Before we knew it the digital advertising world started buzzing with scientific words that we never thought we’d hear - Artificial Intelligence, Machine Learning, Data Crunching, Reinforcement Learning etc, etc. Enough to get your head spinning.
Those terms are now an integral part of the digital landscape. They are the little bolts and gears driving the engine of programmatic media buying. Since programmatic buying is definitely here to stay, there’s never been more need to get into grips with which is which and how that affects the programmatic advertising process.
We’ll start with two of the most commonly used terms “Artificial Intelligence” and “Machine Learning”. Ever wondered what is the difference between them?
Well, it’s not exactly a difference. Here is the simple guide:
Artificial Intelligence is the famous field of study looking for ways to create computers that are capable of intelligent behaviour. A machine is deemed intelligent if it can do things normally associated with human intelligence. To pass the Turing Test and qualify as artificially intelligent, a machine should be able to do for example: natural language processing (i.e. communicate with no trouble on a given language); automated reasoning (using stored information to answer questions and draw new conclusions) and machine learning (the ability to adapt to new circumstances and detect patterns).
Machine Learning is a subset of Artificial Intelligence and it explores the development of algorithms that learn from given data. These algorithms should be able to learn from past experience (i.e. the given data) and teach themselves to adapt to new circumstances and perform certain tasks. In programmatic advertising, for example, Machine Learning algorithms help us navigate through the Big Data pool and extract meaningful information, they help us define our most relevant audience and direct our campaign in such way to best meet their preferences.