In 2004, at the end of a long day of classes, I was putting in some extra hours in my school’s computer lab perfecting my latest project: Acey Deucey. A program powered using my crude understanding of the Visual Basic coding language, this program felt alive to me. Not just because I’d created an interface that looked sort of like a first grader’s drawing of a third-rate casino, but because it was a game that users could play against the computer.
It wasn’t machine learning—but to me, it sure felt intelligent.
I sat before the screen, testing and retesting the code, and feeling almost exactly like Mary Shelley’s Victor Frankenstein: “No one can conceive the variety of feelings which bore me onwards, like a hurricane, in the first enthusiasm of success. Life and death appeared to me ideal bounds, which I should first break through, and pour a torrent of light into our dark world.”
And when the code came together and the program was functional, let me tell you, I might as well have reanimated the dead. Now, I could enter a bet and a number into the program, just as I would tell a dealer, and the game would do the rest. I’d know the total whether I won or lost, and how much money I had at my disposal. The computer would keep track of all that for me at my command. And I felt like a boss.
Flash forward 12 years. Today, on my way to work, I can tap my thumb on the base of my iPhone and be granted access to a host of tools that are downright smart. My phone can recognize which of my photos are selfies, and which are photos of friends and loved ones, and compiles them into albums that I can peruse at my leisure. It knows how to optimize for web connectivity. And soon, Siri’s going to start picking up on my behavior. Instead of singing for me or simply helping me text a friend, in time, she could be cheering me up.
That’s not news to you. After all, as a marketer, you’ve probably seen intelligent technology covered from every angle by any number of publications. Everyone from CIO to Venture Beat, Fast Company, and Marketing Tech News has covered it to some extent. And because there are so many developments in the field and applications for this technology, there’s never a shortage of things to say.
But here’s the thing: for all we read about this marketing technology, few of us know what it actually means. And in our era of smart technology that’s developing at a near-frenetic pace, it’s time we ironed out those questions. After all, if the singularity hits, you’ll want to know exactly what you’re dealing with—and how it came to be.
In this series, I’d like to explore machine-learning-driven technologies, and why they’ve become so important to the marketing industry as late. Today, let’s take a look at the history of this drastic shift in our technological expectations, and learn how it arrived at marketing’s doorstep.
TechTarget’s WhatIs.com defined machine learning as “a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. [It] focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.” It goes on to compare this form of AI to data mining in that it’s designed to “search through data to look for patterns.” However, instead of reporting on that data for a human—say, a marketer—to create narratives and make changes, it digests that data and adjusts its own behavior to adapt.
In defining what it is, though, it’s also helpful to take a closer look at what it isn’t—because, though they’re often conflated, it’s different from AI. Here’s how Fortune broke it down:
As Mark Terenzoni of threat detection firm Sqrrl explained, AI is like building a brain, but one that is unable to produce deterministic outcomes (ones that will produce a predictable outcome) — that’s why mischief makers were able to manipulate Microsoft’s AI chatbot into spewing racist comments.
Machine learning, on the other hand, results in predictable responses and useful predictions. It can detect patterns in giant amounts of data and even present the results in visual graphics that highlight the most salient information.
So while some of the technologies you’re familiar with (think: chatbots, certain voice recognition technologies, and—eventually—certain spelling and grammar checkers) may rely on AI and deep learning to work alongside you, they aren’t necessarily examples of marketing technology that can detect patterns and intelligently adjust their own behavior.
“The world is being quietly reshaped by machine learning,” wrote Alex Hern for The Guardian. An apt observation: while so much of this technology has broken into the mainstream of late, it’s been a part of our world for quite some time—eons, if we’re talking tech years.
Mankind’s fascination with AI—and machine-learning technology, specifically—can be officially traced as far back as 1950, when computer scientist Alan Turing developed the Turing Test to gauge a computer’s intelligence in comparison to that of a human’s. He had predicted that by the year 2000, “computers would be able to fool 30 percent of human judges after five minutes of conversation, and that as a result, one would ‘be able to speak of machines thinking without expecting to be contradicted.'” While that’s not currently a reality we can confirm in 2016, we’re certainly getting close to the uncanny valley where we have to think twice about whether we’re speaking to a robot or a scripted human.
In the decades that followed Turing’s proposal, we’ve come far: as Forbes pointed out, our algorithms have advanced. Technologies have learned to make deductions through explanation-based learning. We’ve taught computers to play chess against humans, to beat them in Jeopardy. Today, intelligent technology’s become so universal that it’s tucking itself into device that existed in as many as 2.6 billion pockets across the globe as of 2015. Our major brands—Google, Apple, Amazon, and Facebook, to name a few—are pumping massive resources into integrating it in their strategies. In fact, according to a recent interview with Google’s Gary Illys for Search Engine Land, machine learning is a big part of the way in which Google improves on its search quality.
And while there’s no telling what the next big development in machine-learning technology will be, one thing’s for sure: if you’re a marketer, you should already be paying close attention.
AI is already an integral thread in the fabric of marketing. It’s inherent in many of the tools we use to collect and analyze data, drive our content strategies, and reach our audiences.
In time, it’ll likely be a major double bonus in our customer service approaches too: imagine having an actual “brand voice” that not only hears out your customers and responds to their challenges in an informed way, but also collects data that you can use to work together to reiterate.
But this technology’s about to do more than just make life easier on the marketing front—it’s going to revolutionize it entirely. In time, it’ll no longer be a matter of marketers interpreting data, restrategizing, then making the shift to a new branded approach. The approach will simply occur, driven by the predictive capabilities of machine-learning-based systems. According to The Split: “As advancements in technology scale exponentially, the divide between teams that do and those that don’t [advance] will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow.” Enter machine-learning technology.
The Split also highlights a second set of considerations for marketers: ethics. And not only through the ability of a highly intelligent technology to manipulate emotions, but also through targeting:
According to Carl Schmidt, cofounder and chief technology officer at Unbounce, “where we are really going to run into ethical issues [with machine-learning-driven marketing] is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.”
We can talk a lot about the future of machine-learning-driven technology, but the fact is that in many ways, brands are already incorporating it into their broader content strategies. Next post, we’ll talk about brands that are using intelligent technology effectively in their strategies today—and how you, too, could get in on the smart tech game.
Check out the next posts in the series: “5 Brands That Are Marketing Smarter with Machine Learning,” and “Machine Learning and the Human Endeavor: An Interview with Paul Roetzer, CEO of PR/2020“