Dynamics of Lifelong Learning, Part II: Human and Machine

In a recent article in The Atlantic, Jeffrey Selingo outlines the reality that of today’s workforce like this:

Previous shifts in how people work have typically been accompanied in the United States by an expansion in the amount of education required by employers to get a good job … Now a third wave in education and training has arrived, argue economists, educators, and workforce-development officials. The level of preparation that worked in the first two waves—adding more time to education early in life—does not seem sufficient in the 21st-century economy. Instead, the third wave is likely to be marked by continual training throughout a person’s lifetime—to keep current in a career, to learn how to complement rising levels of automation, and to gain skills for new work.

On the “Reinventing Corporate Education” panel at SXSW EDU, TC Haldi (Senior Director of MIT xPRO) suggested that universities were still figuring this transition out.  She took as a given that universities didn’t prepare “job-ready” graduates.  More provocatively, she wondered whether this was desirable, or even possible.  (You can read about her collaboration with Boeing and EdX to offer “continual training throughout a person’s lifetime” in the previous part of this blog entry, here.)

Even beyond the constant keeping-up-with-technology aspects of continued education, there’s a second requirement for any worker wanting to keep their job: it’s not just a matter of operating new technologies, but also of differentiating yourself from those technologies.  The systems that you once operated are increasingly capable of operating themselves, and the machines’ learning and adaptation can happen across an entire network, in a single instant, with a single software update.  Re-training and up-skilling a workforce, on the other hand, takes time, and each worker is going to learn according to their unique talents, situation, and interests.

Delivering new content to humans—whether in traditional schools, adult basic ed programs at community colleges, or in MOOC’s consumed on state-of-the-art mobile devices—may itself represent an outdated paradigm.  This sort of schooling–inasmuch as it delivers uniform curricula and models students as vessels in need of filling–is based on the assumption that standardized learning can skill workers for standardized jobs.  As an assumption, it seems better suited to the programming of robots than to the development of human beings.

In robots, homogenization is a feature, not a bug.  If scale and interoperability are priorities for robot skill-building, shouldn’t humans have a different set of priorities?  What if our systems of education were optimized to develop individual adaptation and creative exploration?  And what if the fact that robots are encroaching on work that’s previously been done by humans could be the catalyst for this change?  In what follows, I’ll talk about why I’m hopeful—not just hopeful that this is possible, but hopeful that the crisis we currently face will clarify what is essentially human, and offer more humans the opportunities to pursue their unique human talents.  But for this to happen, it will require not just investment in new educational opportunities, but a transformation in what it means to learn.

Interestingly, both Selingo, in his Atlantic piece, and Tim O’Reilly in WTF? What’s the Future and Why It’s Up to Us look to the same history in order to chart a path forward.  Part III of this entry outlines that history and the future it suggests.

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