Glossary of Key Terms in Artificial Intelligence Explained

May 29, 2026

AI Summary

A new glossary aims to clarify complex AI terminology as the field rapidly evolves. Key concepts include artificial general intelligence, AI agents, and large language models, which are essential for understanding the capabilities and limitations of AI technologies.

Artificial intelligence is creating new terminology that can be confusing, even for experts in the tech field. A glossary has been created to help clarify these terms and is regularly updated as the field evolves.
Artificial general intelligence (AGI) refers to AI systems that can perform tasks better than the average human. Different organizations have slightly varying definitions of AGI.
AI agents are tools that utilize AI to perform complex tasks autonomously, such as booking services or managing code.
API endpoints act as interfaces that allow different software applications to communicate and perform tasks without human intervention.
Chain-of-thought reasoning in AI involves breaking down problems into smaller steps to improve accuracy, particularly in logic and coding.
Coding agents are specialized AI programs that 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 associated with hardware like GPUs and CPUs.
Deep learning is a subset of machine learning that uses artificial neural networks to identify complex patterns in data without human intervention.
Diffusion is a technique used in generative AI to create realistic outputs by reversing a noise process applied to data.
Distillation is a method for creating smaller, more efficient AI models by extracting knowledge from larger models.
Fine-tuning involves further training an AI model on specialized data to enhance its performance for specific tasks.
Generative Adversarial Networks (GANs) consist of two neural networks that compete to produce realistic data outputs.
Hallucination in AI refers to the generation of incorrect information, which poses risks and is a focus for improving AI accuracy.
Inference is the process of running an AI model to make predictions based on previously learned data.
Large language models (LLMs) are AI systems that process and generate human-like text based on vast datasets.
Memory cache optimizes inference by saving calculations for future queries, enhancing efficiency in AI responses.
Neural networks are multi-layered algorithms that form the backbone of deep learning and generative AI.
Open source software allows public access to underlying code, fostering collaboration and transparency in AI development.
Parallelization enables simultaneous processing of tasks in AI, significantly improving training and inference efficiency.
RAMageddon refers to the growing shortage of random access memory chips, impacting the tech industry amid rising AI demands.

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Glossary of Key Terms in Artificial Intelligence Explained

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