(1+ε)-approximate nearest neighbor search - A (1+epsilon)-approximate nearest neighbor search returns a data point whose distance from a query is at most 1+epsilon times the distance of the true...
A* algorithm - A* is a search algorithm that finds the lowest-cost path from a starting point to a goal by combining two numbers for each option: the known cost so far and...
Ablation - An ablation is a controlled experiment that removes one part of a model or system and measures what changes, to learn how much that part actually contributes.
Abstractive Summarization - Abstractive summarization is generating a summary in new words that capture the meaning of the source, rather than selecting and copying the source's own...
Accumulated local effects - Accumulated local effects, or ALE, describe how a fitted model's predictions change with a feature by averaging local prediction differences and...
Action - In AI, an action is anything an agent can do that changes its environment or its relationship to it: moving, sending, writing, booking, buying.
Action model learning - Action model learning is the process of learning descriptions of what actions require and how they change an environment.
Active learning - Active learning is a machine-learning approach where the system chooses which examples it wants labeled next, instead of learning from a fixed or random set.
Adam - Adam is the most widely used algorithm for training neural networks: an optimization method that adjusts each of the model's millions of internal numbers...
Adaptive boosting - Adaptive boosting, or AdaBoost, is an ensemble method that combines many simple, weak models into one strong predictor by training them in sequence, each...
Adaptive Learning Rate Methods - Adaptive learning rate methods are training algorithms that give each parameter of a model its own automatically adjusted step size during learning, instead...
Additive regression - Additive regression is a modeling approach that builds a prediction by adding up the contributions of many simple components, each learned from the data...
Adversarial machine learning - Adversarial machine learning studies deliberate attempts to manipulate machine-learning systems and methods for evaluating or reducing those risks.
Agent experience - Agent experience, often shortened to AX, concerns how effectively AI agents discover, understand and use a product or service on a user's behalf.
Agent harness - An agent harness is the software surrounding an AI model that enables it to work through tasks.
Agent verification - Agent verification is the examination of whether an autonomous software agent satisfies specified requirements.
Agentive logic - Agentive logic is a family of formal approaches for reasoning about what agents do, can bring about or are responsible for bringing about.
Agglomerative clustering - Agglomerative clustering is a method that groups data by merging: it starts with every item as its own tiny cluster, then repeatedly joins the two closest...
AI-assisted software development - AI-assisted software development is the use of AI tools to help people design, write, understand, test and maintain software.
AI-complete - AI-complete describes a problem believed to require the breadth of capability associated with general artificial intelligence to solve fully.
AI infrastructure - AI infrastructure is the combination of computing resources, data systems, networking and operational software used to develop, deploy and maintain AI...
AI Scaling Wall - AI Scaling Wall is an informal expression for a proposed limit or sharp slowdown in the improvements obtained by making AI systems larger or spending more...
AI washing - AI washing is the misleading presentation of a product, service or organization as using AI, or as benefiting from AI, beyond what the available evidence...
AI watermarking - AI watermarking is the embedding of a detectable signal in AI-generated or AI-altered content to help identify its origin.
AIXI - AIXI is a theoretical model of an intelligent agent introduced by Marcus Hutter.
Akaike Information Criterion - The Akaike Information Criterion, or AIC, is a score for comparing statistical models that balances fit against complexity: it rewards models that explain...
Algorithm - An algorithm is a precise, finite set of instructions for solving a problem or completing a task: the same steps, in the same order, producing a result from...
Algorithm selection - Algorithm selection is the task of choosing an algorithm from a set of candidates for a particular problem or problem instance.
Algorithmic accountability - Algorithmic accountability means that people and organizations can be held answerable for how algorithmic systems are designed, used and governed.
Algorithmic inference - Algorithmic inference is a computational approach to statistical inference that explicitly uses a data-generation or sampling mechanism to reason from...
Algorithmic probability - Algorithmic probability assigns probability to an output by considering programs that generate it, with shorter programs receiving greater weight.
Algorithmic transparency - Algorithmic transparency is the availability of understandable information about an algorithmic system's purpose, operation, data, limitations and use.
Almeida - Pineda recurrent backpropagation - Almeida - Pineda recurrent backpropagation is a method for calculating learning gradients in recurrent neural networks that settle to a stable fixed point.
Alternating decision tree - An alternating decision tree is a classification model that alternates between decision conditions and numerical prediction contributions.
Analogical modeling - Analogical modeling, in its specialized sense, is Royal Skousen's exemplar-based approach to predicting behavior from stored examples and their contexts.
And - or tree - An and - or tree represents a problem using two kinds of branching.
Anomaly - An anomaly is a data point or event that departs from what is normal in a way that deserves attention: the unusual transaction, the strange sensor reading...
Anomaly detection - Anomaly detection is the task of finding data points, events or patterns that differ markedly from what is normal, such as a fraudulent payment, a failing...
Application programming interface - An application programming interface, or API, is a defined way for one piece of software to ask another to do something or share data, without needing to...
Apriori Algorithm - The Apriori algorithm is a classic method for finding items that frequently occur together in a large set of transactions and turning them into association...
Argumentation framework - An argumentation framework is a formal representation of arguments and relationships between them, used to analyze which arguments can be accepted together.
ARKA descriptors in QSAR - ARKA descriptors are derived features used in quantitative structure - activity relationship modeling.
Artificial brain - Artificial brain is a broad label for an engineered system intended to reproduce, simulate or draw inspiration from aspects of a biological brain.
Artificial consciousness - Artificial consciousness is the possibility or proposed creation of subjective experience in an artificial system, and the field that investigates it.
Artificial general intelligence - Artificial general intelligence, or AGI, is a hypothetical form of AI that could learn and perform any intellectual task a human can, across domains, rather...
Artificial intelligence of things - Artificial intelligence of things, often shortened to AIoT, combines AI capabilities with connected devices in the Internet of Things.
Artificial intimacy - Artificial intimacy is the experience of closeness, emotional connection or a relationship with an artificial system, such as a conversational AI companion.
ASR-complete - ASR-complete is an informal expression for a task that brings together much of the difficulty of automatic speech recognition and related spoken-language...