The physical world and the digital world are converging. 2-d bar codes and RFID tags serve as physical hyperlinks, connecting a physical object to its digital data heritage. Amazon merges deep data expertise with unparalleled physical logistics to promise same-day delivery in some areas. The so-called Internet of Things connects sensors on physical devices to powerful data analytic capability to increase fuel efficiency, prevent unexpected breakdowns, and smooth traffic flows. Waze, Uber, Airbnb, and many other startups are building new business models at the intersection of mobile apps and the cars, houses, and other attributes of private citizens.
The changes wrought by this combination of digital and physical attributes will be massive. My distinguished engineering colleague Monty Alger suggests two big buckets:
1) Disruptive innovation, in which incumbents are attacked from outside their traditional competitive realm. Content industries are already in disarray: record labels from MP3s and streaming, newspapers from Google and Facebook, non-sports television from streaming, etc. Looking ahead, who knows what Google + Lyft could do to GM in time-shared autonomous electric vehicles, for example?
2) New economies of scale and scope as digital expertise with big data and hyper-scale infrastructure encounter metal-benders and other traditional “atoms” companies. Again, recent history is instructive: Facebook photos vs Kodak; Google searches vs library reference desks; Uber’s 1.5 million drivers vs Kelly Services, the former with 3x the number of contractor/workers and 1/7 the longevity.
We will soon see more companies or other organizations that dwarf the giants of the 20th century: AT&T, IBM, Exxon Mobil, GM. A recent example: last week Peloton achieved “unicorn” status with a $1 billion+ valuation. Its innovation is a $2000 connected stationary bike that lets people join unlimited spin classes (for $39 per month) from their homes. Note that Dorel, Trek, ASI, QBP, and the rest of the traditional bike world were absent from the effort.
How is this combined world different?
This world of bits + atoms has many characteristics that distinguish it from the world of only a few years ago. First, the speed of change has never been so rapid. Uber is valued at $69 billion (bigger than GM and Lockheed Martin, and about the same size as Goldman Sachs) after less than eight years of operation. Nokia and Research In Motion lost the global lead in smartphones, becoming irrelevant in less than five years after the launch of the iPhone. Instagram was acquired by Facebook for $1 billion in 2012, two years after being founded but with 200 million users and only about a dozen employees.
Second, innovations are building on each other; we now have a set of building blocks that can be reconfigured. Tiny high-resolution digital cameras, created by the millions for smartphones, can be used in game consoles, robots, or medical applications. GPS finds its way into everything from missile systems to social network games. The same machine learning algorithms that helped a Google company beat a Go grandmaster are being used to manage energy consumption in the parent company’s data centers.
Third, more and more domains look like Wall Street: sub-second algorithmic trading governs mobile ad placement, while high-speed digital markets are coming into play in energy markets and even at Amazon, where items can change price multiple times a day. (See this story.) Few companies are ready to manage such speeds and volumes of market information, much less to respond accordingly.
The talent needed to invent, manage, and succeed in this world is scarce. The CEO of Symantec predicts there will be 6 million cybersecurity jobs in the global market by 2019, with a quarter of them going unfilled. (One scary implication: the already pathetic state of Internet of Things security will get worse.) Digital tools are becoming more prevalent at all levels of manufacturing, and many entry-level employees currently lack the skills to use CNC machines, 3D printers, and workflow automation software. Looking ahead, robot programming is beyond nearly all industrial employees, even engineers with graduate degrees. But given the speed of digital disruption, internships and entry-level hires often serve to perpetuate the skills gap rather than close it as companies recruit new workers who look a lot like the existing ones. “Big data” is a case in point: many job postings ask for a worker the existing managers cannot define, so buzzwords can replace concrete skills, software packages, and work achievements in the interview, with predictable results.
College curricula are lagging farther and farther behind. Pure bits people in computer science are doing well, as are students with pure atoms skills in domains such as hospitality management or fitness instruction. In the middle -- where many engineering fields, several business majors, and, increasingly, the hard sciences are being challenged to integrate physical and digital processes – several factors combine to prevent learning innovation to address the skills shortfall.
First, curriculum change is shepherded by the very people who succeed under the current regime. Time after time, bold changes get abraded and segmented in committee, and the outcome is incremental shifts to existing turf, fiefdoms, and reward systems. Second, in a research-driven university, professors with grants and publication hurdles often teach what they know and are investigating (push) rather than what companies are figuring out they need to hire (pull). Related to this mismatch, real-world challenges rarely align with academic silos, and interdisciplinary problem-based instruction is rare. Finally, many students bear responsibility for not digging deep or hard enough into their prospective fields of employment until an internship after the junior year, far too late to fine-tune the shape of a major, or to get mentoring in that field. If students don’t know what they need to succeed in the market, professors aren’t incented to know, and company recruiters screen for younger versions of themselves, the status quo gets perpetuated further.
What about STEM, you might ask? Parents, governors, and lobbyists from many corners of the economy hope to improve how the U.S. trains and measures its primary and secondary students. We have computers in more classrooms, Internet connections to most schools, and, slowly, teacher training to make instruction better. Certainly education in quantitative reasoning needs to improve to prepare students for the many complexities posed by the intersections of bits and atoms. But STEM programs, or even STEAM (including the arts) efforts, are necessary but not sufficient.
Because of the mixing of the physical and digital domains, and the increasing speed of change, skills for what some are calling “hybrid jobs” are proving to be in short supply. How many engineers can prepare a project budget that accounts for spare parts, currency effects, and price volatility in raw materials? How many programmers can manage a team? It used to be that only senior executives needed to learn cross-domain skills: marketers learned data analytics when a new division was acquired, engineers learned finance when they got promoted to run a line of business, statisticians learned interviewing techniques as they became responsible for instrument design, not just scoring. But no longer is it a mid-career transition to learn outside one’s educated domain. Now, even entry-level employees must cross disciplines, understand different cultures, and merge “soft” and “hard” skill sets.
Where in college or university does this happen? The notion of general education distribution requirements, in theory, should allow biologists to study materials science or architecture, or roboticists to study psychology. In practice, the combination of loose requirements, student convergence, and minimal incentive for interdisciplinary faculty collaboration means that students learn little outside their major. In one college of business, the two most popular general education courses were introductions to astronomy and to the history of popular music. You can guess the average grade earned in these courses.
Consider the field of robotics as a counterpoint. On a self-driving car team, in humanoid robotics, and in factory automation, the following disciplines intermingle:
- computer science (architectures and programming)
- metallurgy (what can we make this piece from?)
- physics (lidar)
- electro-optics (machine vision)
- material science (batteries)
- mechanical engineering (how can we make the bot strong and light?)
- psychology (how do co-workers understand the robot? How does the robot signal its co-workers?)
- math (path planning is highly complex).
That astronomy course isn’t doing anyone a favor in this marketplace.
What do we propose instead?
Let us begin with a list of objectives, then propose possible ways to achieve them.
1) Education cannot be confined to the traditional K-12, 2-yr college, 4-year college, masters degree, Ph.D. model.
2) Students (who can be defined as anyone with need and desire to learn) will need to learn or re-learn what they need close to when they need it.
3) Students need to learn how to learn over a lifetime, how to express themselves in various ways (words, numbers, animations/simulations, videos, etc), and how to assess the validity and quality of both argument and evidence (classic critical thinking). That is, the core of a liberal arts education has never been more necessary.
4) Students need both hard skills and soft skills, and the ability to tell when each is applicable.
5) Students need to learn self-awareness, both to manage their contributions to a team and to frequently reposition themselves relative to workforce demands and opportunities.
6) Students need to learn the differences between skills advancement in a particular domain and skills integration across domains, and how each is to be achieved.
The ultimate solution to this wish list is a decade or two away. In the interim, we propose as a starting point a model of integrated education that uses student internships as a test bed for industry-student-university collaboration. (Details are being worked out this summer.) Going forward, we foresee a model of integration moving across university departments and even colleges, in line with the robotics example noted above: when companies seek skills rather than majors, the university will have to respond with new course design and delivery models, new skills certification practices, and new funding streams. Those are outside the scope of the initial project, but we feel it is important to position the integrated internship as the initial step in a long, fundamental transition that more adequately supports the need of a global economy, our students and graduates, and the employers who will hire them.