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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

"Word of the layoffs comes over a year after the company made a big hiring push, doubling its size to over 500 people. Stack Overflow did not elaborate on the reasons for the layoff, but its hiring push began near the start of a generative AI boom that has stuffed chatbots into every corner of the tech industry, including coding. That presents clear challenges for a personal coding help forum, as developers get comfortable with AI coding assistance and the very tools that do that are blended into products they use. AI-generated coding answers have also posed problems for the company over the past year. The company issued a temporary ban on users generating answers with the help of an AI chatbot in December last year, but its alleged under-enforcement led to a months-long strike among moderators that was resolved in August; the ban is still in place today. Stack Overflow also announced it would start charging AI companies to train on its site. "

"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"."

"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.)"

"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."

"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.""

"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."

"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."

"Some might scoff at this, and point out that news organisations make their own mistakes all the time - more consequential than my physicist/physician howler, if less humiliating. But cases of bad journalism are almost always warped representations of the real world, rather than missives from an imaginary one. Crucially, if an outlet gets big things wrong a lot, its reputation will suffer, and its audience are likely to vote with their feet, or other people will publish stories that air the mistake. And all of it will be out in the open. You may also note that journalists are increasingly likely to use AI in the production of stories - and there is no doubt that it is a phenomenally powerful tool, allowing investigative reporters to find patterns in vast financial datasets that reveal corruption, or analyse satellite imagery for evidence of bombing attacks in areas designated safe for civilians. There is a legitimate debate over the extent of disclosure required in such cases: on the one hand, if the inputs and outputs are being properly vetted, it might be a bit like flagging the use of Excel; on the other, AI is still new enough that readers may expect you to err on the side of caution. Still, the fundamental difference is not in what you're telling your audience, but what degree of supervision you're exercising over the machine."

""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 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."

"Depending on how legal decisions shake out, AI systems could become a valuable tool to assist creativity, a nuisance ripping off hard-working human musicians, or both."

"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."

"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."

"To what extent do the decisions of these types of algorithms reflect the conscious or unconscious biases of their creators?"

"a word that does not exist; it was invented, defined and used by a machine learning algorithm. "

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

"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.

""AI can pick up missed cues and suggest nudges to bridge the gap in emotional intelligence and communication styles. It can identify optimal ways to discuss common problems and alleviate common misunderstandings based on these different priorities and ways of viewing the world. We could be looking at a different gender dynamics in a decade.""