Before we embed AI directly into our core technology stacks, this themed collection of articles offers a theoretical and foundational grounding in the science of it all. At a time when AI is promising efficiencies across the board, The Science Of Artificial Intelligence breaks down its inner workings to identify how it is being employed across broadcast production environments.

We explore machine learning and how neural networks process the data it gathers, before examining how perceptive, generative, and agentic AI are all being implemented to give media companies an edge across a range of different broadcast disciplines.

It is about as far away from all the hype and hysteria as it’s possible to get. These six themed articles ensure we’re asking the right questions before we implement AI at the very heart of our production workflows. 

Broadcast Standards – The Science Of AI

This Themed Content Collection is a free PDF download which contains 6 original articles.

Article 1: AI – New Foundations
What AI is and what it isn't, but for engineers. We explore its basic principles, define its technology stack and discuss how it applies to broadcast.

Article 2: Machine Learning
Machine Learning is founded on statistical analysis principles and a variety of training methods. But what is it learning and how does it do it?

Article 3: Neural Networks
Neural Networks are well suited to recognizing familiar features in image data, but broadcasters can train them to work even harder.

Article 4: Perceptive AI
Perceptive AI systems have been developing for years but perception has always been about more than just seeing things. We discover how perceptive AI adopts semantic cues to add more context.

Article 5: Generative AI
Generative AI is a great time-saving tool for creating content, but it’s not perfect. Large Language Models and Stable Diffusion can not only help train it, but reduce the creation of hallucinations.

Article 6: Agentic AI
Power is nothing without control, and Agentic AI pulls everything together and orchestrates it in an applied fashion. But how far can we trust it before delegating critical workflow functions?