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AI Glossary
Explanations of terms from the broad field of AI
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Continual Learning
Learning continuously without forgetting past knowledge.
Convolutional Neural Network (CNN)
Architecture for images and signals using convolution layers.
Copyright and AI
Rights in AI input/output and training data.
Creative Use
Using AI for idea generation, writing, or design.
Cross-Validation
Repeated train/test splits for robust estimation.
Customer Support Chatbot
Automated conversational systems for customer service.
Data
Raw information from which models learn.
Data Annotation
Labeling raw data for training AI models.
Data Pipeline
Automated flow from data collection to delivery.
Data Protection (GDPR)
Protection of personal data under EU law.
Data Quality
Accuracy, completeness, and relevance of data.
Deep Learning
Learning with deep neural networks containing many parameters.
Deepfake
AI-generated or manipulated realistic media.
Digital Transformation
Deep organizational change through digital technologies.
EU AI Act
European regulation defining risk-based AI categories.
Edge AI
Running AI models directly on local devices or sensors.
Embedding
Numerical vector representation of tokens or objects.
Evaluation Metrics
Measures for assessing model performance.
Explainable AI (XAI)
Making AI decisions understandable to humans.
Fairness in AI
Principle that models should not discriminate against groups.
Feature
Measurable input attribute used by a model.
Feature Engineering
Transforming raw data into informative model inputs.
Federated Learning
Decentralized training across devices with privacy protection.
Feedforward Network
Layers without feedback; data flow only forward.
Few-Shot Learning
Learning from only a few examples.
Fine-Tuning
Further training a pre-trained model on new data.
Fraud Detection
Detecting suspicious patterns in transactions using AI.
GPU / TPU
Specialized hardware for fast AI training and inference.
Generative Adversarial Network (GAN)
Generator and discriminator in adversarial training.
Gradient Descent
Iterative method to minimize the loss function.
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