Your Complete Guide to AI Terminology

Computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making, and language translation.

A subset of AI that enables computers to learn from data and improve their performance over time without being explicitly programmed for every scenario.

A subset of machine learning that uses artificial neural networks with multiple layers to automatically learn hierarchical representations of data, enabling breakthrough performance in image, speech, and language tasks.

A branch of AI that enables computers to understand, interpret, and generate human language in a valuable way, powering applications like chatbots, translation, and sentiment analysis.

AI models trained on vast amounts of text data that can understand and generate human-like text, powering applications like ChatGPT, content generation, and code assistance.

A family of large language models developed by OpenAI that can generate human-like text, power ChatGPT, and perform a wide range of language tasks through natural conversation.

The practice of designing and refining text inputs (prompts) to get the best possible outputs from AI language models, maximizing accuracy, relevance, and usefulness.

The process of taking a pre-trained AI model and further training it on a specific dataset to specialize it for a particular task, industry, or use case.

A technique that combines information retrieval with language generation, allowing AI models to access external knowledge bases and provide accurate, up-to-date answers grounded in specific documents.

When an AI model generates information that sounds plausible but is factually incorrect or entirely fabricated, often presenting false data with high confidence.

An autonomous AI system that can perceive its environment, make decisions, and take actions to achieve specific goals, often using tools and interacting with external systems.

A computing system inspired by biological neural networks, consisting of interconnected nodes (neurons) that process information through weighted connections and activation functions.

A field of AI that enables computers to interpret and understand visual information from the world, including images and videos.

A machine learning approach where an agent learns to make decisions by taking actions in an environment to maximize cumulative reward.

A technique where a model trained on one task is reused as the starting point for a model on a second related task, dramatically reducing training time and data requirements.

A neural network architecture that uses self-attention mechanisms to process sequential data in parallel, revolutionizing NLP and enabling models like GPT and BERT.

Bidirectional Encoder Representations from Transformers - a pre-trained language model that understands context by reading text bidirectionally.

A numerical representation of data (text, images, etc.) in a continuous vector space where similar items are positioned close together.

A specialized database designed to store and efficiently search high-dimensional vector embeddings, enabling semantic search and similarity matching at scale.

A machine learning approach where models learn to perform new tasks from just a few examples, rather than requiring thousands of training samples.

AI systems that can create new content—text, images, code, music, video—that didn't exist before, rather than just analyzing or classifying existing data.

Systematic and unfair discrimination in AI system outputs, often resulting from biased training data, flawed algorithms, or biased human decisions during development.

AI systems designed to provide clear, understandable explanations for their decisions and predictions, making AI transparent and trustworthy.

AI systems that can process and understand multiple types of data simultaneously—text, images, audio, video—enabling richer understanding and generation.

AI processing that happens locally on devices (phones, cameras, IoT sensors) rather than in the cloud, enabling faster response, privacy, and offline operation.

Artificially generated data created by algorithms rather than collected from real-world events, used to train AI models when real data is scarce, expensive, or privacy-sensitive.

A machine learning approach where models are trained across multiple decentralized devices or servers holding local data, without exchanging the data itself, preserving privacy.

Techniques to reduce the size and computational requirements of AI models while maintaining performance, enabling deployment on resource-constrained devices.

Machine Learning Operations - practices and tools for deploying, monitoring, and maintaining AI models in production environments, similar to DevOps for software.

Techniques to artificially increase training data size by creating modified versions of existing data, improving model performance and reducing overfitting.