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"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

"DeepMind is trying to combine deep learning and algorithms, creating the one algorithm to rule them all: a deep learning model that can learn how to emulate any algorithm, generating an algorithm-equivalent model that can work with real-world data."

"In October 2016, algorithms reacted to negative news headlines about Brexit negotiations by sending the pound down 6% against the dollar in under two minutes, before recovering almost immediately. Knowing which particular headline, or which particular algorithm, caused the crash is next to impossible. When one haywire algorithm started placing and cancelling orders that ate up 4% of all traffic in US stocks in October 2012, one commentator was moved to comment wryly that "the motive of the algorithm is still unclear"."

""In some ways we've lost agency. When programs pass into code and code passes into algorithms and then algorithms start to create new algorithms, it gets farther and farther from human agency. Software is released into a code universe which no one can fully understand.""

"Twitter's image cropping algorithm prefers younger, slimmer faces with lighter skin, an investigation into algorithmic bias at the company has found. The finding, while embarrassing for the company, which had previously apologised to users after reports of bias, marks the successful conclusion of Twitter's first ever "algorithmic bug bounty"."

"Creating an algorithm that discriminates or shows bias isn't as hard as it might seem, however. As a first-year graduate student, my advisor asked me to create a machine-learning algorithm to analyze a survey sent to United States physics instructors about teaching computer programming in their courses."

"On inbuilt bias in algorithms, Sharkey said: "There are so many biases happening now, from job interviews to welfare to determining who should get bail and who should go to jail. It is quite clear that we really have to stop using decision algorithms, and I am someone who has always been very light on regulation and always believed that it stifles innovation."

"Those changes happened relatively slowly, but it seems to me that employment disruption is accelerating. A large reason for this is that what used to be a room-sized super computer now fits in my pocket. Over the last two decades, I have observed a fundamental change in how we can apply advanced algorithms to sensing and controlling systems-the kinds of technology that enable more sophisticated robotic manufacturing. I can remember discussing various algorithms and believing they were well beyond what we could ever implement. Now these same algorithms are considered elementary. They are just some of the changes that have fueled the revolution in manufacturing."

"The founders of Predictim want to be clear with me: Their product-an algorithm that scans the online footprint of a prospective babysitter to determine their "risk" levels for parents-is not racist. It is not biased. "We take ethics and bias extremely seriously," Sal Parsa, Predictim's CEO, tells me warily over the phone. "In fact, in the last 18 months we trained our product, our machine, our algorithm to make sure it was ethical and not biased. We took sensitive attributes, protected classes, sex, gender, race, away from our training set. We continuously audit our model. And on top of that we added a human review process.""

"Researchers fed these algorithms (which function like autocomplete, but for images) pictures of a man cropped below his neck: 43% of the time the image was autocompleted with the man wearing a suit. When you fed the same algorithm a similarly cropped photo of a woman, it auto-completed her wearing a low-cut top or bikini a massive 53% of the time. For some reason, the researchers gave the algorithm a picture of the Democratic congresswoman Alexandria Ocasio-Cortez and found that it also automatically generated an image of her in a bikini. (After ethical concerns were raised on Twitter, the researchers had the computer-generated image of AOC in a swimsuit removed from the research paper.)"

"We have become symbiotic with these machines. We feed them with energy and data, and they reward us with a host of services. But our relationship with them goes deeper. There are multiple layers of feedback loops as we shape algorithms and they shape us, at the individual and collective levels. What framework can we turn to to analyze this complex ecosystem?"

"The greatest propaganda machine in history. Think about it. Facebook, YouTube and Google, Twitter and others - they reach billions of people. The algorithms these platforms depend on deliberately amplify the type of content that keeps users engaged - stories that appeal to our baser instincts and that trigger outrage and fear. It's why YouTube recommended videos by the conspiracist Alex Jones billions of times. It's why fake news outperforms real news, because studies show that lies spread faster than truth. And it's no surprise that the greatest propaganda machine in history has spread the oldest conspiracy theory in history - the lie that Jews are somehow dangerous. As one headline put it, "Just Think What Goebbels Could Have Done with Facebook.""

"The algorithmic manager seems to watch everything you do. Ride-hailing platforms track a variety of personalized statistics, including ride acceptance rates, cancellation rates, hours spent logged in to the app and trips completed. And they display selected statistics to individual drivers as motivating tools, like "You're in the top 10 percent of partners!" Uber uses the accelerometer in drivers' phones along with GPS and gyroscope to give them safe driving reports, tracking their performance in granular detail. One driver posted to a forum that a grade of 210 out of 247 "smooth accelerations" earned a "Great work!" from the boss."

"The streaming video company's recommendation algorithm can sometimes send you on an hours-long video binge so captivating that you never notice the time passing. But according to a study from software nonprofit Mozilla Foundation, trusting the algorithm means you're actually more likely to see videos featuring sexualized content and false claims than personalized interests."

"BEIJING: Handing over a piping hot meal at exactly the time promised, Chinese food delivery driver Zhuang Zhenhua triumphantly tapped his job as complete through the Meituan app -- and was immediately fined half of his earnings. A glitch meant it inaccurately registered him as being late and he incurred an automatic penalty -- one of many ways, he said, delivery firms exploit millions of workers even as the sector booms. Authorities have launched a crackdown demanding firms including Meituan and Alibaba's Ele.me ensure basic labour protections such as proper compensation, insurance, as well as tackling algorithms that effectively encourage dangerous driving."

"Interestingly, the time reversal algorithm itself could prove useful for making quantum computers more precise. "Our algorithm could be updated and used to test programs written for quantum computers and eliminate noise and errors," Lebedev explained."

"As part of her undergraduate research, Alice Xue studied whether a machine could pass a Visual Turing Test by producing images that people cannot tell were made by a machine. Xue trained an algorithm using 2,192 traditional Chinese landscape paintings collected from art museums. The resulting AI-generated paintings were mistaken for being made by humans 55 per cent of the time."

""As expected, we found evidence of a performance improvement over generations due to social learning," the researchers wrote. "Adding an algorithm with a different problem-solving bias than humans temporarily improved human performance but improvements were not sustained in following generations. While humans did copy solutions from the algorithm, they appeared to do so at a lower rate than they copied other humans' solutions with comparable performance." Brinkmann told Motherboard that while they were surprised superior solutions weren't more commonly adopted, this was in line with other research suggesting human biases in decision-making persist despite social learning. Still, the team is optimistic that future research can yield insight into how to amend this."

"The algorithm behind what3words took six months to write. Sheldrick worked on it with two friends he had grown up with. Mohan Ganesalingham, a maths fellow at Trinity College, Cambridge, and Jack Waley-Cohen, a full-time quiz obsessive and question-setter for Only Connect. After the initial mapping was complete, they incorporated an error-correction algorithm, which places similar-sounding combinations a very long way apart."

"And that was a genuine first - the only time I can recall when an algorithmic decision had been challenged in public protests that were powerful enough to prompt a government climbdown. In a world increasingly - and invisibly - regulated by computer code, this uprising might look like a promising precedent."