Tell me how you do your job.
This simple phrase is one way to determine how quickly your job might become automated in the future.
The more clearly that you can describe a task, the easier it may be to create rules for it, whereas, the harder the skill is to describe or enunciate, the more resistant it may be to computerization. MIT economist David Autor named this phenomenon Polanyi’s paradox after the Hungarian economist, philosopher and chemist Michael Polanyi, who famously explained in his work The Tacit Dimension that “we know more than we can tell.”
What we do effortlessly as humans may be least susceptible to automation. There are skills and rules in our human knowledge and capability that lie beneath consciousness. Think about describing how you ride a bike or a horse, how you crack an egg on the side of a bowl, how you adjust your grasp when a cup of coffee is slipping out of your hands, or how you persuade someone when writing a paragraph. These small things illuminate the tacit quality of how we engage with the world around us.
Oddly, some of the high-skill work in demand today, such as computer programming, may become some of the most-easily automated skills in the future. Autor explains that work like mathematics, logical deduction and encoding quantitative relationships—really any work that involves “a set of formal logical tools”—can be automated.
This helps us understand how we can not only survive but also thrive in the future of work that so many futurists predict. Middle-skill jobs that involve adaptability, problem-solving, communication and common sense will persist even as traditional computation, production or clerical occupations may contract. There are even a large set of occupations that require sensorimotor skills that will grow or remain relevant.
At a recent EmTech Next conference on the future of work at the MIT Media Lab, I was amazed by how roboticist after roboticist kept describing just how difficult it is to teach a robot how to grasp an object and simply adjust its grip. This is a talent that most infants learn as they begin to hold a bottle or grasp an adult’s fingertip. Each scientist touted the new technologies they were building, but each also tempered their projections for the work ahead because of huge constraints like a robot’s inability to grasp.
Chief technologist of Amazon Robotics, Tye Brady—who leads the work on more than 100,000 Kiva robots that have revolutionized warehouse fulfillment—described his growing need for 600,000 warehouse workers. The company is desperate for humans precisely because of their ability to pick, grasp, intuit, recognize and move between the retail offerings of the Kiva robots. That’s a rich set of different skills to stitch together at breakneck speed.
This sensorimotor-specific work is hugely in demand because of the dramatic increase in online shopping. And as Autor explains, “There is at present no cost-effective robotic facsimile for these human pickers. The job’s steep requirements for flexibility, object recognition, physical dexterity and fine motor coordination are too formidable.”
Meanwhile, Amazon has found another thing that robots weren’t good at: helping in the hiring process. When it attempted to apply artificial intelligence algorithms to sort resumes, it found that the software had a thing against women and only put forward men as worthy candidates. Unfortunately, Amazon refuses to comment on it.
Nevertheless, think about the chain of human skills that came into play in that hiring experiment, as the leaders of the experiment wondered, “Hey, there are a lot of men in this pile. Where are the women? Are there any women? Why aren’t there any women? What assumptions have we made in creating this sorting algorithm? How did we train the algorithm? Maybe we should rethink using AI as means to hire.” First, observation, then confusion, then critical thinking, then problem-solving, then ethics and then judgment. That’s a lot of human—not AI—skills to stitch together. It may not be difficult to imagine AI making sense of a series of If/Then statements, but it does seem far more remote for AI to adapt and stitch together critical thinking skills with ethical choice-making skills and with judgment—not one but three human skills—in nimble fashion.
On both sides of the spectrum, from manual to analytical capabilities, the tasks that will remain unaffected are the ones “that we only tacitly understand how to perform.” Autor concludes, “Following Polanyi’s observation, the tasks that have proved most vexing to automate are those demanding flexibility, judgment, and common sense—skills that we understand only tacitly.” McKinsey Global Institute reaffirmed this in theirrecent report on productivity and automation:
“Automation will create an opportunity for those in work to make use of the innate human skills that machines have the hardest time replicating: social and emotional capabilities, providing expertise, coaching and developing others, and creativity. For now, the world of work still expects men and women to undertake rote tasks that do not stretch these innate capabilities as far as they could. As machines take on ever more of the predictable activities of the workday, these skills will be at a premium. Automation could make us all more human.”
The work of the future will be robo-human. Workers will complement and augment the work of machines and vice versa. Machines and humans already collaborate and will continue to coordinate even more closely in the future. From chatbots that lead into customer service calls to predictive analytics for students that are channeled to human coaches and advisers, the world is already evolving into a robo-human one.
And if you can’t really describe clearly the kinds of activities you engage in, well, that just might bode well for you. Many of the tasks that can’t be substituted or automated will be the ones that define us as human beings—and make us more human.
Michelle R. Weise (@rwmichelle) is chief innovation officer of Strada Institute for the Future of Work, which will be releasing its latest report called “Robot-Ready: Human + Skills for the Future of Work” on Nov 13.
*This article was first published on EdSurge on November 5, 2018.