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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

""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."

""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.""

""Weapons of math destruction" is how the writer Cathy O'Neil describes the nasty and pernicious kinds of algorithms that are not subject to the same challenges that human decision-makers are. Parole algorithms (not Jure's) can bias decisions on the basis of income or (indirectly) ethnicity. Recruitment algorithms can reject candidates on the basis of mistaken identity. In some circumstances, such as policing, they might create feedback loops, sending police into areas with more crime, which causes more crime to be detected."

"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."

"On the contrary, building large centralized databases and predictive algorithms that make decisions on behalf of humans, and which completely ignore privacy concerns, now seem to be the most efficient way of governing. Algorithms now handle college admissions processes, applicants' selection processes for jobs, where to go to college, what to study in that college, which city is best for you to start your career and raise a family, what part of that city you should live in, and even who you should marry."

"How that effects systems of governance has yet to be fully explored, but there are cautions. "Algorithms are only as good as the data on which they are based, and the problem with current AI is that it was trained on data that was incomplete or unrepresentative and the risk of bias or unfairness is quite substantial," says West. The fairness and equity of algorithms are only as good as the data-programming that underlie them. "For the last few decades we've allowed the tech companies to decide, so we need better guardrails and to make sure the algorithms respect human values," West says. "We need more oversight.""

"This figure compares a traditionally trained algorithm to Aarabi and Guo's heuristically trained neural net."

"MIT researcher's at CSAIL have developed a lane-changing algorithm for self-driving cars. the algorithm allows for aggressive lane changes much like the kind only real drivers would be capable of.   it works by computing 'buffer zones' around autonomous vehicles and reassessing them on the fly. MIT uses a mathematically efficient approach which calculates new buffer zones if the default buffer zones lead to performance that's far worse than a human's driver."

"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.""

"YouTube will rely more on machine learning and less on human reviewers during the coronavirus outbreak. Normally, algorithms detect potentially harmful content and send it to human reviewers for assessment. But these are not normal times, and in an effort to reduce the need for employees and contractors to come into an office, YouTube will allow its automated system to remove some content without human review."

"The use for humans in the world of robotics, even as teachers, is shrinking thanks to AI. NVIDIA Research has announced the creation of Eureka, an AI agent powered by GPT-4 that has trained robots to perform tasks using reward algorithms. Notably, Eureka taught a robotic hand to do pen spinning tricks as well as a human can (honestly, as you can see in the YouTube video below, better than many of us)."

"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."

"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. "

"After being set an overarching goal like maximizing profit, they develop their own strategies based on experience of the market, often with little human oversight. The most advanced also use forms of AI whose workings are opaque even if humans wanted to peer inside."

"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"