DEMYSTIFYING AI & NEUROSCIENCE
What You Need to Know if You’re an Advertiser
Chief Marketing Officer
06 July 2017
AI is quickly becoming the new buzzword in advertising, but there is more to AI than just machine learning, the most talked about aspect in the AI family. Writing exclusively for ExchangeWire, Gregory Yates (pictured below), CMO, RICG, explains the basics of AI, the importance of neuroscience, and how marketers are starting to use AI to bring the future into the reality of today’s advertising.
AI – everyone’s talking about it, but what is it, really? There are a slew of companies out there right now that are jumping on the latest trend train with chatbots, machine-learning capabilities, analytics, and so on; but this can actually backfire and pose a problem because AI is an umbrella term with several components (and subcomponents) that can be leveraged in many different ways. Sure, it can help advertisers make a bigger impact, stay competitive, and reach the right audiences, yet there’s still a big question mark when advertisers look at AI technology. Marketers need to first educate themselves on how it could benefit – or not benefit – their company and their customers.
At the highest level, the power of AI lies in this ability to combine emotions and behavior, overlaid by data, to create personalized experiences delivered to you at the right place, at the right moment, at the right time, with the right device. Remember the movie Minority Report with Tom Cruise? There’s a scene where he’s stressed out and walking by some ‘living’ ads in a mall that read his emotions and try to convince him he needs a beer, a vacation, etc. Here’s the thing – this is actually becoming a reality and there’s a huge opportunity for advertisers to up their AI game.
AI & Neuroscience 101: Understanding the Basics
I want to start by first breaking down the core concepts and components of AI and Neuroscience. The basics can demonstrate the true power and promise of these combined technologies before taking the first step down the AI path. Here are three ways that I break down AI, Neuroscience, and the combination and use of both:
Breaking down AI
In its most basic form, AI can be described as methods to reproduce what’s going on in the human mind. What’s interesting is that 99.9% of AI is determined by eliminating data and/or making assumptions, i.e. regardless of everything else, let’s just come to a conclusion based on a smaller set of things we know. Let’s take a quick look under the AI hood to see the different parts that can be laid out in an almost hierarchy-type fashion.
Machine Learning: This utilizes algorithms to process data and takes those leanings and applies them to like data sets of probabilities of what it thinks is the next outcome (e.g. customer service chatbots for airlines that are predicting future desired flight deals).
Cognitive: This builds on machine learning, but infuses a ‘human mind approach’.
Deep Learning: This takes cognitive to next level and uses neural networks – the brain is composed of neural networks and now computers try to achieve that by simulating this process as though they’re human brains processing the data.
Analytics: This is AI’s most basic form and entails the simple processing of data, there are three most used types: predictive, statistical, and deterministic.
When we dig into the neuroscience side of things, it opens a whole new world of possibilities for advertisers.
Importance of Neuroscience
According to human behavioral studies, an individual can make up their mind up to 10 seconds before they even realize. Neuroscience relates to this emotional component; so where AI can help you understand behaviour, neuroscience taps into one’s emotions so you can engage with them on that level and influence their decision-making process.
Neurometrics: The two tools for this are electroencephalogram (EEG), which measures the brain’s electrical activity, and functional magnetic resonance imaging (fMRI), which measures brain activity by observing changes in blood flow.
Biometrics: These are things that are ‘of the body’, such as facial expressions. The Facial Action Coding System (FACS) is a good example where technology can be used to understand predefined micro expressions of the face. Galvanic Skin Response (GSR) is another helpful biometric which is based on reading your sweat (electrodermal activity) to gauge emotional arousal. And there is also eye tracking, which observes a person’s gaze, fixation, and other eye movements to understand their focus. If you triangulate this data you can truly understand how a person is feeling.
Psychometrics: These are based more on a qualitative Q&A method with testing on a 1:1 basis to gain more colour surrounding the experiment, including recall and other verbal conscious dialogue.