Generative AI has been around for over a year, disrupting public relations industry and making communicators question the future of their work. People are insecure, especially with all the unknowns that technology brings with it.

However, this fear it's preventing people from understanding the capabilities of artificial intelligence, making people think they can't prepare for the future. Unfortunately, many communicators lack the knowledge to accurately describe what this technology is, how it works and what it is capable of doing, both in terms of the organizations they represent and in terms of their general knowledge.

So I've written a short glossary of common AI terms, in plain English, to enable any communicator to understand what these buzzwords mean and explain what's going on.

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it is a technology that enables computers and machines to simulate human thinking and intelligence, and human-level problem solving.

It includes everything from self-driving cars to weather forecasting models, machine learning, robotics and much more. Each of these examples is a “subset” of AI, and whole articles could be written on each of them. However, given that this article is about generative AI, we will dive deep into the lexicon surrounding this type of artificial intelligence.

And to do that, we need to look at the “car learning” subset of AI.

Machine learning

The purpose of machine learning, or “ML,” is the use of algorithms that can learn and generalize information. Basically, a machine learning algorithm is given information. A question is then asked and the algorithm thinks of an answer based on the information given to it.

There are dozens of subfields within machine learning. These include “decision trees” which are used in chatbots. There is “linear regression”, which is useful for predicting what will happen in the future based on past data such as weather patterns. There's also “grouping,” which is how an adtech algorithm knows when and how to sell you a product or service.

All these subgroups take the information that is fed into it to make predictions about the future based on past events. They are all useful and affect our daily life. However, there is another subset of machine learning called “deep learning”. This is the subset in which we find generative AI.

Deep learning

Deep learning means there are more than three layers of neural networks. “Neural networks” are the brains of the algorithm, while “layers” are the depth of thinking an algorithm can do.

In standard machine learning, there is an input layer (ie, what will the weather be like today?); a “thinking” layer, such as taking all the wind, rain and temperature data from past events and applying it to the current situation; and then the output layer (ie the weather forecast will be sunny). All these layers make up the neural network.

With deep learning, there are more than three layers in the neural network. This enables the algorithm to think deeper and more nuanced. In fact, this deep versus shallow way of thinking is where the phrases “deep AI” and “shallow AI” come from.

In addition, with a difference in the amount of layers in the algorithm, the way information is fed into these algorithms is also distinct. This is because a deep learning algorithm is based on underlying patterns.

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Basic models

“Underlying models” are giant repositories of data, where each data point is called a “parameter”. Deep learning models are trained on these basic patterns full of data, then “fine-tuned” to perform in a particular way. Some basic models have over 1 trillion parameters.

There are several basic model types, including “Large language patterns” or “LLM.” They are so called because they are large—they can have over a trillion parameters—and are intended for processing and generating normal human language. Other basic models include vision models for video generation, sound models for generating different types of sounds and even biological models to predict how proteins will interact with each other.

The underlying models are important because they are large repositories of data that any paying subscriber can use. Instead of spending millions of dollars and thousands of hours to compile all this data, a company can subscribe to an already existing model (like OpenAI's model or Google's model) and use this information to train the AI their generative.

Application of AI

These basic patterns provide the basis for “Applications of AI.” The application itself can be anything from a piece of a platform to a full application that adjusts an underlying model to be used in a certain way. A good analogy for an AI application is to look at how are applications in general.

If you view an app in the Apple Store or Google Play, that app is designed to be able to work on the underlying technology infrastructure of that particular app store. AI applications work on the same idea – they are built to work with the underlying technological infrastructure of the AI ​​model.

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So where does generative AI fit in?

“Generative AI” includes models that are specifically designed to generate new content. It is what is created using the knowledge base of the underlying models, along with the fine-tuning that comes from an AI application to get a desired result. So are video generators such as Sora or language generators like the work of Perplexity or ChatGPT.

In short, generative AI is used in AI applications that use deep learning neural networks trained on underlying patterns to generate a unique, never-before-seen piece.

It is important for us as communicators to fully understand these AI terms in order to enable the public to understand how this world-changing technology works. We hope that PR professionals will be able to use this glossary to better communicate what AI is, as well as to better understand how it can be applied in their daily lives.