AI Glossary

101 AI terms explained in simple language. Your go-to reference for understanding AI.

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Accuracy

The proportion of correct predictions among total predictions. Simple metric but can be misleading with imbalanced datasets.

Adversarial Examples

Inputs specifically designed to fool AI models into making mistakes. Important for understanding model robustness and security.

AGI (Artificial General Intelligence)

Hypothetical AI with human-level intelligence across all cognitive tasks. Contrasts with narrow AI, which excels at specific tasks.

AI Agent

An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve goals. Can use tools and APIs.

AI Alignment

The challenge of ensuring AI systems behave in accordance with human values and intentions. Critical for safe and beneficial AI development.

AI Bias

Systematic errors or unfairness in AI systems, often inherited from biased training data. A major concern in responsible AI development.

AI Safety

The field focused on developing AI systems that are safe, reliable, and beneficial. Addresses risks like misalignment, bias, and unintended consequences.

API (Application Programming Interface)

A way for software applications to communicate with each other. Most AI tools offer APIs for programmatic access to their capabilities.

Attention Mechanism

A technique in neural networks that allows models to focus on relevant parts of input data. Core component of transformer architectures.

Autoencoder

A neural network architecture that learns to compress data into a lower-dimensional representation and reconstruct it. Used for dimensionality reduction and generation.

Backpropagation

Algorithm for calculating gradients in neural networks by propagating errors backward through layers. Enables efficient training.

Batch Processing

Processing multiple data items together rather than one at a time. Improves efficiency in AI training and inference.

Batch Size

The number of training examples processed together in one iteration. Affects training speed, memory usage, and model performance.

Beam Search

A search algorithm used in AI text generation that explores multiple probable sequences simultaneously to find optimal outputs.

Chain-of-Thought Prompting

Prompting technique where you ask AI to show its reasoning step-by-step, improving performance on complex tasks.

Classification

A supervised learning task where models assign inputs to predefined categories. Examples include spam detection and image recognition.

Clustering

An unsupervised learning technique that groups similar data points together. Used for customer segmentation, anomaly detection, and more.

Confusion Matrix

A table showing true positives, false positives, true negatives, and false negatives in classification. Used to calculate accuracy, precision, and recall.

Context Window

The maximum amount of text (measured in tokens) that an AI model can process at once. Larger context windows allow for longer conversations and documents.

Contrastive Learning

A self-supervised learning approach where models learn by contrasting similar and dissimilar examples. Used in creating high-quality embeddings.

Cross-validation

A technique for assessing model performance by training and testing on different subsets of data. Helps detect overfitting and estimate generalization.

Data Augmentation

Artificially expanding training datasets by creating modified versions of existing data. Improves model generalization and reduces overfitting.

Deep Learning

A subset of machine learning using neural networks with multiple layers. Enables complex pattern recognition and is the foundation of modern AI.

Diffusion Model

A type of generative AI model that creates images by iteratively refining random noise. Used in tools like Stable Diffusion, DALL-E, and Midjourney.

Dimensionality Reduction

Techniques for reducing the number of features in data while preserving important information. Helps with visualization and computation.

Dropout

A regularization technique that randomly ignores neurons during training to prevent overfitting and improve generalization.

Early Stopping

Halting training when validation performance stops improving, preventing overfitting while maintaining good generalization.

Edge AI

Running AI models on local devices (edge) rather than cloud servers. Improves latency, privacy, and works offline.

Embeddings

Numerical representations of text, images, or other data that capture semantic meaning. Used for similarity search, clustering, and RAG systems.

Ensemble Learning

Combining multiple models to achieve better performance than individual models. Includes techniques like bagging and boosting.

Epoch

One complete pass through the entire training dataset during model training. Models typically train for multiple epochs.

Explainability (XAI)

The ability to understand and interpret how AI models make decisions. Important for trust, debugging, and regulatory compliance.

F1 Score

Harmonic mean of precision and recall, providing a single metric that balances both. Useful when class distribution is imbalanced.

Feature Engineering

The process of creating, transforming, and selecting features (input variables) to improve machine learning model performance.

Federated Learning

Training AI models across multiple decentralized devices without sharing raw data. Enhances privacy in machine learning.

Few-shot Learning

Providing an AI model with a few examples in the prompt to guide its behavior for a specific task without fine-tuning.

Fine-tuning

The process of further training a pre-trained AI model on specific data to adapt it for particular tasks or domains.

GAN (Generative Adversarial Network)

A type of generative AI with two networks: a generator creating samples and a discriminator evaluating them. Used in image generation before diffusion models.

Generative AI

AI systems that create new content such as text, images, audio, or video. Includes LLMs, diffusion models, and other creative AI tools.

GPT (Generative Pre-trained Transformer)

A family of large language models developed by OpenAI. GPT models are trained to predict the next word in a sequence, enabling them to generate coherent text.

Gradient Descent

An optimization algorithm that iteratively adjusts model parameters to minimize the loss function. Foundation of neural network training.

Grounding

Connecting AI outputs to verifiable sources or real-world facts to reduce hallucinations and increase reliability.

Hallucination

When an AI model generates false or nonsensical information that sounds plausible. A common challenge in LLMs that needs to be mitigated through various techniques.

Hyperparameter

Configuration settings for AI model training (like learning rate) that are set before training begins, as opposed to parameters learned during training.

Image Segmentation

Dividing images into regions or segments based on object boundaries. More precise than object detection, identifying exact pixel-level boundaries.

Inference

The process of using a trained AI model to make predictions or generate outputs on new data. Distinct from training phase.

Jailbreaking (AI)

Techniques to bypass safety guardrails in AI systems, making them produce restricted content. A concern for AI safety.

Knowledge Distillation

Training a smaller 'student' model to mimic a larger 'teacher' model, creating more efficient models with similar performance.

Latency

The time delay between input and output in AI systems. Lower latency means faster response times, crucial for real-time applications.

Learning Rate

Hyperparameter controlling how much model parameters change during training. Too high causes instability; too low slows learning.

LLM (Large Language Model)

A type of artificial intelligence model trained on vast amounts of text data to understand and generate human-like text. Examples include GPT-4, Claude, and Gemini.

Loss Function

A mathematical function measuring how far model predictions are from actual values. Models are trained to minimize loss.

Machine Learning (ML)

A branch of AI where systems learn patterns from data without explicit programming. Includes supervised, unsupervised, and reinforcement learning.

Machine Translation

Automatic translation of text or speech between languages using AI. Modern systems use neural networks for high-quality results.

MLOps

Practices for deploying, monitoring, and maintaining machine learning models in production. Combines ML with DevOps principles.

Model Checkpoint

Saving model state during training at specific intervals. Allows resuming training and recovering the best model version.

Model Parameters

The internal variables of an AI model learned during training. Model size is often measured in parameters (e.g., GPT-4 has trillions).

Model Serving

Infrastructure and processes for deploying trained models to make them available for inference in production environments.

Multimodal AI

AI systems that can process and generate multiple types of data such as text, images, audio, and video. Examples include GPT-4 Vision and Gemini.

Named Entity Recognition (NER)

NLP technique that identifies and classifies named entities (people, organizations, locations) in text.

Narrow AI

AI systems designed for specific tasks (like image recognition or language translation) rather than general intelligence. All current AI is narrow AI.

Neural Network

A computing system inspired by biological neural networks in brains. Consists of interconnected nodes (neurons) organized in layers that process and learn from data.

Object Detection

Computer vision technique that identifies and locates objects within images or videos. Used in autonomous vehicles, security, and more.

OCR (Optical Character Recognition)

Technology that converts images of text into machine-readable text. Enhanced by modern AI for better accuracy with handwriting and varied fonts.

Overfitting

When a model learns training data too well, including noise, resulting in poor performance on new data. Avoided through regularization and validation.

Perplexity (Metric)

A measurement of how well a language model predicts text. Lower perplexity indicates better prediction quality.

Precision

Metric measuring the proportion of positive predictions that are actually correct. Important when false positives are costly.

Prompt

The input text or instruction given to an AI model to generate a desired output. Well-crafted prompts are essential for getting quality results from AI tools.

Prompt Engineering

The practice of designing and optimizing prompts to get better responses from AI models. It involves understanding model capabilities and crafting effective instructions.

Quantization

Reducing the precision of model weights (e.g., from 32-bit to 8-bit) to decrease model size and increase inference speed with minimal accuracy loss.

RAG (Retrieval-Augmented Generation)

A technique that enhances AI responses by retrieving relevant information from external sources before generating output. Helps reduce hallucinations and provides up-to-date information.

Recall (Sensitivity)

Metric measuring the proportion of actual positives that are correctly identified. Important when false negatives are costly.

Red Teaming (AI)

Adversarial testing of AI systems to find vulnerabilities, biases, and safety issues before deployment.

Regression

A supervised learning task where models predict continuous numerical values rather than categories. Used for price prediction, forecasting, etc.

Regularization

Techniques to prevent overfitting by constraining model complexity. Includes dropout, L1/L2 penalties, and early stopping.

Reinforcement Learning

A machine learning paradigm where agents learn by interacting with an environment and receiving rewards or penalties for actions.

RLHF (Reinforcement Learning from Human Feedback)

A technique used to train AI models like ChatGPT by incorporating human preferences and feedback into the learning process.

Semantic Search

Search technology that understands meaning and context rather than just matching keywords. Powered by embeddings and vector databases.

Sentiment Analysis

AI technique for determining emotional tone in text (positive, negative, neutral). Used in customer feedback analysis and social media monitoring.

Speech Recognition

AI technology that converts spoken language into text. Used in transcription services, voice assistants, and accessibility tools.

Streaming Response

Delivering AI-generated output progressively as it's created, rather than waiting for complete generation. Improves perceived responsiveness.

Supervised Learning

A machine learning approach where models learn from labeled training data to make predictions on new, unseen data.

Synthetic Data

Artificially generated data that mimics real data. Used when real data is scarce, expensive, or sensitive. Can be created by AI.

System Prompt

Initial instructions given to an AI model that set its behavior, role, and constraints for a conversation or task.

Temperature

A parameter controlling randomness in AI text generation. Lower values make output more deterministic; higher values more creative and varied.

Text Summarization

AI capability to condense long documents into shorter summaries while preserving key information. Can be extractive or abstractive.

Text-to-Image

AI technology that generates images from text descriptions. Powered by diffusion models and other generative techniques.

Text-to-Speech (TTS)

Technology that converts written text into spoken audio. Modern AI TTS systems like ElevenLabs produce highly natural-sounding voices.

Text-to-Video

AI systems that create videos from text prompts. Emerging technology exemplified by tools like Runway, Pika, and Sora.

Token

The basic unit of text that AI models process. A token can be a word, part of a word, or a character. Most AI APIs charge based on token usage.

Tool Use (Function Calling)

AI capability to use external tools, APIs, or functions to accomplish tasks beyond text generation. Enables AI agents.

Top-p (Nucleus Sampling)

A sampling method in text generation that considers only the most probable tokens whose cumulative probability exceeds p. Alternative to temperature.

Training Data

The dataset used to teach an AI model. Quality and diversity of training data greatly impact model performance and bias.

Transfer Learning

Using knowledge from a model trained on one task to improve performance on a related task. Foundation of modern AI, enabling efficient adaptation.

Transformer

A neural network architecture that revolutionized AI by using attention mechanisms. The foundation for modern LLMs like GPT and BERT.

Underfitting

When a model is too simple to capture patterns in data, performing poorly on both training and new data.

Unsupervised Learning

Machine learning where models find patterns in unlabeled data without predefined categories or outcomes.

Vector Database

A specialized database designed to store and query embeddings efficiently. Used in RAG systems and semantic search applications.

Voice Cloning

AI technology that replicates a person's voice from audio samples, allowing text-to-speech in that voice. Raises ethical considerations around consent.

Zero-day (AI Context)

AI capabilities or behaviors that emerge unexpectedly without explicit training, often discovered after deployment.

Zero-shot Learning

The ability of an AI model to perform tasks it wasn't explicitly trained for, without any examples in the prompt.