Jargon Buster

A type of autonomous AI that can make decisions, take actions, and learn on its own to achieve specific goals. It’s akin to having a virtual assistant that can think, reason, and adapt to changing circumstances without needing constant direction. Agentic AI can be used in self-driving cars, supply chain management, cybersecurity, and in healthcare diagnostics and treatment recommendations.

Typically built to do specific tasks such as like answering questions or setting reminders. AI Agents are great at automating simple, repetitive tasks but don’t have the autonomy or decision-making abilities that Agentic AI does. AI Agents are used in customer service via chatbots to answer questions and guide customers through processes, in voice assistants to help set reminders and play music and in productivity tools which can help in things like software development by suggesting code and helping with debugging. 

The AI Content Monetization Protocols (CoMP) is an IAB Tech Lab project focused on creating a secure controlled gateway for publishers to share their content with Large Language Models (LLMs). The aim is to address concerns regarding the growing use of AI agents, LLMs and AI-driven search summaries - protecting the intellectual property of publishers while still allowing AI systems to learn and create outputs from content. Find out more via IAB Tech Lab.

A set of instructions or rules designed to perform a task or solve a problem. In AI, algorithms are often used for learning patterns or making predictions from data. Online, algorithms are commonly used to determine the listings shown via search engines and for automated methods of ad trading and delivery.

A computational model inspired by the structure of the human brain, used to recognise patterns and make decisions.

Systems that simulate human thought processes to solve complex problems, often integrating AI with human-like reasoning.

Refers to the number of computational resources, including CPU, GPU, and memory capacity, available to train and run models. High compute power can enable faster processing, larger models, and handling larger datasets.

A subset of machine learning that uses multi-layered neural networks to model complex patterns in large datasets.

A deep learning framework that pits two neural networks against each other to generate new, synthetic data that closely resembles real data.