Demystifying AI & Neuroscience: What You Need to Know if You’re an Advertiser

Demystifying AI & Neuroscience: What You Need to Know if You’re an Advertiser

ARTIFICIAL INTELLIGENCE

Demystifying AI & Neuroscience: What You Need to Know if You’re an Advertiser

Greg Yates
CMO, RICG

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 behaviour, overlaid by data, to create personalised 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 utilises 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 behavioural studies, an individual can make up their mind up to 10 seconds before they even realise. 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 microexpressions 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.

 

Photograph by Lorem Ipsum via Unsplash

AI, Neuroscience, and Advertising

So, now that we have the nitty gritty scientific items out of the way, let’s look at some examples where AI – or at least parts of it – are being leveraged to help advertisers and brands capitalise on this latest tech craze.

Let’s first look at Chatbots, which are designed to simulate a conversation with another human. Brands across several industries are developing Chatbots, or just bots, that leverage popular messaging platforms like Facebook Messenger, iMessage, and Kik. The power of bots is in the natural-language processing actions that allow brands to better understand you, learn your preferences, and deliver targeted ads that you’ll be interested in. Service-oriented brands are well-positioned to benefit, because bots are becoming important tools for consumer discovery. I recently heard of a company called Forkable, a ‘lunch bot’, that learns what people like to eat and then delivers a different lunch every day to their office. From an advertising perspective, specific campaigns on a bot like this can interact and engage with individual users in a more authentic way based on all of the data it has collected on them over time. So, while bots are definitely still in the nascent phase, they’re starting to demonstrate their value to the advertising community.

When we dig into the neuroscience side of things, it opens a whole new world of possibilities for advertisers. Not only to make creative and relevant ads, but be able to tap into human emotions and subconscious reactions to inform their strategies, so there’s literally no guess work or trial and error that needs to take place. Look at what Facebook is doing with AI – they’re building neuroscience labs that use things like EEG and biometrics to get a deeper understanding of how people react to things like watching shows or scrolling through their news feeds. Their goal is to capitalise on the power of neuroscience by helping advertisers, brands, and publishers maximise the impact of their content across different devices and platforms.

Another great example is a test that was conducted by Nielsen where they looked at 100 ads across 25 brands and tested individuals’ reactions to them by using EEG. The goal was to understand above-average versus below-average sentiment and reactions. They found that the ads that had below-average emotional response decreased a brand’s sales by 16%, whereas above-average responses increased sales by 23% – this is a powerful tool for advertisers.

 

This is all great, but how do I even get started?

The above examples show us that brands are indeed trying to tap into AI to help boost their advertising initiatives, but there’s potential to do so much more. AI can ultimately make those million-dollar advertisements successful and drive sales. Here’s are three things that I tell people about when they’re starting to think about dipping their toes into the AI pool:

Identify the objectives. Focus on what problem you’re trying to solve. You need to build a case to show the need for AI, as it relates to your business objectives and advertising initiatives.

Don’t be afraid. Brands that are willing to grow and drive adoption are winning, whereas brands that have no interest are falling by the wayside – in fact, 80% of AI-adopters replacing their roles will retain and retrain employees, so it isn’t something to be afraid of at all.

Do your research. There’s no one-size-fits-all approach to AI, but start by looking at data you currently have available in-house and leverage it with an AI company to process to understand how it could potentially benefit your advertising strategies. Data can be something as simple as an excel spreadsheet. This will help you understand the insights you can get from AI before taking the next step. It will also help you understand the quality of data and if it should be better structured, formatted, and if there’s anything missing.

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The World of Everything: AI is More Than a Chatbot

The World of Everything: AI is More Than a Chatbot

ARTIFICIAL INTELLIGENCE

The World of Everything: AI is More Than a Chatbot

Greg Yates
Chief Marketing Officer

15 June 2017

As Artificial Intelligence (AI) continues to creep into our daily personal and business lives, brands are starting to feel the pressure to incorporate AI into their marketing and advertising strategies. They are right to feel the pressure – when executed correctly, AI is the key to connecting brands with the right audience and giving them the competitive edge they need in today’s market. Unfortunately what I’m seeing more and more of is people jumping on the AI bandwagon without understanding what it is and the best way to utilize it.

Probably the biggest AI hype right now is around the use of a chatbot, or a service powered by rules and artificial intelligence that allows the user to interact via a chat interface. Instead of clicking buttons, you type (or say) a command and expect the chatbot to execute it. The service could be anything from functional to fun – think Facebook Messenger, Apple’s Siri, Amazon’s Alexa, Slack, Telegram, etc. We’ve seen a surge in chatbots as businesses are using them as a way to communicate with their customers or organizations to communicate with their clients, or organizations to communicate with employees. Millennials have jumped on the ‘bot bandwagon’ in particular, as a recent poll indicates that 86 percent of millennials agree that brands should promote deals, products and services via chatbots. Also about 58 percent of millennials respondents in the same survey said that they had a positive experience when interacting with a chatbot.

Photograph by Hannah Wei via Unsplash

So every brand should start using chatbots, right? Not so fast. Like all AI, chatbots are still in their infancy stage and the technology needs to be refined. A recent report claims that 70% of Facebook’s chatbots “failed to fulfill”, or basically that chatbots couldn’t understand what users were saying, and in some cases humans had to step in and intervene. 70% is a large failure rate – think if 70% of airplane flights “failed,” people would never fly! The issue is looking at AI and chatbots as a ‘one size fits all’ when in reality, brands need customized intelligence and data to successfully deploy and utilize AI.

So how do you get started in developing and executing a chatbot program that will be successful? We need to take it back to basics like you would if developing a product. First you have to identify the business problem you’re trying to solve, the audience it speaks to, and then you worry about the data to fill the AI brain with so the chat bot can learn and get better. Just like when big data became all the rage, companies quickly learned that if they inputted wonky data, they received wonky results. Chatbots are very similar if it starts to learn something inaccurately or is fed wonky data to start it will produce false results and humans will need to reset and intervene.

You also have to take into account what vernacular and vocabulary you want the bot to be able to understand and what you want its knowledge base to be. Most organizations starting out using bots do not even think about these very basic steps before deploying something, so without you teaching the toddler wrong from right the bot will not produce the desired results and whats more will grow in the wrong direction. 

I still think brands should invest in AI and even chatbots, but the key is understanding how to properly use them.

Many banks are starting to use chatbots in the right way, for example  In Sweden, Swedbank’s Nina Web assistant achieved an average of 30,000 conversations per month and first-contact resolution of 78% in its first three months. Nina can handle over 350 different customer questions and answers. Several other banks in the UK and internationally have similar systems in place or are trialling them.

Aeromexico debuted Aerobot last month at Facebook’s F8 conference, which in the first six months took the airline’s automation from 0-96%. The customer service bot even understands various Spanish dialects because the airline and partners fed it historical travel customer data to give it an education from the start.

As we move into the World of Everything (WoE) or a 365° view of everything tangible and intangible that surrounds us and how to use it to our benefit (think how casinos pump scents into their facilities to change players moods), brands need to customize AI into their business strategy and not the other way around. We’ll replace artificial intelligence with cognitive intelligence by coupling biometric data with AI data to create the most robust form of humanized intelligence. We will see a shift from statistics-based AI to the idea of an exactitude-based AI that looks at data and solves it, creating a customized model.

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The World of Everything: Making Sense of the AI Hype

The World of Everything: Making Sense of the AI Hype

ARTIFICIAL INTELLIGENCE

The World of Everything: Making Sense of the AI Hype

Greg Yates
Chief Marketing Officer

09 March 2017

Artificial Intelligence (AI) is everywhere, bringing hype and fear to almost everyone. As we move into an automated future, we have to start considering things like will robots really make human jobs obsolete? According to Oxford University, researchers estimate that ‘robots’ could potentially automate 47% of U.S. jobs within the next 20 years. Scary, right? The buzz around the upcoming SXSW Conference that kicks off later this week is all about AI, so I’ve been asked a lot recently if all of the hype is real?

AI is still in its infancy stage. While it is moving towards its tween stage, the problem now is that people are jumping on the AI bandwagon without fully understanding what it is and how to successfully create it. I equate it to the Wild West – the main AI players are using a statistical methodology that is known to be inaccurate. Companies everywhere are claiming to have AI or be creating AI, using open source platforms and using an AI engine from the same place. In reality, proper AI should be developed by a skilled mathematician, and not a statistician, with a technology background and knowledge broader than simple probabilities to create an AI engine that can be utilized into different mediums. People are rushing into AI to be part of the trend, but like most things, we will start to see a large percentage of the subpar AI phased out. The true and successful AI will move away from the statistics based model we are seeing now, and move into a deterministic model that looks at data and solves it.

Photograph by Thomas Lefebvre via Unsplash

For example look at the Echo that I personally use at home. It has its limitations, however, it should be considered a baby that still needs to learn. Current AI should not be treated like a grandparent full of knowledge with all the wisdom of the world yet; it should be treated like a child with an understanding that it is not going to know information it has not been fed yet. As more organizations start back filling the brain of AI, then the AI will learn more and start to do learning on its own.

The key is combining biometric data with AI data, producing the most robust form of humanized intelligence.

Yes, believe the hype, but be patient with expectations. One day the AI imposters will be gone, and the real AI will change day-to-day operations across all aspects of our lives. The key is combining biometric data with AI data, producing the most robust form of humanized intelligence. We will move away from the concept of artificial intelligence and move towards the idea of cognitive intelligence. The future will be the World of Everything (WoE) or a 365° view of everything tangible and intangible that surrounds us. Our cognition mission will be able to solve for all of it.

Photograph by Lorem Ipsum via Unsplash

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