AI Glossary

Large-scale AI models like Claude 4 and Gemini 2.5 Pro that serve as the foundation for countless applications

AI models trained on vast amounts of text data to understand and generate human-like language

Branch of AI that enables computers to learn and improve from experience without explicit programming

Computing systems inspired by biological neural networks that form the backbone of modern AI

Subset of machine learning using neural networks with multiple layers to model complex patterns

Technique that enhances AI models with real-time access to external knowledge and proprietary data

The art and science of crafting effective prompts to maximize AI model performance (skill worth $150K+)

Process of customizing foundation models for specific tasks and business applications

Technique of applying knowledge gained from one task to improve performance on related tasks

Process of teaching AI models to perform tasks by learning from large datasets

The amount of information AI models can process and remember in a single interaction

Neural network architecture that revolutionized NLP and powers most modern language models

Numerical representations that capture semantic meaning of words, images, or other data

Basic units of text that AI models process, roughly equivalent to words or parts of words

Process of using a trained AI model to make predictions or generate outputs on new data

Interface that allows different software applications to communicate and access AI model capabilities

Programming approach using natural language conversation with AI models like Claude and Cursor

AI field focused on enabling computers to understand, interpret, and generate human language

AI field that enables computers to interpret and understand visual information from images and videos

Autonomous AI systems that can perceive, reason, and act to achieve specific goals independently

AI systems that understand and generate content across text, images, audio, and video

AI architecture using two competing networks to generate increasingly realistic synthetic data

Specialized processors designed for parallel computation, essential for AI training and inference

Google's custom AI chips optimized specifically for machine learning workloads

Facilities housing the computational infrastructure that powers modern AI systems

NVIDIA's term for data centers optimized specifically for AI training and inference workloads

Computing infrastructure that brings AI processing closer to data sources for reduced latency

Specialized databases optimized for storing and querying high-dimensional vector embeddings

Practices and tools for deploying, monitoring, and maintaining machine learning systems in production

Technique to reduce model size and improve inference speed by using lower precision numbers