We’ve already established that Moore’s Law makes the likelihood of artificial intelligence (AI) taking over improbable. The next most common argument against progress is that machines will take all of our jobs. But AI is not an all or nothing proposition. Some jobs will go; others will arrive.
“Artificial intelligence (AI) will have many profound societal effects. It promises potential benefits (and may also pose risks)… We argue that AI will indeed reduce drastically the need for human toil. We also note that some people fear the automation of work by machines and the resulting unemployment. Yet, since the majority of us probably would rather use our time for activities other than our present jobs, we ought thus to greet the work-eliminating consequences of AI enthusiastically.”
That’s essentially still the crux today, but the abstract was written 33 years ago, by Nils J. Nilsson, Professor of Engineering (Emeritus), Stanford University. In 1984, scientists believed AI would likely equal or surpass humans in 20 years — or “surely in 50” Nilsson said. He also thought it would take a “generation or two” before the full impact was felt in employment.
Here we stand in their future, but without either estimate being wholly fulfilled and the full potential of AI still unrealized. At this stage, however, we find the limits of the technology less than the obstacles created by humans’ resistance to change. Part of that opposition is fear of the unknown; the other, fear of being replaced.
Curiosity killed AI
There’s no doubt machine automation will keep increasing, but rather than superseding humans, most experts believe smart technology will augment our own abilities. In fact, new skill sets will be required, pushing growth in many areas.
After all, someone still has to develop, build and maintain, and there’s already a shortage of programmers, data scientists and cyber security specialists IT. For example, by 2020, the U.S. Bureau of Labor Statistics estimates there will be 1.4 million more software development jobs than applicants. The tech sector (jobs at tech companies and tech jobs within enterprises) added nearly 200,000 jobs in the U.S. last year.
Chart credit: CompTIA
It’s also worth noting, The Centre for European Economic Research (ZEW) believes the number of jobs predicted to be lost to automation is exaggerated. (On average, 1 in 10 is automatable.) On the contrary, the firm says digitalization creates more jobs than it destroys.
Still, with machines focusing on the mundane, people can focus on excelling at something more inherently human — curiosity. Machines can ask questions based on what they know. They can also learn (and improve) from what they detect, i.e. offering the right navigation to a driver who goes to the same place repeatedly. But machines can’t figure out why the driver frequents a location, nor do they “care”, so they cannot jump to a helpful conclusion.
For people, curiosity is part of emotional intelligence, which, as whole, makes us more adaptable to our environments. This instinct to know more reveals potential as well, and is increasingly perceived as a differentiator in candidates for employment.
According to Harvard Business Review (HBR), “Curiosity has been hailed as one of the most critical competencies for the modern workplace. It’s been shown to boost people’s employability. Countries with higher curiosity enjoy more economic and political freedom, as well as higher GDPs.”
Humans take note; practice might make perfect, but machines out-optimize us. Staying relevant in the age of AI requires a drive to explore and investigate, to learn for the sake of learning and not simply for an outcome.
Image credit: ZEW
HBR explains: “…computers still lack the ability to venture into new problem domains and connect analogous problems, perhaps because of their inability to relate unrelated experiences. For instance, the hiring algorithms can’t play checkers, and the car design algorithms can’t play computer games… AI will have an edge over humans in a growing number of tasks, but the capacity to remain capriciously curious about anything, including random things, and pursue one’s interest with passion may remain exclusively human.”
White, blue and new
From startups to tech giants and new business cases, companies are investing in AI and creating jobs. Wired has declared coding “the next big blue-collar job,” which doesn’t have to mean the next big innovation.
IBM CEO Ginni Rometty, during an interview with CNBC at the 2017 World Economic Forum, put it this way: “We’ve seen it in the past, whether when people come off of doing farming, they had to learn to read. The industrial area, it was mechanical skills,” she said. “If we would change the basis and align what is taught in school with what is needed with business… that’s where I came up with this idea of ‘new collar.’ Not blue collar or white collar.”
IBM’s Watson is a leading cognitive platform enabling deep learning and incredible applications. Rometty sees machines and humans as a partnership and the company plans to add 6,000 jobs this year, many of which will work on AI initiatives.
Microsoft’s CEO Satya Nadella created guiding principles for the company’s AI efforts that stand to preserve and generate job. “AI must be designed to assist humanity; be transparent; maximize efficiency without destroying human dignity; provide intelligent privacy and accountability for the unexpected; and be guarded against biases.”
Through Microsoft Ventures, the company recently invested in start-up Element AI to “help people and machines work together to increase access to education, teach new skills and create jobs, enhance the capabilities of existing workforces and improve the treatment of diseases…”
Besides removing the burden of tasks we don’t want to perform and enhancing our core competencies, AI is also doing work humans cannot because it’s too dangerous.
And before we panic too much about all the jobs lost, let’s consider the ones that have yet to be conceived. Each tech revolution brings with it untold opportunities and unimaginable jobs. Just ask our head of highly automated driving.