Rise of the Machines: The Future has Lots of Robots, Few Jobs for Humans

Martin Ford

The robots haven’t just landed in the workplace—they’re expanding skills, moving up the corporate ladder, showing awesome productivity and retention rates, and increasingly shoving aside their human counterparts. One multi-tasker bot, from Momentum Machines, can make (and flip) a gourmet hamburger in 10 seconds and could soon replace an entire McDonalds crew. A manufacturing device from Universal Robots doesn’t just solder, paint, screw, glue, and grasp—it builds new parts for itself on the fly when they wear out or bust. And just this week, Google won a patent to start building worker robots with personalities.  

Fast Food Company Develops Robots

Universal Robots: UR3: The world’s most flexible, light-weight table-top robot to work alongside humans

As intelligent machines begin their march on labor and become more sophisticated and specialized than first-generation cousins like Roomba or Siri, they have an outspoken champion in their corner: author and entrepreneur Martin Ford. In his new book, Rise of the Robots, he argues that AI and robotics will soon overhaul our economy. 

There’s some logic to the thesis, of course, and other economists such as Andrew (The Second Machine Age) McAfee have sided generally with Ford’s outlook. Oxford University researchers have estimated that 47 percent of U.S. jobs could be automated within the next two decades. And if even half that number is closer to the mark, workers are in for a rude awakening. 

In Ford’s vision, a full-on worker revolt is on the horizon, followed by a radically new economic state whereby humans will live more productive and entrepreneurial lives, subsisting on guaranteed incomes generated by our amazing machines. (Don’t laugh — even some conservative influencers believe this may be the ultimate means of solving the wealth-inequality dilemma.) 

Sound a little nuts? We thought so—we’re human, after all—so we invited Ford to defend his turf. 

Rise of the Robots

Critics say your vision of a jobless future isn’t founded in good research or logic. What makes you so convinced this phenomenon is real? 

I see the advances happening in technology and it’s becoming evident that computers, machines, robots, and algorithms are going to be able to do most of the routine, repetitive types of jobs. That’s the essence of what machine learning is all about. What types of jobs are on some level fundamentally predictable? A lot of different skill levels fall into that category. It’s not just about lower-skilled jobs either. People with college degrees, even professional degrees, people like lawyers are doing things that ultimately are predictable. A lot of those jobs are going to be susceptible over time. 

Right now there’s still a lot of debate over it. There are economists who think it’s totally wrong, that problems really stem from things like globalization or the fact that we’ve wiped out unions or haven’t raised the minimum wage. Those are all important, but I tend to believe that technology is a bigger issue, especially as we look to the future. 

Eventually I think we’ll get to the point where there’s less debate about whether this is really happening or not. There will be more widespread agreement that it really is a problem and at that point we’ll have to figure out what to do about it. 

Aren’t you relying on some pretty radical and unlikely assumptions? 

People who are very skeptical tend to look at the historical record. It’s true that the economy has always adapted over time. It has created new kinds of jobs. The classic example of that is agriculture. In the 1800s, 80 percent of the U.S. labor force worked on farms. Today it’s 2 percent. Obviously mechanization didn’t destroy the economy; it made it better off. Food is now really cheap compared to what it was relative to income, and as a result people have money to spend on other things and they’ve transitioned to jobs in other areas. Skeptics say that will happen again. 

The agricultural revolution was about specialized technology that couldn’t be implemented in other industries. You couldn’t take the farm machinery and have it go flip hamburgers. Information technology is totally different. It’s a broad-based general purpose technology. There isn’t a new place for all these workers to move. 

You can imagine lots of new industries—nanotechnology and synthetic biology—but they won’t employ many people. They’ll use lots of technology, rely on big computing centers, and be heavily automated. 

So in the all-automated economy, what will ambitious 20-somethings choose to do with their lives and careers? 

My proposed solution is to have some kind of a guaranteed income that incentivizes education. We don’t want people to get halfway through high school and say, ‘Well if I drop out I’m still going to get the same income as everyone else.’ 

Then I believe that a guaranteed income would actually result in more entrepreneurship. A lot of people would start businesses just as they do today. The problem with these types of businesses you can start online today is it’s hard to put enough together to generate a middle-class income. 

If people had an income floor, and if the incentives were such that on top of that they could do other things and still keep that extra money, without having it all taxed away, then I think a lot of people would pursue those opportunities. 

There’s a phenomenon called the Peltzman Effect, based on research from an economist at the University of Chicago who studied auto accidents. He found that when you introduce more safety features like seat belts into cars, the number of fatalities and injuries doesn’t drop. The reason is that people compensate for it. When you have a safety net in place, people will take more risks. That probably is true of the economic arena as well. 

People say that having a guaranteed income will turn everyone into a slacker and destroy the economy. I think the opposite might be true, that it might push us toward more entrepreneurship and more risk-taking. 

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ORIGINAL: Wired

Microsoft's Bill Gates insists AI is a threat

ORIGINAL: BBC
By Kevin Rawlinson BBC News
29 January 2015

Bill Gates said he could not understand why people were not concerned by AI

Humans should be worried about the threat posed by artificial Intelligence, Bill Gates has said.

The Microsoft founder said he didn't understand people who were not troubled by the possibility that AI could grow too strong for people to control.

Mr Gates contradicted one of Microsoft Research's chiefs, Eric Horvitz, who has said he "fundamentally" did not see AI as a threat.

Mr Horvitz has said about a quarter of his team's resources are focused on AI.

During an "ask me anything" question and answer session on Reddit, Mr Gates wrote: "I am in the camp that is concerned about super intelligence. First the machines will do a lot of jobs for us and not be super intelligent. That should be positive if we manage it well.

"A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others on this and don't understand why some people are not concerned."

Watch: Stephen Hawking has warned of the threat AI poses

His view was backed up by the likes of Mr Musk and Professor Stephen Hawking, who have both warned about the possibility that AI could evolve to the point that it was beyond human control. Prof Hawking said he felt that machines with AI could "spell the end of the human race".

Mr Horvitz has said: "There have been concerns about the long-term prospect that we lose control of certain kinds of intelligences. I fundamentally don't think that's going to happen."

He was giving an interview marking his acceptance of the AAAI Feigenbaum Prize for "outstanding advances" in AI research.

Ex Machina explores the relationship between humans and AI robots

"I think that we will be very proactive in terms of how we field AI systems, and that in the end we'll be able to get incredible benefits from machine intelligence in all realms of life, from science to education to economics to daily life."

Mr Horvitz runs Microsoft Research's lab at the parent company's Redmond headquarters. His division's work has already helped introduce Cortana, Microsoft's virtual assistant.

Despite his own reservations, Mr Gates wrote on Reddit that, had Microsoft not worked out, he would probably be a researcher on AI.

"When I started Microsoft I was worried I would miss the chance to do basic work in that field," he said.

Marvel's latest Avengers film features an AI character named Ultron

He added that he believed the firm he founded would see "more progress... than ever" over the next three decades.

"Even in the next 10 [years,] problems like vision and speech understanding and translation will be very good."

He predicted that, in that time, robots would perform tasks such as picking fruit or moving hospital patients. "Once computers/robots get to a level of capability where seeing and moving is easy for them then they will be used very extensively."

He said he was working on a project with Microsoft called "Personal Agent", which he said would "remember everything and help you go back and find things and help you pick what things to pay attention to".

He wrote: "The idea that you have to find applications and pick them and they each are trying to tell you what is new is just not the efficient model - the agent will help solve this. It will work across all your devices."

Forthcoming film CHAPPiE will feature an AI robot that needs to find its place in the world

But he admitted that he felt "pretty stupid" because he cannot speak any language other than English.

"I took Latin and Greek in High School and got As and I guess it helps my vocabulary but I wish I knew French or Arabic or Chinese.

"I keep hoping to get time to study one of these - probably French because it is the easiest... Mark Zuckerberg amazingly learned Mandarin and did a Q&A with Chinese students - incredible," he wrote.


More on This Story

Related Stories

• AI won't run amok, says Microsoft 28 JANUARY 2015, TECHNOLOGY
• Does AI really threaten the future of the human race? 05 DECEMBER 2014, TECHNOLOGY
• Hawking: AI could end human race 02 DECEMBER 2014, TECHNOLOGY

From other news sites

• Guardian.co.uk Artificial intelligence will become strong enough to be a concern, says Bill Gates 
• Huffington Post UK Microsoft Research Chief Says AI Is No Threat. Bill Gates Says It Is. 
• Sydney Morning Herald Artificial intelligence is 'not a threat'
• Melbourne Age Artificial intelligence 'not a threat' 
• The Press NZ Artificial intelligence not a threat 

ORIGINAL: BBC
By Kevin Rawlinson BBC News
29 January 2015

Is AI a Myth?

A few weeks back the technologist Jaron Lanier gave a provocative talk over at The Edge in which he declared ideas swirling around the current manifestation AI to be a “myth”, and a dangerous myth at that. Yet Lanier was only one of a set of prominent thinkers and technologists who have appeared over the last few months to challenge want they saw as a flawed narrative surrounding recent advances in artificial intelligence.

There was a piece in The New York Review of Books back in October by the most famous skeptic from the last peak in AI – back in the early 1980’s, John Searle. (Relation to the author lost in the mists of time) It was Searle who invented the well-know thought experiment of the “Chinese Room”, which purports to show that a computer can be very clever without actually knowing anything at all. Searle was no less critical of the recent incarnation of AI, and questioned the assumptions behind both Luciano Floridi’s Fourth Revolution and Nick Bostrom’s Super-Intelligence.

Also in October, Michael Jordan, the guy who brought us neural and Bayesian networks (not the gentleman who gave us mind-bending slam dunks) sought to puncture what he sees as hype surrounding both AI and Big Data. And just the day before this Thanksgiving, Kurt Anderson gave us a very long piece in Vanity Fair in which he wondered which side of this now enjoined battle between AI believers and skeptics would ultimately be proven correct.

I think seeing clearly what this debate is and isn’t about might give us a better handle on what is actually going on in AI, right now, in the next few decades, and in reference to a farther off future we have to start at least thinking about it- even if there’s no much to actually do regarding the latter question for a few decades at the least.

The first thing I think one needs to grasp is that none of the AI skeptics are making non-materialistic claims, or claims that human level intelligence in machines is theoretically impossible. These aren’t people arguing that there’s some spiritual something that humans possess that we’ll be unable to replicate in machines. What they are arguing against is what they see as a misinterpretation of what is happening in AI right now, what we are experiencing with our Siri(s) and self-driving cars and Watsons. This question of timing is important far beyond a singularitarian’s fear that he won’t be alive long enough for his upload, rather, it touches on questions of research sustainability, economic equality, and political power.

Just to get the time horizon straight, Nick Bostrom has stated that top AI researchers give us a 90% probability of having human level machine intelligence between 2075 and 2090. If we just average those we’re out to 2083 by the time human equivalent AI emerges. In the Kurt Andersen piece, even the AI skeptic Lanier thinks humanesque machines are likely by around 2100.

Yet we need to keep sight of the fact that this is 69 years in the future we’re talking about, a blink of an eye in the grand scheme of things, but quite a long stretch in the realm of human affairs. It should be plenty long enough for us to get a handle on what human level intelligence means, how we want to control it, (which, I think, echoing Bostrom we will want to do), and even what it will actually look like when it arrives. The debate looks very likely to grow from here on out and will become a huge part of a larger argument, that will include many issues in addition to AI, over the survival and future of our species, only some of whose questions we can answer at this historical and technological juncture.

Still, what the skeptics are saying really isn’t about this larger debate regarding our survival and future, it’s about what’s happening with artificial intelligence right before our eyes. They want to challenge what they see as current common false assumptions regarding AI. It’s hard not to be bedazzled by all the amazing manifestations around us many of which have only appeared over the last decade. Yet as the philosopher Alva Noë recently pointed out, we’re still not really seeing what we’d properly call “intelligence”:

Clocks may keep time, but they don’t know what time it is. And strictly speaking, it is we who use them to tell time. But the same is true of Watson, the IBM supercomputer that supposedly played Jeopardy! and dominated the human competition. Watson answered no questions. It participated in no competition. It didn’t do anything. All the doing was on our side. We played Jeapordy! with Watson. We used “it” the way we use clocks.

This is an old criticism, the same as the one made by John Searle, both in the 1980’s and more recently, and though old doesn’t necessarily mean wrong, there are more novel versions. Michael Jordan, for one, who did so much to bring sophisticated programming into AI, wants us to be more cautious in our use of neuroscience metaphors when talking about current AI. As Jordan states it:

I wouldn’t want to put labels on people and say that all computer scientists work one way, or all neuroscientists work another way. But it’s true that with neuroscience, it’s going to require decades or even hundreds of years to understand the deep principles. There is progress at the very lowest levels of neuroscience. But for issues of higher cognition—how we perceive, how we remember, how we act—we have no idea how neurons are storing information, how they are computing, what the rules are, what the algorithms are, what the representations are, and the like. So we are not yet in an era in which we can be using an understanding of the brain to guide us in the construction of intelligent systems.

What this lack of deep understanding means is that brain based metaphors of algorithmic processing such as “neural nets” are really just cartoons of what real brains do. Jordan is attempting to provide a word of caution for AI researchers, the media, and the general public. It’s not a good idea to be trapped in anything- including our metaphors. AI researchers might fail to develop other good metaphors that help them understand what they are doing- “flows and pipelines” once provided good metaphors for computers. The media is at risk of mis-explaining what is actually going on in AI if all it has are quite middle 20th century ideas about “electronic brains” and the public is at risk of anthropomorphizing their machines. Such anthropomorphizing might have ugly consequences- a person is liable to some pretty egregious mistakes if he think his digital assistant is able to think or possesses the emotional depth to be his friend.

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Lanier’s critique of AI is actually deeper than Jordan’s because he sees both technological and political risks from misunderstanding what AI is at the current technological juncture. The research risk is that we’ll find ourselves in a similar “AI winter” to that which occurred in the 1980’s. Hype-cycles always risk deflation and despondency when they go bust. If progress slows and claims prove premature what you often get a flight of capital and even public grants. Once your research area becomes the subject of public ridicule you’re likely to lose the interest of the smartest minds and start to attract kooks- which only further drives away both private capital and public support.

The political risks Lanier sees, though, are far more scary. In his Edge talk Lanier points out how our urge to see AI as persons is happening in parallel with our defining corporations as persons. The big Silicon Valley companies – Google, FaceBook, Amazon are essentially just algorithms. Some of the same people who have an economic interest in us seeing their algorithmic corporations as persons are also among the biggest promoters of a philosophy that declares the coming personhood of AI. Shouldn’t this lead us to be highly skeptical of the claim that AI should be treated as persons?

What Lanier thinks we have with current AI is a Wizard of OZ scenario:

If you talk about AI as a set of techniques, as a field of study in mathematics or engineering, it brings benefits. If we talk about AI as a mythology of creating a post-human species, it creates a series of problems that I’ve just gone over, which include acceptance of bad user interfaces, where you can’t tell if you’re being manipulated or not, and everything is ambiguous. It creates incompetence, because you don’t know whether recommendations are coming from anything real or just self-fulfilling prophecies from a manipulative system that spun off on its own, and economic negativity, because you’re gradually pulling formal economic benefits away from the people who supply the data that makes the scheme work.

What you get with a digital assistant isn’t so much another form of intelligence helping you to make better informed decisions as a very cleverly crafted marketing tool. In fact the intelligence of these systems isn’t, as it is often presented, coming silicon intelligence at all. Rather, it’s leveraged human intelligence that has suddenly disappeared from the books. This is how search itself works, along with Google Translate or recommendation systems such as Spotify, Pandora, Amazon or Netflix, they aggregate and compress decisions made by actually intelligent human beings who are hidden from the user’s view.

Jaron Lanier

Lanier doesn’t think this problem is a matter of consumer manipulation alone: By packaging these services as a form of artificial intelligence tech companies can ignore paying the human beings who are the actual intelligence at the heart of these systems. Technological unemployment, whose solution the otherwise laudable philanthropists Bill Gates thinks is: “eliminating payroll and corporate income taxes while also scrapping the minimum wage so that businesses will feel comfortable employing people at dirt-cheap wages instead of outsourcing their jobs to an iPad”. A view that is based on the false premise that human intelligence is becoming superfluous when what is actually happening is that human intelligence has been captured, hidden, and repackaged as AI.

The danger of the moment is that we will take this rhetoric regarding machine intelligence as reality. Lanier wants to warn us that the way AI is being positioned today looks eerily familiar in terms of human history:

In the history of organized religion, it’s often been the case that people have been disempowered precisely to serve what were perceived to be the needs of some deity or another, where in fact what they were doing was supporting an elite class that was the priesthood for that deity.

That looks an awful lot like the new digital economy to me, where you have (natural language) translators and everybody else who contributes to the corpora that allow the data schemes to operate, contributing mostly to the fortunes of whoever runs the top computers. The new elite might say, “Well, but they’re helping the AI, it’s not us, they’re helping the AI.” It reminds me of somebody saying, “Oh, build these pyramids, it’s in the service of this deity,” but, on the ground, it’s in the service of an elite. It’s an economic effect of the new idea. The effect of the new religious idea of AI is a lot like the economic effect of the old idea, religion.

As long as we avoid falling into another AI winter this century (a prospect that seems as likely to occur as not) then over the course of the next half-century we will experience the gradual improvement of AI to the point where perhaps the majority of human occupations are able to be performed by machines. We should not confuse ourselves as to what this means, it is impossible to say with anything but an echo of lost religious myths that we will be entering the “next stage” of human or “cosmic evolution”.

Indeed, what seems more likely is that the rise of AI is just one part of an overall trend eroding the prospects and power of the middle class and propelling the re-emergence of oligarchy as the dominant form of human society. Making sure we don’t allow ourselves to fall into this trap by insisting that our machines continue to serve the broader human interest for which they were made will be the necessary prelude to addressing the deeper existential dilemmas posed by truly intelligent artifacts should they ever emerge from anything other than our nightmares and our dreams.

Rick Searle, an Affiliate Scholar of the IEET, is a writer and educator living the very non-technological Amish country of central Pennsylvania along with his two young daughters. He is an adjunct professor of political science and history for Delaware Valley College and works for the PA Distance Learning Project.

ORIGINAL: IEET

By Rick Searle  Utopia or Dystopia

Nov 30, 2014

An Aspiring Scientist’s Frustration with Modern-Day Academia: A Resignation

 

Here is a mind-blowing text that was sent to all EPFL researchers (presumably) by a doctoral student during the week-end. It expresses feelings that are worth to think about.

Just to be crystal-clear:

• I am not the author of this text.
• I don’t publish the name of his/her author, since I have no proof that his/her e-mail address was not spoofed. (NOTE: Gene Bunin acknowledges being the author of the letter in his website)

• I don’t think that the exposed facts are a problematic unique to EPFL, nor to any other Swiss university: to the contrary, this is probably a worldwide phenomenon.
• Finally, I would like to make very clear that I did not experience the same feelings at all during my (very happy) PhD times at EPFL. So, don’t try to make any parallel with my own experience.
• Like the author, I don’t have any good idea how to change the system towards a better one.

Still, if you are or have been in the academic world, I think it is worth to invest 10 minutes to read this text.

Dear EPFL,

I am writing to state that, after four years of hard but enjoyable PhD work at this school, I am planning to quit my thesis in January, just a few months shy of completion. Originally, this was a letter that was intended only for my advisors. However, as I prepared to write it I realized that the message here may be pertinent to anyone involved in research across the entire EPFL, and so have extended its range just a bit. Specifically, this is intended for graduate students, postdocs, senior researchers, and professors, as well as for the people at the highest tiers of the school’s management. To those who have gotten this and are not in those groups, I apologize for the spam.

While I could give a multitude of reasons for leaving my studies – some more concrete, others more abstract – the essential motivation stems from my personal conclusion that I’ve lost faith in today’s academia as being something that brings a positive benefit to the world/societies we live in. Rather, I’m starting to think of it as a big money vacuum that takes in grants and spits out nebulous results, fueled by people whose main concerns are not to advance knowledge and to effect positive change, though they may talk of such things, but to build their CVs and to propel/maintain their careers. But more on that later.

Before continuing, I want to be very clear about two things: 

First, not everything that I will say here is from my personal firsthand experience. Much is also based on conversations I’ve had with my peers, outside the EPFL and in, and reflects their experiences in addition to my own.  
Second, any negative statements that I make in this letter should not be taken to heart by all of its readers. It is not my intention to demonize anyone, nor to target specific individuals. I will add that, both here and elsewhere, I have met some excellent people and would not – not in a hundred years – dare accuse them of what I wrote in the previous paragraph. However, my fear and suspicion is that these people are few, and that all but the most successful ones are being marginalized by a system that, feeding on our innate human weaknesses, is quickly getting out of control.

I don’t know how many of the PhD students reading this entered their PhD programs with the desire to actually *learn* and to somehow contribute to science in a positive manner. Personally, I did. If you did, too, then you’ve probably shared at least some of the frustrations that I’m going to describe next.

(1) Academia: It’s Not Science, It’s Business
I’m going to start with the supposition that the goal of “science” is

• to search for truth, 
• to improve our understanding of the universe around us, and 
• to somehow use this understanding to move the world towards a better tomorrow. 

At least, this is the propaganda that we’ve often been fed while still young, and this is generally the propaganda that universities that do research use to put themselves on lofty moral ground, to decorate their websites, and to recruit naïve youngsters like myself.
I’m also going to suppose that in order to find truth, the basic prerequisite is that you, as a researcher, have to be brutally honest – first and foremost, with yourself and about the quality of your own work. Here one immediately encounters a contradiction, as such honesty appears to have a very minor role in many people’s agendas. Very quickly after your initiation in the academic world, you learn that being “too honest” about your work is a bad thing and that stating your research’s shortcomings “too openly” is a big faux pas. Instead, you are taught to “sell” your work, to worry about your “image”, and to be strategic in your vocabulary and where you use it. Preference is given to good presentation over good content – a priority that, though understandable at times, has now gone overboard. The “evil” kind of networking (see, e.g.,http://thoughtcatalog.com/2011/networking-good-vs-evil/) seems to be openly encouraged. With so many business-esque things to worry about, it’s actually surprising that *any* scientific research still gets done these days. Or perhaps not, since it’s precisely the naïve PhDs, still new to the ropes, who do almost all of it.