Archive for April 16th, 2017
I think AI will have a serious impact on jobs within 5 years. It’s going to move fast. Steve Levine writes in Quartz:
You’ve heard about the robots—how they are on their way to vaporize the jobs of tens of thousands of bankers and brokers on Wall Street, in the City of London, and in trading hubs around the world. How they are bent on inflicting similar mayhem in law and accounting firms, and in computer-programming pools.
How, if you wear a white collar, male or female, watch your back.
And how all that’s just for starters. Advances in supercomputers and the understanding of neural networks are combining to create a revolution in robotics, and companies eager for more profitability and cheaper production are ruthlessly grabbing the new technology to automate rote jobs.
Blue-collar workers—forget about it. The robots will kill off the positions of half a million oil-rig hands, up to half the industry’s workforce around the world, along with hundreds of thousands of warehouse employees, Amazon-ized by automated forklifts and other machines. Then there are the drivers—the navigators of taxis and long-haul trucks, who make up some 17% of the adult US work force, adding up to about 7 million people, to be replaced by robot cars if competition from Uber’s roster of of 1.5 million drivers doesn’t put them out of business first. Fast-food workers—the hard-working teens, first-generation immigrants, and return-to-work moms who are the bedrock of burger joints everywhere—are also on the firing line as ordering kiosks begin to take the place of human cashiers.
Estimates of how many jobs in all the robots will wipe out, and when, vary wildly. Economists say somewhere between 9% and 47% of workers in the West could lose jobs to automation over the next two or so decades. They forecast that as much as 40% of the Fortune 500—the companies as a whole—could vanish entirely within a single decade, driven out by algorithms. In China and India, meanwhile, they predict the disappearance of between 25% and 69% of jobs.
Industries have weathered massive disruptions before. Through two centuries of technological revolutions, positions eliminated in one sector have been replaced by even more jobs in others. Perhaps this time will be the same; or perhaps technology will overwhelm the capacity for ingenious humans to invent sufficient new businesses to employ the population.
Perhaps in a lot of cases, not entire jobs, but large percentages of the duties involved will be diverted to robot labor. Either way, it seems clear that change is coming. How quickly it unfolds is another matter. In the case of 19th century British Industrial Age workers, historians note that it took six decades for laborers to resettle and start to win higher wages once factory automation took hold. How long it will take workers to adjust in the age of mass-market robots is not known, but as in the early Industrial Age, they seem likely to face a drawn-out and agonizing transition. . .
This article by Nancy LeBrun from Craftsmanship magazine is from 2015, but it is still interesting.
t six o’clock on a July morning, during one of the hottest stretches in northwest Oregon’s recorded history, Ryan Neil trots out the door of his hilltop home and down a short gravel path in his nursery to check on more than a million dollars worth of small, delicate trees. Neil is a professional bonsai artist, and he’s borrowed heavily against his trees, his house and his property to pursue a quest for a new, all-American form of bonsai that will be recognized as a true art form. If the trees die, so does his dream.
As a major step in his master plan, the 34 year-old Neil, a fit and even-featured all-American type, is mounting a juried exhibit of bonsai in late September at the Portland Museum of Art—an event he conceived, developed and paid for with a loan against his home and business. He’s not sure how he’ll pay off the mountain of debt, which exceeds $420,000, but he’s all in.
The Artisans Cup, as he calls it, attracted six hundred submissions, out of which only seventy have made it to the final round of judging. The top tree will win the Cup and $10,000. Most of the entries are styled in the traditional Japanese manner, but for Neil (who as founder did not enter any of his own trees) the real prize is the chance to proselytize for a shift to a whole new American form. Neil is intent on breaking away from the fifteen hundred years of Japanese traditions that most American bonsai practitioners follow.
Japanese bonsai generally draws from the country’s relatively calm, homogenous landscape with its limited number of species. Instead, Neil wants to see American bonsai embrace the energy and diversity of the American landscape. To do that, he wants bonsai artists to use “trees collected from the harsh conditions of America’s mountains, deserts, and coastlines.” In Neil’s opinion, this would give bonsai artists an opportunity—untapped thus far—to bring out “the unbridled” quality of American trees through “asymmetry and dynamic movement.” And that, Neil argues, would give bonsai a kind of wildness that “speaks to the freedom in American culture.” These trees may be small, but Neil thinks big.
Bonsai is part art, part craft, part horticulture, and part philosophy. It’s sometimes described as a collaboration between man and nature, but at its core, it is about imagining how a tree might grow in the wild, and interpreting that vision in miniature. Or, as Neil puts it, “Bonsai is supposed to take you to the place where that tree was growing without you having to actually go there.” While this may seem to be the most natural of credos, it’s anything but. The bonsai artists’ ultimate worth is measured by how well they can manipulate a tree—sometimes pushing it to its limits—to make a living, changing thing become something of ongoing artistic value. Neil may interpret those limits rather differently from standard bonsai practice, but his vision grew out of years closely studying classical Japanese technique.
Bonsai (correctly pronounced, “bone-sigh” rather than “bahn-zai”) originated in China around 600 A.D, although there is evidence that it may go back another millennium. In Chinese, it’s called Penjing, which means, rather prosaically, “tray scenery.” After Japanese monks imported the practice from China in the 12th century, bonsai was taken up by the aristocracy, which turned the art form into a symbol of high rank and prestige. When Japan opened up to the West, in the mid-19th century, bonsai’s appeal spread. After World War II and the Korean War, the art form gained a whole new audience when American GI’s returned home with some little trees in their baggage. Today, according to bonsai expert and author Peter Warren, the U.S. is one of the countries in which interest in bonsai is rising the fastest. It’s a good time for an artist like Neil to push some boundaries. . .
Continue reading. More photos at the link.
Will Knight writes in MIT’s Technology Review:
Last year, a strange self-driving car was released onto the quiet roads of Monmouth County, New Jersey. The experimental vehicle, developed by researchers at the chip maker Nvidia, didn’t look different from other autonomous cars, but it was unlike anything demonstrated by Google, Tesla, or General Motors, and it showed the rising power of artificial intelligence. The car didn’t follow a single instruction provided by an engineer or programmer. Instead, it relied entirely on an algorithm that had taught itself to drive by watching a human do it.
Getting a car to drive this way was an impressive feat. But it’s also a bit unsettling, since it isn’t completely clear how the car makes its decisions. Information from the vehicle’s sensors goes straight into a huge network of artificial neurons that process the data and then deliver the commands required to operate the steering wheel, the brakes, and other systems. The result seems to match the responses you’d expect from a human driver. But what if one day it did something unexpected—crashed into a tree, or sat at a green light? As things stand now, it might be difficult to find out why. The system is so complicated that even the engineers who designed it may struggle to isolate the reason for any single action. And you can’t ask it: there is no obvious way to design such a system so that it could always explain why it did what it did.
The mysterious mind of this vehicle points to a looming issue with artificial intelligence. The car’s underlying AI technology, known as deep learning, has proved very powerful at solving problems in recent years, and it has been widely deployed for tasks like image captioning, voice recognition, and language translation. There is now hope that the same techniques will be able to diagnose deadly diseases, make million-dollar trading decisions, and do countless other things to transform whole industries.
But this won’t happen—or shouldn’t happen—unless we find ways of making techniques like deep learning more understandable to their creators and accountable to their users. Otherwise it will be hard to predict when failures might occur—and it’s inevitable they will. That’s one reason Nvidia’s car is still experimental.
Already, mathematical models are being used to help determine who makes parole, who’s approved for a loan, and who gets hired for a job. If you could get access to these mathematical models, it would be possible to understand their reasoning. But banks, the military, employers, and others are now turning their attention to more complex machine-learning approaches that could make automated decision-making altogether inscrutable. Deep learning, the most common of these approaches, represents a fundamentally different way to program computers. “It is a problem that is already relevant, and it’s going to be much more relevant in the future,” says Tommi Jaakkola, a professor at MIT who works on applications of machine learning. “Whether it’s an investment decision, a medical decision, or maybe a military decision, you don’t want to just rely on a ‘black box’ method.”
There’s already an argument that being able to interrogate an AI system about how it reached its conclusions is a fundamental legal right. Starting in the summer of 2018, the European Union may require that companies be able to give users an explanation for decisions that automated systems reach. This might be impossible, even for systems that seem relatively simple on the surface, such as the apps and websites that use deep learning to serve ads or recommend songs. The computers that run those services have programmed themselves, and they have done it in ways we cannot understand. Even the engineers who build these apps cannot fully explain their behavior.
This raises mind-boggling questions. As the technology advances, we might soon cross some threshold beyond which using AI requires a leap of faith. Sure, we humans can’t always truly explain our thought processes either—but we find ways to intuitively trust and gauge people. Will that also be possible with machines that think and make decisions differently from the way a human would? . . .