AI Glossary
Essential AI terms and definitions for executives and business leaders
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Foundation Models
Large-scale AI models like Claude 4 and Gemini 2.5 Pro that serve as the foundation for countless applications
Large Language Models (LLMs)
AI models trained on vast amounts of text data to understand and generate human-like language
Machine Learning
Branch of AI that enables computers to learn and improve from experience without explicit programming
Neural Networks
Computing systems inspired by biological neural networks that form the backbone of modern AI
Deep Learning
Subset of machine learning using neural networks with multiple layers to model complex patterns
RAG (Retrieval-Augmented Generation)
Technique that enhances AI models with real-time access to external knowledge and proprietary data
Prompt Engineering
The art and science of crafting effective prompts to maximize AI model performance (skill worth $150K+)
Fine-Tuning
Process of customizing foundation models for specific tasks and business applications
Transfer Learning
Technique of applying knowledge gained from one task to improve performance on related tasks
Model Training
Process of teaching AI models to perform tasks by learning from large datasets
Context Window
The amount of information AI models can process and remember in a single interaction
Transformers
Neural network architecture that revolutionized NLP and powers most modern language models
Embeddings
Numerical representations that capture semantic meaning of words, images, or other data
Tokens
Basic units of text that AI models process, roughly equivalent to words or parts of words
Inference
Process of using a trained AI model to make predictions or generate outputs on new data
API (Application Programming Interface)
Interface that allows different software applications to communicate and access AI model capabilities
Vibe Coding
Programming approach using natural language conversation with AI models like Claude and Cursor
Natural Language Processing (NLP)
AI field focused on enabling computers to understand, interpret, and generate human language
Computer Vision
AI field that enables computers to interpret and understand visual information from images and videos
AI Agents
Autonomous AI systems that can perceive, reason, and act to achieve specific goals independently
Multimodal Models
AI systems that understand and generate content across text, images, audio, and video
Generative Adversarial Networks (GANs)
AI architecture using two competing networks to generate increasingly realistic synthetic data
GPU (Graphics Processing Unit)
Specialized processors designed for parallel computation, essential for AI training and inference
TPU (Tensor Processing Unit)
Google's custom AI chips optimized specifically for machine learning workloads
Data Center
Facilities housing the computational infrastructure that powers modern AI systems
AI Factory
NVIDIA's term for data centers optimized specifically for AI training and inference workloads
Edge Computing
Computing infrastructure that brings AI processing closer to data sources for reduced latency
Vector Database
Specialized databases optimized for storing and querying high-dimensional vector embeddings
MLOps
Practices and tools for deploying, monitoring, and maintaining machine learning systems in production
Model Quantization
Technique to reduce model size and improve inference speed by using lower precision numbers
Quick Reference: Current AI Models (2025)
Leading Foundation Models
Creative & Multimodal Models
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