The Smart Machine Age: A New Game Requires New Rules
We can be humble and live a good life with the aid of the machines or we can be arrogant and die.
— Norbert Wiener
Norbert Wiener, an MIT mathematics professor and computer science pioneer, wrote those words in 1948 in a recently discovered unpublished essay for the New York Times. He literally meant them as an apocryphal warning about the dangers to humanity of uncontrolled advances in automation and artificial intelligence. For decades, such dire predictions remained on the fringe of societal concerns and relevant only to science fiction fans. The technologies that were only a gleam in Wiener's eye, however, have finally come to fruition.
Smart machines are becoming autonomous and able to tackle nonroutine cognitive tasks previously thought the exclusive purview of people. Machines are gaining natural language capabilities, voice and facial recognition, and the ability to draft sports columns and analyze due diligence documents better and faster than many human reporters or lawyers. Thanks to advances in automated perception, sensors, and robotics, machines are now able to handle what had previously prevented them from tackling nonroutine manual jobs as well, such as driving cars, picking out products from warehouse shelves, and sorting mail. High-functioning humanoid robots can now be seen on hospital floors and in hotels, restaurants, museums, and shopping malls. They aren't just flipping burgers behind the scenes: they're interacting with patrons and patients — like "Connie," the robot concierge Hilton began rolling out in 2016 in lobbies across the country in partnership with IBM Watson.
With respect to nonroutine cognitive jobs, using automated tools and algorithms, machines can now handle data analytics, pattern recognition, and deductive reasoning. Machines are becoming better than a roomful of Wharton graduates at devising portfolio investment theory for hedge funds and better than a team of Sloan-Kettering doctors at diagnosing illnesses. With investments from companies like Google, implantable biometric sensors will soon allow us to monitor our own health. Facial expression analysis software will detect the emotions and engagement of others better than our own minds. A group of researchers from MIT and the Masdar Institute, who conducted the first quantitative study of skill content changes in occupations between 2006 and 2014, concluded, "For any given skill one can think of, some computer scientist somewhere may already be trying to develop an algorithm to do it."
Combining the development of artificial neural codes and networks that model the human brain with access to Big Data, programmers can give machines the ability to process information and learn on a level that rivals and may soon exceed that of the human race.
Machines quite literally are now beating us at our own games. In March 2016 in what many artificial intelligence (AI) experts touted as the match of the century, AlphaGo — a computer program developed by Google's DeepMind AI company — defeated South Korean Go master Lee Se-dol four matches to one in the ancient Chinese strategy game. Almost twenty years after IBM's supercomputer DeepBlue bested the chess champion Gary Kasparov, AlphaGo's victory still surprised many experts who predicted that it would take at least another decade to develop a computer program with the ability to outwit and out-strategize a Go master in arguably the most complicated human board game ever invented. The CEO of DeepMind, Demis Hassabis, said that algorithms used for AlphaGo "one day can be used in all sorts of problems, from health care to science."
Plenty of today's technology experts, from Silicon Valley entrepreneurs to current MIT and University of Oxford academics, have sounded alarms about the potentially devastating impacts to our economy and society because of such recent and imminent technology advances. We repeat Wiener's warning here, however, not because we believe that the robot apocalypse is around the corner but because we believe that it's crucial to our relevancy as human workers and the vitality of the organizations for which we work that we pause and acknowledge the drastic changes coming and prepare ourselves to not only survive but to thrive.
We believe that there's a path to successfully navigating these strange new highly automated waters, but many of us will have to fundamentally change our views of what it means for humans to be "smart" and what it takes for humans to succeed and reach their fullest potential. To do otherwise — to ignore the impact and fail to prepare for what's to come — would indeed be a foolhardy exercise in human arrogance.
Smart Machines and a New Era
There's a growing consensus among most computer science experts, economists, and business leaders that smart machines — whether humanoid robots or invisible networked connections — that can learn, think, and perform both manual and cognitive tasks in most cases better than their human counterparts could be the biggest game changer both personally and organizationally since the Industrial Revolution. It's likely that the business, education, and leadership models created for the Industrial Revolution could become obsolete. Technological and scientific advances in artificial intelligence, the Internet of Things, virtual reality, robotics, nanotechnology, deep learning, mapping the human brain, and biomedical, genetic, and cyborg engineering could fundamentally change how all of us — from laborers to knowledge workers — live and find livelihood.
Technology that can learn and even program itself will become ubiquitous in homes, factories, and offices and soon displace even the highly educated people who have thought that their professions are immune to the risks of automation, including accountants, business managers, doctors, lawyers, journalists, researchers, architects, higher-education teachers, and consultants. Artificial intelligence — deep learning or machine learning — will be especially transformative in this regard. Speaking at a technology industry conference in May 2016, Jeff Bezos, the founder of Amazon, stated, "It's probably hard to overstate how big of an impact it's going to have on society over the next 20 years."
Andrew Ng, an associate professor of computer science at Stanford University, a chief scientist at Baidu, and chairman and cofounder of Coursera, recently told the Wall Street Journal: "The age of intelligent machines will see huge numbers of individuals unable to work, unable to earn, unable to pay taxes. Those workers will need to be retrained — or risk being left out in the cold. We could face labor displacement of a magnitude we haven't seen since the 1930s."
Similarly, Kevin Kelly, co-founder of Wired magazine, says in his new book The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future: "It is hard to imagine anything that would 'change everything' as much as cheap, powerful, ubiquitous artificial intelligence. ... The advantages gained from cognifying inert things would be hundreds of times more disruptive to our lives than the transformations gained by industrialization."
In the next two decades, technological advances could displace as many as eighty million US workers, according to the chief economist of the Bank of England, or 47 percent of the US workforce, based on a 2013 study by leading researchers at Oxford University. According to a study by McKinsey & Company, by adapting technologies already demonstrated as of 2015, as many as 45 percent of the job tasks US workers are currently paid to do could be automated. Not even the most highly skilled or highly paid are safe. McKinsey also estimated that current technology could be adapted to replace at least 20 percent of a CEO's work activities.
The result is that no longer will human scale be necessary for value creation in most fields. Without question, technology will transform how most businesses operate and are staffed in terms of both numbers and job requirements and skills. Routine jobs in hierarchical organizations — both those requiring manual and those requiring cognitive skills — will rapidly disappear. Most businesses in the near future will be staffed by some combination of smart robots, smart machines, and humans, and the job and skill requirements for each will be in flux.
In addition, the kind of long-term employment at stable organizations that characterized previous generations will be rare. The percentage of "contingent workers," including part-time, temporary, and independent contractors, has been on the rise and recently made up a whopping 40 percent of the workforce, according to an April 2015 report of the US Government Accountability Office. Another recent study predicted that by 2020, over half of the country's workforce will be consultants, freelancers, and independent contractors, cobbling together their own gigs.
Martin Ford, a Silicon Valley entrepreneur and the author of Rise of the Robots: Technology and the Threat of a Jobless Future, recently argued that "emerging industries will rarely, if ever, be highly labor-intensive"; rather, they'll be more like YouTube and Instagram, "where we've come to expect tiny work forces and huge valuations and revenues." Similarly, Tony Wagner argues: "While the Intels, IBMs, and Genentechs of the last century employed hundreds of thousands (the majority of whom were low- and middle-skilled workers), the Googles, Facebooks, and Twitters of the 21st century will employ an order of magnitude fewer employees. Almost all of them will be creative problem-solvers." Howard Gardner made a similar statement: "The future belongs to those organizations, as well as those individuals that have made an active lifelong commitment to learning."
In the age of these smart machines — what we're calling the Smart Machine Age or SMA — operational excellence may well become almost totally technology-driven, making human innovation the key to value creation. Organizations will need their people to be hyperlearners who can adapt to rapidly changing environments. These needs are unlike what was required in the command-and-control-style organizations of the Industrial Age or more recently with respect to the repetitive and routine nature of knowledge work. Agility, adaptability, and responsiveness also will be required for most, and thus organizational efficiency will be necessary but no longer sufficient. The type of human learning that will be required is continuous and iterative learning, where one's beliefs are constantly stress-tested against changing phenomena and adapted to better reflect reality. Those human processes are not efficient. In fact, they are hard and emotionally messy.
What's Left for Humans to Do?
Humans can no longer add value by merely accumulating or analyzing knowledge. The creation of new knowledge is increasing exponentially, and it's now believed that most knowledge has a less than three-year shelf life. What you think you "know" is so quickly out of date that you must continually update your learning. Moreover, it'll be impossible for humans to know more facts or concepts than a smart machine or be able to process, remember, recall, pattern match, and synthesize more data faster or more accurately than smart machines such as Google's AlphaGo and IBM's Jeopardy!-winning Watson.
Instead, to be marketable and stay relevant in the SMA, humans will need to excel at the kinds of jobs and skills that either complement technology or are those that technology cannot do well — at least not yet. That list includes critical thinking, innovative thinking, creativity, and high emotional engagement with others that fosters relationship building and collaboration. Collectively we refer to these as the SMA Skills. (Note that by creativity we mean to refer to the original expression of ideas and thoughts, including through art and otherwise. By innovation, we mean to refer to the commercialization of new ideas, methods, or things.)
Other jobs that will remain in the near future are those manual jobs requiring customized tasks and physical dexterity, but here we're focusing on the cognitive skills remaining for the majority of us who consider ourselves knowledge workers. Regardless of job or position, most of us will have to think and behave more like scientists, entrepreneurs, and artists and better engage socially and emotionally with others. The SMA Skills amount to our summary of the conclusions drawn by leading business and education leaders, economists, and researchers at MIT, Oxford, McKinsey & Company, the World Economic Forum, and the National Educational Association, among many other experts on the most important human skills in the twenty-first century.
The purpose of this book, however, is not to justify or debate the primacy of the four SMA Skills or to address, for example, when and if computers will ever achieve a human level of creativity. Much has already been written about the need to better incorporate twenty-first-century skills into primary and secondary education and job training programs and to close the skills gap to maintain US competitiveness in the global economy. Our purpose is to focus on how we humans can excel at those skills and thrive in the SMA. Unfortunately, for reasons of both nature and nurture, most of us face challenges in that regard.
Why SMA Skills Are So Hard for Humans
While the SMA Skills are what humans increasingly will need to master to stay relevant, they're far from easy to execute well. We need to understand that many of today's business leaders and managers have not been trained to develop or cultivate critical and innovative thinking, creativity, and high emotional engagement with others. They were raised, educated, and trained instead in an era when higher-order thinking and emotional skills were not deemed essential for the majority of workers. Most of today's adults have had no formal training in how to think, how to listen, how to learn and experiment through inquiry, how to emotionally engage, how to manage emotions, how to collaborate, or how to embrace mistakes as learning opportunities. This is because US society (note that we're addressing these issues from the perspective of Western and particularly US culture) favors high grades over mastery, aggressiveness and competitiveness, and the avoidance of failure at all costs — all of which hinder thinking, creating, relating, and learning at our best.
Our humanness is a blessing and a curse
We can all probably agree that SMA Skills constitute what humans can do at their best and brightest. When we're functioning at our highest level, we're able to think critically and innovatively, be creative, and relate socially and emotionally to and collaborate with others. That's our human advantage over the "bots" and algorithms. The good news is that recent research in neuroscience and cognitive, social, and educational psychology has begun to show us the environments, mindsets, and behaviors most conducive to enabling this kind of higher-order thinking, relating, and creating. The bad news is that most of us are really bad at creating those environments and embodying those underlying mindsets and behaviors because of both human nature and how we've been nurtured, which together generate two big inhibitors to learning and thinking: a preoccupation with protecting our own egos and a fear of failing and looking bad.
Let's take critical thinking, for instance. The Oxford English Dictionary defines it as "the objective analysis and evaluation of an issue in order to form a judgment." The key word is objective, and it's this objectivity that underlies the cognitive psychologist Daniel Willingham's more elaborate definition of critical thinking: "seeing both sides of an issue, being open to new evidence that disconfirms your ideas, reasoning dispassionately, demanding that claims be backed by evidence, deducing and inferring conclusions from available facts, solving problems, and so forth."
Critical thinking is different from our usual way of thinking precisely because being "objective" is so difficult to do. You may believe that you're thinking critically much of the time, but chances are you aren't doing it as well as you think you are, as well as you could, or as well as increasingly you'll need to. Scientific research has revealed just how hard it can be for humans to think and behave at their best in our modern world because of basic human biology and evolution. Our strong inclination is to be confirmation-biased and emotionally defensive thinkers.
As Daniel Kahneman, a psychologist and Nobel Laureate, explains in his treatise Thinking, Fast and Slow, we've evolved to have two systems of thinking. System 1 is fast, automatic, and subconscious — we can think of this as our intuition, which is not flying by the seat of our pants necessarily but relying on the internal beliefs, ideas, and perceptions that we consciously or unconsciously form from our experiences. Psychologists refer to this bundle of beliefs, ideas, and perceptions as our "mental models." They enable us to pattern match and make connections and associations that are quick and often subconscious. System 2 is our slow, deliberate, and effortful process of reasoning — it's closer to critical-type thinking, but not always quite there, as we'll explain further.