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Adoption is usually inferred from logins, which measures presence rather than use. A fuller picture comes from the interaction itself: what a person asks, which rules and content served the answer...
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A/B testing, also called split testing or controlled experimentation, is the practice of randomly assigning users (or sessions, or visits) to two or more variants of an experience and measuring the...
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Abstraction is how industries absorb volatility. Database drivers made applications survive engine changes; hypervisors made workloads survive hardware; container runtimes made deployments survive...
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Abstractive Summarization generates new sentences that capture the meaning of source material — paraphrasing, restructuring, and synthesizing rather than copying existing text. The approach mirrors...
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Thresholds make deployment decisions accountable. Without one, readiness is a judgement call made under commercial pressure, and systems go live because the date arrived. With one, the question be...
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Remediation handled document by document is unsustainable above a few thousand files, and handled as a project produces an estate compliant on one date and drifting immediately afterwards. Scaling...
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Confirmation is the control that separates a helpful agent from an alarming one. The design question is which actions require it, because confirming everything trains people to click through and c...
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The Action Space is the set of all possible actions an AI agent can take in its environment — every tool it can call, every API it can hit, every command it can issue. The concept comes from reinfo...
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An Activation Function introduces non-linearity into a neural network, allowing it to learn complex patterns rather than just linear relationships. Without activation functions, even a hundred-laye...
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Active Learning lets a model request labels for the data points it finds most informative, dramatically reducing labeling cost. Instead of randomly labeling thousands of examples, the model identif...
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Adam, short for Adaptive Moment Estimation, is the adaptive optimization algorithm introduced by Kingma and Ba in 2014 that has become the default optimizer for deep learning including LLM pretrain...
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This entry has been superseded by the consolidated definition of the same term.
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AdamW is a variant of the Adam optimizer introduced by Loshchilov and Hutter in 2017 that decouples weight decay from the gradient-based parameter updates, producing better generalization and train...
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This entry has been superseded by the consolidated definition of the same term.
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Adapter layers are a PEFT technique introduced by Houlsby et al. in 2019 that inserts small bottleneck feed-forward modules between the frozen layers of a pretrained transformer , training only the...
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This entry has been superseded by the consolidated definition of the same term.
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An Adversarial Attack is a deliberate attempt to manipulate an AI system through crafted inputs designed to cause incorrect or unintended behavior. Famous adversarial attacks include the demonstrat...
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An Adversarial Example is an input crafted to fool an AI model — often by introducing perturbations imperceptible to humans but devastating to model accuracy. The phenomenon was first widely docume...
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Adversarial examples are inputs deliberately crafted to cause an AI model to produce incorrect or harmful outputs, often by introducing perturbations imperceptible to humans but disruptive to the m...
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Authority is distinct from capability. An agent may be technically able to send mail, write to a system of record or approve a request, and separately permitted to do so. Conflating the two is how...
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Budgets designed for human users assume human pacing. An agent issues requests at machine rate and can exhaust a monthly allocation in an afternoon, which makes per-user ceilings an inadequate con...
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Agent handoff is the pattern where one LLM agent delegates a conversation or task to another agent better suited to handle it — analogous to a customer-service representative transferring a call to...
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Agent memory is the persistent state that an agentic LLM system maintains across interactions, enabling continuity, learning, and personalization beyond a single conversation. Common memory types i...
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This entry has been superseded by the consolidated definition of the same term.
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This entry has been superseded by the consolidated definition of the same term.
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An agent supervisor is the meta-agent in a multi-agent system whose role is not to do the work directly but to plan, dispatch, monitor, and synthesize across subordinate agents — essentially playin...
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Agentic AI refers to systems where language models plan, reason, and act with significant autonomy — chaining tools, calling APIs, and pursuing multi-step goals without step-by-step human direction...
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AGI (Artificial General Intelligence) refers to AI systems that match or exceed human-level cognitive ability across the full range of cognitive tasks — including reasoning, learning, planning, cre...
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Selection only becomes a real decision when switching is cheap. Where it is expensive, the organization standardizes and defends the standard, and the choice calcifies into an assumption nobody re...
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Most organizations have an AI acceptable-use policy and no mechanism that enforces it. Staff acknowledge it during onboarding, and thereafter compliance depends on recollection and goodwill. Enfor...
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AI Accountability is the principle that humans — not algorithms — bear responsibility for the outcomes of AI systems. It requires clear chains of responsibility, decision rights, and consequences f...
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An AI Agent is a system that perceives its environment, makes decisions, and takes actions to achieve goals — often calling tools or APIs autonomously. Modern AI agents combine an LLM brain with a ...
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AI Alignment is the technical and philosophical challenge of making AI systems pursue goals that match human values and intentions — including goals their designers didn't anticipate but would endo...
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An AI Approval Workflow is a structured process that routes proposed AI use cases through legal, security, AI ethics, and business approvers before development or deployment. Typical workflows incl...
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An AI Assistant is a conversational AI system that helps users accomplish tasks via natural language. Consumer examples include OpenAI's ChatGPT, Anthropic's Claude, Google's Gemini, Apple Intellig...
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An AI Audit Trail is a complete, tamper-evident record of every AI interaction — including who used the AI, when, what they asked, what context was retrieved, what the model produced, and what acti...
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The Blueprint for an AI Bill of Rights is a non-binding policy framework published by the White House Office of Science and Technology Policy (OSTP) in October 2022, articulating five principles fo...
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This entry has been superseded by the consolidated definition of the same term.
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An AI Center of Excellence (CoE) is a centralized team that sets standards, builds reusable platforms, and supports business-unit AI delivery across an enterprise. The CoE typically owns AI strateg...
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Prompt and skill edits are code changes with production impact, and they are routinely made without any of the controls a code change would attract — no review, no staging, no rollback plan, no re...
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AI Compliance is the operational practice of meeting legal, regulatory, and contractual obligations applicable to AI systems — including evidence collection, documentation, audit response, and ongo...
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AI Conformity is the state of an AI system meeting the requirements of applicable laws, standards, and contractual obligations. The term is used broadly to encompass conformity assessment under the...
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An AI Copilot is an AI assistant embedded inside a tool — a code editor, document app, CRM, or design platform — that supports the user without taking full control. The term was popularized by GitH...
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AI Discrimination occurs when an AI system produces decisions that disadvantage protected groups in ways prohibited by law, contract, or AI ethics policy. The concept extends traditional anti-discr...
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AI Documentation is the collection of artifacts describing an AI system — model cards, datasheets for datasets, system architecture diagrams, evaluation reports, risk assessments, monitoring runboo...
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AI Ethics is the field that examines how AI systems should be designed and deployed to respect human values, rights, and well-being. Core principles across most frameworks include fairness, account...
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An AI Ethics Board is a cross-functional group of internal and external stakeholders that reviews high-risk AI use cases and advises leadership on AI ethics and AI policy. Boards typically include ...
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AI Fairness is the discipline of ensuring AI systems treat individuals and groups equitably across protected and relevant characteristics. Fairness is not one thing — researchers have catalogued do...
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AI Governance is the framework of policies, roles, controls, and processes an organization uses to manage AI responsibly across its lifecycle — from idea to retirement. Mature AI governance include...
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An AI Impact Assessment, also called an Algorithmic Impact Assessment or AIA, is a structured documentation and review process that evaluates the potential impacts of an AI system on individuals, g...
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This entry has been superseded by the consolidated definition of the same term.
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An AI Incident is an event in which AI behavior causes — or could have caused — harm, financial loss, regulatory violation, or significant operational disruption. Real-world AI incidents tracked in...
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The AI Lifecycle describes the stages an AI system passes through: ideation, design, data collection, training, validation, deployment, monitoring, iteration, and retirement. Each stage involves sp...
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AI Lifecycle Management governs every stage of an AI system's life — from ideation and design through development, validation, deployment, monitoring, and eventual retirement. Each stage has distin...
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An AI Maturity Model assesses how advanced an organization's AI capabilities are across dimensions like strategy, data, talent, infrastructure, governance, and ethics — typically scoring each on a ...
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An AI Notified Body is a third-party organization designated by an EU member state to perform conformity assessments for certain high-risk AI systems under the EU AI Act. Notified Bodies must demon...
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The AI Operating Model defines how AI is organized, funded, governed, and delivered inside a company — the org chart, roles, decision rights, and processes of an enterprise AI function. Common patt...
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AI Orchestration coordinates multiple models, tools, prompts, and data sources to deliver complex enterprise AI workflows. Rather than a single LLM call, orchestration platforms route requests thro...
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AI Penetration Testing extends traditional security pen testing to AI-specific attack surfaces. While AI red teaming focuses broadly on undesirable behavior including bias and safety, AI pen testin...
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An AI Plugin extends a language model with access to external capabilities — search, calculations, databases, browsing, image generation, or specialized APIs. The category was popularized by ChatGP...
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AI Policy is a written organizational guideline that defines acceptable AI use, required controls, and roles and responsibilities for everyone interacting with AI systems. Typical AI policies cover...
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AI Red Teaming is the practice of probing AI systems for vulnerabilities, biases, jailbreaks, prompt-injection susceptibility, and unsafe behaviors before adversaries find them in production. Red t...
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AI Risk Management identifies, assesses, mitigates, and monitors risks specific to AI systems across the lifecycle. Risk categories include AI-specific concerns (bias, hallucination, prompt injecti...
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An AI Risk Register is a living catalog of identified AI risks across the enterprise — each entry capturing risk description, likelihood, impact, mitigation status, and owner. Common risk categorie...
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AI Safety is the field focused on preventing AI systems from causing harm — through accidents, misuse, misalignment, or unforeseen capabilities. AI safety operates at multiple levels: technical saf...
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An AI Skill is a packaged, reusable capability that combines a prompt, a knowledge base, tools, and configuration into a single discoverable unit — like a function in software, but for AI capabilit...
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AI Stewardship assigns clear ownership and accountability for each AI system to a specific person or team responsible for its design, deployment, and lifecycle outcomes. The principle borrows from ...
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AI Strategy is the long-term plan that aligns AI investments with business objectives, technical capabilities, and risk tolerance. A mature AI strategy answers fundamental questions: which use case...
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An AI System Inventory is a comprehensive registry of every AI system an organization builds, buys, or uses — including third-party APIs, embedded vendor AI, and internal models. The inventory typi...
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An AI taxonomy is the structured hierarchical classification system applied to AI-related content — documents, model outputs, retrieved chunks, conversations, user queries — that enables filtering,...
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AI Transparency is the practice of making AI systems' purpose, capabilities, limitations, training data, and behaviors visible to relevant stakeholders. Transparency operates at multiple levels: to...
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An AI Use Case Registry catalogs every AI application across an organization — what it does, who owns it, what risks it carries, and what controls apply. Each use case is typically registered befor...
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An AI use policy answers four questions: which tools are approved, what categories of information may be entered into them, when human review is mandatory, and what happens when someone gets it wr...
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An AI Vulnerability is a weakness in an AI system that can be exploited by attackers to cause unauthorized behavior, data leakage, or harm. Categories include prompt injection (manipulating model b...
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AI watermarking is the family of techniques for embedding detectable signatures in AI-generated content — text, images, audio, video — that allow downstream verifiers to determine that the content ...
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AIOps (Artificial Intelligence for IT Operations) applies AI to IT infrastructure management — including event correlation, anomaly detection, predictive maintenance, and root-cause analysis across...
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Air-gapping is the strongest isolation posture and the one most often assumed impossible for AI, because the prevailing model is a hosted API. It becomes feasible when inference runs locally, at w...
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An Algorithm is a precise, step-by-step procedure for solving a problem or producing an output. In AI, algorithms range from simple decision trees and linear regression to sophisticated deep neural...
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Algorithmic accountability is the policy and engineering principle that organizations deploying automated decision-making systems must be answerable for those decisions — including the ability to e...
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The Algorithmic Accountability Act is proposed U.S. federal legislation (versions introduced 2019, 2022, and 2023) that would require companies to conduct impact assessments of automated decision s...
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Algorithmic Bias is systematic, repeatable unfairness in an AI system's output that disadvantages certain groups — often along lines of race, gender, age, disability, or socioeconomic status. Famou...
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ALiBi, short for Attention with Linear Biases, is a positional encoding technique introduced by Press, Smith, and Lewis in a 2021 paper that adds linear-distance penalties directly to attention sco...
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This entry has been superseded by the consolidated definition of the same term.
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Alignment tax is the term used to describe the trade-off between safety properties and raw capability that often emerges when LLMs undergo RLHF , refusal training , Constitutional AI , or other saf...
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This entry has been superseded by the consolidated definition of the same term.
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AlpacaEval is an automated evaluation framework for chat-tuned LLMs released by Stanford's Tatsu Lab in 2023, scoring models by win rate against a reference model (originally text-davinci-003) on a...
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This entry has been superseded by the consolidated definition of the same term.
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Alt text, the alt attribute on HTML image elements, is the textual alternative that conveys an image's information to users who cannot see it — primarily screen-reader users who hear the alt text s...
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Coverage initiation is a commitment rather than a citation. A research firm that initiates coverage takes on the obligation to follow a vendor's trajectory, which means it has judged the company m...
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Angular distance is a variant of cosine similarity that converts the cosine value into a proper distance metric satisfying the triangle inequality, computed as arccos(cosine_similarity) divided by ...
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Annoy, an acronym for Approximate Nearest Neighbors Oh Yeah, is an open-source ANN library released by Spotify in 2015 that uses random projection trees for efficient similarity search. The algorit...
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True anonymization is a high bar and frequently claimed for processing that does not meet it. Removing direct identifiers leaves quasi-identifiers — dates, locations, rare conditions, role titles ...
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Generation is variable by design, which is unhelpful when consistency is the requirement. Promotion converts a good answer into a fixed one: after review, the response is stored and served for equ...
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Conventional AI cost scales with adoption: more users ask more questions and the bill rises proportionally, which makes success expensive and creates pressure to limit usage. Reuse inverts the cur...
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Caching improves consistency and cost and introduces a failure mode: an answer that was correct when approved continues to be served after the policy it described was amended. Nothing errors, and ...
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An approval chain is a sequence of required decisions, and governing it means three things are fixed in advance rather than negotiated per document: who holds each decision, what happens when one ...
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Approval is a point-in-time judgement about a specific artefact under specific conditions. Lineage records those conditions, which is what makes an old approval meaningful — an answer approved two...
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Approximate Nearest Neighbor (ANN) search finds the closest matches in a vector space without examining every candidate — trading a small amount of accuracy for enormous speed gains. Exact nearest ...
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Approximate Nearest Neighbor search, abbreviated ANN, is a class of algorithms that find vectors close to a query vector without guaranteeing they are the absolute closest, trading a small accuracy...
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ARC, short for the AI2 Reasoning Challenge, is a benchmark introduced by Allen Institute for AI in 2018 containing 7,787 grade-school-level multiple-choice science questions from US standardized te...