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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

"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"."

"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 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 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."

"Vast sums are being spent by governments across the industrialized and developing worlds on automating poverty and in the process, turning the needs of vulnerable citizens into numbers, replacing the judgment of human caseworkers with the cold, bloodless decision-making of machines. "

"His team trained a machine learning algorithm to spot words and phrases associated with bullying on social media site AskFM, which allows users to ask and answer questions. It managed to detect and block almost two-thirds of insults within almost 114,000 posts in English and was more accurate than a simple keyword search. Still, it did struggle with sarcastic remarks."

"My job was to use technology to make the low-level content moderators' work more efficient. For example, we created a tool that allowed them to throw a video clip into our database and search for similar content. When I was at ByteDance, we received multiple requests from the bases to develop an algorithm that could automatically detect when a Douyin user spoke Uyghur, and then cut off the livestream session. The moderators had asked for this because they didn't understand the language. Streamers speaking ethnic languages and dialects that Mandarin-speakers don't understand would receive a warning to switch to Mandarin."

The ensuing controversy has sparked renewed debates about the ways in which algorithms can perpetuate biases, yielding unintended and often offensive results.

"Luddism refuses to die because each innovation creates a pool of people who feel economically or culturally dispossessed. The greater the leap forward, the wider the chasm of obsolescence. And the scale of the digital revolution defies hyperbole. No area of human activity is undisrupted. "

"One great way to tell the difference is to ask AI recruiting companies what they use artificial intelligence, machine learning and/or deep learning for. Hopefully the hiring firm can what it's using the new technology for and not just that it is. If not it's time to dig a bit deeper."

"huge public corporations launch products that are known to "hallucinate" "

Motka Manjhi had been back and forth to the ration shop four or five times, his wife said, but on each occasion he returned empty-handed. His thumbprint, needed to prove his identity, wasn't registering on the new system."

""The problem," Leveson wrote in a book, "is that we are attempting to build systems that are beyond our ability to intellectually manage.""