Glossary of Key AI Terms and Concepts Explained

May 9, 2026

AI Summary

Artificial intelligence is rapidly evolving, creating a need for a clearer understanding of its terminology. This glossary provides definitions for key AI concepts, from artificial general intelligence to neural networks, helping users navigate the complex language of AI.

Artificial general intelligence (AGI) refers to AI systems that can perform tasks at or above human capability in various domains, with differing definitions from organizations like OpenAI and Google DeepMind.
An AI agent is a tool that utilizes AI to perform complex tasks autonomously, distinguishing it from simpler chatbots.
API endpoints act as interfaces that allow different software applications to communicate, enabling AI agents to automate tasks without human intervention.
Chain-of-thought reasoning in AI involves breaking down problems into smaller steps to enhance accuracy, particularly in logic and coding tasks.
A coding agent is a specialized AI that autonomously writes, tests, and debugs code, functioning like a highly efficient intern.
Compute refers to the computational power necessary for AI models to operate, relying on hardware like GPUs and CPUs.
Deep learning is a subset of machine learning that uses artificial neural networks to identify patterns in data, requiring large datasets for effective training.
Diffusion technology underpins many generative AI models, learning to reverse the process of data destruction to recreate original content.
Distillation is a method for creating smaller, more efficient AI models by transferring knowledge from a larger model.
Fine-tuning involves further training an AI model on specialized data to optimize its performance for specific tasks.
Generative Adversarial Networks (GANs) consist of two neural networks competing against each other to produce realistic data outputs.
Hallucination in AI refers to the generation of incorrect information, posing risks for reliability and accuracy.
Inference is the process of running an AI model to make predictions based on previously learned data.
Large language models (LLMs) are advanced AI systems that process and generate human-like text based on extensive training data.
Memory cache enhances inference efficiency by storing previous calculations for quicker access in future queries.
Neural networks are multi-layered algorithms that form the foundation of deep learning and generative AI, inspired by human brain structures.
Open source software allows public access to underlying code, fostering collaboration and innovation in AI development.
Parallelization in AI refers to executing multiple calculations simultaneously, crucial for training and inference efficiency.
RAMageddon describes the growing shortage of random access memory chips, impacting the tech industry amid rising AI demands.

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Glossary of Key AI Terms and Concepts Explained

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