPR Article 22 restricts such decisions when they have legal or similarly significant effects on a…
Automated Machine Learning (AutoML)
- Definition
- Automated machine learning (AutoML) is software that automates the steps of building a model, including data preparation, algorithm selection, hyperparameter tuning, and evaluation.
Automation Bias
- Definition
- Automation bias is the human tendency to accept an automated system's output without checking it, even when the output is wrong and the person has the evidence to catch it.
Autotelic AI
- Definition
- Autotelic AI is a research approach in which agents generate, choose, and pursue their own goals, driven by curiosity or learning progress instead of by tasks a person assigns.
Backpropagation
- Definition
- Backpropagation is the algorithm that computes how much each weight in a neural network contributed to the error, so gradient descent knows how to adjust it.
Baseline Model
- Definition
- A baseline model is a simple reference model, such as predicting the average or the most common outcome, used to judge whether a more complex model adds real value.
Batch
- Definition
- A batch is a group of data examples processed together in one step, either during training, where it drives a single parameter update, or during inference, where it improves throughput.
Batch Inference
- Definition
- Batch inference is the practice of running a model over a large set of records at once on a schedule, such as scoring every account overnight, instead of answering requests one at…
Batch Size
- Definition
- Batch size is the number of training examples a model processes before updating its parameters once, a hyperparameter that trades off memory use, training speed, and model quality.
Bayesian Network
- Definition
- A Bayesian network is a probabilistic model that represents variables as nodes in a directed graph and encodes how each depends on its causes, so evidence about some variables updates beliefs about…
Benchmark
- Definition
- A benchmark is a standardized set of tasks and a scoring method used to compare AI models against each other, giving a repeatable measure of capability on a defined problem.
BERT
- Definition
- BERT (Bidirectional Encoder Representations from Transformers) is a language model from Google that reads text in both directions at once and became the standard base for text classification, entity extraction, and search…
Bias (AI)
- Definition
- Bias in AI is a systematic error in a model's outputs. It can be statistical, a consistent gap between predictions and reality, or social, a pattern that treats groups of people unequally.
Bias Detection and Fairness Auditing
- Definition
- Bias detection and fairness auditing are the methods used to find out whether a model treats groups of people differently and to document whether those differences are justified.
Bias-Variance Tradeoff
- Definition
- The bias-variance tradeoff is the balance between a model that is too simple to capture the pattern and one so flexible that it chases noise in the training data.
Binary Classification
- Definition
- Binary classification is the machine learning task of sorting each input into one of exactly two classes, such as fraud or legitimate, and it is judged by how it balances the two…
Biohybrid Intelligence
- Definition
- Biohybrid intelligence combines living biological material, such as cultured neurons or brain organoids, with electronics and AI software to process information. It remains an early research field.
Black Box Model
- Definition
- A black box model is a model whose inputs and outputs are visible but whose internal reasoning a person cannot follow, because of its complexity or because its vendor keeps the details…
Boosting
- Definition
- Boosting is an ensemble technique that trains a sequence of simple models, each concentrating on the examples its predecessors got wrong, and combines them into one accurate model.
Bounding Box
- Definition
- A bounding box is the rectangle drawn around an object in an image to mark its position and size. Detection models predict them, and annotators draw them to create training data.
Brute Force Search
- Definition
- Brute force search is a problem-solving method that tries every possible candidate solution and checks each one, guaranteeing the best answer at a cost in time that grows explosively with the size…
Business Intelligence (BI)
- Definition
- Business intelligence (BI) is the practice and tooling for turning operational data into reports, dashboards, and analyses that tell managers what happened and how the business is performing.
Business Value of AI
- Definition
- The business value of AI is the measurable gain an AI system delivers in cost, revenue, risk, or speed, net of what the company spends to build, run, and maintain it.
Calibration
- Definition
- Calibration is the degree to which a model's confidence scores match real outcomes, so that predictions made with a score of 0.8 turn out to be correct about eight times in ten.
Canary Deployment
- Definition
- A canary deployment releases a new model or software version to a small share of real traffic first, compares its behavior with the current version, and widens the rollout only when the…
Canonical Schema
- Definition
- A canonical schema is one agreed, system-neutral definition of a business entity, such as a customer, order, or invoice, that every connected system's data is translated into before it is shared or…
Canonical Transform
- Definition
- A canonical transform is the mapping logic that converts data from one source system's native format into a canonical schema, or from the canonical schema back into a target system's format.
Central Processing Unit (CPU)
- Definition
- A central processing unit (CPU) is the general-purpose processor in every computer, built to run a few tasks very quickly in sequence, and it handles most of the work around an AI…
Chain-of-Thought Prompting
- Definition
- Chain-of-thought prompting asks a language model to write out its intermediate reasoning steps before giving a final answer, which improves accuracy on arithmetic, logic, and multi-step business rules.
Champion-Challenger Testing
- Definition
- Champion-challenger testing is a continuous method for improving a production model by running candidate models alongside the current one, comparing their results on the same cases, and promoting a challenger that proves…
Change Data Capture (CDC)
- Definition
- Change data capture (CDC) is a technique that detects inserts, updates, and deletes in a source database as they happen and forwards only those changes to downstream systems.
Change Management for AI
- Definition
- Change management for AI is the planned work of preparing people, roles, and procedures for a new AI system so that the change is understood, accepted, and still in place months after…
Chatbot
- Definition
- A chatbot is a software program that holds a text or voice conversation with a person to answer questions or complete simple tasks. Modern chatbots run on large language models grounded in…
ChatGPT
- Definition
- ChatGPT is a conversational AI assistant from OpenAI, released in November 2022, that lets people interact with GPT language models through a chat interface on the web, desktop, and mobile.
Checkpoint
- Definition
- A checkpoint is a saved snapshot of a model's weights, and often its optimizer state, taken at a point during training so the run can be resumed, compared, or rolled back.
Chunking
- Definition
- Chunking is the step in a retrieval-augmented generation pipeline that splits documents into smaller passages so they can be embedded, searched, and supplied to a language model within its context window.
Churn Prediction
- Definition
- Churn prediction is the use of a model to estimate which customers are likely to leave, or sharply cut their business, within a set period, so a team can act before they…
Citizen Developer
- Definition
- A citizen developer is an employee outside the IT department who builds applications, automations, or AI agents for their own team using no-code or low-code tools that IT has approved.
Class (Machine Learning)
- Definition
- A class is one of the discrete categories a classification model assigns to each input, such as approved or declined for a loan application, or invoice, contract, and other for a document…
Class Balance
- Definition
- Class balance describes how evenly the categories a model predicts are represented in the training data; class imbalance, where one outcome is rare, distorts both training and accuracy.
Classic Machine Learning
- Definition
- Classic machine learning is the family of established statistical algorithms, such as linear and logistic regression, decision trees, support vector machines, and naive Bayes, that remain the best choice for most tabular…
Classification
- Definition
- Classification is the supervised learning task of assigning each input to one of a set of predefined categories, such as approve or decline, spam or legitimate, or one of several ticket queues.
Classifier
- Definition
- A classifier is a trained machine learning model that assigns an input to one of a set of categories, usually by producing a score for each category and selecting the highest.
Classifier Performance
- Definition
- Classifier performance is the set of measures that describe how well a classification model separates its classes, including accuracy, precision, recall, F1 score, and the area under the ROC curve.
Claude
- Definition
- Claude is a family of large language models and a conversational AI assistant developed by Anthropic, first released in March 2023 and offered through a chat product and an API.
Closed-Source Model
- Definition
- A closed-source model is an AI model whose weights and training details stay private, so customers can use it only through the provider's hosted API or product under the provider's terms.
Cloud Computing
- Definition
- Cloud computing is the delivery of servers, storage, networking, and software over the internet from a provider's data centers, rented on demand and billed by use instead of bought outright.
Cloud GPU
- Definition
- A cloud GPU is a graphics processing unit rented by the hour or by reservation from a cloud provider, giving an organization AI hardware without buying or housing it.
Clustering
- Definition
- Clustering is an unsupervised machine learning technique that sorts records into groups so that members of each group are more similar to one another than to members of other groups.
Coding Agent
- Definition
- A coding agent is an AI agent that writes, runs, and tests software in a code repository, working through multi-step programming tasks and returning the finished change for a developer to review.
Coefficient of Determination (R-Squared)
- Definition
- The coefficient of determination, or R-squared, measures how much of the variation in an outcome a regression model explains, where 1 means a perfect fit and 0 means no better than predicting…
Cognitive Bias
- Definition
- A cognitive bias is a predictable pattern in human judgment that departs from logic or evidence. In AI work, these biases enter training data through human decisions and shape how people use…
Cohen’s Kappa
- Definition
- Cohen's kappa is a statistic that measures how much two raters agree on categorical labels beyond the agreement chance alone would produce. It is widely used to check the consistency of training…
Collaborative AI
- Definition
- Collaborative AI is an approach in which AI systems work alongside people or other AI systems on a shared task, each handling the steps it does best and passing work back and…
Compute
- Definition
- Compute is the processing capacity, measured in processor time and operations per second, that an AI system consumes to train a model or to produce answers from a trained one.
Computer Use (AI)
- Definition
- Computer use is the ability of an AI agent to operate software as a person does, reading the screen and responding with mouse clicks and keystrokes, so it can work in applications…
Computer Vision
- Definition
- Computer vision is the field of AI that lets software extract meaning from images and video, such as identifying objects, reading text, or measuring parts. It drives inspection, document capture, and safety…
Computer Vision Platform
- Definition
- A computer vision platform is software that covers the full life cycle of vision models: collecting and labeling images, training, deploying to cameras or servers, and monitoring results in production.
Concept Drift
- Definition
- Concept drift is a change in the relationship between a model's inputs and the outcome it predicts, so that the same inputs now lead to a different result than they did in…
Condition Monitoring
- Definition
- Condition monitoring is the continuous or periodic measurement of an asset's health, such as vibration, temperature, or oil quality, to detect developing faults before they cause a breakdown.
Confidence Score
- Definition
- A confidence score is a number a model attaches to each prediction to indicate how sure it is, usually on a scale from zero to one, so that uncertain outputs can be…
Confidence Threshold
- Definition
- A confidence threshold is the cutoff score above which a model's prediction is accepted automatically and below which it is rejected or sent to a person for review.
Confidential Computing
- Definition
- Confidential computing protects data while it is being processed by running workloads in hardware-isolated, encrypted memory that the host operating system, the cloud operator, and other tenants cannot read.
Conformity Assessment
- Definition
- A conformity assessment is the procedure by which the provider of a high-risk AI system demonstrates, before placing it on the EU market, that the system meets the requirements of the EU…
Confusion Matrix
- Definition
- A confusion matrix is a table that counts a classifier's correct and incorrect predictions for each class, showing which classes the model confuses with which.
Container
- Definition
- A container is a packaged unit of software that bundles an application with its libraries and settings so it runs the same way on any machine. In AI work, containers carry models,…
Content Moderation
- Definition
- Content moderation is the review of user-generated or AI-generated content to decide whether it is allowed, using classifiers, written policies, and human reviewers for the cases a model cannot settle.
Content Provenance
- Definition
- Content provenance is a verifiable record of where a piece of digital content came from and how it was edited, attached to the file as signed metadata.
Context Window
- Definition
- The context window is the maximum number of tokens a language model can take in at once, covering the system prompt, the conversation so far, any retrieved documents, and the answer it…
Continual Learning
- Definition
- Continual learning is the ability of a model to keep learning from new data over time while retaining what it already knows, instead of being retrained from scratch or frozen after one…
Continuous Batching
- Definition
- Continuous batching is an inference server technique that adds new requests to the running batch and removes finished ones at every generation step, keeping the GPU busy and raising throughput.
Conversational AI
- Definition
- Conversational AI is the set of technologies that lets software hold a natural, multi-turn dialogue with people by text or voice, combining speech recognition, language understanding, dialogue management, and back-end integration.
Convolutional Filter
- Definition
- A convolutional filter is a small grid of weights that slides across an image to detect a local pattern such as an edge or texture. Stacks of learned filters form a convolutional…
Convolutional Neural Network (CNN)
- Definition
- A convolutional neural network is a deep learning model that scans images with small learned filters to detect edges, textures, and shapes, making it the standard architecture for visual recognition tasks.
Copilot (AI Assistant)
- Definition
- A copilot is an AI assistant embedded in a work application that drafts, summarizes, and answers questions from the content the user has open, while the person keeps control of the final…
Correlation
- Definition
- Correlation is a statistical measure of how closely two variables move together, usually expressed as a coefficient between minus one and plus one. It shows association but does not show that one…
Counterfactual Explanation
- Definition
- A counterfactual explanation states the smallest change to a model's inputs that would have produced a different outcome, for example what would have turned a declined application into an approved one.
Cross-Functional AI Teams
- Definition
- Cross-functional AI teams are small project groups that combine a business owner, frontline experts, engineers, IT security, and compliance staff to take one AI use case into production and keep it running.
Cross-Validation
- Definition
- Cross-validation is a method for estimating how well a model will perform on new data by training and testing it several times on different splits of the same dataset. It is most…
Dashboard
- Definition
- A dashboard is a screen that displays a chosen set of metrics and charts, refreshed from live or scheduled data, so a team can monitor performance and spot problems at a glance.
Data
- Definition
- Data is recorded facts, measurements, and events, stored so that people and software can use them. In a business it includes orders, invoices, sensor readings, documents, and system logs.
Data Annotation
- Definition
- Data annotation is the work of marking raw data with the labels a model should learn, such as drawing boxes around objects in images or tagging clauses in contracts.
Data Catalog
- Definition
- A data catalog is a searchable inventory of an organization's data assets that records what each dataset contains, where it lives, who owns it, how fresh it is, and whether it can…
Data Classification
- Definition
- Data classification is the process of labeling data by sensitivity, such as public, internal, confidential, or restricted, so that handling rules and access controls can be applied consistently.
Data Cleansing
- Definition
- Data cleansing is the process of finding and correcting errors, duplicates, inconsistencies, and gaps in a dataset so that reports, systems, and AI models work from accurate records.
Data Contract
- Definition
- A data contract is a formal agreement between the team that produces a dataset and the teams that consume it, specifying the schema, meaning, quality rules, and freshness the producer commits to.
Data Dictionary
- Definition
- A data dictionary is a reference document or catalog that lists each data element in a system, with its name, definition, type, allowed values, source, and owner, so people interpret data consistently.
Data Drift
- Definition
- Data drift is a change in the makeup of the data a model receives in production, such as shifted averages or new categories, compared with the data it was trained on.
Data Engineer and Data Architect
- Definition
- A data architect designs how an organization's data is structured, stored, and connected, and a data engineer builds and runs the pipelines that move it. An AI program needs both roles.
Data Exfiltration
- Definition
- Data exfiltration is the unauthorized transfer of data out of an organization, whether by an outside attacker, a malicious insider, or an AI agent manipulated into sending information to an external destination.
Data Fabric
- Definition
- A data fabric is an architecture that connects data across many systems and locations through a shared layer of metadata, integration, and access control, without first moving it into one store.
Data Format
- Definition
- A data format is the agreed arrangement of characters or bytes that tells software how to read a file or message. CSV, JSON, XML, Parquet, PDF, and EDI are common formats in…
Data Governance
- Definition
- Data governance is the set of roles, policies, and processes that decides who owns each kind of data, who may use it and how, and how its quality and security are maintained.
Data Granularity
- Definition
- Data granularity is the level of detail at which data is recorded or summarized, from individual transactions and sensor readings up to daily, monthly, or regional totals.
Data Historian
- Definition
- A data historian is a time-series database that records readings from industrial equipment and control systems at high speed and keeps them for years, forming the raw material for maintenance and energy…
Data Image
- Definition
- A data image is a unified, continuously updated representation of an organization's data, assembled from many source systems into one consistent model that reports, machine learning models, and AI agents can read.
Data Integration Architecture
- Definition
- Data integration architecture is the overall design for how data moves between an organization's systems, covering which systems are sources, which consume data, what patterns carry it, and where it is transformed…
Data Integration Design
- Definition
- Data integration design is the detailed specification for a particular data flow between systems, stating what data moves, how fields map and transform, when it runs, and how failures are handled.
Data Labels
- Definition
- Data labels are the known answers attached to training examples, such as "fraud" on a transaction or "defect" on a photo, that a supervised learning model learns to reproduce.
Data Lake
- Definition
- A data lake is a central store that holds raw data of any type, from database tables to log files and documents, in its original form until someone needs to analyze it.
Data Lakehouse
- Definition
- A data lakehouse is a data platform that stores all data as open files in low-cost object storage while adding the tables, transactions, and governance of a data warehouse on top.
Data Lineage
- Definition
- Data lineage is the documented path data takes from its original source through every transformation, system, and calculation to the report, dashboard, or AI model that uses it.
Data Loss Prevention (DLP)
- Definition
- Data loss prevention (DLP) is a set of tools and policies that detect sensitive data in motion or at rest and block or flag attempts to move it outside approved boundaries.
Data Mesh
- Definition
- A data mesh is an organizational approach to analytics in which each business domain owns and publishes its own data as a product, under shared standards and a common platform.
Data Mining
- Definition
- Data mining is the process of discovering patterns, relationships, segments, and anomalies in large existing datasets using statistics and machine learning, usually to explain behavior rather than to serve live predictions.
Data Model
- Definition
- A data model describes the things a business keeps records about, the facts stored for each, and how those things relate, giving databases, reports, and AI tools a shared structure to follow.
Data Observability
- Definition
- Data observability is the continuous monitoring of data pipelines and tables for freshness, volume, schema changes, and anomalies, so that bad or missing data is detected before anyone acts on it.
Data Orchestration
- Definition
- Data orchestration is the coordination of the many jobs in a data pipeline, deciding when each step runs, what it depends on, and what happens when one fails.
Data Persistence
- Definition
- Data persistence is the storage of data so that it survives after the program, session, or server that created it stops, usually by writing it to a database, disk, or object storage.
Data Pipeline
- Definition
- A data pipeline is an automated sequence of steps that moves data from where it is produced to where it is used, transforming, validating, and loading it along the way.
Data Poisoning
- Definition
- Data poisoning is an attack that corrupts the data an AI system learns from or retrieves, so that the model later gives wrong answers, hides a backdoor, or favors the attacker.
Data Quality
- Definition
- Data quality is how well data fits its intended use in reports, transactions, and AI models, measured by whether it is accurate, complete, consistent, timely, valid, and free of duplicates.
Data Residency
- Definition
- Data residency is the requirement that specific data be stored and processed within a defined geographic location, usually to satisfy a law, regulation, or customer contract.
Data Science
- Definition
- Data science is the discipline of drawing knowledge, explanations, and predictions from data by combining statistics, programming, machine learning, and an understanding of the business domain.
Data Silo
- Definition
- A data silo is a store of business data that one team or system controls and that other parts of the company cannot easily access, combine, or reconcile with their own.
Data Sovereignty
- Definition
- Data sovereignty is the principle that data is subject to the laws and governance of the country where it is collected or stored, and the practice of keeping control of it accordingly.
Data Validation
- Definition
- Data validation checks data against defined rules before it is stored or used, so that incomplete, malformed, or contradictory records are stopped before they reach reports, models, or other systems.
Data Visualization
- Definition
- Data visualization is the presentation of data as charts, maps, and other graphics so that patterns, trends, and outliers can be seen at a glance and acted on by people who do…
Data Warehouse
- Definition
- A data warehouse is a database designed for reporting and analysis, holding cleaned, structured data integrated from operational systems and organized so business questions can be answered quickly and consistently.
Data-Driven AI
- Definition
- Data-driven AI learns its behavior from patterns in historical data rather than from rules written by experts. Machine learning and deep learning are data-driven; its counterpart is knowledge-driven AI built on explicit…
Dataset
- Definition
- A dataset is a defined collection of records assembled for a purpose, such as training or testing a machine learning model, with a known source, scope, and structure.
Datasheets for Datasets
- Definition
- Datasheets for datasets are structured documents that record why a dataset was built, what it contains, how it was collected and labeled, and which uses it does and does not support.
Decision Intelligence
- Definition
- Decision intelligence is a discipline that designs business decisions explicitly, linking the available actions to expected outcomes through data, models, and rules, then tracking results to improve the next decision.
Decision Support System
- Definition
- A decision support system (DSS) is software that helps people make decisions by gathering relevant data, applying models or rules, and presenting options or recommendations, while a person keeps the final choice.
Decision Traceability
- Definition
- Decision traceability is the ability to reconstruct, after the fact, how an AI system reached a specific decision: the inputs it saw, the rules or model it applied, the steps it took,…
Decision Tree
- Definition
- A decision tree is a model that predicts an outcome by asking a sequence of yes-or-no questions about a record, following the answers down branches until it reaches a leaf that holds…
Deep Learning
- Definition
- Deep learning is machine learning with neural networks that have many layers, which learn their own features from raw data such as images, audio, and text instead of relying on hand-built inputs.
Deep Learning Framework
- Definition
- A deep learning framework is a software library that supplies the building blocks for defining, training, and running neural networks, handling the underlying math and GPU acceleration so developers describe the model…
Deep Neural Network (DNN)
- Definition
- A deep neural network is a neural network with several hidden layers between input and output, which allows it to learn layered representations of complex data such as images, audio, and text.
Deepfake
- Definition
- A deepfake is synthetic audio, video, or imagery generated by AI to depict a real person saying or doing something they never did, usually convincingly enough to deceive a viewer.
Demand Forecasting
- Definition
- Demand forecasting is the estimation of how much of a product or service customers will want in a future period, by item, location, and time, so that purchasing, production, and staffing can…
Descriptive Analytics
- Definition
- Descriptive analytics is the analysis of historical data to summarize what happened, using counts, totals, averages, and trends presented in reports and dashboards.
Deterministic Algorithm
- Definition
- A deterministic algorithm is a procedure that always produces the same output from the same input by following the same steps. Tax calculations, pricing rules, and most traditional business software work this…
Diagnostic Analytics
- Definition
- Diagnostic analytics is the examination of historical data to explain why an outcome occurred, by drilling into segments, comparing periods, and testing which factors moved together with the result.
Differential Privacy
- Definition
- Differential privacy is a mathematical guarantee that a statistic or model changes almost imperceptibly whether or not any one person's record is included, which bounds what anyone can learn about that person…
Digital Disruption
- Definition
- Digital disruption is a shift in a market caused by digital technology or a digital business model that changes how customers buy and makes established products, channels, or pricing less competitive.
Digital Transformation
- Definition
- Digital transformation is the redesign of a company's processes, customer interactions, and decisions around software and data, so that work runs through connected systems instead of paper, spreadsheets, and manual handoffs.
Digital Twin
- Definition
- A digital twin is a software model of a physical asset, process, or facility, kept current with sensor and operational data, so planners can monitor it, test changes, and predict its behavior…
Dimensionality Reduction
- Definition
- Dimensionality reduction is a set of techniques that cut the number of variables describing each record while keeping most of the useful information, which speeds up models, filters noise, and makes data…
Direct Preference Optimization (DPO)
- Definition
- Direct preference optimization (DPO) is a method for aligning a language model to human preferences by training directly on pairs of preferred and rejected responses, without a separate reward model.
Discriminative Model
- Definition
- A discriminative model is a machine learning model that learns the boundary between classes or the direct mapping from inputs to a label, without modeling how the input data itself is generated.
Document Automation
- Definition
- Document automation is software that assembles contracts, letters, proposals, and reports from approved templates, business rules, and system data, and can use a language model to draft the passages that vary.
Domain Adaptation
- Definition
- Domain adaptation is the set of techniques for making a model trained on data from one setting perform well on data from a different but related setting.
DORA (Digital Operational Resilience Act)
- Definition
- DORA, the Digital Operational Resilience Act, is the EU regulation that requires financial entities to manage ICT risk, report incidents, test resilience, and control the third-party technology providers they depend on, including…
DPIA and FRIA
- Definition
- A DPIA is the GDPR's data protection impact assessment for high-risk processing of personal data, and a FRIA is the EU AI Act's fundamental rights impact assessment that certain deployers of high-risk…
Dynamic Pricing
- Definition
- Dynamic pricing is the practice of changing prices frequently in response to demand, capacity, timing, and competitor prices, usually with a model that recommends each price within limits the business sets.
Edge AI
- Definition
- Edge AI is the running of machine learning models directly on local devices such as cameras, gateways, industrial PCs, and phones, so decisions are made where the data is captured.
Edge Computing
- Definition
- Edge computing is the processing of data on or near the devices that generate it, such as sensors, machines, and store systems, instead of sending everything to a central data center or…
Elastic Cloud Computing
- Definition
- Elastic cloud computing is the ability to add or remove computing resources automatically as demand changes, so capacity grows during a peak and shrinks back when the peak passes.
Embeddings
- Definition
- Embeddings are numeric vectors that represent the meaning of text, images, or other data so that items with similar meaning sit close together and can be compared, searched, and clustered by a…
Encryption at Rest and in Transit
- Definition
- Encryption at rest protects stored data such as databases, files, and backups, and encryption in transit protects data moving across networks, so stolen disks or intercepted traffic cannot be read without the…
Energy Management
- Definition
- Energy management is the practice of measuring, controlling, and reducing the energy a company uses across buildings, equipment, and processes, using meters, control systems, and analytics to lower cost and emissions.
Energy-Aware Routing (Green AI)
- Definition
- Energy-aware routing is a form of model routing that includes energy use in the decision, sending each request to the smallest model, hardware, or time slot that meets its quality needs.
Ensemble Learning
- Definition
- Ensemble learning combines the predictions of several machine learning models into one answer, so that their separate errors partly cancel and the result is more accurate and more stable than any single…
Enterprise AI
- Definition
- Enterprise AI is the use of artificial intelligence inside a company's core operations, connected to its systems of record and run under the same security, governance, and support as other business software.
Enterprise AI Platform
- Definition
- An enterprise AI platform is the shared software layer on which a company builds and runs its AI applications, combining model access, data connections, identity and access control, agent tooling, monitoring, and…
Epoch
- Definition
- An epoch is one complete pass through the entire training dataset during model training, and the number of epochs is a hyperparameter that controls how long training runs.
Ethical AI
- Definition
- Ethical AI is the practice of designing, deploying, and operating AI systems so that they respect people's rights, treat them fairly, and remain under meaningful human accountability. It turns stated values into…
ETL and ELT
- Definition
- ETL and ELT are the two main patterns for moving data from source systems into an analytics platform, differing in whether the data is transformed before or after it is loaded.
EU AI Act
- Definition
- The EU AI Act is the European Union's regulation of artificial intelligence, sorting AI systems by risk and imposing duties on the companies that build or use them, inside and outside Europe.
Experiment Tracking
- Definition
- Experiment tracking is the practice of recording every model training run, including its code version, data, hyperparameters, metrics, and outputs, so results can be compared and reproduced later.
Expert Systems
- Definition
- An expert system is software that captures a specialist's knowledge as explicit rules and facts and applies them with an inference engine to solve problems or recommend decisions in a narrow domain.
Explainable AI (XAI)
- Definition
- Explainable AI (XAI) is the set of methods that make an AI model's outputs understandable to people, by showing which inputs drove a result and how the result would change if they…
F1 Score
- Definition
- The F1 score is a single measure of classifier quality that combines precision and recall into their harmonic mean, ranging from zero to one, with one meaning every prediction and every real…
Facial Recognition
- Definition
- Facial recognition is a biometric technology that identifies or verifies a person by comparing the geometry of their face with stored templates. It is tightly regulated and carries high privacy and bias…
Fairness (AI)
- Definition
- Fairness in AI is the property that a model's predictions or decisions do not systematically disadvantage people because of characteristics such as race, sex, age, or disability. It is measured with specific,…
False Negatives
- Definition
- False negatives are cases a model predicts as negative that are actually positive, such as a failing bearing the maintenance model marked healthy. Statisticians call them Type II errors.
False Positive Rate
- Definition
- The false positive rate is the share of actual negative cases that a model wrongly flags as positive, calculated as false positives divided by false positives plus true negatives. It is the…
False Positives
- Definition
- False positives are cases a model predicts as positive that are actually negative, such as a legitimate card purchase declined as fraud. Statisticians call them Type I errors.
Feature Engineering
- Definition
- Feature engineering is the work of turning raw data into input variables that help a machine learning model predict well, using domain knowledge to create, transform, and select the columns the model…
Feature Extraction
- Definition
- Feature extraction is the conversion of raw data such as images, audio, or text into a compact set of numeric values that capture its useful content, so that a model can learn…
Feature Importance
- Definition
- Feature importance is a measure of how much each input variable contributes to a model's predictions. Teams use it to check a model's logic, catch information leakage, and explain the model to…
Feature Store
- Definition
- A feature store is a data system that defines, stores, and serves the input variables machine learning models use, so the same feature definitions feed both model training and live predictions.
Features
- Definition
- Features are the input variables a machine learning model learns from, such as an account's age or an invoice's amount. Their quality usually matters more to accuracy than the choice of algorithm.
Federated Learning
- Definition
- Federated learning trains a shared machine learning model across several data holders without moving their raw data to a central location; only model updates travel between the participants and a coordinator.
Feedback Loop
- Definition
- A feedback loop is the path by which an AI system's results, such as user corrections or real-world outcomes, return as data that shapes its next version, whether by design or by…
Few-Shot Learning
- Definition
- Few-shot learning is a model's ability to learn a new task or category from only a handful of examples, with one-shot learning as the special case of a single example.
Few-Shot Prompting
- Definition
- Few-shot prompting is the technique of including a small number of worked examples of input and desired output in a prompt so that the model copies their format and judgment on new…
Field Validation
- Definition
- Field validation checks a single value, such as a date, phone number, or account code, for the right type, format, length, and range before a system accepts it.
Fine-Tuning
- Definition
- Fine-tuning is the process of continuing to train a pre-trained model on a smaller, task-specific dataset so that it adapts to a company's language, formats, and decisions.
Foundation Model
- Definition
- A foundation model is a large AI model trained on broad data that can be adapted to many different tasks through prompting, retrieval, or fine-tuning instead of being built for a single…
Fraud Detection
- Definition
- Fraud detection is the use of rules, statistics, and machine learning to spot transactions, claims, or account activity that are likely deceptive, so they can be blocked or reviewed before money is…
Function Calling
- Definition
- Function calling is a language model capability where the model, given descriptions of available functions, responds with a structured request to run one, and the application executes it and returns the result.
Gaussian Mixture Model (GMM)
- Definition
- A Gaussian mixture model (GMM) describes data as a blend of several bell-shaped distributions, one per group, and gives each record a probability of belonging to each group.
GDPR (and AI)
- Definition
- The GDPR is the European Union's data protection law, and it governs any AI system that trains on or processes personal data of people in the EU, including automated decisions about them.
General-Purpose AI (GPAI) Model
- Definition
- A general-purpose AI (GPAI) model is the EU AI Act's legal term for a model, such as a large language model, that performs many distinct tasks and can be built into many…
Generalization
- Definition
- Generalization is a model's ability to make accurate predictions on data it did not see during training, which is the only ability that matters once the model is deployed.
Generalized Linear Models (GLM)
- Definition
- Generalized linear models extend linear regression to outcomes that are not normally distributed, such as counts, yes-or-no events, and skewed amounts, by linking a weighted sum of inputs to the outcome through…
Generative Adversarial Network (GAN)
- Definition
- A generative adversarial network is a pair of neural networks trained against each other, one producing synthetic data and the other judging whether it is real, until the output becomes convincing.
Generative AI
- Definition
- Generative AI is a class of AI models that produce new content, such as text, images, code, or audio, by learning the patterns in large collections of existing examples.
Generative Model
- Definition
- A generative model is a machine learning model that learns the distribution of its training data well enough to produce new examples that resemble it, such as text, images, audio, or tabular…
Governance Plane
- Definition
- A governance plane is the layer of an AI platform that enforces identity, permissions, policy, and audit logging on every model call and agent action, separate from the models themselves.
GPT (Generative Pre-Trained Transformer)
- Definition
- GPT, short for Generative Pre-Trained Transformer, is a family of large language models built on the transformer architecture and trained to predict the next token in a sequence of text.
GPU Memory (VRAM)
- Definition
- GPU memory (VRAM) is the fast memory on a graphics card that holds a model's weights and working data during inference, and its size decides which models can run and how many…
Gradient Descent
- Definition
- Gradient descent is the optimization algorithm that trains most machine learning models by repeatedly adjusting parameters in the direction that reduces the loss function.
Gradient-Boosted Decision Trees (GBDT)
- Definition
- Gradient-boosted decision trees (GBDT) are an ensemble method that adds small decision trees one at a time, each fitted to the errors left by those before it, and are usually the most…
Graph Neural Network (GNN)
- Definition
- A graph neural network is a neural network that operates on data arranged as nodes and edges, passing information along connections so that each node's representation reflects its neighbors.
Graphics Processing Unit (GPU)
- Definition
- A graphics processing unit (GPU) is a processor built to run thousands of simple calculations at once, which makes it the standard hardware for training and running neural networks.
GraphRAG (Knowledge Graph RAG)
- Definition
- GraphRAG is a retrieval-augmented generation approach that stores entities and their relationships in a knowledge graph so that a language model can answer questions requiring connections across many documents.
Grid Search
- Definition
- Grid search is a hyperparameter tuning method that trains and evaluates a model on every combination of values from a predefined list for each setting.
Ground Truth
- Definition
- Ground truth is the verified correct answer for a data example, used to train a model and to measure how often its predictions are right.
Grounding
- Definition
- Grounding is the practice of tying a generative AI model's answers to verified sources, such as retrieved documents or database records, so the output reflects real facts instead of the model's memory.
Guardrails
- Definition
- Guardrails are the controls placed around a generative AI model that check inputs and outputs against rules so the system stays within its intended scope, tone, and legal limits.
Hallucination
- Definition
- A hallucination is output from a generative AI model that is fluent and confident but false, such as an invented fact, citation, number, or claim that an action was completed.
High-Risk AI System
- Definition
- A high-risk AI system is one the EU AI Act places in its most regulated permitted tier because it affects safety or fundamental rights, triggering strict duties for its provider and its…
HIPAA (and AI)
- Definition
- HIPAA, the Health Insurance Portability and Accountability Act, is the US law that sets privacy and security rules for patient health information, including the information AI systems process for providers, health plans,…
Holdout Data
- Definition
- Holdout data is a portion of a dataset deliberately kept out of model training, so the model can be evaluated on examples it has never seen before it goes into use.
Hosted Model
- Definition
- A hosted model is an AI model that a provider runs on its own infrastructure and offers to customers through an API, so the customer sends data in and receives results without…
Human Oversight
- Definition
- Human oversight is the set of design features and assigned people that let an organization monitor an AI system, interpret its output, and override or stop it. The EU AI Act requires…
Human-in-the-Loop
- Definition
- Human-in-the-loop is a design in which a person reviews, corrects, or approves an AI system's output before it takes effect, so that a human decision stands between the model and the outcome.
Hybrid AI
- Definition
- Hybrid AI combines symbolic methods such as rules, ontologies, and constraint solvers with statistical or neural methods such as machine learning and language models, so each handles the part of a problem…
Hybrid Deployment
- Definition
- A hybrid deployment runs part of an AI system on infrastructure the organization owns and part in a public cloud, placing each workload where its data sensitivity, cost, and capacity needs are…
Hybrid Search
- Definition
- Hybrid search combines keyword matching with vector similarity in one retrieval step, so that exact identifiers are matched literally while related passages are still found by meaning.
Hyperautomation
- Definition
- Hyperautomation is a strategy for automating business processes end to end by combining workflow tools, robotic process automation, document AI, process mining, and AI agents under one set of controls.
Hyperparameters
- Definition
- Hyperparameters are the settings chosen before training begins, such as learning rate, batch size, and tree depth, that control how a model learns rather than what it learns.
Image Generation
- Definition
- Image generation is the use of AI models, mostly diffusion models, to create new images from text prompts or reference pictures. Businesses use it for marketing visuals, product mockups, and synthetic training…
Image Recognition
- Definition
- Image recognition is the ability of software to identify what an image contains, such as a product, a person, a scene, or a defect, by comparing it with patterns learned from labeled…
Image Segmentation
- Definition
- Image segmentation assigns every pixel in an image to a class or an object, producing exact outlines instead of boxes. It is used to measure areas, isolate regions, and find irregular defects.
ImageNet
- Definition
- ImageNet is a large labeled image dataset, with over a million photographs sorted into a thousand categories in its benchmark subset, on which modern deep learning for computer vision first proved itself.
Inference
- Definition
- Inference is the stage in which a trained model is used to produce predictions or generated output for new inputs, as distinct from training, which is where the model learns.
Inference Endpoint
- Definition
- An inference endpoint is the network address where a deployed model accepts requests and returns predictions or generated text, whether the model runs in a vendor's cloud or on the company's own…
Inference Server
- Definition
- An inference server is the software that loads a trained model onto GPUs, accepts requests over the network, and schedules them for efficient generation, turning model weights into a usable service.
Information Leakage
- Definition
- Information leakage, also called data leakage, occurs when training data contains information that will not be available at prediction time, which inflates test results and fails in production.
Instruction Tuning
- Definition
- Instruction tuning is a training stage that teaches a pre-trained language model to follow directions by showing it many examples of instructions paired with good responses.
Intelligent Automation
- Definition
- Intelligent automation combines rule-based automation, such as workflow software and robotic process automation bots, with AI models that read documents, classify requests, and make predictions, so processes can handle inputs a script…
Intelligent Character Recognition (ICR)
- Definition
- Intelligent character recognition (ICR) is OCR extended to handwriting. It uses neural networks trained on handwritten samples to read hand-printed fields on forms, checks, and notes, with a confidence score per field.
Intelligent Document Processing (IDP)
- Definition
- Intelligent document processing (IDP) combines OCR, classification, and language models to turn invoices, contracts, forms, and other documents into validated structured data that flows into business systems.
Internet of Things (IoT)
- Definition
- The Internet of Things (IoT) is the network of physical devices with sensors and connectivity that collect and transmit data about equipment, environments, and goods without human input.
Interpretability
- Definition
- Interpretability is the degree to which a person can understand how a model turns inputs into outputs by examining the model itself, such as its rules, weights, or structure.
Inventory Planning
- Definition
- Inventory planning is the process of deciding how much of each item to stock, where to hold it, and when to reorder, so that a business meets demand without tying up cash…
IoT Platform
- Definition
- An IoT platform is the software layer that connects, manages, and secures fleets of devices, ingests their sensor data, and makes it available to applications, analytics, and AI models.
ISO/IEC 42001
- Definition
- ISO/IEC 42001 is the international standard for an AI management system, a certifiable set of policies, roles, and controls for developing and using AI responsibly across an organization.
Jailbreak
- Definition
- A jailbreak is a prompt or conversation designed to make a language model ignore its safety training and operating instructions so that it produces content or takes actions it was built to…
JSON
- Definition
- JSON (JavaScript Object Notation) is a lightweight text format for exchanging data as nested key-value pairs and lists, used by most web APIs and by AI tools to pass structured results.
Jupyter Notebook
- Definition
- A Jupyter notebook is an interactive document that combines executable code cells, their outputs, charts, and explanatory text, and it is the standard workspace for data exploration and model prototyping.
k-Nearest Neighbors (k-NN)
- Definition
- The k-nearest neighbors (k-NN) algorithm predicts the label or value of a new record from the k most similar records with known outcomes, using their majority vote or their average.
Key Performance Indicator (KPI)
- Definition
- A key performance indicator (KPI) is a measurable value, tied to a target, that a business tracks to judge whether a process, team, or strategy is performing as intended.
Know Your Customer (KYC)
- Definition
- Know your customer (KYC) is the process regulated firms use to verify who a customer is, understand what the customer does, and rate the risk of the relationship at onboarding and afterward.
Knowledge Base (AI)
- Definition
- An AI knowledge base is the curated collection of documents, records, and procedures that a generative AI system retrieves from when it answers questions about a specific company or domain.
Knowledge Cutoff
- Definition
- The knowledge cutoff is the date after which a language model's training data stops, so the model has no built-in knowledge of events, products, rules, or prices that appeared later.
Knowledge Distillation
- Definition
- Knowledge distillation is a technique that trains a smaller student model to reproduce the behavior of a larger teacher model, so the result runs faster and cheaper while keeping most of the…
Knowledge Graph
- Definition
- A knowledge graph is a database that stores facts as entities connected by named relationships, such as a supplier that provides a part used in a product, so systems can query and…
Knowledge Representation
- Definition
- Knowledge representation is the branch of AI concerned with how facts, concepts, relationships, and rules are encoded so that a program can store them, reason over them, and explain its conclusions.
Knowledge-Driven AI
- Definition
- Knowledge-driven AI reasons from knowledge that people have written down explicitly, such as rules, ontologies, and knowledge graphs, rather than from statistical patterns learned from data. It is transparent and precise but…
Kubernetes
- Definition
- Kubernetes is open-source software that runs and manages containers across a cluster of machines, restarting failed workloads, scaling copies up and down, and assigning GPUs to the services that need them.
KV Cache
- Definition
- The KV cache is the GPU memory an inference server uses to store the attention keys and values of every token already processed, so each new token is generated without recomputing the…
Large Language Model (LLM)
- Definition
- A large language model (LLM) is a neural network trained on very large amounts of text to predict the next token, which lets it draft, summarize, translate, classify, and answer questions in…
Large Models
- Definition
- Large models are AI models with billions or trillions of parameters trained on very large datasets, a category that includes language, vision, and multimodal models. Size brings broader capability and higher running…
Learning Rate
- Definition
- The learning rate is the hyperparameter that sets how large a step gradient descent takes when it updates a model's parameters after each batch of training examples.
Least Privilege
- Definition
- Least privilege is the security principle that every user, service, and AI agent gets only the access its task requires, and only for as long as the task lasts.
Levels of Agent Autonomy
- Definition
- Levels of agent autonomy are a graded scale of how much an AI agent may decide and do without a person, from only suggesting actions to acting alone and reporting afterward.
LIME (Local Interpretable Model-Agnostic Explanations)
- Definition
- LIME (Local Interpretable Model-Agnostic Explanations) is a technique that explains one prediction from any machine learning model by testing small variations of the input and fitting a simple model to the results.
Linear Regression
- Definition
- Linear regression is a statistical method that predicts a numeric outcome as a weighted sum of input variables, fitting the weights so the predictions come as close as possible to observed values.
LLM Observability
- Definition
- LLM observability is the practice of recording and analyzing what a language model application does in production, including prompts, responses, tool calls, latency, token usage, errors, and quality signals.
LLM-as-a-Judge
- Definition
- LLM-as-a-judge is an evaluation method in which a language model scores or ranks the outputs of another model against a rubric, replacing or supplementing human review at scale.
Logistic Regression
- Definition
- Logistic regression is a statistical model that predicts the probability of a yes-or-no outcome from input variables, and it remains the most widely used and most explainable classification method in business.
Long Context
- Definition
- Long context describes language models and workloads that handle prompts of hundreds of thousands of tokens, enough to supply whole documents or long histories instead of retrieving short excerpts.
Long Short-Term Memory (LSTM)
- Definition
- Long short-term memory is a type of recurrent neural network with gates that control what information is kept, updated, or forgotten, allowing it to learn patterns that span long sequences.
LoRA (Low-Rank Adaptation)
- Definition
- LoRA (Low-Rank Adaptation) is a fine-tuning method that freezes a model's original weights and trains small added matrices instead, producing a compact adapter that changes the model's behavior.
Loss Function
- Definition
- A loss function is the formula that scores how far a model's predictions are from the correct answers during training, giving the optimizer a single number to minimize.
Low Code
- Definition
- Low code is a way of building applications with visual tools and prebuilt components while allowing developers to add hand-written code where the visual blocks cannot express the needed logic or integration.
Low-Dimensional Representation
- Definition
- A low-dimensional representation describes a complex item, such as a document, image, customer, or product, with a short list of numbers that keeps the properties that matter for a task.
Machine Learning (ML)
- Definition
- Machine learning is the branch of artificial intelligence in which software learns patterns from data and improves at a task through experience instead of following rules written by a programmer.
Machine Learning Hardware Requirements
- Definition
- Machine learning hardware requirements are the processors, memory, storage, and network capacity a project needs, which depend on whether the work is classic machine learning, deep learning training, or language model inference.
Machine Learning Pipeline
- Definition
- A machine learning pipeline is an automated, repeatable sequence of steps that turns raw data into a trained, evaluated, and deployable model, so that retraining runs the same way every time.
Machine Translation
- Definition
- Machine translation is the automatic conversion of text or speech from one human language to another by software, now performed by neural models that read whole sentences and follow terminology instructions.
Machine Vision
- Definition
- Machine vision is the use of industrial cameras, lighting, and image analysis software to inspect, measure, guide, and identify parts on a production line, usually in real time and tied to plant…
Managed AI Service
- Definition
- A managed AI service is an arrangement in which a provider operates an AI system on the customer's behalf, handling hosting, updates, monitoring, security patches, and support, while the customer uses the…
Master Data Management (MDM)
- Definition
- Master data management (MDM) is the discipline of keeping one agreed, governed record for each core business entity, such as a customer, product, supplier, or site, across every system that uses it.
Mean Absolute Error (MAE)
- Definition
- Mean absolute error (MAE) is the average size of a model's prediction errors, ignoring direction, expressed in the same units as the value being predicted, such as tickets, days, or units sold.
Mean Absolute Percent Error (MAPE)
- Definition
- Mean absolute percent error (MAPE) is the average size of a forecast's errors expressed as a percentage of the actual values, which allows accuracy to be compared across items of very different…
Medallion Architecture
- Definition
- Medallion architecture is a layered design for a data lake or lakehouse in which data moves through bronze (raw), silver (cleaned and conformed), and gold (business-ready) tables.
Metadata
- Definition
- Metadata is data that describes other data, such as a file's author and creation date, a table's schema and owner, or a record's source and sensitivity classification.
Misclassification Rate
- Definition
- The misclassification rate is the share of all predictions a classifier gets wrong, calculated as false positives plus false negatives divided by all cases. It equals one minus accuracy.
Mixture of Experts (MoE)
- Definition
- Mixture of experts (MoE) is a model architecture that splits parts of a neural network into several expert sub-networks and activates only a few of them for each token, lowering compute per…
MLOps
- Definition
- MLOps (machine learning operations) is the set of practices and tools for moving machine learning models into production and keeping them accurate, monitored, and governed over their working life.
Modality
- Definition
- A modality is a type of data that an AI model can take in or produce, such as text, images, audio, video, or sensor readings. Models are often described by the modalities…
Model Architecture
- Definition
- Model architecture is the structural design of a model: the types of layers it contains, how they connect, and how data flows from input to output, independent of the learned weights.
Model Card
- Definition
- A model card is a structured document published with an AI model that describes its intended uses, training data, evaluation results, limitations, and license, so buyers and auditors can judge its fitness.
Model Context Protocol (MCP)
- Definition
- The Model Context Protocol (MCP) is an open standard that lets AI applications connect to tools and data sources through one common interface, so any compliant agent can use any compliant server.
Model Deployment
- Definition
- Model deployment is the process of moving a trained model out of the lab and into a running system where it receives real requests, returns predictions, and is monitored, secured, and updated…
Model Drift
- Definition
- Model drift is the gradual or sudden decline in a deployed model's accuracy because the data it sees in production, or the patterns behind that data, no longer match what it learned…
Model Evaluation
- Definition
- Model evaluation is the measurement of how well a model performs against defined criteria, using held-out data, task-specific tests, and safety checks, before and after it is put into service.
Model Inputs and Outputs
- Definition
- Model inputs and outputs are the data a model receives and the results it returns, defined by type, format, and meaning, forming the contract between the model and the systems around it.
Model Maintenance
- Definition
- Model maintenance is the ongoing work that keeps a deployed model accurate, secure, and compliant, including retraining, recalibration, software updates, revalidation, and eventual retirement.
Model Monitoring
- Definition
- Model monitoring is the ongoing tracking of a deployed model's inputs, outputs, accuracy, and operational health, with alerts that tell its owners when performance degrades or the data changes.
Model Prototyping
- Definition
- Model prototyping is the fast, early stage of a machine learning project in which a team builds rough models on real data to learn whether the problem can be solved before investing…
Model Registry
- Definition
- A model registry is a central catalog of trained models that records each version's lineage, evaluation results, approvals, and lifecycle stage, so teams know exactly which model is running where.
Model Risk Management
- Definition
- Model risk management is the discipline of inventorying, validating, and controlling the models a company relies on for decisions, developed in banking under SR 11-7 and now applied to AI systems generally.
Model Robustness
- Definition
- Model robustness is a model's ability to keep producing correct results when its inputs are noisy, unusual, shifted away from the training data, or deliberately manipulated by an attacker.
Model Routing
- Definition
- Model routing is the practice of sending each AI request to the most suitable model, local or cloud, based on the task type, cost, latency requirement, and sensitivity of the data involved.
Model Serving
- Definition
- Model serving is the software and infrastructure that keeps a trained model loaded in memory, accepts requests over a network, runs inference, and returns results reliably enough for applications to depend on.
Model Size
- Definition
- Model size is the scale of a model, usually expressed as its parameter count, which determines how much memory it needs, how fast it runs, and what hardware can serve it.
Model Training
- Definition
- Model training is the process of adjusting a machine learning model's parameters to fit example data, so the model learns patterns it can apply to new cases. Using the trained model afterward…
Model Tuning
- Definition
- Model tuning is the iterative process of adjusting a model's hyperparameters, features, and training setup to improve its performance on validation data.
Model Validation
- Definition
- Model validation is the independent review of a model's design, data, performance, and limitations to confirm it is fit for its intended use before and during deployment.
Model Weights
- Definition
- Model weights are the learned numerical parameters of a trained model, saved as files that can be loaded to run the model, shared under a license, or fine-tuned further.
Model-Driven Architecture
- Definition
- Model-driven architecture is a way of building software in which a formal model of the business domain, its objects, relationships, and rules, is the primary artifact, and databases, interfaces, and application code…
Monte Carlo Simulation
- Definition
- Monte Carlo simulation is a method that estimates the range and likelihood of possible outcomes by running a model thousands of times, each time drawing uncertain inputs at random from their probability…
Multi-Agent Orchestration
- Definition
- Multi-agent orchestration is the coordination layer that decides which agent runs when, passes work and results between agents, and tracks the state of a task across them until it completes.
Multi-Agent System
- Definition
- A multi-agent system is an AI setup in which several specialized agents work on parts of a task and exchange results, instead of one agent handling everything. Each agent has its own…
Multi-Cloud
- Definition
- Multi-cloud is the use of services from two or more cloud providers within one organization, whether by deliberate design or because different teams and acquisitions chose different vendors.
Multi-Tenancy
- Definition
- Multi-tenancy is an architecture in which one running instance of a platform serves several separate customers or business units, called tenants, keeping each tenant's data, users, and configuration isolated from the others.
Multimodal AI
- Definition
- Multimodal AI is AI that can take in, reason over, or produce more than one type of data, such as text, images, audio, and video, within a single model or system.
Naive Bayes
- Definition
- Naive Bayes is a family of fast probabilistic classifiers that apply Bayes' theorem and assume every feature is independent of the others given the class, which makes them a common baseline for…
Named Entity Recognition (NER)
- Definition
- Named entity recognition (NER) is a natural language processing technique that finds and labels specific items in text, such as people, companies, dates, amounts, and identifiers, turning unstructured documents into structured fields.
Narrow AI (Weak AI)
- Definition
- Narrow AI, also called weak AI, is an AI system built to perform one task or a closely related set of tasks, such as scoring credit risk or reading invoices. Every AI…
Natural Language Generation (NLG)
- Definition
- Natural language generation (NLG) is the production of readable text by software from data, instructions, or other text. It is the output half of NLP and the basis of most generative AI…
Natural Language Processing (NLP)
- Definition
- Natural language processing (NLP) is the branch of AI that lets software read, interpret, and produce human language. It turns contracts, emails, call transcripts, and tickets into data a business can search,…
Natural Language Query
- Definition
- Natural language query is the ability to ask a question of a database or reporting tool in plain English and receive the answer as a table, chart, or sentence computed from live…
Natural Language Understanding (NLU)
- Definition
- Natural language understanding (NLU) is the part of NLP that works out what text or speech means, turning an open-ended sentence into an intent and extracted fields that software can act on.
Neural Architecture Search
- Definition
- Neural architecture search is an automated method that explores many possible neural network designs and selects the one that performs best under given accuracy and compute constraints.
Neural Network
- Definition
- A neural network is a machine learning model made of layers of simple computing units, connected by adjustable weights, that learns to map inputs to outputs by tuning those weights on examples.
Neuro-Symbolic AI
- Definition
- Neuro-symbolic AI integrates neural networks, which learn from data, with symbolic reasoning over explicit rules and knowledge, so that one system can both perceive messy inputs and reason with guaranteed logic.
NIST AI Risk Management Framework
- Definition
- The NIST AI Risk Management Framework is a voluntary US guide for managing AI risk through four functions, Govern, Map, Measure, and Manage, that companies adapt to their own systems and sector.
No Code
- Definition
- No code is an approach to building software through visual tools, forms, and prebuilt components instead of written programming, letting business users assemble applications, workflows, and AI agents themselves.
Noise (Data)
- Definition
- Noise is random or irrelevant variation in data that carries no useful signal, such as sensor jitter, typos, and inconsistent labels, which a model can mistake for real patterns.
Normalization
- Definition
- Normalization rescales numeric features to a common range or distribution so that no single feature dominates a model because of the units it happens to be measured in.
Null Error Rate
- Definition
- The null error rate is the share of predictions that would be wrong if a model always predicted the most common class. A model's error rate must fall below it before its…
Object Detection
- Definition
- Object detection is a computer vision task that finds every object of interest in an image, labels each one, and marks its position with a bounding box and a confidence score.
Object Tracking
- Definition
- Object tracking follows a detected object from frame to frame in a video, assigning it a persistent identity so a system can count objects, measure dwell time, and trace paths.
On-Premises AI
- Definition
- On-premises AI is AI that runs on servers physically located in an organization's own facilities, keeping models, prompts, and data inside buildings and networks the organization controls.
ONNX (Open Neural Network Exchange)
- Definition
- ONNX (Open Neural Network Exchange) is an open file format for trained machine learning models that lets a model built in one framework run in a different runtime, programming language, or device.
Ontology
- Definition
- An ontology is a formal, machine-readable definition of the concepts in a domain, their properties, and the relationships between them, giving people and systems one shared vocabulary for the same things.
Open-Source Model
- Definition
- An open-source model is an AI model whose weights, training code, and ideally training data are published under a license that permits anyone to use, study, modify, and redistribute it.
Open-Weight Model
- Definition
- An open-weight model is an AI model whose trained weights are published for download and self-hosting, usually without the training data, and often under a license that restricts certain uses.
OpenAI-Compatible API
- Definition
- An OpenAI-compatible API is a model-serving interface that accepts the same request and response format as OpenAI's API, so most client libraries and applications can switch models by changing a URL.
Optical Character Recognition (OCR)
- Definition
- Optical character recognition (OCR) converts images of printed text, such as scans, photos, and PDFs, into machine-readable characters that software can search, extract, and process.
Optimization
- Definition
- Optimization is the mathematical method of choosing values for decision variables that maximize or minimize an objective while satisfying constraints. Linear and integer programming are its main business forms, from truck loading…
Orchestrator Agent
- Definition
- An orchestrator agent is an AI agent whose job is to break a task into subtasks, assign them to specialized agents or tools, review the results, and assemble the final output.
Outsourced Labeling
- Definition
- Outsourced labeling is paying an external vendor or crowd to annotate images and other training data. It scales labeling quickly but raises quality control and confidentiality issues that need contractual and technical…
Overfitting
- Definition
- Overfitting happens when a model learns the training data so closely, including its noise, that it performs well on that data and poorly on new data.
Parameter-Efficient Fine-Tuning (PEFT)
- Definition
- Parameter-efficient fine-tuning (PEFT) is a family of methods that adapt a large pre-trained model by training only a small number of added or selected parameters instead of the whole network.
Parameters (Model)
- Definition
- Model parameters are the numerical values, mainly weights and biases, that a model learns during training, and their count is the standard measure of a model's size.
Permission-Aware Retrieval
- Definition
- Permission-aware retrieval is the practice of restricting the documents a generative AI system can retrieve to those the requesting user is authorized to see, enforced before the model receives any text.
Personally Identifiable Information (PII)
- Definition
- Personally identifiable information (PII) is any data that can identify a specific person on its own or combined with other data, such as a name, address, account number, or biometric record.
Physical AI
- Definition
- Physical AI is AI that perceives and acts in the physical world through machines such as robots, smart cameras, drones, and autonomous vehicles, so its errors have physical consequences.
PII Redaction
- Definition
- PII redaction is the automated removal or masking of personal identifiers from text, documents, audio, or images before the content is stored, shared, or sent to an AI model.
Planning (AI)
- Definition
- Planning in AI is the process by which a system chooses and sequences actions to reach a goal from a known starting state, considering the effects of each action. It is a…
Post-Market Monitoring
- Definition
- Post-market monitoring is the ongoing collection and review of evidence about how an AI system performs once it is in real use. The EU AI Act requires it of every provider of…
Pre-Trained Model
- Definition
- A pre-trained model is a model that has already been trained on a large general dataset and is distributed for others to use directly or adapt to their own tasks.
Pre-Training
- Definition
- Pre-training is the first and largest phase of building a model, in which it learns general patterns from a huge unlabeled dataset before any task-specific training.
Precision
- Definition
- Precision is the share of a model's positive predictions that are actually correct, calculated as true positives divided by all predicted positives. It is also called positive predictive value.
Prediction
- Definition
- A prediction is the output a trained model produces for a new input, such as a category, a number, a ranked list, or a probability, before the true outcome is known.
Predictive AI
- Definition
- Predictive AI uses historical data to forecast what will happen or to classify what something is, producing a number, a category, or a probability rather than new content. It is the counterpart…
Predictive Analytics
- Definition
- Predictive analytics is the use of statistical models and machine learning on historical data to estimate the likelihood of future outcomes, such as which customers will churn or which machines will fail.
Predictive Maintenance
- Definition
- Predictive maintenance uses sensor data and machine learning to forecast when equipment is likely to fail, so repairs can be scheduled shortly before failure instead of on a fixed calendar or after…
Predictive Model
- Definition
- A predictive model is a trained statistical or machine learning model that estimates an unknown outcome, such as a future event or a hidden attribute, from known inputs.
Prescriptive Analytics
- Definition
- Prescriptive analytics is the use of optimization, simulation, and decision rules on top of predictions to recommend or automate the specific action that best meets a business objective.
Prevalence
- Definition
- Prevalence is the share of cases in a population that actually belong to the positive class, such as the fraction of circuit boards that are defective. It shapes how precision and accuracy…
Private AI
- Definition
- Private AI is artificial intelligence that runs on infrastructure the organization controls, using models it owns or has the rights to run, so that prompts, documents, and outputs never leave its environment.
Private AI Platform
- Definition
- A private AI platform is the software layer that runs models, agents, and integrations on an organization's own infrastructure, with identity, access control, and audit logging built in.
Probabilistic Algorithm
- Definition
- A probabilistic algorithm uses random choices or probability estimates in its steps, so the same input can produce different outputs, or an output expressed as a likelihood rather than a certainty.
Problem Tractability
- Definition
- Problem tractability is the judgment of whether a business problem can realistically be solved with the data, methods, and computing resources available, within the time and accuracy the business needs.
Process Mining
- Definition
- Process mining is a technique that reconstructs how a business process actually runs from the timestamped event logs recorded by ERP, CRM, ticketing, and other business systems.
Process Optimization
- Definition
- Process optimization is the systematic improvement of a business or production process so that it delivers better results, such as shorter cycle times, lower cost, fewer errors, or higher yield, within its…
Production Environment
- Definition
- A production environment is the live set of servers, software, and data where an AI system serves real users and real business processes, as distinct from the development and test environments used…
Production Scheduling
- Definition
- Production scheduling is the assignment of manufacturing jobs to specific machines, lines, crews, and time slots, in a sequence that meets due dates while respecting capacity, materials, and changeover rules.
Programming Languages for Machine Learning
- Definition
- The programming languages for machine learning are the languages used to build, train, and deploy models; Python dominates, while R, SQL, Scala, C++, Rust, and Julia each serve particular stages or environments.
Prompt
- Definition
- A prompt is the text sent to a generative AI model to get a response, including the instructions, the question, any supplied documents or examples, and the required output format.
Prompt Caching
- Definition
- Prompt caching stores the processed form of a prompt prefix, such as a long system prompt or document, so later requests that begin the same way skip that work and respond faster.
Prompt Chaining
- Definition
- Prompt chaining is the technique of breaking a task into a sequence of smaller prompts, where the output of each step becomes the input to the next, with checks between steps.
Prompt Engineering
- Definition
- Prompt engineering is the discipline of designing, testing, and maintaining the instructions given to a generative AI model so that it produces correct, consistent output for a specific business task.
Prompt Injection
- Definition
- Prompt injection is an attack in which instructions hidden in content a language model processes, such as an email, document, or web page, cause it to act against its operator's intent.
Prompt Template
- Definition
- A prompt template is a reusable prompt with placeholders that an application fills with the user's question, retrieved documents, or record data at run time, so every request is built the same…
Proof of Concept (POC)
- Definition
- A proof of concept (POC) is a short, tightly scoped test that shows whether an AI approach can perform a specific task well enough on real company data to justify building it…
Provider vs Deployer
- Definition
- Under the EU AI Act, a provider develops an AI system and places it on the market under its own name, while a deployer uses the system in its operations, and each…
PyTorch
- Definition
- PyTorch is an open-source deep learning framework, started at Meta and now governed by the PyTorch Foundation, that has become the most widely used tool for building and training neural networks, including…
Quantization
- Definition
- Quantization stores a model's weights in lower-precision numbers such as FP8, INT8, or INT4 so the model needs less memory and runs faster, at the cost of a small loss in accuracy.
Random Forest
- Definition
- A random forest is an ensemble of many decision trees, each trained on a random sample of the data and a random subset of features, whose predictions are combined by voting or…
Rate Limiting
- Definition
- Rate limiting caps how many requests or tokens a user, application, or team may send to an AI model in a given period, protecting capacity and controlling cost.
ReAct Pattern
- Definition
- The ReAct pattern is an agent design in which a language model alternates between writing a short reasoning step and taking an action, then reads the result before deciding what to do…
Real-Time Inference
- Definition
- Real-time inference is serving a model's prediction on demand, within milliseconds to a few seconds of the request, so that the result can be used inside a live transaction or conversation.
Reasoning Model
- Definition
- A reasoning model is a large language model trained to work through a problem in intermediate steps before giving its final answer, trading speed and cost for accuracy on harder tasks.
Recall (Sensitivity)
- Definition
- Recall is the share of all actual positive cases that a model correctly identifies, calculated as true positives divided by true positives plus false negatives. It is also called sensitivity or the…
Recommendation System
- Definition
- A recommendation system is software that predicts which products, content, or actions a person is most likely to want and ranks them, using past behavior, item attributes, and context.
Recurrent Neural Network (RNN)
- Definition
- A recurrent neural network is a neural network that processes a sequence one step at a time while carrying a hidden state forward, so earlier inputs influence how later ones are interpreted.
Reflective AI
- Definition
- Reflective AI is an AI system or agent that checks its own outputs or actions against a goal, finds errors, and revises its work, either before answering or on its next attempt.
Regression
- Definition
- Regression is the family of supervised learning methods that predict a continuous numeric value, such as a price, a quantity, or a duration, from a set of input variables.
Regression Performance
- Definition
- Regression performance is the set of measures that describe how close a model's numeric predictions are to actual values, including mean absolute error, root mean square error, and R-squared.
Regularization
- Definition
- Regularization is any technique that penalizes complexity during model training so the model fits general patterns in the data instead of memorizing individual records.
Reinforcement Learning
- Definition
- Reinforcement learning is machine learning in which an agent learns by acting in an environment and receiving rewards or penalties, gradually finding a policy that earns the most reward over time.
Reinforcement Learning from Human Feedback (RLHF)
- Definition
- Reinforcement learning from human feedback (RLHF) is a training method that teaches a language model to produce answers people prefer by learning from human rankings of its outputs.
Remaining Useful Life (RUL)
- Definition
- Remaining useful life (RUL) is an estimate of how long a component or machine can keep operating before it fails or reaches a defined end-of-life limit, expressed in hours, cycles, miles, or…
Reporting Bias
- Definition
- Reporting bias occurs when the frequency of events, outcomes, or opinions in a dataset differs from their frequency in reality because people record some things more readily than others.
Reranking
- Definition
- Reranking is a second scoring pass that takes the candidate passages returned by a search and reorders them with a more accurate model so the best evidence reaches the language model first.
Responsible AI
- Definition
- Responsible AI is the practice of developing and operating AI systems so that they are fair, transparent, accountable, safe, and respectful of privacy, backed by concrete policies, controls, and review processes rather…
Retrieval-Augmented Generation (RAG)
- Definition
- Retrieval-augmented generation (RAG) is a technique that finds relevant passages from a company's own documents and supplies them to a language model so that it answers from current, verifiable sources.
Ridge Regression
- Definition
- Ridge regression is linear regression with a penalty on large coefficients, which stabilizes the model when input variables are highly correlated or numerous and reduces overfitting.
Risk Management System (EU AI Act)
- Definition
- A risk management system under the EU AI Act is the documented, continuous process a provider of a high-risk AI system must run to identify, evaluate, and reduce risks across the system's…
Robotic Process Automation (RPA)
- Definition
- Robotic process automation (RPA) is software that automates repetitive, rule-based computer tasks by performing the clicks and keystrokes a person would make in an application's screens. It works best on stable, high-volume…
Robotics
- Definition
- Robotics is the engineering of machines that sense their surroundings, decide what to do, and act physically, with AI supplying perception, planning, and control for tasks from warehouse picking to assembly.
ROC Curve
- Definition
- A receiver operating characteristic (ROC) curve plots a classifier's true positive rate against its false positive rate at every decision threshold, showing the trade-off between catching real cases and raising false alarms.
Role-Based Access Control (RBAC)
- Definition
- Role-based access control (RBAC) is a method of managing permissions in which access rights are attached to job roles, and people receive access by being assigned to the roles that match their…
Root Cause Analysis
- Definition
- Root cause analysis is a structured method for finding the underlying reason a problem occurred, so that the fix prevents it from happening again instead of treating only the symptom.
Root Mean Square Error (RMSE)
- Definition
- Root mean square error (RMSE) is the square root of the average squared difference between predicted and actual values. Because errors are squared, it penalizes large misses more heavily than mean absolute…
Sandboxing
- Definition
- Sandboxing is the practice of running an AI agent's actions, such as executing code or browsing the web, inside an isolated environment with limited access, so mistakes or attacks cannot reach production…
Scalable AI
- Definition
- Scalable AI is an AI deployment designed to keep performing as users, data volume, and use cases grow, through capacity planning, load testing, and architecture that adds compute without redesign.
Scenario Planning (What-If Analysis)
- Definition
- Scenario planning, also called what-if analysis, is the practice of testing how plans and results change under different assumptions, from one altered input in a model to several complete versions of the…
Scheduling (AI)
- Definition
- Scheduling in AI is the assignment of tasks to resources and time slots under constraints such as capacity, deadlines, and sequence rules, using search and optimization methods to find feasible, high-quality schedules…
Schema
- Definition
- A schema is the formal definition of how data is structured, naming the tables or objects, the fields within them, each field's type, and the relationships and rules that connect them.
Secrets Management
- Definition
- Secrets management is the practice of storing, distributing, rotating, and auditing the credentials that software and AI agents use to reach other systems, such as API keys, passwords, and tokens.
Selection Bias
- Definition
- Selection bias arises when the data used to train or evaluate a model is not representative of the population the model will serve, because of how the records were chosen.
Self-Hosted AI
- Definition
- Self-hosted AI is AI software and models that an organization installs and operates on servers it manages, rather than consuming them as a vendor's hosted service. The servers can be on-premises or…
Self-Supervised Learning
- Definition
- Self-supervised learning trains a model on unlabeled data by hiding part of each example and teaching the model to predict it, which is how large language models and other foundation models are…
Semantic Layer
- Definition
- A semantic layer is a business-friendly model placed between raw data tables and the tools that query them, defining metrics, dimensions, and relationships once so every report and AI assistant uses the…
Semantic Search
- Definition
- Semantic search is a retrieval method that finds documents by meaning rather than by matching exact words, using embeddings to return the passages closest to what the user asked.
Semantic Skills
- Definition
- Semantic skills are the capabilities that let an AI system work out what language means, including word sense, context, and intent, so it can act on requests however they are phrased.
Semi-Structured Data
- Definition
- Semi-structured data has organizing markers such as tags or key-value pairs but no fixed table schema, which is why formats like JSON and XML sit between databases and free text.
Semi-Supervised Learning
- Definition
- Semi-supervised learning trains a model on a small set of labeled examples together with a much larger set of unlabeled data, cutting the cost of labeling while improving accuracy.
Sentiment Analysis
- Definition
- Sentiment analysis is the use of natural language processing to determine the attitude expressed in text, usually as positive, negative, or neutral, so that customer feedback can be counted and tracked over…
Session (AI Conversation)
- Definition
- A session is one continuous conversation between a user or system and an AI model or agent, with its own history, state, and identity, kept separate from every other conversation.
Shadow AI
- Definition
- Shadow AI is the use of AI tools, models, or features at work without the knowledge or approval of IT, security, or governance owners. It most often starts with company data pasted…
Shadow Mode
- Definition
- Shadow mode runs a new model on live production inputs alongside the current system, records its outputs for comparison, and takes no action on them, so the new model can be judged…
Shapley Values (SHAP)
- Definition
- Shapley values are a game theory method that splits a single model prediction among its input features, showing how much each one pushed the result up or down. SHAP is the most…
Siamese Network
- Definition
- A Siamese network is a neural network that runs two inputs through identical twin branches and compares the results, learning to judge whether the inputs match rather than what class they belong…
Simulation
- Definition
- Simulation is the use of a computer model that imitates how a real system behaves over time, so a business can test changes, compare options, and see likely outcomes before committing real…
Single Sign-On (SSO)
- Definition
- Single sign-on (SSO) is an authentication method that lets people sign in once through a central identity provider and then use many applications, including AI tools, without separate passwords.
Single Source of Truth
- Definition
- A single source of truth is the one agreed location where a given piece of business data is maintained, so that every report, system, and person reading it gets the same value.
Single-Tenant Deployment
- Definition
- A single-tenant deployment is an AI system in which the servers, model instance, databases, and configuration serve one organization only, with nothing shared with other customers of the provider or platform.
Small Language Model (SLM)
- Definition
- A small language model (SLM) is a language model with a few billion parameters or fewer, sized to run on a single GPU, a CPU, or an edge device while handling narrow,…
SOC 2 (and AI)
- Definition
- SOC 2 is an attestation report in which an independent CPA firm tests a service provider's controls over customer data. It covers a vendor's AI features only when they sit inside the…
Software Development Kit (SDK)
- Definition
- A software development kit (SDK) is a packaged set of libraries, tools, and documentation that lets developers build on a platform or call its API from a specific programming language.
Sovereign AI
- Definition
- Sovereign AI is AI capability that a nation or organization controls within its own legal jurisdiction, covering the models, the data, the infrastructure, and the rules that govern them.
Specificity
- Definition
- Specificity is the share of actual negative cases that a model correctly identifies as negative, calculated as true negatives divided by true negatives plus false positives. It is the complement of the…
Speculative Decoding
- Definition
- Speculative decoding speeds up text generation by letting a small draft model propose several tokens that the large model then verifies in one pass, producing the same output faster.
Speech Recognition (ASR)
- Definition
- Speech recognition, or automatic speech recognition (ASR), is the conversion of spoken audio into written text by software, making calls, dictation, and meetings searchable and usable by other AI systems.
Stochastic Optimization
- Definition
- Stochastic optimization is optimization in which some inputs, such as demand, prices, or lead times, are uncertain and modeled as probability distributions, so the chosen plan performs well across many possible outcomes.
Streaming (LLM Responses)
- Definition
- Streaming is the delivery of a language model's response token by token as it is generated, so the user sees text appear immediately instead of waiting for the full answer.
Streaming Data
- Definition
- Streaming data is data that is produced and processed continuously as a flow of individual events, such as sensor readings or transactions, rather than collected and handled in scheduled batches.
Structure Learning (Graph Learning)
- Definition
- Structure learning is the process of discovering the structure of relationships among variables from data, such as which edges belong in a Bayesian network or how entities in a graph connect.
Structured Data
- Definition
- Structured data is information organized into fixed fields with defined types, such as rows and columns in a database table, so software can query, sort, and aggregate it directly.
Structured Outputs
- Definition
- Structured outputs are model responses that conform to a predefined format such as a JSON schema, so that downstream software can parse the result without interpreting free text.
Sub-Symbolic (Connectionist) AI
- Definition
- Sub-symbolic AI, also called connectionist AI, stores what it knows as numeric weights across many simple units, as in neural networks, rather than as readable rules. Deep learning and large language models…
Summarization
- Definition
- Summarization is the use of a language model to condense a long text into a shorter one that keeps the important points, in a format and length set by the prompt.
Supervised Learning
- Definition
- Supervised learning is machine learning in which a model is trained on examples that carry the correct answer, so it can predict that answer for new, unlabeled records.
Supply Chain Management
- Definition
- Supply chain management is the coordination of materials, products, information, and payments from suppliers through production and distribution to customers, balancing service levels, inventory, and cost.
Support Vector Machine (SVM)
- Definition
- A support vector machine (SVM) is a supervised learning algorithm that separates classes with the boundary that leaves the widest possible margin between them. It suits small and medium-sized datasets with many…
Symbolic AI
- Definition
- Symbolic AI represents knowledge as explicit symbols, rules, and logical relationships that a program manipulates to reason and reach conclusions. It dominated early AI and survives in rules engines, planners, and knowledge…
Synthetic Data
- Definition
- Synthetic data is artificially generated data that reproduces the statistical properties of real data without containing real records, used to train and test models when real data is scarce, sensitive, or unbalanced.
System Prompt
- Definition
- A system prompt is the standing set of instructions an application gives a language model before any user message, defining the model's role, rules, tone, tools, and the boundaries of what it…
Target Function
- Definition
- The target function is the unknown true relationship between a model's inputs and the outcome it predicts, which training tries to approximate from labeled examples.
Taxonomy
- Definition
- A taxonomy is a hierarchical classification scheme that organizes items such as products, documents, or support tickets into categories and subcategories, so that people and systems label and find them consistently.
Temperature and Sampling
- Definition
- Temperature and sampling are the settings that control how a language model picks each next token, from always choosing the most likely option to drawing from a wider spread of possibilities.
Tensor Processing Unit (TPU)
- Definition
- A tensor processing unit (TPU) is a chip Google designed specifically for the matrix arithmetic of neural networks, available only through Google's cloud and used mostly for large-scale training.
TensorFlow
- Definition
- TensorFlow is an open-source deep learning framework developed by Google for building, training, and deploying neural networks, known for its production deployment tooling and its support for mobile and edge devices.
Test Data
- Definition
- Test data is a set of labeled examples withheld from model training and tuning and used once, at the end, to estimate how the finished model will perform on new cases.
Text Classification
- Definition
- Text classification is the task of assigning a piece of text to one or more predefined categories, such as routing a support email to billing, shipping, or returns.
Text-to-Speech (TTS)
- Definition
- Text-to-speech (TTS) is the generation of spoken audio from written text by software, used in phone systems, voice assistants, and accessibility tools to read dynamic content aloud in a natural voice.
Text-to-SQL
- Definition
- Text-to-SQL is the translation of a plain-language question into a SQL statement that a database can run, performed by a language model given the schema and business definitions.
Time Series
- Definition
- A time series is a sequence of measurements of one quantity recorded at successive points in time, such as hourly machine temperature or daily sales, where the order of the observations carries…
Time Series Forecasting
- Definition
- Time series forecasting is the use of historical, time-ordered data to predict future values of a quantity, such as next month's demand for a product or tomorrow's inbound call volume.
Time to First Token (Latency)
- Definition
- Time to first token is the delay between sending a prompt and receiving the first piece of the model's answer, the latency figure users notice most in an interactive AI feature.
Token-Based Pricing
- Definition
- Token-based pricing is the billing model for hosted AI services in which the customer pays for the number of tokens sent to and generated by the model, with input and output often…
Tokenization
- Definition
- Tokenization is the step that splits text into tokens, the word pieces a language model reads and produces, and the unit in which context windows, throughput, and usage-based pricing are measured.
Tokens
- Definition
- Tokens are the units of text a language model reads and writes, usually a word or a fragment of a word. Token counts determine context limits, speed, and the cost of each…
Tokens per Second (Throughput)
- Definition
- Tokens per second measures how fast a language model produces text, either for a single response or as the total output of a server across all users at once.
Tool Governance
- Definition
- Tool governance is the set of policies and controls that determine which tools an AI agent may call, with what data, under whose identity, and with what approval or logging requirements.
Tool Use
- Definition
- Tool use is the ability of a language model to call external functions, such as a database query, a search, or an API, and use the returned data to complete a task.
Topic Modeling
- Definition
- Topic modeling is an unsupervised learning technique that discovers the themes running through a large collection of documents without being told in advance which categories to look for.
Total Cost of Ownership (AI)
- Definition
- Total cost of ownership for AI is the full cost of running an AI capability over its life, including hardware or GPU time, power, people, software, model licenses, and support, rather than…
Training Data
- Definition
- Training data is the set of examples a machine learning model learns from during training. Its coverage, label quality, and history largely determine how the model behaves on new cases.
Transfer Learning
- Definition
- Transfer learning reuses a model trained on one large task as the starting point for a related task, so the new task needs far less data and compute.
Transformer
- Definition
- A transformer is a neural network architecture that uses attention to weigh the relationship between every element in a sequence at once, and it is the foundation of modern large language models.
Transparency (AI)
- Definition
- Transparency in AI is the practice of telling people, in understandable terms, when AI is being used, what it does, what data it relies on, and what its limits are.
Transparency Obligations
- Definition
- Transparency obligations are the EU AI Act's duties to tell people when they are interacting with an AI system, to mark AI-generated content, and to give deployers of high-risk systems the information…
Tree-Based Models
- Definition
- Tree-based models are machine learning models built from decision trees, either a single tree or an ensemble of many, such as a random forest or gradient-boosted trees, and are the usual first…
True Negatives
- Definition
- True negatives are cases a model correctly predicts as negative, such as a routine email that a phishing filter correctly lets through. They are usually the largest cell in a confusion matrix.
True Positives
- Definition
- True positives are cases a model correctly predicts as positive, such as a shipment flagged as likely to arrive late that does arrive late. They are the numerator of both precision and…
Trustworthy AI
- Definition
- Trustworthy AI is an AI system that is valid, reliable, safe, secure, accountable, transparent, explainable, privacy-enhanced, and fair, as defined by the NIST AI Risk Management Framework. Each property is something a…
Turing Test
- Definition
- The Turing test is a thought experiment in which a machine counts as intelligent if a human judge, conversing by text, cannot tell it from a person. It is a cultural benchmark…
Type System
- Definition
- A type system in an AI platform is a catalog of formally defined object types, with attributes, relationships, and rules, that describes data, models, and services so applications are built against types…
Underfitting
- Definition
- Underfitting happens when a model is too simple to capture the real patterns in the data, so it performs poorly on both the training data and new data.
Unified Data
- Definition
- Unified data is business information from many source systems brought together under shared identifiers and definitions, so that one query, report, or AI agent can work across all of it consistently.
Unstructured Data
- Definition
- Unstructured data is information without a predefined data model, such as documents, emails, images, audio, and chat transcripts, which must be interpreted before software can analyze it.
Unsupervised Learning
- Definition
- Unsupervised learning is machine learning on data without labeled answers, where the model finds structure on its own, such as natural groupings, unusual records, or a compact summary of many variables.
Use Case (AI)
- Definition
- An AI use case is a specific business task where AI does defined work, described by who uses it, what data it reads, what it produces, and how success is measured.
Use Case Prioritization
- Definition
- Use case prioritization is the process of scoring candidate AI projects on business value, feasibility, and risk, then ranking them so a company funds the few most likely to deliver first.
Validation Data
- Definition
- Validation data is a portion of labeled data held out of training and used during development to compare candidate models, tune their settings, and decide when to stop training.
Vector Database
- Definition
- A vector database is a data store built to hold embedding vectors with their metadata and to find the vectors nearest to a query quickly, which makes it the retrieval layer for…
Vector Search
- Definition
- Vector search finds the stored items whose embedding vectors are closest to a query vector, which returns results by similarity of meaning instead of by matching the exact words.
Video Analytics
- Definition
- Video analytics applies computer vision to live or recorded video to detect events, count objects, and measure activity, turning camera footage into alerts and operational data.
Virtual Assistant
- Definition
- A virtual assistant is a conversational AI application that helps a person complete tasks across several systems on request, such as booking a room, checking a balance, or updating a ticket.
Vision-Language Model (VLM)
- Definition
- A vision-language model (VLM) is a large language model that also accepts images, so it can describe photos, read documents, answer questions about charts, and follow instructions that mix text and pictures.
Visual Inspection (AI Quality Inspection)
- Definition
- AI visual inspection uses cameras and trained vision models to check products for defects, missing components, and assembly errors during production, replacing or assisting manual inspection.
Visual Search
- Definition
- Visual search finds items that look like a query image instead of matching typed keywords. It converts images to embeddings and retrieves the nearest matches from a catalog or archive.
Web Scraping
- Definition
- Web scraping is the automated extraction of data from websites by software, and a web crawler discovers pages by following links. Both feed pricing, research, and AI training data pipelines.
Workflow Automation
- Definition
- Workflow automation is the use of software to move tasks, documents, and data through a defined sequence of steps, using rules and triggers so each step starts without someone passing the work…
XGBoost
- Definition
- XGBoost is an open-source software library for training gradient-boosted decision trees, valued for its accuracy on tabular data, its speed on ordinary hardware, and its support across common programming languages.
Zero Trust
- Definition
- Zero trust is a security model in which no user, device, or service is trusted because of its network location, and every request to every resource is authenticated and checked against policy.
Zero-Shot Learning
- Definition
- Zero-shot learning is a model's ability to perform a task or recognize a category it was never explicitly trained on, using only a description of what is wanted.
Zero-Trust AI Agent
- Definition
- A zero-trust AI agent is an agent deployed under zero-trust security principles: it authenticates for every request, holds only the permissions a task needs, and has every action verified and logged rather…
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