The June, 2015 issue of the Harvard Business Review includes a spotlight on Man and Machine: Knowledge Work in the Age of the Algorithm. Another example that, as a recent article observed, “Artificial intelligence is suddenly everywhere…”
People have long worried about the impact of technology on society, whether discussing railroads, electricity, and cars in the Industrial Age, or the Internet, mobile devices and smart connected products now permeating just about all aspect of our lives. But the concerns surrounding AI may well be in a class by themselves. Like no other technology, AI forces us to explore the very boundaries between machines and humans.
Some experts fear that at some future time, sentient, superintelligent AI machines might pose an “existential risk” that “could spell the end of the human race.” Others are dismissive of such dire concerns while agreeing that we must work hard to ensure that our complex AI systems do what we want them to do.
Whether right or wrong, these long term worries are still decades into the future. Much more immediate is the impact of our smart machines on jobs and the economy. Will AI turn out like other major innovations, - e.g., steam power, electricity, cars, - highly disruptive in the near term, but ultimately beneficial to society? Or, will our smart machines take over not just low-skilled tasks but high-skilled ones too? What will life be like in such an AI future, when highly intelligent machines, - many far surpassing human cognitive capabilities, - are all around us?
While generally optimistic about technology’s long term benefits, the authors are quite concerned about the major challenges we’re already facing, in particular, the disappearance of many mid-level jobs and the stagnation of average incomes, first in the US and other advanced economies but over time in emerging economies as well.
“Digital technologies are doing for human brainpower what the steam engine and related technologies did for human muscle power during the Industrial Revolution,” notes McAfee. “They’re allowing us to overcome many limitations rapidly and to open up new frontiers with unprecedented speed. It’s a very big deal. But how exactly it will play out is uncertain… we’re at an inflection point today, at the dawn of what we call the Second Machine Age.”
“This era will be better for the simple reason that, thanks to digital technologies, we’ll be able to produce more: more health care, more education, more entertainment, and more of all the other material goods and services we value. And we’ll be able to extend this bounty to more and more people around the world while treading lightly on the planet’s resources.”
“But digitization has brought with it some thorny challenges,” adds Brynjolfsson. “That shouldn’t be a shock. Throughout history, positive economic developments have often had unpleasant side effects. For instance, the first Industrial Revolution created vast wealth but also brought us pollution and disease and the exploitation of child labor.”
“Digitization is creating new types of economic disruption. In part, this reflects the fact that as computers get more powerful, companies have less need for some kinds of workers. Even as it races ahead, technological progress may leave some people - perhaps even a lot - behind… Computers and robots are learning many basic skills at an extraordinary pace.”
“As the Second Machine Age progresses, will there be any jobs left for humans?,” the interviewer asked. Brynjolfsson and McAfee mentioned three skills areas where, - at least for now, - humans are still far superior.
- High-end creativity including “great new business ideas, scientific breakthroughs, novels that grip you, and so on. Technology will only amplify the abilities of people who are good at these things.”
- Emotion and interpersonal relations, including “caring, nurturing, coaching, motivating, leading, and so on. Through millions of years of evolution, we’ve gotten good at deciphering other people’s body language… Machines are way behind there.”
- Dexterity and mobility. “It’s unbelievably hard to get a robot to walk across a crowded restaurant, bus a table, take the dishes back into the kitchen, put them in the sink without breaking them, and do it all without terrifying the restaurant’s patrons. Sensing and manipulation are hard for robots.”
In the end, it all comes down to whether we can learn to race with rather than against the machines. Over the past two centuries we’ve successfully adapted to the Industrial age machines. It would have made no sense to look at the Industrial Revolution as a race between humans and steam power to see who is strongest, or between humans and cars to see who is faster.
Similarly, how can we now learn to adapt to and work with our increasingly smart machines? This was the subject of Beyond Automation, - the lead article in HBR’s Man and Machine special section, - by professor and author Tom Davenport and HBR editor at large Julia Kirby.
The key, they write, is to “reframe the threat of automation as an opportunity for augmentation… What if, rather than asking the traditional question - What tasks currently performed by humans will soon be done more cheaply and rapidly by machines? - we ask a new one: What new feats might people achieve if they had better thinking machines to assist them? Instead of seeing work as a zero-sum game with machines taking an ever greater share, we might see growing possibilities for employment.”
In such an augmentation environment, humans and machines support each other. The machines makes the human much more productive, and the human ensures that the computer is doing a good job, is on the look-out for common-sense mistakes that computers often make because they’re difficult to codify, and makes sure that the machine keeps learning and improving. After analyzing a wide variety of cases, the authors propose five key ways for humans to partner and collaborate with intelligent machines, so together they can do things much better than either could on its own.
Step Up: Head for higher intellectual ground. “There will always be jobs for people who are capable of more big-picture thinking and a higher level of abstraction than computers are. In essence this is the same advice that has always been offered and taken as automation has encroached on human work: Let the machine do the things that are beneath you, and take the opportunity to engage with higher-order concerns.”
Step Aside: Big-picture, abstract thinking may be an option for a small fraction of the workforce. “But a lot of brain work is equally valuable and also cannot be codified. Stepping aside means using mental strengths that aren’t about purely rational cognition but draw on what the psychologist Howard Gardner has called our ‘multiple intelligences.’ You might focus on the ‘interpersonal’ and ‘intrapersonal’ intelligences - knowing how to work well with other people and understanding your own interests, goals, and strengths.”
Step In: This means knowing how to monitor and improve the work of computers. Computers are excellent at analyzing large data sets looking for correlations and patterns. But they are not as good as humans at separating true causal relationships from mere statistical correlations. “Taxes may increasingly be done by computer, but smart accountants look out for the mistakes that automated programs - and the programs’ human users - often make. Ad buying in digital marketing is almost exclusively automated these days, but only people can say when some ‘programmatic’ buy would actually hurt the brand and how the logic behind it might be tuned.”
Step Narrowly: Find a deep and narrow professional specialty that’s not worth automating. “Those who step narrowly find such niches and burrow deep inside them. They are hedgehogs to the stepping-up foxes among us. Although most of them have the benefit of a formal education, the expertise that fuels their earning power is gained through on-the-job training - and the discipline of focus.”
Step Forward: This means “constructing the next generation of computing and AI tools. It’s still true that behind every great machine is a person - in fact, many people… Someone intuits the human need for a better system; someone identifies the part of it that can be codified; someone writes the code; and someone designs the conditions under which it will be applied… [S]potting the right next opportunity for automation requires much more than technical chops. If this is your strategy, you’ll reach the top of your field if you can also think outside the box, perceive where today’s computers fall short, and envision tools that don’t yet exist.”
“The strategy that will work in the long term, for employers and the employed, is to view smart machines as our partners and collaborators in knowledge work,” write Davenport and Kirby in their concluding paragraph. “By emphasizing augmentation, we can remove the threat of automation and turn the race with the machine into a relay rather than a dash. Those who are able to smoothly transfer the baton to and from a computer will be the winners.”