AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
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A/B Testing
A controlled experiment comparing two versions (A and B) of a system, feature, or model to determine which performs better. Users are randomly assigned to each version and outcomes are measured.
Accountability
The principle that there must be clear responsibility and liability for AI system decisions and their outcomes. Someone must be answerable when AI causes harm.
Accuracy
The percentage of correct predictions out of all predictions made by a model. While intuitive, accuracy can be misleading for imbalanced datasets.
Activation Function
A mathematical function applied to the output of each neuron in a neural network that introduces non-linearity. Without activation functions, a neural network would just be a series of linear transformations.
Active Learning
A training strategy where the model identifies the most informative unlabeled examples and requests human labels only for those. This minimizes labeling effort by focusing on the examples that matter most.
Adam Optimizer
An adaptive optimization algorithm that combines momentum and adaptive learning rates for each parameter. Adam maintains running averages of both gradients and squared gradients.
Adversarial Attack
An input deliberately crafted to fool an AI model into making incorrect predictions. Adversarial examples often look normal to humans but cause models to fail spectacularly.
Adversarial Training
A defense technique where adversarial examples are included in the training data to make the model more robust against attacks. The model learns to handle both normal and adversarial inputs.
Agent Memory
Systems that give AI agents persistent storage for facts, preferences, and conversation history across sessions. Memory enables agents to build cumulative knowledge over time.
Agentic AI
AI systems designed to operate with high autonomy — planning, executing, and adapting without constant human oversight. Agentic AI emphasizes independent action-taking to accomplish user goals.
Agentic Memory Systems
Architectures for managing different types of memory in AI agents — working memory for current tasks, episodic memory for past interactions, and semantic memory for accumulated knowledge.
Agentic RAG
An advanced RAG pattern where an AI agent dynamically decides what to retrieve, how to refine queries, and when to search again based on the quality of initial results.
Agentic Workflow
A multi-step process where an AI agent autonomously plans, executes, evaluates, and iterates on tasks, making decisions at each step rather than following a fixed pipeline.
AI Agent
An AI system that can autonomously plan, reason, and take actions to accomplish goals. Unlike simple chatbots, agents can use tools, make decisions, execute multi-step workflows, and adapt their approach based on results.
AI Alignment Tax
The performance cost of making AI models safer and more aligned with human values. Safety training sometimes reduces raw capability on certain tasks.
AI Chip
A semiconductor designed specifically for artificial intelligence workloads, optimized for the mathematical operations (matrix multiplication, convolution) that neural networks require.
AI Coding Assistant
An AI tool that helps developers write, debug, review, and refactor code through natural language interaction and code completion. Modern coding assistants use LLMs fine-tuned on code.
AI Democratization
Making AI technology accessible to a broader range of people and organizations, regardless of technical expertise or resources. Includes open-source models, no-code tools, and affordable APIs.
AI Ethics
The study of moral principles and values that should guide the development and deployment of AI systems. It addresses questions of fairness, accountability, transparency, privacy, and the societal impact of AI.
AI Governance
The frameworks, policies, processes, and organizational structures that guide the responsible development, deployment, and monitoring of AI systems within organizations and across society.
AI Literacy
The ability to understand, evaluate, and effectively use AI systems. AI literacy includes knowing what AI can and cannot do, how it works at a conceptual level, and how to critically assess AI outputs.
AI Maturity Model
A framework that describes the stages of an organization's AI capability, from initial experimentation through scaled deployment to AI-driven transformation.
AI Memory
Systems that give AI models the ability to retain and recall information across conversations or sessions. Memory enables persistent context, user preferences, and accumulated knowledge.
AI Orchestration Layer
The middleware that coordinates AI model calls, tool execution, memory management, and error handling in complex AI applications. It manages the flow between components.
AI Product Management
The discipline of managing AI-powered products, which requires understanding both traditional product management and the unique characteristics of AI systems (uncertainty, data dependency, continuous learning).
AI Regulation
Government rules and legislation governing the development, deployment, and use of artificial intelligence. AI regulation is rapidly evolving worldwide.
AI Risk Management
The systematic process of identifying, assessing, mitigating, and monitoring risks associated with AI systems. NIST's AI Risk Management Framework provides a comprehensive approach.
AI Safety
The research field focused on ensuring AI systems operate reliably, predictably, and without causing unintended harm. It spans from technical robustness to long-term existential risk concerns.
AI Supply Chain
The end-to-end ecosystem of components needed to build and deploy AI, from chip manufacturing and cloud infrastructure through data, models, tools, and applications.
AI Transformation
The comprehensive organizational change process of integrating AI across business functions, processes, and strategy. It goes beyond individual AI projects to fundamentally rethink how work gets done.
Alignment
The challenge of ensuring AI systems behave in ways that match human values, intentions, and expectations. Alignment aims to make AI helpful, honest, and harmless.
Annotation
The process of adding labels, tags, or metadata to raw data to make it suitable for supervised machine learning. Annotation can involve labeling images, transcribing audio, or tagging text.
Anomaly Detection
Techniques for identifying data points, events, or observations that deviate significantly from expected patterns. Anomalies can indicate fraud, equipment failure, security breaches, or other important events.
Anthropic
An AI safety company founded by former OpenAI researchers, focused on building safe and beneficial AI. Anthropic developed Claude and pioneered Constitutional AI.
API
Application Programming Interface — a set of rules and protocols that allow different software applications to communicate with each other. In AI, APIs let developers integrate AI capabilities into their applications.
Approximate Nearest Neighbor
An algorithm that finds vectors approximately closest to a query vector, trading perfect accuracy for dramatic speed improvements. ANN makes vector search practical at scale.
Artificial General Intelligence
A hypothetical AI system with human-level cognitive abilities across all domains — able to reason, learn, plan, and understand any intellectual task that a human can. AGI does not yet exist.
Artificial Intelligence
The broad field of computer science focused on creating systems capable of performing tasks that typically require human intelligence. This includes learning, reasoning, problem-solving, perception, and language understanding.
Artificial Superintelligence
A theoretical AI system that vastly surpasses human intelligence across all domains including creativity, problem-solving, and social intelligence. ASI remains purely hypothetical.
ASIC
Application-Specific Integrated Circuit — a chip designed for a single specific purpose. In AI, ASICs like Google's TPUs are designed exclusively for neural network operations.
Attention Head
A single attention computation within multi-head attention. Each head independently computes attention scores, allowing different heads to specialize in different types of relationships.
Attention Map
A visualization showing which parts of the input an AI model focuses on when making predictions. Attention maps reveal the model's internal focus patterns.
Attention Mechanism
A component in neural networks that allows the model to focus on the most relevant parts of the input when producing each part of the output. It assigns different weights to different input elements based on their relevance.
Attention Score
The numerical value representing how much one token should focus on another token in the attention mechanism. Higher scores mean stronger relationships between tokens.
Attention Sink
A phenomenon in transformers where the first few tokens in a sequence receive disproportionately high attention scores regardless of their content, acting as 'sinks' for excess attention.
Attention Window
The range of tokens that an attention mechanism can attend to in a single computation. Different attention patterns (local, global, sliding) use different window sizes.
Audit
A systematic examination of an AI system's data, algorithms, processes, and outcomes to verify compliance, fairness, accuracy, and adherence to stated principles.
Autoencoder
A neural network that learns to compress data into a lower-dimensional representation (encoding) and then reconstruct it back (decoding). It learns what features are most important for faithful reconstruction.
AutoML
Automated Machine Learning — tools and techniques that automate the end-to-end process of applying machine learning, including feature engineering, model selection, and hyperparameter tuning.
Autonomous Agent Framework
A software framework providing the infrastructure for building AI agents including planning, memory, tool integration, error handling, and multi-agent coordination.
Autonomous AI
AI systems capable of making decisions and taking actions independently without continuous human guidance. Autonomous AI can plan, execute, and adapt to changing circumstances on its own.
Autonomous Vehicle
A vehicle that can navigate and operate without human input using AI systems for perception (cameras, lidar), decision-making, and control. Self-driving technology uses computer vision, sensor fusion, and planning.