AI Glossary

AI systems that find patterns, anomalies, and insights in large datasets, used for tasks like fraud detection, medical imaging analysis, and business intelligence.

A broad field of computer science focused on building systems that can perform tasks normally requiring human intelligence, such as understanding language, recognizing patterns, and making decisions.

The maximum amount of text (measured in tokens) that a language model can consider at once — including both the input prompt and the generated output.

A subset of machine learning that uses neural networks with many layers to learn increasingly abstract representations of data, powering breakthroughs in language, vision, and generation.

A generative AI architecture that creates images, video, and other media by learning to gradually remove noise from random static until a coherent output emerges.

The process of further training a pre-trained model on a smaller, task-specific dataset to improve its performance on that particular task or domain.

Artificial intelligence that creates new content — text, images, video, audio, or code — by learning patterns from existing data and producing original outputs in response to prompts.

The date beyond which a language model has no training data, meaning it cannot know about events, discoveries, or changes that occurred after that point.

A neural network trained on massive text corpora that can understand and generate human language, typically with billions of parameters.

A subset of AI where systems learn patterns from data rather than following explicitly programmed rules, improving their performance as they see more examples.

A neural network architecture that scales model capacity efficiently by routing each input through only a small subset of specialized sub-networks ("experts"), keeping compute costs manageable even as total model size grows.

AI systems that can process, understand, and generate across multiple types of data — text, images, audio, video, and code — within a single model.

The core mechanism of large language models: given a sequence of text, predict the most likely next piece (token), then repeat to generate coherent text one token at a time.

The initial training phase where a language model learns general language patterns from a massive text corpus, before being fine-tuned for specific tasks or behaviors.

AI systems that forecast outcomes based on historical data patterns, used for tasks like demand forecasting, risk assessment, and recommendation engines.

A training technique where human preferences are used to fine-tune a language model through reinforcement learning, teaching it to produce responses that humans judge as helpful, accurate, and safe.

A machine learning approach where an agent learns by taking actions in an environment and receiving rewards or penalties, gradually discovering which strategies produce the best outcomes.

A compact AI language model — typically under 10 billion parameters — designed to run efficiently on edge devices and single GPUs while delivering strong task-specific performance.

A machine learning approach where the model learns from labeled examples — input-output pairs where the correct answer is provided during training.

The basic unit of text that a language model processes — typically a word, subword, or punctuation mark, roughly equivalent to 3/4 of an English word.

The dataset used to teach a machine learning model, containing the examples and patterns the model learns to recognize and reproduce.

A neural network architecture that powers modern AI by processing entire input sequences simultaneously through an attention mechanism, rather than reading them word by word.

A machine learning approach where the model finds patterns and structure in data without labeled examples, discovering groupings and relationships on its own.