On the other hand there is reason to suspect he's smarter than I.
From Irving Wladawsky-Berger:
Last week I discussed a recent Pew Research report on the impact of AI, robotics and other advanced technologies on the future of jobs. The report was based on the responses of nearly 1,900 experts to a few open-ended questions, including “Will networked, automated, artificial intelligence (AI) applications and robotic devices have displaced more jobs than they have created by 2025?” The expert’s responses to this question where divided down the middle.
Beyond predictions based on responses to a survey, can one develop an overall framework to help analyze these critical issues? Along this line, I like a recent paper by MIT economist David Autor, - Polanyi’s Paradox and the Shape of Employment Growth. The paper was presented at the annual Jackson Hole Federal Reserve symposium, a gathering of some of the world’s most prominent central bankers, finance experts and academics, where the theme this year was “Re-evaluating Labor Market Dynamics”. The paper carefully laid out its arguments based on existing empirical evidence. Let me summarize its key points.
Computers have made huge advances in automating many physical and cognitive human tasks, especially those tasks that can be well described by a set of rules. But, Professor Autor argues, despite continuing advances in AI and robotics, the “challenges to substituting machines for workers in tasks requiring flexibility, judgment, and common sense remain immense.”
Central to his argument is the concept of tacit knowledge, first introduced in the 1950s by scientist and philosopher Michael Polanyi. Explicit knowledge is formal, codified, and can be readily explained to people and captured in a computer program. Tacit knowledge, on the other hand, is the kind of knowledge we are often not aware we have, and is therefore difficult to transfer to another person, let alone to a machine. Generally, this kind of knowledge is best transmitted through personal interactions and practical experiences. Everyday examples include speaking a language, riding a bike, driving a car, and easily recognizing many different objects and animals.
“We can know more than we can tell,” noted Polanyi in what Autor refers to as Polanyi’s paradox. This seeming paradox succinctly captures the fact that we tacitly know a lot about the way the world works, yet are not able to explicitly describe this knowledge.
The paper builds on Autor’s earlier research on the polarization of job opportunities in the US, where he examined the changing dynamics of the US labor market by looking at three different segments:Earlier:
- high skill, high wage jobs, where opportunities have significantly expanded, with the earnings of the college educated workers needed to fill such jobs rising steadily over the past thirty years;
- low skill, low wage jobs, which have also been expanding, while their wage growth, particularly since 2000, has been flat to negative;
Many mid-skill activities involve relatively routine tasks, that is, tasks or processes that can be well described by a set of rules. They include blue-collar manual activities such as manufacturing and other forms of production, as well as white-collar, information-based activities like accounting, record keeping, dealing with simple customer service questions, and many kinds of administrative tasks. “Because the core tasks of these occupations follow precise, well understood procedures, they are increasingly codified in computer software and performed by machines,” writes Autor. “This force has led to a substantial decline in employment in clerical, administrative support and, to a lesser degree, production and operative employment.”...MUCH MORE
- mid skill, mid wage jobs which have been declining, while their wage growth has also declined over the years, especially since 2000.
AI, Robotics, and the Future of Jobs
