Seriously Curious

by Tom Standage

  The simplest type of brain computer is a cochlear implant. These devices transform sound waves into electrical signals, to stimulate the auditory nerve directly. The computer controlling this process sits behind the ear, connected to a microphone and a wearable battery pack. It transmits both power and soundwaves – transformed into electromagnetic signals – to an implant just inside the skull, next to the ear. That implant receives the signal wirelessly, translates it into an electrical current and passes it down a wire, past the biological machinery of the ear, to an electrode embedded in the auditory nerve. Another sort of brain computer is called a neurostimulator, a device used in the treatment of Parkinson’s disease. It is usually implanted under the skin on the chest or lower back. It sends electrical signals to parts of the brain called the basal ganglia, which are associated with control of voluntary movement.

  Now a new kind of brain computer is emerging from Silicon Valley – albeit one that is, for now, still on the drawing board. Entrepreneurs think that devices could go beyond simply replacing lost functions: they dream of connecting the brain directly to computers and to the internet to give it entirely new functions that are beyond human beings’ abilities today. Imagine Google searches that deliver their result to the brain before the question is consciously asked; or direct, brain-to-brain communication, in which messages are sent using thought alone. Elon Musk, with his new company Neuralink, and Bryan Johnson, with a slightly older company called Kernel, are leading the charge. For the time being, the function of the brain is not understood in enough detail to read and write information at this level of linguistic communication. But for the optimists of Silicon Valley, avid readers of science-fiction novels in which such devices are commonplace, it is only a matter of time.

  The link between video games and unemployment

  In 2017 the video-gaming industry racked up sales of about $110bn, making it one of the world’s largest entertainment industries. The games on offer run the gamut from time-wasting smartphone apps to detailed, immersive fantasy worlds in which players can get lost for days or weeks. Indeed, the engrossing nature of games may be cause for concern. In 2016 four economists published a paper suggesting that high-quality video games – an example of what they call “leisure luxuries” – are contributing to a decline in work among young people in America, and especially young men. Given the social and economic importance of early adulthood, such a trend could spell big trouble. But are video games really causing the young to turn on and drop out?

  In making the link between gaming and work, the economists Mark Aguiar, Mark Bils, Kerwin Charles and Erik Hurst point to compelling data. Between 2000 and 2015, the employment rate for men in their 20s without a college education dropped by ten percentage points, from 82% to 72%. Such men often live at their parents’ homes and tend not to marry at the same rate as their peers. They, do, however, play video games. For each hour less the group spent in work, time spent at leisure activities rose by about an hour, and 75% of the increased leisure time was accounted for by gaming. Over the same period games became far more graphically and narratively complex, more social and, relative to other luxury items, more affordable. It would not be surprising if the satisfaction provided by such games kept some people from pursuing careers as aggressively as they otherwise might (or at all).

  To draw a firm conclusion, however, would take a clearer understanding of the direction of causation. While games have improved since the turn of the century, labour-market options for young people have got worse. Hourly wages, adjusted for inflation, have stagnated for young college graduates since the 1990s, while pay for new high-school graduates has declined. The share of young high-school and college graduates not in work or education has risen; in 2014 about 11% of college graduates were apparently idle, compared with 9% in 2004 and 8% in 1994. The share of recent college graduates working in jobs which did not require a college degree rose from just over 30% in the early 2000s to nearly 45% a decade later. And the financial crisis and recession fell harder on young people than on the population as a whole. For people unable to find demanding, full-time work (or any work at all) gaming is often a way to spend some of one’s unwanted downtime, rather than a disincentive to work; it is much more a symptom of other economic ills than a cause.

  Games will go on getting better, and the share of jobless or underemployed young Americans choosing to game rather than focus on their careers will probably grow. That is not necessarily something to lament. Games are often rewarding and social, and spending time gaming indoors may provide an alternative to getting involved in undesirable or antisocial activities. If the pull of work is not strong enough to overcome the desire to game, the first response should be to ask whether more can be done to prepare young people for good jobs – and to make sure that there are some around when those young people enter the workforce.

  What do robots do all day?

  You have probably never heard of FANUC, the world’s largest maker of industrial robots. But the chances are that you own a product built by one of its 400,000 machines. Established in 1956, the Japanese company supplies robots that build cars for Ford and Tesla, and metal iPhone cases for Apple. The firm distinguishes itself from competitors by the colour of its robots’ whizzing mechanical arms, which are painted bright yellow. Its factories, offices and employee uniforms all share the same hue. FANUC is at the forefront of a booming market for robots that shows little sign of slowing. According to the International Federation of Robotics, unit sales of industrial robots grew by 15% in 2015, while revenues increased 9% to $11bn. In 2016 turnover in North America rose by 14%, to $1.8bn. ABI Research, a consultancy, reckons that the industry’s sales will triple by 2025.

  The popular narrative about robots is that they are stealing human workers’ jobs. A paper published by the National Bureau of Economic Research broadly supports this belief, estimating that each additional robot in the American economy reduces employment by 5.6 workers. But the relationship between automation and employment is not always straightforward. One big trend is the growth of “collaborative robots”, smaller and more adaptable machines designed to work alongside humans and increase their productivity. Barclays, a bank, thinks that between 2016 and 2020, sales of these machines will increase more than tenfold. Adopting robots has made it economical for some manufacturers in high-wage countries to “re-shore” production from poorer countries. In 2017 Adidas, a sportswear firm, began producing running shoes in a German factory staffed by robots and 160 new workers.

  The life robotic

  Global industrial robots

  Source: International Federation of Robotics

  FANUC is not taking its dominance for granted. The company is working on smarter, more customisable robots and is investing heavily in artificial intelligence. Its efforts to adapt in the rapidly evolving robotics industry can be seen even in the firm’s new approach to colours. When the company unveiled its first collaborative robot, CR-35iA, its trademark yellow had been replaced with green.

  Why 5G might be both faster and slower than previous wireless technologies

  “Faster, higher, stronger,” goes the Olympic motto. So it was only appropriate that the fifth generation of wireless technology, “5G” for short, should get its first showcase at the 2018 Winter Olympics in Pyeongchang, South Korea. Once fully developed, 5G is supposed to offer download speeds of at least 20 gigabits per second (4G manages about half that at best) and response times (“latency”) of below 1 millisecond. That means 5G networks will be able to transfer a high-definition movie in two seconds and respond to requests in less than a hundredth of the time it takes to blink an eye. But 5G is not just about faster and broader wireless connections.

  The technology could also enable all sorts of new services. One example would be real-time virtual- or augmented-reality streaming. At the Olympics, for example, many contestants were followed by 360-degree video cameras. At special venues sports fans could don virtual-reality goggles to put themselves right
into the action. 5G is also supposed to become the connective tissue for the internet of things, interconnecting everything from smartphones and wireless sensors to industrial robots and self-driving cars. This will be made possible by a technique called “network slicing”, which allows operators to create bespoke networks that give each set of devices exactly the kind of connectivity they need to job a particular job.

  Despite its versatility, it is not clear how quickly 5G will take off. The biggest brake will be economic. When the GSMA, an industry group, asked 750 telecoms bosses in 2017 about the most salient impediment to delivering 5G, more than half cited the lack of a clear business case. People may always want more bandwidth, but they are not willing to pay much more for it – an attitude that even the lure of the fanciest virtual-reality applications may not change. And building 5G networks will not be cheap. Because they operate at higher radio frequencies, 5G networks will require more antennae, base stations and fibre-optic cables.

  Although it can deliver data more quickly, 5G technology will arrive slowly. Analysts expect network operators to roll out 5G more gradually than the previous wireless generation – and only in places where the numbers add up. Some will initially use the technology to provide super-fast “fixed” wireless links between stationary antennae, which is less tricky to do. Others may use 5G to get more out of the spectrum they already own. Yet others will focus on building 5G networks to serve densely populated cities. In other words, 5G’s trajectory is likely to resemble that of 3G, which was launched in the early 2000s. It disappointed until it found its “killer application” with the smartphone, later that decade. And it was only with 4G that mobile networks actually lived up to the promises of 3G, such as being able to watch video streams. To really get the benefits that are promised for 5G, people may have to wait for 6G.

  Mobile phones are more common than electricity in much of sub-Saharan Africa

  A decade after mobile phones began to spread in Africa, they have become commonplace even in the continent’s poorest countries. In 2016, two-fifths of people in sub-Saharan Africa had mobile phones. Their rapid spread has beaten all sorts of odds. In most African countries, less than half the population has access to electricity. In a third of those countries, less than a quarter does. Yet in much of the continent people with mobile phones outnumber those with electricity, despite the fact that they may have to walk for miles to get a signal or to recharge their phones’ batteries.

  A current problem

  Sources: IEA; GSMA

  Mobile phones have transformed the lives of hundreds of millions for whom they were the first, and often the only, way to connect with the outside world. They have made it possible for poor countries to leapfrog much more than landline telephony. Mobile-money services, which enable people to send cash straight from their phones, have in effect created personal bank accounts that people can carry in their pockets. By one estimate, the M-Pesa mobile-money system alone lifted about 2% of Kenyan households out of poverty between 2008 and 2014. Technology cannot solve all of Africa’s problems, but it can help with some of them.

  Why self-driving cars will mostly be shared, not owned

  When will you be able to buy a driverless car that will work anywhere? This commonly asked question contains three assumptions: that autonomous vehicles (AVs) will resemble cars; that people will buy them; and that they will be capable of working on all roads in all conditions. All three of those assumptions may be wrong. Although today’s experimental vehicles are modified versions of ordinary cars, with steering wheels that eerily turn by themselves, future AVs will have no steering wheel or pedals and will come in all sorts of shapes and sizes; pods capable of carrying six or eight people may prove to be the most efficient design. Rather than working everywhere, these pods will initially operate within geographically limited and well-mapped urban areas. And they will be shared “robotaxis”, summoned when needed using a ride-hailing app. The first self-driving vehicle you ride in will be shared, not owned, for a combination of technological and economic reasons.

  The technology needed to get vehicles to drive themselves has not yet been perfected, but it has improved enormously over the past decade and is on the verge of working reliably, at least in relatively simple urban environments with good weather. This explains why Phoenix, Arizona, is a popular place to test AVs; Waymo, the self-driving car unit of Google’s parent company, hopes to launch a robotaxi service there by the end of 2018, based on Chrysler Pacifica minivans. Other robotaxi services will appear in the coming years in other cities, and the areas they cover will gradually be expanded. The initial deployment of self-driving vehicles as robotaxis makes sense because they only need to work within a particular area – and because the sensors needed for a fully autonomous AV to sense its surroundings and figure out how to respond currently cost more than the vehicle itself. That is less of a problem for a shared robotaxi, however, which will be in use and generating revenue for several hours a day. (Private cars, by contrast, are used on average only about 5% of the time.)

  So economics and practicality dictate that AVs will start out as shared robotaxis. Eventually, perhaps by 2030 or so, the cost of sensors will fall and it will no longer be prohibitively expensive to buy your own self-driving vehicle. The question then is whether you would want to. For people living in cities, robotaxis could offer a far cheaper and more convenient alternative to car ownership. At the moment, travelling by Uber or another ride-hailing service costs around $2.50 a mile; but take away the driver, and that cost could fall to $0.70 a mile, reckon analysts at UBS, a bank. That is less than the $1.20 a mile it costs, on average, to run a private car (when fuel, insurance, servicing and other costs are factored in). So if robotaxis really work as advertised, many urbanites could ditch their cars and save thousands of dollars a year. UBS predicts that by 2035, 80% of people will use robotaxis in cities where they are available, and that urban car ownership will fall by 70%.

  No doubt some people will still want to own a car, and will buy a self-driving one. But the total number of vehicles on the road will fall by about half from its current level, UBS predicts, and by 2050 those vehicles will be split roughly equally between robotaxis and privately owned AVs. The robotaxis, being in almost constant use, will account for the vast majority of miles travelled. With fewer private vehicles needing to be parked, vast swathes of land currently wasted on parking will be available for other uses, such as housing. As cars did in the 20th century, AVs will redefine retailing and reshape cities, as well as providing a convenient new form of mobility. As with cars, which lead to road deaths, pollution and congestion, there are likely to be unanticipated (and unpleasant) consequences for society from autonomous vehicles, such as a loss of privacy and the potential to use them as a means of social control. Removing the horse from horse-drawn carriages was an apparently simple change that had far-reaching effects. Similarly, there is much more to autonomous vehicles than simply removing the need for a driver – and much of their impact will be a consequence of the fact that they will mostly be shared, not owned.

  How ride-hailing apps reduce drink-driving

  Gun violence in America gets plenty of attention, but cars kill more people. Around 40,000 people a year die on American roads, more than all fatalities caused by firearms (of which two-thirds are suicides, not homicides). The death rate from motor accidents in America, around 12 people per 100,000, is more than twice that of western Europe. The grim toll of motor-vehicle deaths is widely seen as unavoidable, given that the United States is a large, sprawling country primarily designed around the automobile. But around a third of these deaths has involved drunk drivers, suggesting that there is, in fact, substantial room for improvement. Indeed, it appears that the advent of ride-hailing apps like Uber and Lyft has had a welcome impact on road safety.

  According to a working paper by Jessica Lynn Peck of the Graduate Centre at the City University of New York, the arrival of Uber in New York City may have helped reduce alcohol-relate
d traffic accidents by 25–35%, as people opt to hail a ride home after a night out, rather than driving themselves. Uber was first introduced in the city in May 2011, but did not spread through the rest of the state. The study uses this as a natural experiment. To control for factors unrelated to Uber’s launch, such as adverse weather conditions, Ms Peck compares accident rates in each of New York’s five boroughs to those in the counties where Uber was not present, picking those that had the most similar population density and pre-2011 drunk-driving rate.

  The four boroughs which were quick to adopt Uber – Manhattan, Brooklyn, Queens and the Bronx – all saw decreases in alcohol-related car crashes relative to their lookalike counties. By contrast, Staten Island, where Uber caught on more slowly, saw no such decrease. It should not take ride-hailing apps to curb drunk driving, but any reduction is worth hailing.

  Worth hailing

  Alcohol-related crashes in New York City

  Difference* in the number of crashes in boroughs when compared with similar counties

  Source: “New York City Drunk Driving After Uber” by J. L. Peck, 2017