Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Neural Networks
Activation Function
A mathematical function that determines the output of a neural network layer.
Machine Learning
Active Learning
A machine learning paradigm where the model can query a user to obtain labels for new data points actively.
signal-processing
adaptive-filtering
A technique for dynamically adjusting filter parameters based on input signal characteristics.
Machine Learning
adaptive-learning
A method where the system optimizes its learning process based on user interactions and performance.
Machine Learning
Adversarial Training
Adversarial training improves model robustness by training on deliberately perturbed inputs that are designed to expose failures under a defined threat model.
Security
adversarial-example
An input designed to fool a machine learning model into making incorrect predictions.
Agents
Agent memory
Agent memory is the state an AI agent keeps across steps or sessions, such as scratchpad notes, retrieved facts, user preferences, or task history.
Agents
Agent2Agent protocol
Agent2Agent, or A2A, is an open protocol from Google for agent-to-agent communication, capability discovery, task management, and artifact exchange.
Agents
Agentic AI
Agentic AI refers to AI systems that can plan, call tools, maintain task state, and take multi-step actions toward a goal.
Agent security
Agentjacking
Agentjacking is an informal term for hijacking an AI agent's tools, context, or execution path so it performs attacker-directed actions.
Safety
AI guardrails
AI guardrails are controls that constrain, monitor, or validate model behavior before outputs or tool actions reach users or systems.
General Concepts
Algorithm
A step-by-step procedure for calculations or problem-solving.
machine-learning
anomaly-detection
A technique used to identify unusual patterns or outliers in data.
Foundations
Artificial general intelligence
Artificial general intelligence (AGI) is a research goal: a system that can learn and carry out a wide range of tasks, not one narrow job. No model in this catalog is AGI.
Foundations
Artificial intelligence
Artificial intelligence (AI) is the field of building software that performs tasks which usually need human judgment, such as understanding language, recognizing patterns, planning, or generating text, images, and audio.
Deep Learning
Artificial Neural Network (ANN)
A computational model inspired by the way biological neural networks function.
Deep Learning
Attention
Attention is the mechanism that lets a transformer weigh which other tokens matter when computing the next representation.
Interactive Technology
Augmented Reality
An interactive experience that overlays digital information onto the real world.
neural networks
autoencoder
An autoencoder is a type of neural network used for unsupervised learning of efficient representations.
Data Science
automated-data-analysis
The use of algorithms and software to analyze data without human intervention.
Machine Learning
AutoML
AutoML automates parts of the machine-learning workflow such as preprocessing, model selection, and hyperparameter search.