Here is a startling fact -jobs are going to machines at an alarming rate. Consequences are in motion to alter everything we take for granted about our work and the ways in which we humans exist. The world has already gone from linear to parabolic. By 2011 Watson, IBM’s artificial intelligence, question and answer computer, was capable of beating Jeopardy champions Ken Jennings and Brad Rutter. But, a new class of machine known as “deep learning” has crossed us over to computers thinking faster and better than humans. Advances in technology are now so powerful we can expect to see a lot more artificial intelligence (AI) soon.
A Chinese board game Go’s undisputed, five time champion Lee Se-dol, the world’s best living Go player recently dueled a computer. China’s top players predicted Lee would not lose a single game, but Lee went on to lose all but one of five match games. Such a feat is incredible. Go is a super, ultra, mega chess game. AI has surpassed specific tasks, and can now learn collective behavior using algorithms.
Because of “deep learning”, voice recognition is more accurate and powerful now. Voice interfaces are being added to more apps, which now have smart or cognitive capabilities. AI learns from consumers consumption and provides real, valuable recommendations about behavior to target markets automatically. If this change sounds exaggerated, step back and look at what computer technology has been doing to employment so far:
Computer technology is already eating jobs and has been since 1990. Work can be divided into four types: routine and nonroutine, cognitive and manual. Routine work is the same day in and day out, while nonroutine varies. Within these two varieties, it is the work that requries mostly our brains (cognitive) and the work that requires mostly our bodies (manual). All four once saw growth, but routine stagnated in 1990. This happened because routine labor is easiest for technology to replace. Rules can be written for work that doesn’t change and then turned over to machines.
Unfortunately, it is the same routine work that once formed the American middle class. Henry Ford transformed routine manual work by paying people middle class wages to perform it. Routine cognitive work that once filled US office spaces. Many of the latter jobs are increasingly unavailable. Only two kinds of jobs are left, the ones that require so little thought we pay people little to do them, and jobs that required so much thought we pay people well to do them. Two engines out of four will keep the plane flying, but what happens when the two remaining engines fail? The advancing fields of robotics and AI represent those final two engines because, for the first time, we are successfully teaching machines to learn.
At the same time Big Data is growing expodentially. In 2013 SINTEF estimated that 90% of all information in the world had been created in the prior two years. By 2015 every minute we were liking 4.2 million things on Facebook, uploading 300 hours of video to YouTube, and sending 350,000 tweets. Lots of data is exactly what machines need in order to learn to learn. The combinatioin of deep learning and big data has resulted in astounding accomplishments just in the past year. Google’s DeepMind AI has learned how to read and comprehend what it reads through thousands of annotated news articles. DeepMind has also taught itself to play dozens of Atari 2600 video games better than humans.
Anytime now is the answer in the 21st century for any question involving something new machines can do better than human, and wrapping our heads around the transformation of the world’s workforce is very difficult. One thing certain is droves of workers will be displaced and soon. If just the truck drives in the US were replaced by driveless trucks, 3.5 million people would be out of work. We need to recognized what it means for exponentila technological change to enter the labor market for nonroutine jobs for the first time ever. Machines that can learn mean nothing humans do as a job is uniquely safe anymore. From hamburgers to healthcare, machines can be created to successfully perform tasks with no need or less need for humans, and at lower costs than humans.
One more example, and we’ll address the known solution for the impending displaced workers. Amelia is one AI being beta-tested by companies. Created by IPsoft, she has learned how to perform the work of call center employees. She can learn in seconds what takes us months, and she can do it in 20 languages. By learning more over time, she successfully handled one of every ten calls in the first week, but by the end of the second month, she could resolve six of ten calls.
The hope of workforce labor and economic growth as a counter force to this major transformation is entrepreneurship, the creation of cognitive, innovation designing better companies and countless new startups. Small business is the backbone of our economy. Successful entrepreneurs are naturally competitive, think outside the box, and see through easy answers to how an industry can benefit from a fresh take. The SBA said in 2012 small busineses created 64% of the new jobs in the previous decade:
- New businesses challenge the existing market.
- Market disruption causes new job fields to open.
- Small businesses are more flexible to change.
- Competition pushes companies to streamline.
- Their ideas create new products and services.
- Entrepreneurship historically turns bad economies.
- Managed economies (China) encourage it as crucial.
- Experience has proved that entrepreneurship can be taught.
- Entrepreneurship flourishes in US capitalism and freedoms.
- Self-employed are passionate, willing to work harder.
- Business creation is job creation.
- Entrepreneurs also create, improve social change.
Because entrepreneurship is a way of thinking (the entrepreneurial mindset), it is learned over time and by experience. Introducing the power to choose, opportunity recognition, action on ideas, pursuit of knowledge, wealth creation, building a brand, creating community, and the power of persistence should be inserted into early education. In Georgia there is a program, the International Entrepreneurship Institute’s Real Ledge, that trains K12 teachers of all grades to introduce entrepreneurial thinking and experiential exercises to curriculum.
High school students have been dramatically turned from failure to success in one or two years by”catching the entreprenship bug”. Two national programs of note, the NFTE, Network for Teaching Entrepreneurship, and YEA, Youth Entrepreneurship Academy, use mentorship and project competitions to reward startup ideas. Entrepreneurship teaches human success, how to evaluate and identify beliefs and assumptions, problem solving, oral and written communication, teamwork, and community engagement.
Entrepreneur students come out critical thinkers able to validate their business ideas through inquiry and analysis. Even mature and experienced works, who make changes in the Gig Economy, are far better prepared by entrepreneurship skills. Due to shifts from an industrial to a knowledge economy, temporary positions are common and organizations contract with independent works for short-term engagements. These workers need skills to survive self-employed even now ahead of the impending loss of routine labor. They will need to understand entrepreneurship to thrive.
It is past time to offer exposure to entrepreneurial thinking in K12 schools from coast-to-coast, make one year of entrepreneurship education mandatory in all community colleges and universities, and fund workforce development centers with state-of-the-art evidenced-based (aka lean) entrepreneurship training. There is still time to save the giantic freight train headed straight at routine and cognitive labor if we wake-up now and accept entrepreneurship as the answer.
McKinsey research say that up to one-third of U. S. workers -and 800 million globally- could be displaced by 2030. The researchers found that “60 percent of occupations have at least 30 percent of constituent work activities that could be automated.” “Income polarization could continue in the United States and other advanced economies,” they added. McKinsey suggests that governments can help retrain workers or supplement income as people adjust. “To help transition to a future with increased automation, businesses and policymakers will need to act to keep people employed,” suggests the McKinsey research. We know that action is to embrace entrepreneurship training as the best solution to massive unemployment.
Clinton E. Day is an entrepreneurship adjunct professor and author, more on website http://clintoneday.com.
Special thanks to Scott Santens, moderator of BasicIncome on Reddit, a fervent believer in basic income as a solution to the displacement, whose article made the deep learning argument.