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. 

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.

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.

Generative AI (Gen AI) can create original content, for example text or images tailored to a request. It learns from data that it has been trained on, to produce new things. When it comes to digital advertising, Generative AI enables automated and optimised delivery of highly personalised content and experiences based on individual preferences, behaviours, and past interactions.

A large language model is a machine learning model that uses a huge amount of data to perform natural language processing tasks. LLMs can generate, translate, summarise, and predict text. They can also answer questions and perform other tasks. LLMs can automate tasks like content creation, personalise ads, and analyse customer data.