Understanding Key AI Terms: A Glossary for Navigating the Evolving Landscape

Sep 7, 2026

AI Summary

As artificial intelligence continues to advance, a new vocabulary has emerged to describe its concepts and technologies. This glossary provides plain-English definitions of essential AI terms, helping individuals in tech, investment, and general interest to keep pace with the rapidly evolving field.

AI is creating a new language with terms like LLMs, RAG, RLHF, and opaque recurrence, which is a reasoning technique in OpenAI's Astra model.
Artificial general intelligence (AGI) refers to AI that can perform tasks better than the average human, with varying definitions from different organizations.
An AI agent is a tool that autonomously performs complex tasks, while API endpoints allow software to interact with other applications.
Chain-of-thought reasoning improves AI problem-solving by breaking down tasks into smaller steps.
A coding agent can autonomously write, test, and debug code, functioning like a highly efficient intern.
Compute refers to the computational power necessary for AI models to operate, often linked to hardware like GPUs and CPUs.
Deep learning involves multi-layered neural networks that can identify data features independently, requiring extensive data for training.
Diffusion is a technique used in generative AI models to create data by reversing a noise process.
Distillation extracts knowledge from larger models to create smaller, efficient versions, often used in AI development.
Fine-tuning optimizes AI models for specific tasks by training them with specialized data.
Generative Adversarial Networks (GANs) consist of two neural networks that compete to produce realistic data outputs.
Hallucination refers to AI generating incorrect information, which poses risks for reliability.
Inference is the process of making predictions using trained AI models, reliant on the model's prior training.
Large language models (LLMs) are used in AI assistants and are built from vast datasets to understand language patterns.
Memory cache enhances inference efficiency by storing previous calculations to reduce processing time.
The Model Context Protocol (MCP) allows AI models to connect to external tools without custom integration.
Mixture of Experts architecture activates only relevant sub-networks for tasks, improving efficiency.
Neural networks are the foundational structures for deep learning, inspired by the human brain's interconnected pathways.

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Understanding Key AI Terms: A Glossary for Navigating the Evolving Landscape

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