By BRADY EVAN WALKER
You Say You Want a Revolution
Proclamations on the next industrial revolution aren’t hard to come by. The trifecta of burgeoning artificial intelligence, robotics, and otherworldly graphics processing units seem on the brink of surpassing even the dreams of The Jetsons. We’ll even get even flying cars, probably self-driving.
But what is an industrial revolution? It can quite plainly be defined as a process whereby something that was once costly becomes cheap.
Machine Learning is a Prediction Tool
In a paper published by Harvard Business Review called “Managing the Machines” co-authored by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, the authors assert that the vast wonders of computer power over the last half a century have basically reduced the cost of a single thing: arithmetic.
The bottom line is that, as simplistic as it seems, computers are really just incredibly powerful calculators. Improvements in the technology have made arithmetic such a cheap resource that its applicability expanded rapidly, allowing people to create things like digital photography and Grindr.
But with machine learning coming into its own, we’re starting to see the easy abundance of another resource: prediction, “the ability to take information you have and generate information you do not have.” (ibid)
The authors argue that advances in AI boil down to an enhancement of our ability to make predictions and automate those predictions.
Facial recognition technology doesn’t recognize a face — it predicts that this face correlates to that person, just as a human does. And just as a human uses sensory information to predict that road conditions and the behavior of other cars should influence their driving, so do self-driving cars.
From the paper, a clarification on what is meant by “prediction”:
We most often think about prediction as determining what will happen in the future. For example, machine learning can be used to predict whether a bank customer will default on a loan. There is also a great deal of useful data we do not have that is not about the future. For example, mass retailer Target predicted which of its customers were pregnant based on their purchasing behavior. This was not predicting the future: The customers were already pregnant. Instead, it was filling in missing data in a way that proved useful to the company.
Anatomy of a Task
To further clarify the role of machine learning in the coming years, the authors outlined the anatomy of a task. The central component of a task is obviously the action, but before that comes data. With large amounts of well-processed data, any predictor — human or machine — will make more accurate predictions. After the prediction comes the action, and after the action, the outcome and feedback, which is dumped into the database. This post-action feedback will then feed directly into better predictions for the next task.
But there’s a missing component in the outline sketched above: judgment of the action and the outcome.
In the case of a machine learning platform like Persado, users are relieved of the burden of predicting the exact language and emotional valence for a given message to get the highest ROI. Instead, their role as producers of language is minimized and their role as the final judges of the machine’s predictions and their own brand voice is brought to the fore.
Rather than taking the creative wind out of your sails, it enhances your output and can dramatically shorten delivery while consistently building your ROI.
Think of it this way:
Writers have different priorities: some obsess over writing elegant sentences and others focus on profound psychological and philosophical insight. James Patterson’s priority is simply to tell a thrilling story. He’s one of the best at doing this, but he has more ideas than he could ever execute. But Patterson alone could never have written 130 novels in 38 years!
From The Telegraph:
Instead, 23 other authors have been paid out of Patterson’s own pocket to write them for him. It is not a fact he hides: every novel that is written by somebody else declares so on the cover, under Patterson’s name. Patterson is responsible for the vision and plotline of each novel and series and will give a detailed outline to the hired writer in question. The co-author…will then draft the early chapters. Patterson will read, revise and demand new drafts until he is satisfied.
Patterson has found a way to focus on his strengths and the part of writing that he enjoys most, story design. This tactic has made him a very rich man. AI predictive tech will similarly let marketers and advertisers focus on what they do best.
The Next Hottest Skill in the Job Market
The rapid advancement of AI tech and neuroscience will inevitably automate humankind completely out of the equation in many regards. But automating us out of predictive roles will inevitably lead to placing greater value on human-led tasks.
In the language of economics, just as the returns to numeracy and literacy increased with the diffusion of computing and the demand for golf balls rises with lower prices of golf clubs, the most valuable skills in the future will be those that are complementary to prediction: those related to judgment.
For instance, if AI automates the role of a diagnosing and prescribing doctor completely, “[n]ursing skills related to physical intervention and emotional comfort.”
The Next Creative Era of Marketing
In the case of marketing, this may mean that we’re on the edge of a new Creative Golden Age. The last two decades have seen a rise in the quantification and ROI-ification of creative efforts. The outsized and increasingly diverse scale that marketing teams, now more digital than traditional, must face in the 21st century might soon be a burden relieved.
At Persado, our aim is to leverage technology to relieve the pressure of crucial but time-consuming tasks like testing and refining short digital ad copy, email subject lines, and more. We give marketers room to breathe to focus on brand voice, content marketing, and the more exciting, engaging, and enjoyable aspects of the job.
If the most data-dependent tasks can be automated, then marketing and advertising teams across industries will see a rising value in their own creativity and artistic insight, something that can never be offloaded to a computer to handle.