Group items tagged

"Unskilled manual laborers have felt the pressure of automation for a long time - but, increasingly, they're not alone. The last few years have been a bonanza of advances in artificial intelligence. As our software gets smarter, it can tackle harder problems, which means white-collar and pink-collar workers are at risk as well. Here are eight jobs expected to be automated (partially or entirely) in the coming decades. Call Center Employees call-center Telemarketing used to happen in a crowded call center, with a group of representatives cold-calling hundreds of prospects every day. Of those, maybe a few dozen could be persuaded to buy the product in question. Today, the idea is largely the same, but the methods are far more efficient. Many of today's telemarketers are not human. In some cases, as you've probably experienced, there's nothing but a recording on the other end of the line. It may prompt you to "press '1' for more information," but nothing you say has any impact on the call - and, usually, that's clear to you. But in other cases, you may get a sales call and have no idea that you're actually speaking to a computer. Everything you say gets an appropriate response - the voice may even laugh. How is that possible? Well, in some cases, there is a human being on the other side, and they're just pressing buttons on a keyboard to walk you through a pre-recorded but highly interactive marketing pitch. It's a more practical version of those funny soundboards that used to be all the rage for prank calls. Using soundboard-assisted calling - regardless of what it says about the state of human interaction - has the potential to make individual call center employees far more productive: in some cases, a single worker will run two or even three calls at the same time. In the not too distant future, computers will be able to man the phones by themselves. At the intersection of big data, artificial intelligence, and advanced

"There's an increasingly popular solution to this problem: why not let an intelligent algorithm make hiring decisions for you? Surely, the thinking goes, a computer is more able to be impartial than a person, and can simply look at the relevant data vectors to select the most qualified people from a heap of applications, removing human bias and making the process more efficient to boot."

"Brain scans, however, are quite telling, especially when analyzed with an algorithm, Brent and his colleagues discovered. "We're trying to figure out what's going on in somebody's brain when they're thinking about suicide," says Brent.  These scans, taken using fMRI, or functional magnetic resonance imaging, show that strong words such as 'death,' 'trouble,' 'carefree,' and 'praise,' trigger different patterns of brain activity in people who are suicidal, compared with people who are not. That means that people at risk of suicide think about those concepts differently than everyone else-evidenced by the levels and patterns of brain activity, or neural signatures."

"Facebook is mired in a series of controversies about the curation of its news feed, from its broadcasting live killings, to editing out an iconic photo of the Vietnam war, to accusations of political bias. It recently tried to smooth the process out by firing its human editors … only to find the news feed degenerated into a mass of fake and controversial news stories."

"Uber knows when your phone battery is running low because its app collects that information in order to switch into power-saving mode. But Chen swears Uber would never use that knowledge to gouge you out of more money. "We absolutely don't use that to kind of like push you a higher surge price, but it's an interesting kind of psychological fact of human behavior," Chen said. Uber's surge pricing uses a proprietary algorithm that accounts for how many users are hailing rides in an area at a given time. Customers are apparently less willing to believe that when the multiplier is a round number like 2.0 or 3.0, which seems more like it could have been arbitrarily made up by a human."

"Generally, it questions the advance of computing in the absence of a deeper knowledge of how the human brain perceives the world to be computed. We want computers to have perception, yet we know little about the workings of our own human perception."

"For now, however, Hammond tries to reassure journalists that he's not trying to kick them when they're down. He tells a story about a party he attended with his wife, who's the marketing director at Chicago's fabled Second City improv club. He found himself in conversation with a well-known local theater critic, who asked about Hammond's business. As Hammond explained what he did, the critic became agitated. Times are tough enough in journalism, he said, and now you're going to replace writers with robots? "I just looked at him," Hammond recalls, "and asked him: Have you ever seen a reporter at a Little League game? That's the most important thing about us. Nobody has lost a single job because of us.""

""Could teaching be trumped by a learning machine? Are we beginning to glimpse the possibility of machines that teach themselves to teach? They learn what works, what doesn't and deliver ever better performance. We see the embryonic evidence for this in adaptive learning systems, that are truly algorithmic, and do use machine learning, to improve as they deliver. The more students they teach, the better they get. They even tech themselves. This is not science fiction. This is real AI, in real software, delivering real courses, in real institutions." - Donald Clark"

"Facebook announced late Friday that it had eliminated jobs in its trending module, the part of its news division where staff curated popular news for Facebook users. Over the weekend, the fully automated Facebook trending module pushed out a false story about Fox News host Megyn Kelly, a controversial piece about a comedian's four-letter word attack on rightwing pundit Ann Coulter"

""No-limit Texas Hold'em is a game of incomplete information where the AI must infer a human player's intentions and then act in ways that incorporate both the direct odds of winning and bluffing behaviour to try to fool the other player." The designers said their computer didn't "bluff" the human players. But by learning from its mistakes and practising its moves at night between games, the AI was working out how to defeat its human opponents."

"This algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We show that the sounds predicted by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that they convey significant information about material properties and physical interactions."

"From a computer science perspective, learning the behaviour of a single user is tough. This is the small data problem; unlike big data, where patterns and trends easily emerge, individual human beings can be unpredictable and can change behaviour, which is not helpful for pattern-hunting algorithms."

"The really significant thing about AlphaGo is that it (and its creators) cannot explain its moves. And yet it plays a very difficult game expertly. So it's displaying a capability eerily similar to what we call intuition - "knowledge obtained without conscious reasoning". Up to now, we have regarded that as an exclusively human prerogative. It's what Newton was on about when he wrote "Hypotheses non fingo" in the second edition of his Principia: "I don't make hypotheses," he's saying, "I just know.""

"It might not sound like much, but the success of the Minwa supercomputer, which can sort a million images into a thousand predefined categories with an error rate less than the typical human, makes it the latest secret weapon of the company known as "China's Google", Baidu."

"Automated decision-making is appealing to businesses as it can save time and eliminate human emotional volatility. "People have a bad day and it then colors their perception of the world and they make different decisions. In a hedge fund that's a big deal," he added."

"My personal coding projects have presented similarly thorny ethical questions. Should I write a computer program that will download the communications of thousands of teenagers suffering from eating disorders posted on an anorexia advice website? Write a program to post anonymous, suicidal messages on hundreds of college forums to see which colleges offer the most support? My answer to these questions, incidentally, was "no". But I considered it. And the glory and peril of computers is that they magnify the impact of your whims: an impulse becomes a program that can hurt thousands of people."

"Rather than being "taught" medical knowledge, the AI was asked to learn using unsupervised deep neural networks, known as autoencoders, without being given any medical knowledge. The researchers developed a method for translating the features found by the AI-only numbers initially-into high-resolution images that can be understood by humans."