Preparing Tomorrow’s Workers for an AI Future

by | Feb 19, 2019 | Learning Design

Getting Smart has a nice post on the need to prepare all learners for the uncertain future of work. With regards to what, specifically, the future of work will look like, the post’s author writes:

That question – what future of work will emerge – is unanswerable, making it critical to help young people, along with other education and employment stakeholders, plan for multiple possible futures. From today’s vantage point, we can identify two critical drivers of change shaping the future of readiness for further learning, work and life: the rise of smart machines and the decline of full-time employment. But we cannot yet know what extent of technological unemployment we will face or how much support individuals will have in navigating the changing employment landscape.

Smart machines and automation powered by AI will certainly have a dramatic impact on the future of work. And, while we may not know exactly what that future will look like, we can identify some of the skills and competencies that workers will need to be successful.

To understand what skills and competencies workers of the future will require, it’s helpful to review some of the more recent advancements in AI and machine learning. Here are a few of the recent announcements.

In each of these instances, an A engine has been provided extensive databases of information and then been taught to analyze that information and produce models similar to what it has been analyzing. For faces, a large database of human faces. For the text-generator, a massive database of text samples. IBM’s” Miss Debater (formerly known as Project Debater) pulls arguments from its database of 10 billion sentences taken from newspapers and academic journals.”

While current AI achievements are impressive, presently there are limitations, as champion debater Harish Natarajan, the person who defeated Miss Debater, points out.

“Debating is…more complicated for a machine… At its core, successful debating involves three components,” he wrote in a LinkedIn post. “First, a debater needs to process large amounts of information and construct relevant arguments. Second, debating involves [explaining] complicated arguments in a clear and structured way. Third, you need to make those arguments matter to the audience. This requires the careful use of language, emotions, rhetoric, and examples. While a machine should excel in the first, the latter two may be challenging.”

Natarajan’s observations underscore both where current AI models excel, and where they fall short. Today’s AI is particularly adept at studying pre-existing information and rules, creating models from those, and then learning to replicate the models. This applies to language translation (oral, written, and gesture-based), voice recognition and modeling, the optical recognition of patterns, and driving vehicles. Essentially, any type of information that can be reduced to a replicable model with quantifiable variations, regardless of how complex, is fertile ground for today’s AI.

What does this mean for the future of work and workers?

Kai-Fu Lee, the former President of Google China, recently pointed out that “the rise of AI also comes the fall of other types of jobs, especially those that are routine, including white collar, entry level, and blue-collar, which are most at risk. “As long as a job is routine, can be defined by an objective function and is quantitative in nature, over time, it will eventually get displaced,” he said.

According to a recent report from the Bookings Institution:

Jobs that were more vulnerable to automation were more likely to be found in rural towns like Kokomo, Ind., and Hickory, N.C., the report said, while those in coastal cities like San Jose and the District of Columbia were more likely to be safe. “Less-educated heartland states and counties specialized in manufacturing and low-end service industries could be especially hard-hit by automation in the A.I. era, whereas well-educated states and counties along the Boston-Washington corridor and on the West Coast appear less exposed,” the report said. “In parallel fashion, smaller, less-educated communities will struggle relatively more with A.I.-phase automation, while larger, better-educated cities will experience less disruption.”

For workers and workforce training, this means helping people develop skills and competencies beyond information expertise (processing and analyzing information is something AI will do better than humans). We need to prepare future workers to excel beyond routine and repetitive tasks so that they can thrive in the rapidly evolving professional environments of tomorrow.

We need to prepare our future works to embrace change both creatively and collaboratively.

At the very least, I think this means focusing on the following skills and competencies in our college and vocational training.

  • Critical thinking
  • Creative analysis and problem-solving
  • Collaborative teamwork and communication

These abilities will prepare students to add value to employers in areas where AI either doe snot excel or proves less disruptive.

Rob Reynolds, Ph.D.
Executive Director, TEL Library

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