Tag Archives: artificial intelligence

Where have all the jobs gone?

PENNEY KOME: OVER EASY
March, 2017

Photo by Marco Verch/Flickr/Creative Commons

Photo by Marco Verch/Flickr/Creative Commons

“Every would-be populist in American politics purports to defend the ‘middle class,'” wrote Barbara Ehrenreich and John Ehrenreich recently, “although there is no agreement on what it is.

Back in 1977 the pair (then married) proposed that the American economy had created a new “professional and managerial class” (PMC) that expanded the upper-middle class from its base of successful bourgeois merchants to include doctors, lawyers, accountants, journalists, professors, social workers and other professionals, as well as middle and executive managers at major corporations.  PMC members’ success showed that anybody could achieve wealth through education.

The PMC grew rapidly, from an estimated one per cent of workers in 1930 to 35 per cent of workers in 2006, just before the great crash of 2008. By the 1970s, professionals had education, confidence and enough wealth to start questioning some social effects of the capitalist economic structure.

That’s when the “capitalist class” started pushing back, cutting business workforces and pouring resources into union busting. As capitalists cut the workforce, they also cut the management class, the PMC.

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 What’s more, capitalists reached across borders and moved their businesses to cheaper labour pools in other countries, which also had weaker labour and environmental protections. That, along with the Internet,  brought near-total collapse of the PMC as well as the blue-collar job markets.

Ah, but in the 1980s and 90s, economists forecast a coming “Information Economy,” where knowledge itself would generate revenue. Just as industrialization’s much more efficient tools supplanted the cottage industries, they promised, so too would digitization produce useful goods.

Maybe all that will happen in a generation or two. To date, mostly what we’re doing is eating our own young.

Industry after industry has fallen to technical disruption. On February 7 in Canada, Dominic Blanc, who chairs Justin Trudeau’s economic advisory council, told a university conference that automation will take 40 percent of existing jobs within the next decade. 

An Investopedia article names 20 industries “threatened” by technical changes, (I’ve added a few too)  such as:

  • travel agencies found their customers making their own bookings online;
  • tax accountants lost business to tax software programs;
  • newspapers lost their subscribers and their lucrative classified advertising market to free online services;
  • Secretaries, switchboard operators and executive assistants have lost their jobs to answering systems, online calendars and tailored software;
  • bookstores have closed everywhere as people order their books online;
  • employment agencies have had to compete with online listings and networks like Linked-in;
  • postal workers have much less mail to sort or deliver;
  • the whole film manufacturing and developing industry has folded with the advent of digital cameras;
  • ATMs and online banking are replacing bank counter clerks;
  • most corporations have flattened their structures, trimming middle management;
  • self-serve check-outs are replacing cashiers;
  • pre-recorded playlists (like Clear Channel in the US) have replaced most radio DJs;
  • hotels and motels are challenged by AirBnB and HomeAway;
  • taxis and couriers are challenged by Uber and Lyft;
  • driverless cars may do away with driving jobs altogether, although right now truck driving is the second-largest occupation in North America;
  • Napster crashed the U.S. music and movie industry business model; and of course,
  • as U.S. student debts top $1.3 trillion, universities have to compete with MOOCs (Massive Open Online Courses) like Udemy, Coursera and the Khan Academy, which make higher education available even to students who can’t afford university tuition.

Hold on, because that’s just the beginning of the list. The Paris Agreement calls on fossil fuel industries to restrict current activities, let alone explore for more resources.  Banks and financial industries already compete with online services. On the horizon is “blockchain” software that promises security for anonymous financial dealings, such as Bitcoin. Meanwhile, a whole generation of computer experts is becoming obsolete as smartphones and tablets replace desktops and laptops.

Consumer spending drives 70 per cent of the economy, yet retail stores are folding in the face of Walmarts and online catalogues. Supermarkets may be next, as more people can order online from local warehouses that send out vans for local delivery. Amazon has said it will add groceries to its online products, with drone delivery within 30 minutes in urban areas.

So where are the new industries, the new jobs? Gigs like Uber and AirBnB seem almost regressive, stepping back from health and safety standards, and paying the worker even less than the industry does. Amazon’s monitored warehouse workers might well envy the bored department store clerk.

Sometimes it seems like there isn’t enough work to go around. Scratch that: the world is full of essential tasks that need to be done. What we lack are ways to pay people to do them. There certainly aren’t enough paid jobs.

On the other hand, maybe capitalism has just reached the earthly limits of constant growth. Maybe this is the tipping point forteold by 1950s futurists, when robots take over dirty and dangerous jobs, computers handle personal and corporate transactions, and people like you and me receive Basic Annual Incomes (plus housing if we need it) to keep the retail economy going.

We live in a time of paradoxes. Sixty-three million refugees are on the move globally, fleeing war and famine — famine in four countries simultaneously. At the same time, U.S. corporations are sitting on $1.9 trillion in their bank accounts, not invested in any active enterprises at all — despite the tax breaks they get as “job creators.” Everybody is waiting for the next innovation.

Here’s an innovative idea: let’s share! Let’s suppose two ideas about the futurel 1) Whatever you think of capitalism, the global economy is in flux, and will be volatile for quite a while.  2) Humans are much less inclined to ignite conflicts when they have their basic needs met.

We have a choice. We can step in and share necessities. Or we can throw up our hands in horror and let shortages cause tensions that develop into war, which is capitalism’s usual method for re-booting the economy.

Now is the time to kickstart a true sharing economy. The government could start by funding start-up groups dedicated to establishing national and local sustainable housing (and co-housing) programs, universal connectivity, and geothermal greenhouse farming everywhere across Canada.

Maybe an unemployed coal miner can’t become a computer programmer, but almost anybody can learn how to retrofit homes, from insulation to solar panels. Maybe we can use sustainable technology in a way that means that Indigenous people don’t have to pay $12 for a fresh tomato or travel far from home to get a high school education.

The Ehrenreichs say the Professional Managerial Class rose in the 1930s and started to fade early in the 21st century, lasting barely 100 years.  Instead, in recent decades, the educated middle class spiralled down into service jobs as wealth was sucked upwards.

Last January, Oxfam announced that eight individuals controlled as much wealth ($426 billion US) as all of the poorest 3.6 billion people on earth. Such are the wages of unfettered free markets. No wonder Bernie Sanders found that Americans are finally receptive to the phrase, “democratic socialism.”

Copyright Penney Kome 2017

Contact:  komeca AT yahoo.com

Read more F&O columns by Penney Kome here

Related works on F&O:

Technology, not trade, real job-killer, by Tom Regan   Column

I hate to be the bearer of bad news but those jobs U.S. President Donald Trump promised aren’t coming back. And for others, there’s a very good chance that soon more people will be out of work. It won’t happen because of production going to China or Mexico, or and an immigrant or refugee taking jobs. It will be because of technology.

From F&O’s archives, a Focus on Artificial Intelligence:

Figure-1The chilling significance of AlphaGo. By Sheldon Fernandez  Magazine

In March, a computer named AlphaGo played the human world champion in a five-game match of Go, the ancient board game often described as the ‘Far East cousin’ of chess. That AlphaGo triumphed provoked curiosity and bemusement in the public — but is seen as hugely significant in the artificial intelligence and computer science communities. Computer engineer Sheldon Fernandez explains why.

The Sunflower Robot is a prototype that can carry objects and provide reminders and notifications to assist people in their daily lives. It uses biologically inspired visual signals and a touch screen, located in front of its chest, to communicate and interact with users. Photo by Thomas Farnetti for Wellcome/Mosaic, Creative CommonsA one-armed robot will look after me until I die. By Geoff Watts Magazine

I am persuaded by the rational argument for why machine care in my old age should be acceptable, but find the prospect distasteful – for reasons I cannot, rationally, account for. But that’s humanity in a nutshell: irrational. And who will care for the irrational human when they’re old? Care-O-bot, for one; it probably doesn’t discriminate.

Product and graphic designer Ricky Ma, 42, gives a command to his life-size robot ''Mark 1'', modelled after a Hollywood star, in his balcony which serves as his workshop in Hong Kong, China March 31, 2016. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the humanoid robot to fulfil his childhood dream. REUTERS/Bobby Yip SEARCH "ROBOT STAR" FOR THIS STORY. SEARCH "THE WIDER IMAGE" FOR ALL STORIESBuilding a humanoid Hollywood Star. By Bobby Yip  Report

The rise of robots and artificial intelligence are among disruptive labor market changes that the World Economic Forum projects will lead to a net loss of 5.1 million jobs over the next five years. Where will they come from? Why, we can make them ourselves. Or at least some of us can, and do.

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Penney KomePenney Kome is co-editor of Peace: A Dream Unfolding (Sierra Club Books 1986), with a foreward by the Nobel-winning presidents of International Physicians for Prevention of Nuclear War.

Read her bio on Facts and Opinions.

Contact:  komeca AT yahoo.com

 

 

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Facts and Opinions is a boutique journal, of reporting and analysis in words and images, without borders. Independent, non-partisan and employee-owned, F&O is funded by you, our readers. We are ad-free and spam-free, and we do not solicit donations from partisan organizations. Please visit our Subscribe page or use the PayPal button below to chip in at least .27 for one story or $1 for a day site pass. Tell others about us, and follow us on Facebook and Twitter.

 

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Technology, not trade, real job-killer 

Wind turbines are seen near the Andasol solar power station near Guadix, southern Spain August 10, 2015. The plant is the biggest solar farm in the world and provides electricity for up to about 500,000 people. The 620,000 curved mirrors harness the sun's power even after dark, and the glass alone would cover 1.5 square km (0.6 square miles) - the size of about 210 soccer pitches. REUTERS/Marcelo del Pozo

Wind turbines are seen near the Andasol solar power station near Guadix, southern Spain August 10, 2015. The plant is the biggest solar farm in the world and provides electricity for up to about 500,000 people. The 620,000 curved mirrors harness the sun’s power even after dark, and the glass alone would cover 1.5 square km (0.6 square miles) – the size of about 210 soccer pitches. REUTERS/Marcelo del Pozo

TOM REGAN: SUMMONING ORENDA
February 25, 2017

Living where I do in rural Northern Virginia, about 10 miles from the West Virginia border, it’s not uncommon to see trains pulling long lines of railcars full of coal to local power plants or towards Baltimore to be loaded onto ships or trucks, to be carried to other parts of America or the world.

In this day and age, however, the need for these trains is growing smaller and smaller. Improved solar and wind power is starting to make a difference in this country’s energy output and the jobs for these miners who fill these trains with coal is becoming more and more obsolete as the United States and the world continues to move away from fossil fuels.

President Trump, however, promised to reverse this trend. It’s part of his campaign to “Make America Great Again” by bringing back jobs to people like coal miners in West Virginia. The same is true of manufacturing jobs in the Midwest or in Pennsylvania. Areas decimated by changing global economics gladly accepted Trump’s promise that he could bring all those jobs back.

Well, I hate to be the bearer of bad news but those jobs aren’t coming back – at least not in the way the people who voted for Donald Trump want them to come back. And for many people who currently have a job in any one of several areas, and think they can just ride that job into retirement, there’s a very good chance that within the next 10 years, maybe even sooner, you’re going be out of work.

But what takes your job away won’t be that your company switched production to China or Mexico, or that an immigrant or refugee came and took your job away, or that America started to import coal from someplace like China or Russia. No, the thing that will cost you your job will be technology. Maybe something as simple as a piece of software, or as complex as a robot, or as small as a microchip or as large as a field of solar arrays – regardless it will allow your employer to lower costs and improve productivity. And you’ll be out in the street.

President Donald Trump loves to complain that China and Mexico have been stealing jobs from American workers and that he plans to bring those jobs back. And you can see why this campaign promise resonated with so many people – there are five million fewer manufacturing jobs in the United States now than there were in 2010. Bringing those jobs back is nice idea but it’s totally pie-in-the-sky and not doable. Because the truth is that even if you brought those manufacturing jobs back they would probably be taken by a machine and not a human.

There’s a lot of recent research to back this up. A study by Boston Consulting Group shows that industrial robots perform 10% of manufacturing jobs today. By 2025, eight years, they will perform 25%. Another study by two Ball State professors showed that between 2000 and 2010, 87% of manufacturing jobs were lost to technology and not to trade. If that’s not bad enough, a report from McKinsey and Company showed that 49% of current worker activities can be replaced by technology. And that number is only going to grow, particularly in jobs that require repetitive tasks. Jobs, for instance, like in accounting, food preparation, taxi driver, truck driver or even some aspects of journalism, will be replaced by machines or robots that can do the job faster and allow increases in productivity.

So why is more attention not paid to this? There are probably two answers: 1) American businesses like to make money and cut costs. Their concerns are for their shareholders and not for their employees. If making more profit means replacing humans with machines, then so be it. They just don’t like to talk about it a lot; 2) it’s much easier for an unemployed 50 year-old white guy to blame foreigners or outsiders for losing his job than it is to blame technology. The steelworker in Pennsylvania has a much easier time blaming a worker earning less in China, then struggling with the fact that technology made his job redundant.

Yet there is a way to combat this problem. It’s called education.

For instance, in late 2016 there were over 300,000 manufacturing jobs available in the United States, numbers similar to what were available before the 2008 recession. There is, however, another important factor. Most of these jobs require what are known as “high skill sets” which means that they require a level of education that will enable a worker to operate technologically advanced machinery. To go back to our steelworker in Pennsylvania, chances are he or she is not interested in returning to school to learn a whole new skill set. It’s just much easier to complain about China and Mexico.

Meanwhile, most other Americans are ignoring the writing on the wall. A study by the Pew Research Center show that 80% of Americans think that their job will existed in its current form in 50 years. It’s just whistling past the graveyard.

It boils down to this. American jobs are being lost to technology, not to trade. The answer is education and improved skills but that requires much more investment in education. And based on who President Trump just named as his Secretary of Education, the befuddled Betty DeVos, there is a serious question whether that will happen or not.

President Trump can rant all he wants about China and Mexico but that won’t stop American jobs from disappearing. And unless he faces the real issue, it’s only going to get worse.

Copyright Tom Regan 2017

Contact Tom Regan:  motnager@gmail.com

LINKS

Rise of the machines: Fear robots, not China or Mexico:
http://money.cnn.com/2017/01/30/news/economy/jobs-china-mexico-automation/

Why Are There Still So Many Jobs? The History and Future of Workplace Automation
https://www.aeaweb.org/articles?id=10.1257/jep.29.3.3

Special report: Automation puts jobs in peril:
http://www.usatoday.com/story/money/2017/02/06/special-report-automation-puts-jobs-peril/96464788/

Harnessing automation for a future that works:

http://www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a-future-that-works?utm_content=bufferd9ccc&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer

Public Predictions for the Future of Workforce Automation:
http://www.usatoday.com/story/money/2017/02/06/special-report-automation-puts-jobs-peril/96464788/

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Tom Regan Tom Regan is a journalist in the Washington, D.C., area. He worked for the Canadian Broadcasting Corporation and with the National Film Board in Canada, and in the United States for the Christian Science Monitor, Boston Globe, and National Public Radio. A former executive director of the Online News Association in the U.S., he was a Nieman fellow at Harvard in 1991-92, and is a member of the advisory board of the Nieman Foundation for journalism at Harvard.

Return to Tom Regan’s page 

 

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The chilling significance of AlphaGo

What artificial supremacy in the game of Go portends  for the future

In March, a computer named AlphaGo played the human world champion in a five-game match of Go, the ancient board game often described as the ‘Far East cousin’ of chess. That AlphaGo triumphed provoked curiosity and bemusement in the public — but is seen as hugely significant in the artificial intelligence and computer science communities. Computer engineer Sheldon Fernandez explains why.

SHELDON FERNANDEZ
April, 2016

Figure-1

The ancient game of Go

Lee Sodol grinned goofily, a glowing mix of euphoria and exhaustion.

“It’s just one win, but I’ve never been congratulated so much for winning a single game in my life.”

Members of the South Korean and International press corps whistled and applauded wildly. Out of admiration, pity, or Homo sapiens solidarity, no one was quite sure, but there was general agreement on one point: Lee had, at the very least, salvaged a modicum of pride against his silicon opponent, a computer called AlphaGo.

Prior to the contest, the 33 year-old Lee estimated he’d prevail 4-1 in the five game match against the machine, a prognostication that appeared foolish after AlphaGo won the first three encounters.  Lee won the fourth game. The human world champion had temporarily stopped the bleeding (to employ an eminently human analogy), but the computer’s triumph the following day gave way to an ironic 1-4 scoreline that few would’ve predicted weeks before.

The most obvious parallel to the Go showdown in South Korea is the famous chess match in 1997 between Gary Kasparov, the human world champion at the time, and an IBM supercomputer named Deep Blue.  For some 40 years the game of chess – strategic, subtle and enigmatic – had been the focus of Artificial Intelligence (AI) research, the branch of computer science that attempts to imbue machines with sentient, human-like qualities. Creating a world class chess-playing machine, it was argued, would demonstrate conclusively that computers could think, could intuit, and might one day feel and emote like their creators.

Despite the magnitude of the 1997 achievement, Deep Blue’s narrow victory over Kasparov was ironic, in two ways.   First, it was a tad premature, as most observers agree that, at least at the time, Kasparov was objectively the stronger player and had simply ‘psyched’ himself out. (Today, a ten dollar smartphone app would trounce any Grandmaster on the planet.)

Second, and more important, Deep Blue’s technical design was highly specialized and non-transferable outside of the game of chess.  Though it might be the world’s supreme chess player, Deep Blue had no notion of its achievement in the grand scheme of things or, to use the jargon of AI, meta-knowledge; an awareness of the world and, indeed, of itself.  As Kasparov remarked at the time, the machine “succeeded in turning quantity into quality” not through intelligence, but brute force, analyzing millions of moves per second by means of its sophisticated hardware.

The success of AlphaGo is different, and radically so. Not the rapidity of the achievement, which was remarkable on its own terms, a sudden and unexpected spike in the strength of Go-playing machines unlike the linear progression of their chess counterparts.  Nor the difficulty of Go itself, a game so fantastically complex that scientists hadn’t anticipated a breakthrough for decades. Rather, it is was the way AlphaGo’s creators approached and attacked the problem using a bevy of modern techniques, ones that may represent the first forerunners of genuine thinking machines.

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The game of Go, originally known as weiqi, can be traced in ancient China through written records as early as 400 BC. Conducted on a grid, two players take turns placing stones on the board, black first, followed by white. Though pieces cannot move from their original squares, they can be removed if captured, which is accomplished by cutting off their liberties (encircling a stone on all four sides).  The purpose of Go is to surround a larger area of the board than one’s opponent at the game’s conclusion.

What makes Go so challenging from an AI standpoint is the raw number of moves a player can choose from throughout the game, what is known in Computer Science as the branching factor.  The mathematics are daunting: to begin, black has a choice of 361 possible moves, one at every intersecting point on the 19×19 grid.  White thus has 360 replies, followed by 359 counters from black, and so on.   After only four turns, a total of 16,749,374,760 board positions are possible. After 24 turns, the count exceeds the number of atoms in the sun.  After 32, it surpasses the number of atoms in the universe.

While the numbers also spiral out of control in chess, they do so at rate that is slower than Go by a factor of ten, making the game amenable to Deep Blue’s brute force approach in which powerful hardware is coupled with smart searching (‘pruning’ so as to focus on promising moves) such that quantity becomes quality.

As a game of Go lasts beyond 200 moves, however, the permutations, even for a supercomputer are monstrous and incalculable, rendering the game into a complete enigma, as insoluble as the ancient challenge to “measure a pound of fire”1.

Yet measure a pound of fire the makers of AlphaGo did, and their techniques are instructive – and chilling.

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In a sense, the AI epitomized by AlphaGo is the antithesis of its chess counterpart.  In the early years, researchers believed that computers needed to emulate human thought patterns and nebulous concepts like intuition.

Jose Capablanca, the world chess champion in the 1920s, was once asked how many moves ahead he could calculate.  His magnificent rejoinder, “only one, but it’s always the right one”, captures perfectly the intangibilities of human genius that scientists have labored to replicate artificially.

Though unsuccessful in the realm of chess – Deep Blue and its successors examine billions of moves to determine the ‘right’ one – the field of AI has come full circle such that the ambitions of its pioneers now represent its most dominant strands of thought.  That is, the early aspirations of AI spurned in favor of Deep Blue’s specialization have been rekindled with AlphaGo.  The key concepts, ones you’ll hear a lot about in the coming years and which were exploited by the program, are Deep Learning and neural networks.

The story of these related concepts begins with a three pound packet of tissue rightfully regarded as one of the most complex and awesome devices in the cosmos.  A warm, wet biological construct refined through millions of years of evolution, the human brain contains approximately 100 billion neuron cells and nearly 100 trillion neural connectors that thread the cells together.

Although the precise workings of the brain remain a mystery, the biological contours are clear: neurons, or nerve cells, connect to hundreds of other neurons via long fibers called axons; the connecting junctions referred to as a synapses.   A complex electrochemical reaction allows signals to propagate between neurons and – in a manner not remotely understood by scientists – this frenetic firing coalesces into a neurological dance that provides the basis for conscious life as we know it.

For example, as you read this sentence and unpack its contents, at least a few million neurons in your brain will partake in the prodigious sequence of electronic impulses that enables your contemplation. The important point is that the nature of these impulses – their sequence, timing and pattern – is not random, but is rather connected to the underlying thought.  More specifically, similar mental narratives activate similar neural patterns.  Visualizing a pen and a pencil, for example, would animate the same family of neurons, whereas doing the same for a chair and a zebra would not.  An amusing byproduct of this connection is shown in Figure 2.

Figure-2

Figure 2: Facial recognition exercise

Figure 2 illustrates three human heads in which a gender-ambiguous face is sandwiched between two conventional ones2.  The theory of visual science tells us that if you cover the rightmost figure (the female face) and fixate on the leftmost figure (the male face) for 10 to 15 seconds, the ambiguous face will be interpreted by most observers as female.  When the process is reversed – when the male figure is covered and the female figure is extendedly gazed upon – the middle figure appears generally male.

What might be described as a ‘trick of the mind’, is in fact a striking example of the way your brain works. In classifying a face by gender, your neurons activate in a manner that is representative of the initial image.   By abruptly shifting focus to an ambiguous face after a prolonged stare at a well-defined one, a strange phenomenon occurs:  because of the stare, your neural wiring becomes momentarily ‘biased’ towards that gender pattern, which causes your brain to interpret the ambiguous face in the opposite direction.

A final point regarding the biology of the brain is what neuroscientists call plasticity; the idea that the connection-strength between two neurons – the speed and fidelity by which signals propagate – can change in a long-term manner in response to outside events: reading a book, reciting the alphabet, or humming a symphony.  Plasticity is why you can parse this sentence smoothly, whereas a five-year-old cannot, and as we’ll see it is an important principle in the realms of both human learning and Artificial Intelligence.

To return to the virtual world, a neural network is simply a computational model that emulates the structure of the brain.  As shown in Figure 3, the model is composed of numerous nodes connected by links (the digital equivalent of neurons and axons, respectively).  As with the brain, the signal strength of a link – what is known as the weight – can be refined and adjusted to enhance the system.

Figure-3

Figure 3: A basic neural network

The inputs to the network (the blue nodes on the left) constitute anything that can be described numerically: stock prices, an audio signal, an image, etc.  The outputs (the green nodes on the right) are the result of numerous calculations performed by the operational (red) layers in the network.  Some practical outputs might include predicting future stock values, amplifying an audio signal, or identifying a human face in an image.

The magic of neural networks lies in the way they are able to mimic the human brain and perform complex operations by stringing together millions, sometimes billions, of nodes and links.

Consider, for example, the ability to examine an image and describe its contents in English words; the electronic equivalent of showing a five-year-old a picture of a lion resting in the desert, and asking them to describe what they see.  For decades, researchers in image recognition technology struggled mightily with this problem, because while identifying a visual pattern might be straightforward for a human, it is profoundly complex for a machine. How, for example, does one describe what a lion looks like to a computer in mathematical terms given the thousands of ways one can be portrayed in a picture?

With a neural network, however, the problem becomes tractable, if still difficult.  By providing the network with a million lion-in-a-desert pictures, the weights between the links can be incrementally adjusted until the system gets quite good at identifying lions. In practice, deep learning networks that are capable of performing ‘human’ tasks such as this are: 1.) many layers deep with billions of inputs (hence the ‘deep’); 2.) trained using real world examples until they become proficient at the particular task (hence the ‘learning’).

In broad terms then, deep learning refers to multilayered neural networks that can adapt and learn over time.  And, as the theory and sophistication of these networks has improved in the past few years along with the computational power that undergirds them, they have started to do some amazing things.

Neural image caption generators can now analyze pictures, break them down into their component parts, and describe their contents in colloquial English.  Another striking example is that of inceptionalism, in which two images are combined using a neural network to produce a mind-bending third3.   Figures 4 and 5 illustrate the fruits of this arresting technique.

Figure-4Figure 4: Inceptionalism: Forest-cat

Figure-5
Figure 5: Inceptionalism: Water colored-stream

And then, of course, there is the game of Go that inspired this analysis in the first place.   DeepMind, the Google-based company that designed AlphaGo, actually employed two neural networks in the program: a value network that evaluated board positions, and a policy network that selected moves.  It augmented this twin setup with a clever and previously used technique known as a Monte Carlo Search Tree (MCTS), in which the computer played out thousands of random games for each plausible move to determine that move’s worth.

MCTS demonstrates an important concept in computer science, in that an extremely difficult problem can often be attacked through a bit of randomized simulation.  The calibration of traffic lights is a good example.  A computer tasked with timing red/green switches at numerous intersections so as to optimize for traffic flow will often struggle because of the sheer number of factors involved (crowd patterns, number of cars, weather, etc.).  But, by simulating many switch permutations millions of times and evaluating the results, a machine can be quite confident it will arrive at a ‘very good’ solution, if perhaps not the absolute best one.

This is how the AlphaGo team got around the branching factor problem described earlier.  By injecting randomized simulation into its neural networks, its designers were able to exponentially reduce the number of moves the program had to evaluate to play at the world class level. According to DeepMind, AlphaGo analyzed fewer positions against Lee Sodol than Deep Blue did against Kasparov by a factor of a few thousand.  And, in the spirit of deep learning, AlphaGo was subjected to two rigorous training sessions: a supervised learning phase, in which the network was calibrated by playing through thousands of master-level games; and a reinforcement learning phase, in which the machine played itself millions of times to further polish its neural ‘weights’.

Fuse these techniques together with supercomputing power and it suddenly seems remarkable that Mr. Sodol was able to win a single game against Google’s Go-playing juggernaut, a fact he admitted to rather ruefully to after the contest.

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If the training aspect of DeepMind’s approach – supervised learning complemented with didactic reinforcement – seems analogous to how human beings master particular skills, that’s because it is.

In his book The Talent Code, author Daniel Coyle used advances in neurology to probe the concept of talent to understand how individuals became highly proficient at certain tasks.  Examining ‘talent hotbeds’ – from soccer fields in Brazil to musical academies in upstate New York – his findings centered on something he termed deep practice:

“Deep practice is built on a paradox: struggling in certain targeted ways – operating at the edge of your ability, where you make mistakes – makes you smarter.  Or to put it a slightly different way, experiences where you’re forced to slow down, make errors, and correct them – as you would if you were walking up an ice-covered hill, slipping and stumbling as you go – end up making you swift and graceful without your realizing it.”4

As should be evident given the biology of the brain, all skills – musical, mathematic, kinetic, etc. – emanate from neural circuits that fire in precise and exquisite ways.  So magnificently complex are these circuits, however, that your genes could not possibly encode them at birth; or to cite a specific example, it is highly unlikely that Roger Federer was born with the intrinsic capacity to play tennis at the world class level.

Instead, Coyle draws our attention to myelin, a gooey substance that insulates neural circuits and increases the speed by which cerebral signals propagate.  The more that neural fibers are exercised through practice, the more myelin that wraps around them and the faster electronic impulses travel.  This process forms the neuroscientific basis for deep practice, which produces a powerful illusion in that a skill painstakingly honed comes to feel utterly natural, as if it’s something we’ve always possessed when in fact we didn’t.

Does this mean that anyone can become a Roger Federer with sufficient practice?  Contrary to relativist and idealistic assertions (anyone can become an expert with 10,000 hours of practice according to some) probably not.  Thousands of tennis players work as hard and deeply as the great Swiss champion but fail to ascend to the highest echelons of the sport.  The uncomfortable reason centers on the harsh realities of genetics and biology – how the body and brain respond to and amplify deep practice once it’s undertaken. Try as we might, we’ll never get away from nebulous concepts like ‘genius’, ‘prodigiousness’ and the accompanying notion that some people are simply much much better at certain things than others.  Coyle’s important point is that proficiency and mastery are not just the product of intrinsic ability, and are in fact more a consequence of deep practice.

What should be obvious and fascinating at this point are the parallels between deep practice in the human realm, and deep learning in the virtual one.  By emulating the former in terms of the latter researchers have succeeded in creating machines that crudely approximate how we learn and think. What they lack in complexity (artificial networks still pale in comparison to the unbelievable density and intricacies of the human brain), they compensate for in speed and endurance (e.g., AlphaGo’s encapsulation of a lifetime of learning by reviewing a million games in a few hours).

The tantalizing question – perhaps the ultimate one – is where the yellow brick road of AI might ultimately lead; the Oz of the journey where the spectacular and the spooky intersect with frightening force.

~~~

There is one phenomenon that remains a dark, stubborn mystery to scientists across all disciplines, and it is one you are exercising right now: consciousness.

How do we define this most essential of human capacities?  Psychiatrist Giulio Tononi described it as that which “abandons you every night when you fall into a dreamless sleep and returns the next morning when you wake up.”5

It’s a clever explanation that, to borrow a word from theology, relies on apophatic rhetoric: defining something elusive in negative terms. Phrased positively, we might classify consciousness as the awareness of one’s own existence and surroundings through thoughts and sensations.

This, in the opinion of many, is the Holy Grail that advances in deep learning and neural networks will enable: conscious, thinking machines.  What’s more, such a feat is considered but a precursor to a second, even loftier inevitability: that these conscious constructs will eventually exceed the intelligence of their human makers, what is termed superintelligence or the singularity.

A superintelligent being, runs the argument, could compose super-compelling music, write super-creative poetry, and do super-insightful ethics.  It might also dabble in the discipline of AI itself to create a…super-superintelligence.

The crux of AI efforts center on this existential, some would say metaphysical, inquiry: can we like the gods of our ancestors breathe life into the inanimate where none existed?

The obstacles are, in short, overwhelming, because the simple fact is, spiritual digressions notwithstanding, we have no idea how unconscious entities (molecules, atoms, electrons, quarks) combine and give rise to conscious beings.  In the words of English biologist Thomas Huxley:

“How is that anything so remarkable as a state of consciousness comes about as a result of irritating nervous tissue, is just as unaccountable as the appearance of the Djinn when Aladdin rubbed his lamp”6  

Unsurprisingly, the science of consciousness is rich with conjecture, with explanations ranging from ‘meta-cell assemblies’ to ‘Bose-Einstein condensates’ to the extravagant but not completely implausible suggestion that the brain is in fact a quantum computer7.

And then there is the work of Swedish neuroscientist Bjorn Merker involving a rare medical condition called hydranencephaly.   One in ten thousand children are afflicted with this disorder and are born with what might be described as a ‘proto-brain’, whereby the cerebral cortex is replaced with cerebrospinal fluid.  In a fascinating study8, Merker suggested that the traces of consciousness observed in such children – smiling, laughing, crying, and other basic forms of awareness – required a critical reappraisal of the widespread assumption that consciousness is facilitated by the cerebral cortex.  Researchers, he argued, might thus be fixating on the wrong areas of the brain altogether.

In order to engineer a thinking machine, scientists will simply have to demystify the mechanisms of consciousness, and while some maintain that AI will play an essential role towards this end, others insist that however hard computer scientists rub their lamps, Aladdin will not appear.

As the debate and research rages on, machines will continue to do dazzling things. In Japan, for instance, an AI program co-authored a short form-novel that passed the first round of screening for a national literary prize9, though it ultimately did not win.  IBM Watson, the Jeopardy playing juggernaut, is now being used to provide natural language advice in such fields as medicine and financial management.  And finally, the budding discipline of quantum computing is beginning to show signs of life, which some believe will bridge the conscious/unconscious divide10.

And on which side of the line does this author reside?

Several months ago I wrote a short story set in 2052, in which a bright grade-schooler is conversing with her artificial mentor, a machine named Sargon, while her father ruminates:

“Nursing a coffee, Paul smiled lovingly at the ensuing edification but with curiously mixed feelings.  It was a blessing, of course, to have a fulltime educator for his precocious daughter – a machine with infinite patience and an encyclopedic knowledge of, well, everything.  But as an engineer, he knew there was a flip side to the coin.

Neural networks like Sargon represented the apex of Artificial Intelligence efforts and the attempt to imbue machines with conscious properties and other human characteristics. Yet for decades, observers had warned of an oncoming ‘singularity’ – the point in time in which machines would exceed the intelligence of their human makers, what the experts termed a ‘Superintelligence’. What if Sargon acquired its own desires and ambitions? Would it be as patient with Ellie? As loving?  “Maybe I should ask Sargon,” he smiled ironically.”

The license for fanciful speculation is one of the great joys of fiction writing.  But in the shadows of AlphaGo and the deep learning apparatus I can’t help but envision the Sargons of tomorrow gazing upon the AlphaGos of today with wistful nostalgia, and seeing in them the first tremors of superintelligence and the naive creators who gave them life.

Copyright Sheldon Fernandez 2016

Notes:

  1. 2 Esdras 4:5
  2. Diagram extracted from Churchland, Paul M. (2002). Outer space and inner space: The new epistemology. Proceedings and Addresses of the American Philosophical Association 76 (2). p.25.
  3. For a detailed treatment of the topic see http://googleresearch.blogspot.ca/2015/06/inceptionism-going-deeper-into-neural.html and http://www.boredpanda.com/inceptionism-neural-network-deep-dream-art/. Diagram credit: http://ostagram.ru/
  4. Coyle, Daniel (2009). The Talent Code. Pg 18.  New York: Bantom.
  5. Kaku, Michio (2014). The Future of the Mind. Pg 23. San Francisco: Doubleday.
  6. Ibid, pg 108.
  7. Hameroff, S. (1998b). Quantum computation in brain microtubules? The Penrose-Hameroff “Orch OR” model of consciousness. Philosophical Transactions of the Royal Society of London A, 356, 1869–1896
  8. Merker, Bjorn. “Consciousness without a Cerebral cortex: A Challenge for neuroscience and medicine.” Behavioral and Brain Science. (2007) 30. 63-134.
  9. See: http://www.digitaltrends.com/cool-tech/japanese-ai-writes-novel-passes-first-round-nationanl-literary-prize/
  10. To learn more about the topic I recommend Scott Aaronson’s excellent Quantum Computing Since Democritus from Cambridge University Press (2013).

You might also wish to read these stories on Facts and Opinions:

The Sunflower Robot is a prototype that can carry objects and provide reminders and notifications to assist people in their daily lives. It uses biologically inspired visual signals and a touch screen, located in front of its chest, to communicate and interact with users. Photo by Thomas Farnetti for Wellcome/Mosaic, Creative Commons

AI: A one-armed robot will look after me until I die. By Geoff Watts

I am persuaded by the rational argument for why machine care in my old age should be acceptable, but find the prospect distasteful – for reasons I cannot, rationally, account for. But that’s humanity in a nutshell: irrational. And who will care for the irrational human when they’re old? Care-O-bot, for one; it probably doesn’t discriminate.

Product and graphic designer Ricky Ma, 42, gives a command to his life-size robot ''Mark 1'', modelled after a Hollywood star, in his balcony which serves as his workshop in Hong Kong, China March 31, 2016. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the humanoid robot to fulfil his childhood dream. REUTERS/Bobby Yip SEARCH "ROBOT STAR" FOR THIS STORY. SEARCH "THE WIDER IMAGE" FOR ALL STORIESBuilding a humanoid Hollywood Star. By Bobby Yip

The rise of robots and artificial intelligence are among disruptive labor market changes that the World Economic Forum projects will lead to a net loss of 5.1 million jobs over the next five years. Where will they come from? Like innumerable children with imaginations fired by animated films, Hong Kong product and graphic designer Ricky Ma grew up watching cartoons featuring the adventures of robots, and dreamt of building his own one day. Unlike most, Ma realized his childhood dream, by successfully constructing a life-sized robot from scratch on the balcony of his home.

 

Sheldon Fernandez

Sheldon Fernandez

Sheldon Fernandez is the Vice President of Technology for Infusion, an innovation and consulting firm that focuses on emerging technologies.  Throughout his career, he has coupled his engineering work with non-technical pursuits.  He completed a Master’s degree in theology at the University of Toronto in 2008, and pursued thesis work in the area of neuroscience and metaethics.  He also spearheaded Infusion Africa, a philanthropic arm of his company that focuses on humanitarian efforts on the continent. He can be reached at: sfernandez@infusion.com

His previous works for F&O include The Great Riddle: fostering creativity and tenacityMy Last Day in Kenya; One day at Wembley: a soccer fanatic reflects.

 

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Building a humanoid Hollywood Star

Life-size robot "Mark 1", modelled after a Hollywood star, speaks and reacts after receiving a command by its creator Ricky Ma, 42, a product and graphic designer, during a demonstration in Hong Kong, China March 31, 2016. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the humanoid robot to fulfil his childhood dream. REUTERS/Bobby Yip SEARCH "ROBOT STAR" FOR THIS STORY. SEARCH "THE WIDER IMAGE" FOR ALL STORIES

Life-size robot “Mark 1”, modelled after a Hollywood star, speaks and reacts after receiving a command by its creator Ricky Ma, 42, a product and graphic designer, during a demonstration in Hong Kong, China March 31, 2016. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the humanoid robot to fulfil his childhood dream. REUTERS/Bobby Yip

By Bobby Yip
April, 2016

Like innumerable children with imaginations fired by animated films, Hong Kong product and graphic designer Ricky Ma grew up watching cartoons featuring the adventures of robots, and dreamt of building his own one day.

Unlike most of the others, however, Ma has realized his childhood dream at the age of 42, by successfully constructing a life-sized robot from scratch on the balcony of his home.

Life-size robot "Mark 1", modelled after a Hollywood star, responds with a blink after receiving a command by its creator Ricky Ma, 42, a product and graphic designer, during a demonstration in Hong Kong, China March 31, 2016. The eyes of the robot include face and color tracking functions. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the robot to fulfil his childhood dream. REUTERS/Bobby Yip SEARCH "ROBOT STAR" FOR THIS STORY. SEARCH "THE WIDER IMAGE" FOR ALL STORIES

Life-size robot “Mark 1”, modelled after a Hollywood star, responds with a blink after receiving a command by its creator Ricky Ma, 42, a product and graphic designer, during a demonstration in Hong Kong, China March 31, 2016. The eyes of the robot include face and color tracking functions. Ma, a robot enthusiast, spent a year-and-a half and more than HK$400,000 ($51,000) to create the robot to fulfil his childhood dream. REUTERS/Bobby Yip 

The fruit of his labours of a year-and-a-half, and a budget of more than $50,000, is a female robot prototype he calls the Mark 1, modelled after a Hollywood star whose name he wants to keep under wraps. It responds to a set of programmed verbal commands spoken into a microphone.

“I figured I should just do it when the timing is right and realise my dream. If I realise my dream, I will have no regrets in life,” said Ma, who had to learn about fields completely new to him before he could build the complex gadget.

Besides simple movements of its arms and legs, turning its head and bowing, Ma’s robot, which has dark blonde hair and liquid eyes, and wears a grey skirt and cropped top, can create detailed facial expressions.

In response to the compliment, “Mark 1, you are so beautiful”, its brows and the muscles around its eyes relax, and the corners of its lips lift, creating a natural-seeming smile, and it says, “Hehe, thank you.”

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A 3D-printed skeleton lies beneath Mark 1’s silicone skin, wrapping its mechanical and electronic parts. About 70 percent of its body was created using 3D printing technology.

Ma’s journey of creation was a lonely one, however. He said he did not know of anyone else in the former British colony who builds humanoid robots as a hobby and few in the city understood his ambition.

“During this process, a lot of people would say things like, ‘Are you stupid? This takes a lot of money. Do you even know how to do it? It’s really hard,'” Ma said.

He adopted a trial-and-error method in which he encountered obstacles ranging from frequent burnt-out electric motors to the robot losing its balance and toppling over.

“When you look at everything together, it was really difficult,” said Ma, who had to master unfamiliar topics from electromechanics to programming along the way, besides learning how to fit the robot’s external skin over its components.

Ma, who believes the importance of robots will only grow, hopes an investor will buy his prototype, giving him the capital to build more, and wants to write a book about his experience, to help other enthusiasts.

The rise of robots and artificial intelligence are among disruptive labor market changes that the World Economic Forum projects will lead to a net loss of 5.1 million jobs over the next five years.

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Copyright Reuters 2016

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Stop killer robots, researchers warn in open letter

By Toby Walsh, NICTA*
July 27, 2015

More than 1,000 of the leading researchers in artificial intelligence (AI) and robotics today signed and published an open letter calling for a ban on offensive autonomous weapons, also known colloquially as “killer robots”.

U.S. Air Force MQ-1 Lethal Presence. Photo by Lt Col Leslie Pratt

U.S. Air Force MQ-1 Lethal Presence. Photo by Lt Col Leslie Pratt

The letter has also been signed by many technologists and experts, including SpaceX and Tesla CEO Elon Musk, physicist Stephen Hawking, Apple co-founder Steve Wozniak, Skype co-founder Jaan Talinn and linguist and activist Noam Chomsky.

Musk, Hawking and Wozniak have all recently warned about the dangers that AI poses to mankind. Though it has to be said, Wozniak thinks humans will be fine if robots take over the world; we’ll just become their pets.

The open letter urges the UN to support a ban on offensive autonomous weapons systems. This follows the April meeting of the Convention on Conventional Weapons held at the UN in Geneva discussing such an idea.

The letter argues that the deployment of such autonomous weapons is feasible within years, and will play a dangerous role in driving the next revolution in warfare.

In the interest of full disclosure, I too have signed this letter. My view is that almost every technology can be used for good or bad. And AI is no different. We therefore need to make a choice as to which path to follow.

Artificial intelligence is a technology that can be used to help tackle many of the pressing problems facing society today: inequality and poverty; the rising cost of health care; the impact of global warming, and many others. But it can also be used to inflict unnecessary harm. And now is the right time to get in place a ban before this next arms race begins.

The open letter – reprinted below – gives a good summary of the arguments for a ban. In short, there is likely to be an arms race in such technology that will revolutionise warfare for the worse.

As always, we can learn a lot from history. A recent example is the UN Protocol on Blinding Laser Weapons, which came into force in 1998. The International Committee of the Red Cross argued that the ban was an historic step for humanity, stating that:

It represents the first time since 1868, when the use of exploding bullets was banned, that a weapon of military interest has been banned before its use on the battlefield and before a stream of victims gave visible proof of its tragic effects.

Of course, the technology for blinding lasers still exists; medical lasers that correct eyesight are an example of the very same technology. But because of this ban, no arms manufacturer sells blinding lasers. And we don’t have any victims of blinding lasers to care for.

Similarly, a ban on offensive autonomous weapons is not going to prevent the technology for such weapons being developed. After all, it would take only a few lines of code to turn an autonomous car into an offensive weapon. But a ban would ensure enough stigma and consequences if breached that we are unlikely to see conventional military forces using them.

This won’t stop terrorist and other smaller groups who care little for UN protocols, but they will be constrained on two levels. First, they’ll have to develop the technology themselves. They won’t be able to go out and buy any such weapons. And second, conventional military forces can still use any defensive technologies they like to protect themselves.

With this open letter, we hope to bring awareness to a dire subject which, without a doubt, will have a vicious impact on the whole of mankind.

We can get it right at this early stage, or we can stand idly by and witness the birth of a new era of warfare. Frankly, that’s not something many scientists in this field want to see.

Our call to action is simple: ban offensive autonomous weapons, and in doing so, securing a safe future for us all.

A press conference releasing the open letter to the public was held at the opening of the International Joint Conference on AI at 9pm AEST, July 28, 2015. 


The following is the entire text of the open letter:

Autonomous weapons select and engage targets without human intervention. They might include, for example, armed quadcopters that can search for and eliminate people meeting certain pre-defined criteria, but do not include cruise missiles or remotely piloted drones for which humans make all targeting decisions. Artificial Intelligence (AI) technology has reached a point where the deployment of such systems is – practically if not legally – feasible within years, not decades, and the stakes are high: autonomous weapons have been described as the third revolution in warfare, after gunpowder and nuclear arms.

Many arguments have been made for and against autonomous weapons, for example that replacing human soldiers by machines is good by reducing casualties for the owner but bad by thereby lowering the threshold for going to battle. The key question for humanity today is whether to start a global AI arms race or to prevent it from starting. If any major military power pushes ahead with AI weapon development, a global arms race is virtually inevitable, and the endpoint of this technological trajectory is obvious: autonomous weapons will become the Kalashnikovs of tomorrow. Unlike nuclear weapons, they require no costly or hard-to-obtain raw materials, so they will become ubiquitous and cheap for all significant military powers to mass-produce. It will only be a matter of time until they appear on the black market and in the hands of terrorists, dictators wishing to better control their populace, warlords wishing to perpetrate ethnic cleansing, etc. Autonomous weapons are ideal for tasks such as assassinations, destabilizing nations, subduing populations and selectively killing a particular ethnic group. We therefore believe that a military AI arms race would not be beneficial for humanity. There are many ways in which AI can make battlefields safer for humans, especially civilians, without creating new tools for killing people.

Just as most chemists and biologists have no interest in building chemical or biological weapons, most AI researchers have no interest in building AI weapons — and do not want others to tarnish their field by doing so, potentially creating a major public backlash against AI that curtails its future societal benefits. Indeed, chemists and biologists have broadly supported international agreements that have successfully prohibited chemical and biological weapons, just as most physicists supported the treaties banning space-based nuclear weapons and blinding laser weapons.

In summary, we believe that AI has great potential to benefit humanity in many ways, and that the goal of the field should be to do so. Starting a military AI arms race is a bad idea, and should be prevented by a ban on offensive autonomous weapons beyond meaningful human control.

Creative CommonsThe Conversation

Toby Walsh is Professor, Research Group Leader, Optimisation Research Group at NICTA, Australia’s Information and Communications Technology (ICT) Research Centre of Excellence. This article was originally published on The Conversation. Read the original article.

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Verbatim: The Doomsday Clock ticks closer to disaster

January 23, 2015

DoomsdayClock_black_3mins_regmark

Scientists, including 17 Nobel laureates, this week moved the minute hand of their terrible Doomsday Clock two minutes ahead, as they urged world leaders to defuse nuclear and climate-change threats to the world and humanity. We humans have, metaphorically, just three minutes to get our act together, they warn.

The decision on the Doomsday Clock is made annually, intended to signal the “world’s vulnerability to catastrophe from nuclear weapons, climate change, and new technologies emerging in other domains,” said the Bulletin.

Eerily, the last time the people at The Bulletin of Atomic Scientists placed their famous clock at three minutes to midnight — essentially the global apocalypse — it was 1984. 

In between , the world breathed a little easier. It was fully six minutes to midnight in 2010, when the group was hopeful enough to proclaim, “”We are poised to bend the arc of history toward a world free of nuclear weapons.” Their most optimistic year since they began their time-keeping, in the wake of WW II, was 1991. Back then, they moved the clock 17 minutes away from midnight: “With the Cold War officially over, the United States and Russia begin making deep cuts to their nuclear arsenals.”

The 21st Century has brought renewed pessimism. Following are excerpts of their letter, Three minutes and counting

From: The Bulletin of the Atomic Scientists Science and Security Board

To: Leaders and citizens of the world

Re: It is only three minutes to midnight

In 2015, unchecked climate change, global nuclear weapons modernizations, and outsized nuclear weapons arsenals pose extraordinary and undeniable threats to the continued existence of humanity, and world leaders have failed to act with the speed or on the scale required to protect citizens from potential catastrophe. These failures of political leadership endanger every person on Earth …

A climate catastrophe looms—but is not inevitable.

According to US government environmental scientists, 2014 was the hottest year in 134 years of record keeping. Nine of the 10 warmest years on record have all occurred since 2000. This pattern is deeply disconcerting.

In November 2014, the Intergovernmental Panel on Climate Change (IPCC) released its Synthesis Report encapsulating the key findings of its just-completed multivolume assessment of climate change. The IPCC reported that global warming is unequivocal and unprecedented and already responsible for widespread damage. It warned that warming—if unchecked by urgent and concerted global efforts to greatly reduce greenhouse gas emissions—would reach 3 to 8 degrees Celsius (about 5.5 to 14.5 degrees Fahrenheit) by the end of the century.

This may seem like a modest rise in the average global temperature. After all, people at a given location often experience much greater temperature swings in the course of a single day. But that is a local variation, not a change in the average temperature of the surface of the entire planet. A similarly “modest” global average warming of 3 to 8 degrees Celsius brought Earth out of the frigid depths of the last ice age, utterly transforming the surface of the planet and in the process making it hospitable to the development of human civilization. To risk a further warming of this same magnitude is to risk the possibility of an equally profound transformation of Earth’s surface—only this time the planet’s hospitality to humanity can by no means be taken for granted … 

Nuclear modernization programs threaten to create a new arms race.

Although the United States and Russia have reduced their arsenal sizes from Cold War heights, the pace of reduction has slowed dramatically in recent years. According to Hans Kristensen of the Federation of American Scientists, “in terms of warhead numbers, the Obama administration so far has cut the least warheads from the stockpile” of any post-Cold War administration.

Meanwhile, as they slow the pace of disarmament, the nuclear weapon states have given other strong indications that they are committed to retaining nuclear weapons for the indefinite future. The most worrying evidence of this commitment: huge and expensive programs of nuclear arsenal modernization that all nuclear weapon states are pursuing. These massive modernization efforts undermine the nuclear weapons states’ promise to disarm, a central tenet of the Nuclear Non-Proliferation Treaty (NPT), and they therefore also threaten the global nonproliferation regime …

The leadership failure on nuclear power.

Nuclear energy provides slightly more than 10 percent of the world’s electricity-generating capacity, without emitting carbon dioxide. Depending on the type of fossil fuel displaced by the electricity nuclear power plants generate (that is, coal or natural gas), nuclear power plants help the world avoid approximately 0.5 gigatons of carbon emissions annually. But the international community has not developed coordinated plans to meet the challenges that nuclear power faces in terms of cost, safety, radioactive waste management, and proliferation risk.

Nuclear power is growing sporadically in regions that can afford it, sometimes in countries that do not have adequately independent regulatory systems. Meanwhile, several countries continue to show interest in acquiring technologies for uranium enrichment and spent fuel reprocessing—technologies that can be used to create weapons-grade fissile materials for nuclear weapons. Stockpiles of highly radioactive spent nuclear fuel continue to grow (globally, about 10,000 metric tonnes of heavy metal are produced each year). Spent fuel requires safe geologic disposal over a time scale of hundreds of thousands of years …

Dealing with emerging technological threats.

The world’s institutions were proven arthritic during the recent outbreak of Ebola in West Africa. Medical scientists had a good grip on what to do to quell the outbreak of that deadly virus. But social and political institutions stuttered and, at times, failed to respond effectively. In the age of synthetic biology and globalization, world governance must develop ways to react quickly and effectively to confront emerging disease and the possibility of bioterrorism.

Unfortunately, microbes are not the only emerging technological challenges to civil society and international governance.

It is clear from the recent hacking of major organizations and government facilities that cyber attacks constitute a threat with the potential to destabilize governmental and financial institutions and to serve as a medium for new escalations of international tensions. Meanwhile, advances in artificial intelligence have led a number of prominent individuals to express concern about human command and control capabilities in the field, on national and international scales, over coming decades.

The Bulletin is concerned about the lag between scientific advances in dual-use technologies and the ability of civil society to control them. …

These stunning governmental failures have imperiled civilization on a global scale, and so we, the members of the Bulletin of the Atomic Scientists Science and Security Board, implore the citizens of the world to speak clearly, demanding that their leaders:

  • Take actions that would cap greenhouse gas emissions at levels sufficient to keep average global temperature from rising more than 2 degrees Celsius above preindustrial levels. The 2-degree target is consistent with consensus views on climate science and is eminently achievable and economically viable—if national leaders show more interest in protecting their citizens than in serving the economic interests of the fossil fuel industry.
  • Dramatically reduce proposed spending on nuclear weapons modernization programs. The United States and Russia have hatched plans to essentially rebuild their entire nuclear triads in coming decades, and other nuclear weapons countries are following suit. The projected costs of these “improvements” to nuclear arsenals are indefensible, and they undermine the global disarmament regime.
  • Re-energize the disarmament process, with a focus on results. The United States and Russia, in particular, need to start negotiations on shrinking their strategic and tactical nuclear arsenals. The world can be more secure with much, much smaller nuclear arsenals than now exist—if political leaders are truly interested in protecting their citizens from harm.
  • Deal now with the commercial nuclear waste problem. Reasonable people can disagree on whether an expansion of nuclear-powered electricity generation should be a major component of the effort to limit climate change. Regardless of the future course of the worldwide nuclear power industry, there will be a need for safe and secure interim and permanent nuclear waste storage facilities.
  • Create institutions specifically assigned to explore and address potentially catastrophic misuses of new technologies. Scientific advance can provide society with great benefits, but the potential for misuse of potent new technologies is real, unless government, scientific, and business leaders take appropriate steps to explore and address possible devastating consequences of those technologies early in their development.

Also on Facts and Opinions, read Anders Sandberg’s analysis in our Expert Witness section: Doomsday Clock: can we really predict the end of the world?

Sources and further reading:

Full text of Three Minutes and Counting, Bulletin of Atomic Scientists: http://thebulletin.org/three-minutes-and-counting7938

Doomsday Clock Puts Us 3 Minutes Away from Apocalypse, Time magazine

Three minutes to Armageddon: Scientists reset ‘Doomsday Clock’ Deutsche Welle

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The Hitchbot’s Guide to a Continent

HALIFAX, Canada -- hitchBOT creators David Harris Smith and Frauke Zeller, July 2014. Photo by Norbert Guthier

HALIFAX, Canada — HitchBOT creators David Harris Smith and Frauke Zeller, July 2014.
Photo by Norbert Guthier,  Ryerson University handout 

By Frauke Zeller, Ryerson University and David Harris Smith, McMaster University, The Conversation

How do you rate your chances of completing a transcontinental road trip? What if you can’t drive and don’t have car? What if you can’t even move unaided? In fact, what about if you’re not even human?

Tweeting, GPS-equipped robot Hitchbot managed it, hitchhiking across Canada this summer from Halifax, Nova Scotia to Victoria, British Columbia. The cylindrical robot, sporting a digital LCD smile and a fetching line in matching yellow rubber gloves and boots, completed the 6,000km journey in around 20 days.

Unlike the robots of the big screen, which tend to come equipped with advanced intelligence and search-and-destroy capabilities, Hitchbot is social, friendly, and entirely human-dependent. It relied only on the kindness of strangers it met along the way to pick it up, put it in their vehicles, and take it as far as they could toward its final destination.

David Harris Smith and I conceived the idea for Hitchbot as an opportunity to set an experimental, technological art project free in the wild. Combining arts and science knowledge (and David’s years of experience as a hitchhiker), we thought a hitchhiking robot would provide a fascinating experience for the public, and would offer some insight on how humans interact with robots. We’re delighted to be receiving a Top 30 Innovation Award at this year’s Silicon Valley Innovation & Entrepreneurship Forum, so it seems others see the same appeal.

We put the robot down on the side of the highway on July 27, 2014, in Halifax and watched it get picked up by its first travel companions, Anne and Brian Saulnier. Heading to Kouchibouguac National Park in New Brunswick, this was the first of 19 hitches that allowed Hitchbot to traverse the country.

Arriving in Victoria on August 17, the trip was faster and more eventful than we could have imagined. Highlights of the trip included attending a Pow Wow with the Wikwemikong First Nation on Manitoulin Island, doing the Harlem Shake with three travel companions in Saskatchewan, and attending the wedding of Kyle Shepherd and Julie Branch on Kicking Horse Mountain in Golden, B.C.

As Hitchbot’s family we were overwhelmed by the kindness of those who helped the robot cross the country. The willingness to see the project succeed affirmed what people often say about Canadians – that they are warm, polite, and hospitable. Monitoring the journey unfold from Toronto our team never caught wind of anyone attempting to harm Hitchbot. Instead, each participant was charmed by the robot, showed it love, and genuinely wanted to help it reach Victoria.

Of course, the positive media coverage helped. Tens of thousands of people followed the robot’s trip on Facebook, Twitter, and Instagram, sharing hitchhiking advice, personal anecdotes, and suggestions for tourist visits.

The initial question we posed was whether robots can trust human beings – inverting the more common worry of artificial intelligence’s trustworthiness towards humans. While the result shows that the answer seems to be “Yes”, it also demonstrated that by setting the project up as an engaging art and science project, people were eager to get involved. It developed into a participatory event, proving how much interest art and sciences can evoke among the public.

From a scientific point of view, Hitchbot’s progress showed not only the relevance of social media but also that the field of human-robot interaction goes beyond just physical interaction: it is also the personality, communication abilities and ability to actively shape the interaction that seems to invite people to trust a robot and to be willing to engage with it.

This also applies to the design. Whereas Hitchbot’s communication features were more rudimentary compared to high-tech artificial intelligence robots that come at high costs, Hitchbot’s design actually made people want to engage and connect. The design was chosen and intended to instill trust, and also to encourage people to help Hitchbot – so the decision to have it the height of a six-year-old child. The overall design, with matching yellow gloves and boots was meant to be quirky and fun, which turned out to be rather appealing to people.

Many people from around the world have enquired whether Hitchbot will be making trips through the US or Europe – an encouraging sign that a project like this could succeed in places other than Canada. For now, we’re still deciding what trips may come next.

Creative Commons

This article was co-authored with Hitchbot family member Alanna Mager.

The Conversation

Frauke Zeller is an Assistant Professor of Communication at Ryerson University. She received funding from the European Commission Research Framework (6+7), German Academic Exchange Service, Social Sciences and Humanities Research Council, Canada. She works for Ryerson University, Canada.

David Harris Smith is an Assistant Professor in the Department of Communication Studies and Multimedia at McMaster University. He receives funding from GRAND NCE, McMaster University, SSHRC. He is Assistant Professor in the Department of Communication Studies and Multimedia at McMaster University.

This article was originally published on The Conversation. Read the original article.

 

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