In Ten Years, Robots Could Replace More Than 4 Million Workers

Robots could replace human workers in up to four million jobs in Britain over the next decade, according to research conducted by UK market research firm YouGov on behalf of the Royal Academy of the Arts. This accounts for 15 percent of the workforce in the country’s private sector.

Researchers quizzed business leaders on how they see automation and artificial intelligence affecting their industry over the coming years. Over 20 percent of employers in finance, accounting, transportation, and distribution stated that they expect more than 30 percent of jobs in the field to be automated by 2027.

We’re already seeing more robots enter the workforce, from robot construction workers to drones that can deliver vital medical supplies. New technology is offering up benefits to the world of work that simply can’t be ignored, but it’s crucial that we consider the impact that it will have on society as a whole.

Chiefly, businesses have to make sure that the millions of workers who are replaced by robots and other automated systems aren’t left behind.

Many robots are simply better equipped to perform menial tasks than humans are. They don’t get bored, they can be designed for a specific purpose, and if they break, they can generally be fixed with relative ease. We simply can’t compete on a level playing field — but we can work alongside our synthetic colleagues.

Robots can raise overall productivity by doing the dirty, difficult, or otherwise unpleasant jobs that human workers would rather avoid. This frees up those people to perform tasks that require a level of judgement or original thought that a robot would not be capable of providing. Many experts would argue that we can have the best of both worlds.

“The UK must make the most of the economic opportunities that new technologies offer,” said Frances O’Grady, the general secretary of British national trade union federation the TUC, speaking to The Guardian. “Robots and AI could let us produce more for less, boosting national prosperity. But we need to talk about who benefits — and how workers get a fair share.”

There have been several different solutions outlined in response to this problem. Some argue that a tax on robots is the best way to ensure that no one is left unable to support themselves, while others would push for universal basic income to become the norm.

The biggest question is how quickly automation is going to be adopted. If it’s a steady process, it will be easier to transition human workers in other roles to help take advantage of increased productivity. If it’s sudden, this will be much harder — and as many as four million workers in Britain, and millions more worldwide, stand to be stuck in a very undesirable situation.

A Robot Magic Kingdom? Disney Wants Huggable Humanoids to Play Characters

The signature characters who wander around Disney theme parks taking photos with kids and signing autographs may be played by huggable humanoid robots in the future.

Disney Enterprises filed a U.S. patent application for a “soft body robot for physical interaction with humans” that would act like an animated character, reported the Orlando Sentinel last week (April 7). The patent describes a rigid robot with pliable chambers filled with fluid or air. Designed to reduce collision impacts with humans, these chambers could sense pressure and adjust its inflation appropriately.

Sketches of a prototype, and the robot’s description, parallel the design of Baymax, a soft-bodied robot from Disney’s 2014 film “Big Hero 6,” according to the Orlando Sentinel. Specific characters were not named in the application, however.

“It’s hard to know why Disney decides to file for a patent, but they have been looking at soft-body robots since ‘Big Hero,'” theme park writer Jim Hill told the Sentinel. “Disney is still terrified that even with this soft technology, a robot could accidentally harm a child. They do a lot of testing.”

Though robots are already used throughout Disney’s parks — including some free-roaming characters like Push the Talking Trash Can and Lucky the Dinosaur — the patent filing notes that it is difficult to ensure complete safety in human-robot interactions, the Orlando Sentinel reported.

Disney inventors have already tested two prototypes, according to the patent application. In these tests, “the robot was robust to playful, physical interaction,” which likely means that tests were successful in inflating the robot for safe contact.

Disney officials did not offer comment on the application, according to the Orlando Sentinel.

Jeb Bush is Extremely Concerned About Automation Job Loss

Former Florida Governor Jeb (!) Bush wants us all out in the streets to bring change to America. No, the former Republican presidential candidate isn’t trying to revive his campaign, he just wants to warn us of the imminent crisis facing the American workforce if the government doesn’t prepare workers for the rise of the automated workplace.

Bush told AM 970 New York’s John Catsimatidis that the country is essentially training its children to do jobs that will belong to robots by the time they graduate, according to the Washington Examiner.

“People should be marching in the streets demanding that we change how we educate K-12, higher education, job training,” Bush said. “We need life long in skills development so people can live purposeful lives.”

Bush has actually presented education as a central part of a strong job market for years, and used claims of Florida’s success in this area as part of his 2016 campaign. In Florida, one of his primary methods of improving the state’s jobs number was to invite the sort of manufacturing work that is now under serious threat of automation.

“This is not something that’s science fiction,” he said. “This is happening as we speak. And yet we still have this big skills gap.”

Last week, the National Bureau of Economic research published a study claiming that automation was taking jobs at a surprising rate — up to three jobs per every new robot in a 1000-man workforce — supporting the idea that automation will need to be offset by education and re-training programs.

Bush also took the opportunity to get in a dig at his former rival, President Donald Trump. “We have got to sort out what we stand for,” Bush said. “Presidential leadership would be helpful here.”

Trump has been largely silent on automation, although his treasury secretary Steven Mnuchin isn’t worried about the prospect.

But where American jobs will inevitably decline, it’s worth noting that automation will create jobs in other fields as well. The world needs more talent in certain areas like advanced database management and machine learning, even as other white collar professions also start to feel the heat.

The Examiner’s article also hits on another common thread in the conversation around automation: just what in the hell life is going to be about, if not work? Bush worries that Americans are in danger of losing the opportunity to lead “purposeful lives” in which they contribute to the economy. While that’s probably not the biggest concern for people trying to generate enough income to pay the rent, it is something worth thinking about. A Jeb-style mass reorientation toward computer skills and other, more academic areas of study could produce a workforce full of highly educated people with low or no incomes, or at least exacerbate that problem. If that doesn’t work, a system of guaranteed minimum income, might just institution of work entirely — which probably doesn’t match up with Bush’s conservative ideals.

Nation Expected to Lose 30% of Jobs to Automation in 15 Years

Whether we like it or not, robots are making an impact in the job market. Experts predict that almost a million jobs will be replaced by robots in 2030, while companies like apple are justifying such predictions. This may also be a boon to governments that wish to cut costs, and almost 80 percent of administrative work will likely be automated in the course of the next 15 years.

We’re expected to see changes in sales, customer service, transportation, shipping and logistics, healthcare, and legal paraprofessionals. The consultancy firm PricewaterhouseCooper (PWC) took a look at the future of one of the world’s super-powers — the U.K.

In a few years even a developed country like Britain might lose a significant portion of its work force — about 30 percent — to automation, leaving 10 million workers without a job. Breaking the numbers down in terms of the sexes, this means that 35 percent of jobs currently held by men are at risk. Women are expected to fare slightly better, with only 26 percent of jobs currently held by women expected to be replaced by robots.  While sectors such as wholesale and administrative work are most likely to get the replacement, the health care and social work industries might keep the automation at bay for now.

PWC’s chief economist, John Hawksworth, asserted in a PWC press release that this is because “manual and routine tasks are more susceptible to automation, while social skills are relatively less automatable.” In light of this prediction, the PWC’s team does offer several solutions, including increasing education, spreading potential gains from automation, and considering a form of Universal Basic Income (UBI).

HOW A SOCIETY WITHOUT JOBS COULD WORK

A UBI is gaining traction around the world as potential solution to global automation. While certain entrepreneurs dislike the notion or feel that we aren’t ready for it yet, countries like Finland, Canada, and even cities in the U.S. are experimenting with the system.

A UBI guarantees every citizen a monthly income regardless of any additional salaries they may accrue. While some urge for a complete replacement of all social programs with UBI, others suggest just a partial consolidation. In order to pay for the program as a whole in the U.S., experts suggest possibly eliminating tax cuts that represent upwards of $540 billion for the wealthy or reducing the $853 billion budget on defense.

Will UBI provide as sustainable solution to living in an automated world? We might just have to wait 15 years to find out.

Your Child’s Next Teacher Could Be a Robot

Today, those looking for a non-traditional education have limited access to online classrooms, especially ones that are for-credit and affordable. But Thomas Frey predicts that, within 14 years, learning from robots will be entirely commonplace — even for children.

Frey is a futurist who began as an engineer at IBM and went on to found the DaVinci Institute, a networking firm and think tank for technical innovation to bring about a brighter future. Frey gives lectures and interviews on strategies for progress to high-profile audiences at places like NASA, the New York Times, and various Fortune 500 companies. He told Business Insider that he sees a future where innovators will enhance and improve the current landscape of online education.

“I’ve been predicting that by 2030 the largest company on the internet is going to be an education-based company that we haven’t heard of yet,” Frey said in the interview.

Frey claims that, in order for students to learn through an advanced online course, we must construct an educational program that learns its students’ individual proclivities and preferred learning strategies.

“It learns what your interests are, your reference points” Frey said. “And it figures out how to teach you in a faster and faster way over time.”

EQUITABLE EDUCATION

Regardless of the effectiveness of online learning platforms, there is still an inherent societal distrust of robots, especially within sectors like education. In fact, in a recent survey by the European Commission, it was found that 30 to 34 percent of people thought that robots should be entirely banned from education. But Frey doesn’t go so far as to argue education bots will replace traditional schooling outright. Also, as technology progresses, it is possible that these fears and opinions will change.

If Frey is correct about the future of online education, it could propel many to levels of education they could not otherwise achieve. Students around the world have limited access to public education, quality one-on-one help from a teacher, and advancement beyond their assigned grade or classes. However,  many of these students are gaining access to computers and the internet. A vastly improved online education system could provide the opportunity and resources underprivileged students need to fulfill their educational aspirations.

So, while robot teachers might sound a little scary for some, they could allow for more affordable and accessible education around the world. No longer would students have to live in districts with certain levels of wealth just to receive decent education. No longer would students be constantly overwhelmed or, conversely, bored by lessons that advance too quickly or too slowly. Perhaps, instead of taking our children’s jobs, robots could prepare them for a career they would love.

This Robot Surgeon Is Outperforming Human Doctors

Imagine you needed a life-saving operation. Would you choose an experienced, human surgeon to perform the procedure or a robot? According to Dr. Peter Kim, vice president of the Sheikh Institute for Pediatric Surgical Innovation, the machine might be the better choice.

Dr. Kim is specifically thinking of STAR (which stands for smart tissue autonomous robot), a robotic surgeon. The machine uses advanced 3D imaging to ‘see’ its subjects, along with sensing technology that lets it work with greater precision than humans are capable of.

As a result, it is able to operate with fewer complications and better outcomes than even the most experienced human doctor.

But, Dr. Kim says, the robot’s not likely to stand in for human doctors any time soon. “The goal is not to simply take away or replace surgeons, but really enhance surgeons’ capacity and capability,” he said.

Kim also foresees a future where technology like this can be a democratizing force, making complex medical procedures available to more and more people around the world.

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How Automation is Going to Redefine What it Means to Work

On December 2nd, 1942, a team of scientists led by Enrico Fermi came back from lunch and watched as humanity created the first self-sustaining nuclear reaction inside a pile of bricks and wood underneath a football field at the University of Chicago. Known to history as Chicago Pile-1, it was celebrated in silence with a single bottle of Chianti, for those who were there understood exactly what it meant for humankind, without any need for words.

Now, something new has occurred that, again, quietly changed the world forever. Like a whispered word in a foreign language, it was quiet in that you may have heard it, but its full meaning may not have been comprehended. However, it’s vital we understand this new language, and what it’s increasingly telling us, for the ramifications are set to alter everything we take for granted about the way our globalized economy functions, and the ways in which we as humans exist within it.

The language is a new class of machine learning known as deep learning, and the “whispered word” was a computer’s use of it to seemingly out of nowhere defeat three-time European Go champion Fan Hui, not once but five times in a row without defeat. Many who read this news, considered that as impressive, but in no way comparable to a match against Lee Se-dol instead, who many consider to be one of the world’s best living Go players, if not the best. Imagining such a grand duel of man versus machine, China’s top Go player predicted that Lee would not lose a single game, and Lee himself confidently expected to possibly lose one at the most.

What actually ended up happening when they faced off? Lee went on to lose all but one of their match’s five games. An AI named AlphaGo is now a better Go player than any human and has been granted the “divine” rank of 9 dan. In other words, its level of play borders on godlike. Go has officially fallen to machines, just as Jeopardy did before it to Watson, and chess before that to Deep Blue.

“AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic.”

So, what is Go? Very simply, think of Go as Super Ultra Mega Chess. This may still sound like a small accomplishment, another feather in the cap of machines as they continue to prove themselves superior in the fun games we play, but it is no small accomplishment, and what’s happening is no game.

AlphaGo’s historic victory is a clear signal that we’ve gone from linear to parabolic. Advances in technology are now so visibly exponential in nature that we can expect to see a lot more milestones being crossed long before we would otherwise expect. These exponential advances, most notably in forms of artificial intelligence limited to specific tasks, we are entirely unprepared for as long as we continue to insist upon employment as our primary source of income.

Let the above chart sink in. Do not be fooled into thinking this conversation about the automation of labor is set in the future. It’s already here. Computer technology is already eating jobs and has been since 1990.

ROUTINE WORK

All work can be divided into four types: routine and nonroutine, cognitive and manual. Routine work is the same stuff day in and day out, while nonroutine work varies. Within these two varieties, is the work that requires mostly our brains (cognitive) and the work that requires mostly our bodies (manual). Where once all four types saw growth, the stuff that is routine stagnated back in 1990. This happened because routine labor is easiest for technology to shoulder. Rules can be written for work that doesn’t change, and that work can be better handled by machines.

Distressingly, it’s exactly routine work that once formed the basis of the American middle class. It’s routine manual work that Henry Ford transformed by paying people middle class wages to perform, and it’s routine cognitive work that once filled US office spaces. Such jobs are now increasingly unavailable, leaving only two kinds of jobs with rosy outlooks: jobs that require so little thought, we pay people little to do them, and jobs that require so much thought, we pay people well to do them.

If we can now imagine our economy as a plane with four engines, where it can still fly on only two of them as long as they both keep roaring, we can avoid concerning ourselves with crashing. But what happens when our two remaining engines also fail? That’s what the advancing fields of robotics and AI represent to those final two engines, because for the first time, we are successfully teaching machines to learn.

NEURAL NETWORKS

I’m a writer at heart, but my educational background happens to be in psychology and physics. I’m fascinated by both of them so my undergraduate focus ended up being in the physics of the human brain, otherwise known as cognitive neuroscience. I think once you start to look into how the human brain works, how our mass of interconnected neurons somehow results in what we describe as the mind, everything changes. At least it did for me.

As a quick primer in the way our brains function, they’re a giant network of interconnected cells. Some of these connections are short, and some are long. Some cells are only connected to one other, and some are connected to many. Electrical signals then pass through these connections, at various rates, and subsequent neural firings happen in turn. It’s all kind of like falling dominoes, but far faster, larger, and more complex. The result amazingly is us, and what we’ve been learning about how we work, we’ve now begun applying to the way machines work.

One of these applications is the creation of deep neural networks – kind of like pared-down virtual brains. They provide an avenue to machine learning that’s made incredible leaps that were previously thought to be much further down the road, if even possible at all. How? It’s not just the obvious growing capability of our computers and our expanding knowledge in the neurosciences, but the vastly growing expanse of our collective data, aka big data.

BIG DATA

Big data isn’t just some buzzword. It’s information, and when it comes to information, we’re creating more and more of it every day. In fact we’re creating so much that a 2013 report by SINTEF estimated that 90% of all information in the world had been created in the prior two years. This incredible rate of data creation is even doubling every 1.5 years thanks to the Internet, where in 2015 every minute we were liking 4.2 million things on Facebook, uploading 300 hours of video to YouTube, and sending 350,000 tweets. Everything we do is generating data like never before, and lots of data is exactly what machines need in order to learn to learn. Why?

Imagine programming a computer to recognize a chair. You’d need to enter a ton of instructions, and the result would still be a program detecting chairs that aren’t, and not detecting chairs that are. So how did we learn to detect chairs? Our parents pointed at a chair and said, “chair.” Then we thought we had that whole chair thing all figured out, so we pointed at a table and said “chair”, which is when our parents told us that was “table.” This is called reinforcement learning. The label “chair” gets connected to every chair we see, such that certain neural pathways are weighted and others aren’t. For “chair” to fire in our brains, what we perceive has to be close enough to our previous chair encounters. Essentially, our lives are big data filtered through our brains.

DEEP LEARNING

The power of deep learning is that it’s a way of using massive amounts of data to get machines to operate more like we do without giving them explicit instructions. Instead of describing “chairness” to a computer, we instead just plug it into the Internet and feed it millions of pictures of chairs. It can then have a general idea of “chairness.” Next we test it with even more images. Where it’s wrong, we correct it, which further improves its “chairness” detection. Repetition of this process results in a computer that knows what a chair is when it sees it, for the most part as well as we can. The important difference though is that unlike us, it can then sort through millions of images within a matter of seconds.

This combination of deep learning and big data has resulted in astounding accomplishments just in the past year. Aside from the incredible accomplishment of AlphaGo, Google’s DeepMind AI learned how to read and comprehend what it read through hundreds of thousands of annotated news articles. DeepMind also taught itself to play dozens of Atari 2600 video games better than humans, just by looking at the screen and its score, and playing games repeatedly. An AI named Giraffe taught itself how to play chess in a similar manner using a dataset of 175 million chess positions, attaining International Master level status in just 72 hours by repeatedly playing itself. In 2015, an AI even passed a visual Turing test by learning to learn in a way that enabled it to be shown an unknown character in a fictional alphabet, then instantly reproduce that letter in a way that was entirely indistinguishable from a human given the same task. These are all major milestones in AI.

However, despite all these milestones, when asked to estimate when a computer would defeat a prominent Go player, the answer even just months prior to the announcement by Google of AlphaGo’s victory, was by experts essentially, “Maybe in another ten years.” A decade was considered a fair guess because Go is a game so complex I’ll just let Ken Jennings of Jeopardy fame, another former champion human defeated by AI, describe it:

Go is famously a more complex game than chess, with its larger board, longer games, and many more pieces. Google’s DeepMind artificial intelligence team likes to say that there are more possible Go boards than atoms in the known universe, but that vastly understates the computational problem. There are about 10¹⁷⁰ board positions in Go, and only 10⁸⁰ atoms in the universe. That means that if there were as many parallel universes as there are atoms in our universe (!), then the total number of atoms in all those universes combined would be close to the possibilities on a single Go board.

Such confounding complexity makes impossible any brute-force approach to scan every possible move to determine the next best move. But deep neural networks get around that barrier in the same way our own minds do, by learning to estimate what feels like the best move. We do this through observation and practice, and so did AlphaGo, by analyzing millions of professional games and playing itself millions of times. So the answer to when the game of Go would fall to machines wasn’t even close to ten years. The correct answer ended up being, “Any time now.”

NONROUTINE AUTOMATION

Any time now. That’s the new go-to response in the 21st century for any question involving something new machines can do better than humans, and we need to try to wrap our heads around it.

We need to recognize what it means for exponential technological change to be entering the labor market space for nonroutine jobs for the first time ever. Machines that can learn mean nothing humans do as a job is uniquely safe anymore. From hamburgers to healthcare, machines can be created to successfully perform such tasks with no need or less need for humans, and at lower costs than humans.

Amelia is just one AI out there currently being beta-tested in companies right now. Created by IPsoft over the past 16 years, she’s learned how to perform the work of call center employees. She can learn in seconds what takes us months, and she can do it in 20 languages. Because she’s able to learn, she’s able to do more over time. In one company putting her through the paces, she successfully handled one of every ten calls in the first week, and by the end of the second month, she could resolve six of ten calls. Because of this, it’s been estimated that she can put 250 million people out of a job, worldwide.

Viv is an AI coming soon from the creators of Siri who’ll be our own personal assistant. She’ll perform tasks online for us, and even function as a Facebook News Feed on steroids by suggesting we consume the media she’ll know we’ll like best. In doing all of this for us, we’ll see far fewer ads, and that means the entire advertising industry — that industry the entire Internet is built upon — stands to be hugely disrupted.

A world with Amelia and Viv — and the countless other AI counterparts coming online soon — in combination with robots like Boston Dynamics’ next generation Atlas portends, is a world where machines can do all four types of jobs and that means serious societal reconsiderations. If a machine can do a job instead of a human, should any human be forced at the threat of destitution to perform that job? Should income itself remain coupled to employment, such that having a job is the only way to obtain income, when jobs for many are entirely unobtainable? If machines are performing an increasing percentage of our jobs for us, and not getting paid to do them, where does that money go instead? And what does it no longer buy? Is it even possible that many of the jobs we’re creating don’t need to exist at all, and only do because of the incomes they provide? These are questions we need to start asking, and fast.

DECOUPLING INCOME FROM WORK

Fortunately, people are beginning to ask these questions, and there’s an answer that’s building up momentum. The idea is to put machines to work for us, but empower ourselves to seek out the forms of remaining work we as humans find most valuable, by simply providing everyone a monthly paycheck independent of work. This paycheck would be granted to all citizens unconditionally, and its name is universal basic income. By adopting UBI, aside from immunizing against the negative effects of automation, we’d also be decreasing the risks inherent in entrepreneurship, and the sizes of bureaucracies necessary to boost incomes. It’s for these reasons, it has cross-partisan support, and is even now in the beginning stages of possible implementation in countries like Switzerland, Finland, the Netherlands, and Canada.

The future is a place of accelerating changes. It seems unwise to continue looking at the future as if it were the past, where just because new jobs have historically appeared, they always will. The WEF started 2016 off by estimating the creation by 2020 of 2 million new jobs alongside the elimination of 7 million. That’s a net loss, not a net gain of 5 million jobs. In a frequently cited paper, an Oxford study estimated the automation of about half of all existing jobs by 2033. Meanwhile self-driving vehicles, again thanks to machine learning, have the capability of drastically impacting all economies — especially the US economy as I wrote last year about automating truck driving — by eliminating millions of jobs within a short span of time.

And now even the White House, in a stunning report to Congress, has put the probability at 83 percent that a worker making less than $20 an hour in 2010 will eventually lose their job to a machine. Even workers making as much as $40 an hour face odds of 31 percent. To ignore odds like these is tantamount to our now laughable “duck and cover” strategies for avoiding nuclear blasts during the Cold War.

All of this is why it’s those most knowledgeable in the AI field who are now actively sounding the alarm for basic income. During a panel discussion at the end of 2015 at Singularity University, prominent data scientist Jeremy Howard asked “Do you want half of people to starve because they literally can’t add economic value, or not?” before going on to suggest, ”If the answer is not, then the smartest way to distribute the wealth is by implementing a universal basic income.”

Moshe Vardi expressed the same sentiment after speaking at the 2016 annual meeting of the American Association for the Advancement of Science about the emergence of intelligent machines, “we need to rethink the very basic structure of our economic system… we may have to consider instituting a basic income guarantee.”

Even Baidu’s chief scientist and founder of Google’s “Google Brain” deep learning project, Andrew Ng, during an onstage interview at this year’s Deep Learning Summit, expressed the shared notion that basic income must be “seriously considered” by governments, citing “a high chance that AI will create massive labor displacement.”

When those building the tools begin warning about the implications of their use, shouldn’t those wishing to use those tools listen with the utmost attention, especially when it’s the very livelihoods of millions of people at stake? If not then, what about when Nobel prize winning economists begin agreeing with them in increasing numbers?

No nation is yet ready for the changes ahead. High labor force non-participation leads to social instability, and a lack of consumers within consumer economies leads to economic instability. So let’s ask ourselves, what’s the purpose of the technologies we’re creating? What’s the purpose of a car that can drive for us, or artificial intelligence that can shoulder 60% of our workload? Is it to allow us to work more hours for even less pay? Or is it to enable us to choose how we work, and to decline any pay/hours we deem insufficient because we’re already earning the incomes that machines aren’t?

What’s the big lesson to learn, in a century when machines can learn?

I offer it’s that jobs are for machines, and life is for people.

Kuri, A Helpful and Interactive Home Robot That Plays, Entertains, and Performs Useful Tasks

Kuri is a helpful home robot designed to interact with humans to play, entertain, and perform useful tasks like setting reminders. In one promotional video, a young girl with diabetes uses the robot to remind her to check her blood sugar.

The robot has a camera behind one eye that can take still images and video, microphones it can use to locate the source of a sound, and speakers for playing media or making cute robot noises. Kuri also uses on-board sensors to map its surroundings to avoid obstacles and pitfalls as it moves.

The makers of Kuri are now accepting preorders their website and expect to begin shipping by the holiday season 2017.

Kuri also promises not to try to destroy civilization or rise up and enslave humankind.

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Experts Say The Next Generation of Robots Probably Won’t Want To Kill Us Yet

A team of artificial intelligence (AI) experts has found no evidence that AI poses an imminent threat to humanity, which should come as good news if you’re feeling uneasy about the rapid advancements being made in robotics.

In fact, their report is pretty positive about everything AI-related, saying that within the next 15 years, the technology should be making all our lives better, particularly in the fields of transport, healthcare, education, and security.

The Artificial Intelligence and Life in 2030 paper is the first to be published by the AI100 project – an ongoing 100-year study led by Stanford University and a standing committee of AI specialists from across the world.

The report says that – for now, at least – AI doesn’t have the capacity to plot world domination.

“No machines with self-sustaining long-term goals and intent have been developed, nor are they likely to be developed in the near future,” the researchers write.

“Instead, increasingly useful applications of AI, with potentially profound positive impacts on our society and economy are likely to emerge between now and 2030.”

So we’re safe for now, then. But the report warns that AI will create “new challenges” for our society and economy, and that the right decisions need to be made to ensure everyone feels the benefit of smarter computing.

For the purposes of the report, the team imagined how artificial intelligence might transform an average North American city in 2030, as well as looking at where AI has brought us up to this point.

For most of us, autonomous transport and self-driving cars are likely to be the first area where we’ll have to put our trust in AI, according to the report.

Transport is also one of the fields where there’s been most progress in recent times, as shown in Tesla Motor’s self-driving Autopilot system, which can already park itself and handle highway driving.

Predicting the future is notoriously tricky, and the report doesn’t make too many grand assumptions about what we can expect in 2030, but it does say that AI is likely to make it easier for huge databases to be mined for insights in education, law enforcement, and healthcare.

It’s in this background number-crunching and analysis that AI is going to have the most impact, according to the AI100 scientists – think computer systems that can recognize a human face instantly, or generate a series of automated test questions for thousands of students taking an online course.

The report is also clear that our attitude towards AI is going to go a long way towards shaping its development – if governments and citizens approach the technology positively, a more positive outcome is likely.

That means while AI will likely replace humans in some jobs, it could also create new roles, and offer new ways for societies to create wealth and leisure time.

“If society approaches these technologies primarily with fear and suspicion, missteps that slow AI’s development or drive it underground will result, impeding important work on ensuring the safety and reliability of AI technologies,” the researchers write.

“On the other hand, if society approaches AI with a more open mind, the technologies emerging from the field could profoundly transform society for the better in the coming decades.”

Those thoughts are in line with recent comments from none other than Stephen Hawking, who reckons that it’s down to us whether AI turns out to be a force for good or bad.

According to one of the researchers, Peter Stone from the University of Texas, the usual depiction of AI in pop culture – think killer robots and hostile sentient systems – is something of a misconception, and one we might want to let go of.

“Any technology has upsides and potential downsides and can be used by people in evil ways,” he told Andy Meek at Fast Company. “On balance, I’m highly optimistic that artificial intelligence technologies are going to improve the world.”

Robots Are Coming For Your Job

Robots are coming and there’s nothing we can do to stop them besides convincing everybody to stop inventing robots. Which won’t happen. People love inventing robots.

Market researcher Forrester estimates that by 2021, six percent of jobs in this great future Trumpocracy of ours will be done by robots.

Which, to be honest, is fine, because people are getting old pretty fast all of the young people are too high and mighty to do the most rote jobs.

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