Ever since IBM’s Deep Blue defeated then-world chess champion Garry Kasparov in a six-game contest in May 1997, humanity has been looking over its shoulder as computers have been running up the inside rail. What task that we thought was our exclusive preserve will they conquer next? What jobs will they take? And what jobs will be left for humans when they do?
The pessimistic case was partly set out in the Channel 4 series Humans about a near-future world where intelligent, human-like robots would do routine work, or stand on streets handing out flyers, while some people worked (law and policing seemed to get a pass, mostly), but others were displaced — and angry.
In May, Martin Ford, author of Rise of the Robots: Technology and the Threat of Mass Unemployment, described the concern for both white and blue-collar workers as that Humans-style world approaches: “Try to imagine a new industry that doesn’t exist today that will create millions of new jobs. It’s hard to do.”
Illustration: Yusha
However, there is an optimistic view of the same process: that the pairing of computers and robots will free humans from drudgery and dangerous work; and free people to use their imaginations and interact with each other in more personal ways, and especially in ways that computers cannot, simply because they are not human.
“Humans [will] increasingly work side by side with robots, software agents and other machines,” said J.P. Gownder, lead author of a report called The Future of Jobs, 2025: Working Side by Side with Robots, produced for the research company Forrester in August.
Gownder pointed out — as many have — that throughout history, automation and technology have repeatedly created more jobs overall than they have destroyed. We do not have lamplighters any more, but we have huge industries built around street lights and electricity supply.
However, in the robotic world, will the new jobs be better jobs? In his book published in January, The Glass Cage: Where Automation is Taking Us, writer Nicholas Carr argues that computers are taking over too much from us — or rather, that we are too willing to give up charge of things to machines — and that jobs are becoming deskilled as a result.
Carr points to the origins of automation, after World War II, when Ford Motor Co was installing new machinery to do “automatic business” on assembly lines.
“Control over a complex industrial process had shifted from worker to machine,” he said.
He said that automation had already raised its head during World War II, through the need to get hard-to-maneuver anti-aircraft guns to shoot down bombers by letting machinery move their aim, according to targets picked by gunners from screens. The humans’ task was made easier, but it was abstracted from the process and outcome.
What is indisputable is that robots equipped with computer vision and paired with artificial intelligence (AI) systems — often called “machine learning,” “deep learning” or “neural network” systems — will take over more of the work that humans do today.
Foxconn is one of the world’s biggest manufacturers of electronics, with giant factories in China that assemble smartphones, tablets and computers for Apple and other companies. It is working on robot-driven factories that will inevitably mean fewer of those jobs for humans.
Meanwhile, South Korean electronics giant Samsung has been given a grant by the South Korean government to develop high-precision robots to take over the work now done by humans, also in China, where rising wages are squeezing profit margins.
Of course, that leads to the question: what new jobs will those displaced factory workers go on to do? Nobody knows; yet everyone is sure, despite Ford’s fears, that they must exist.
Yet as we head toward that future, there are also ethical and legal reefs to navigate. Isaac Asimov introduced his famous “Three Laws of Robotics” for Runaround, a science fiction story set in 2015.
In July, an article appeared in the journal Nature, pointing out that “working out how to build ethical robots is one of the thorniest challenges in artificial intelligence.” That month, a 22-year-old worker installing a robot at a Volkswagen plant in Germany was killed when it was accidentally activated. Clearly, Asimov’s laws have not arrived yet.
However, robots that kill — especially “intelligent” ones — are very much on the mind of those who worry most publicly about the AI-robot combination. Stephen Hawking told the BBC it “could spell the end of the human race” as it took off on its own and redesigned itself at an ever-increasing rate.
Elon Musk, the billionaire who brought us PayPal and Tesla Motors, called AI “our biggest existential threat.”
Steve Wozniak, the cofounder of Apple, told the Australian Financial Review in March that “computers are going to take over from humans, no question” and that he now agreed with Hawking and Musk that “the future is scary and very bad for people... Eventually [computers will] think faster than us and they’ll get rid of the slow humans to run companies more efficiently.”
Nick Bostrom might not have a similar claim to fame, but he is an Oxford University philosopher who argues in his book Superintelligence: Paths, Dangers, Strategies that self-improving AI could enslave or kill humans if it wanted to, and that controlling such machines could be impossible.
However, there is no sign so far of inherently intelligent killer robots, or “anthropogenic AI,” as it is also called. Reviewing Bostrom’s book, scientist Edward Moore Geist suggested that it “is propounding a solution that will not work to a problem that probably does not exist.”
According to Imperial College London professor of cognitive robotics Murray Shanahan, “properly general intelligence” is comparatively easy to describe, but hard to enact.
“The hallmark of properly general intelligence is the ability to adapt an existing behavioral repertoire to new challenges, and to do so without recourse to trial and error or to training by a third party,” he writes in his book, The Technological Singularity.
However, to do that requires two capacities that AI tends not to display: common sense and creativity. On common sense, Shanahan gives the example of finding the people who normally work inside a building instead standing outside it in the rain.
“What are you doing?” might prompt the answer “Standing outside” from a computer, whereas a human would respond “Fire alarm” — recognizing the common understanding that exists between the speakers.
Meanwhile, creativity can be demonstrated by animals in problem solving, as well as by humans, such as a crow that bent straight wires to create hooks to get food. However, it is hard to say that computers have ever shown it.
It might be that they will — and to that end, Musk, with the backing of Loopt entrepreneur Sam Altman, has poured US$1 billion into a new nonprofit organization, OpenAI.org, which aims to create an open-sourced AI that surpasses human intelligence, but whose products are “usable by everyone, instead of by, say, just Google.”
However, our real problem seems to be that the growth in computing power — which roughly doubles every 18 months, but grows geometrically, because we have so many more connected devices now — is outstripping our ability to reframe our ethical and legal approach to computers’ decisions.
Even a technology that sounds innocuous and helpful, such as self-driving cars, is not immune from ethical and legal questions. For instance: if such a car needs to brake abruptly to save those on board, is there any responsibility toward people in cars behind?
If someone shunts a self-driving car with nobody at the wheel into a third car, who is responsible for the damage to the third car? The self-driving car’s owner? Its programmer?
The ways in which computers solve “human” problems repeatedly turn out very unlike the methods humans use. Take chess: Studies have found that the best human players look at a narrow set of moves, which they explore in depth, “pruning” among alternatives to find the best sequence. Computers, by contrast, look at every possible move, and essentially use brute force to pick the best at any time; they cannot decide that a particular move will surprise or upset an opponent, or choose a tricky one because the other player is short on time to decide. Compared with humans, chess-playing computers have no subtlety, except by accident.
In June, the US Defense Advanced Research Projects Agency held a competition for self-propelled robots that could work where humans cannot — say, to go into nuclear reactors and shut down operations. The winners took away millions of dollars; but the “blooper reel” of tumbling, stumbling, staggering robots has had nearly half a million views on YouTube. Sometimes, we like robots to be fools.
Carr in May wrote in the New York Times that, while it might feel as though the best way to remove error from any system is to remove the humans — because they are the ones we hear about who opened the wrong spigot, or turned off the wrong engine on the jet — in fact, humans repeatedly perform “feats of perception and skill that lie beyond the capacity of the sharpest computers.”
For example, Google’s self-driving cars have been hit 11 times in 2.74 million kilometers of travel by dozy humans, while causing no accidents directly themselves. However, the humans inside them have to stay alert and at the wheel, because the software has a glitch about every 485km of driving and it hands control back to the “driver.”
What has not yet been figured out is how much warning the human needs to take over. Is it 20 seconds? 10? One? Is it the same for everyone? What if the “driver” falls asleep because the rest of the journey has been so boring, but there is a crash when the computer was fully in charge: is that their fault, or the computer’s, or the programmers’? It is enough to make a lawyer cry with delight.
However, there is no doubt we have to face up to the social changes that are coming our way. Last month, Bank of America published a lengthy report that concluded that the “rise of the intelligent machines” constituted “the next industrial revolution,” with AI-driven robots “becoming an integral part of our lives as providers of labor, mobility, safety, convenience and entertainment.”
Sales of robots grew by 29 percent last year, with North America seeing the third consecutive year of record sales. Potential long-term effects include the replacement of existing jobs by automation — 47 percent of jobs in the US could be automated, Bank of America calculated — and the growth of inequality, as skilled workers are increasingly in demand, while unskilled ones are not.
However, it is not necessarily the low-paid jobs that would be affected — nor the high-paid ones that would be safe. The World Economic Forum last month published a graphic as part of an analysis into robots and jobs that suggests that chief executives’ jobs are probably safe — but so are those of landscaping and groundskeeping workers, despite an order of magnitude in difference in their hourly pay. The emerging consensus, such as it is, seems to be that jobs requiring careful human-to-human contact — hairdresser, surgeon and so on — should be safest from the robot insurgency.
What is most likely is that “work” will grow in complexity as AI-based systems take over the simpler tasks. “Computer” used to be a job title for humans who did calculations; now their entire function can be replicated by a cell in a spreadsheet, but jobs still exist.
Losing at chess has not made us stop playing chess, either. Kasparov himself has run championships of “centaur chess” — humans playing with the direct aid of computers during the game, which has turned out sometimes to lift the humans’ chess rating above both their own and that of the computer program.
And if it can happen in chess, why not work?
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