AI Glossary
The definitive dictionary for AI, Machine Learning, and Governance terminology. From Flash Attention to RAG — look up any term.
E
Early Stopping
A regularization technique where training is halted when the model's performance on validation data stops improving, even if training loss continues to decrease. It prevents overfitting by finding the optimal training duration.
Edge Inference
Running AI models directly on local devices (phones, IoT sensors, cameras) rather than sending data to the cloud. This reduces latency, preserves privacy, and works without internet connectivity.
Elastic Weight Consolidation
A technique for continual learning that identifies which weights are important for previously learned tasks and penalizes changes to those weights during new learning.
Embedding
A numerical representation of data (text, images, etc.) as a vector of numbers in a high-dimensional space. Similar items are placed closer together in this space, enabling machines to understand semantic relationships.
Embedding Dimension
The number of numerical values in a vector embedding. Higher dimensions can capture more nuanced relationships but require more storage and computation.
Embedding Drift
Changes in embedding vector distributions over time as the underlying data, vocabulary, or user behavior shifts. Drift degrades retrieval quality in RAG and search systems.
Embedding Fine-Tuning
Adapting a pre-trained embedding model to a specific domain or task by further training it on domain-specific data, improving retrieval quality for specialized applications.
Embedding Model
A specialized model designed to convert text, images, or other data into vector embeddings. Embedding models are optimized for producing meaningful numerical representations rather than generating text.
Embedding Space
The high-dimensional geometric space in which embeddings exist. In this space, the distance and direction between points encode semantic relationships between the items they represent.
Embeddings as a Service
Cloud APIs that convert text or other data into vector embeddings without requiring users to host or manage embedding models themselves.
Emergent Behavior
Capabilities that appear in large AI models that were not explicitly trained for and were not present in smaller versions. Emergent abilities seem to appear suddenly at certain scale thresholds.
Encoder-Decoder
An architecture where the encoder compresses input into a fixed representation and the decoder generates output from that representation. This structure is used in translation, summarization, and image captioning.
Ensemble Learning
A strategy that combines multiple models to produce better predictions than any single model alone. Ensemble methods leverage the diversity of different models to reduce errors.
Epoch
One complete pass through the entire training dataset during model training. Models typically require multiple epochs to learn effectively, with each pass refining the model's understanding.
Ethical AI
AI development practices that explicitly consider moral implications, societal impact, and human values throughout the design, development, and deployment lifecycle.
Ethical Hacking of AI
The practice of systematically testing AI systems for vulnerabilities, biases, and failure modes with the goal of improving safety and robustness before malicious actors find the same weaknesses.
ETL
Extract, Transform, Load — a data integration process that extracts data from source systems, transforms it into a usable format, and loads it into a destination system.
EU AI Act
The European Union's comprehensive regulatory framework for artificial intelligence, establishing rules based on risk levels. It categorizes AI systems from minimal to unacceptable risk with corresponding compliance requirements.
Evaluation
The systematic process of measuring an AI model's performance, safety, and reliability using various metrics, benchmarks, and testing methodologies.
Evaluation Framework
A structured system for measuring AI model performance across multiple dimensions including accuracy, safety, fairness, robustness, and user satisfaction.
Evaluation Harness
A standardized testing framework for running AI models through suites of benchmarks and evaluation tasks. It ensures consistent, reproducible evaluation across models.
Existential Risk
The risk that advanced AI systems could pose a threat to the long-term survival or flourishing of humanity. This is the most serious concern in the AI safety research community.
Expert System
An early AI system that mimics human expertise in a specific domain using a knowledge base of rules and facts. Expert systems were the dominant AI approach in the 1980s.
Explainability
The ability to understand and articulate how an AI model reaches its decisions or predictions. Explainable AI (XAI) makes the decision-making process transparent and comprehensible to humans.
Explainable AI
The subfield focused on making AI decision-making processes understandable to humans. XAI techniques provide insights into why a model made a specific prediction.
Exploding Gradient
A training problem where gradients become extremely large during backpropagation, causing weight updates to be so drastic that the model becomes unstable and training diverges.
Exploration vs Exploitation
The fundamental tradeoff in reinforcement learning between trying new actions (exploration) to discover potentially better strategies and using known good actions (exploitation) to maximize current reward.