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The Rise of Computer-Aided Explanation

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Michael Nielsen has an interesting article in Quanta:

Imagine it’s the 1950s and you’re in charge of one of the world’s first electronic computers. A company approaches you and says: “We have 10 million words of French text that we’d like to translate into English. We could hire translators, but is there some way your computer could do the translation automatically?”

At this time, computers are still a novelty, and no one has ever done automated translation. But you decide to attempt it. You write a program that examines each sentence and tries to understand the grammatical structure. It looks for verbs, the nouns that go with the verbs, the adjectives modifying nouns, and so on. With the grammatical structure understood, your program converts the sentence structure into English and uses a French-English dictionary to translate individual words.

For several decades, most computer translation systems used ideas along these lines — long lists of rules expressing linguistic structure. But in the late 1980s, a team from IBM’s Thomas J. Watson Research Center in Yorktown Heights, N.Y., tried a radically different approach. They threw out almost everything we know about language — all the rules about verb tenses and noun placement — and instead created a statistical model.

They did this in a clever way. They got hold of a copy of the transcripts of the Canadian parliament from a collection known as Hansard. By Canadian law, Hansard is available in both English and French. They then used a computer to compare corresponding English and French text and spot relationships.

For instance, the computer might notice that sentences containing the French word bonjour tend to contain the English word hello in about the same position in the sentence. The computer didn’t know anything about either word — it started without a conventional grammar or dictionary. But it didn’t need those. Instead, it could use pure brute force to spot the correspondence between bonjour and hello.

By making such comparisons, the program built up a statistical model of how French and English sentences correspond. That model matched words and phrases in French to words and phrases in English. More precisely, the computer used Hansard to estimate the probability that an English word or phrase will be in a sentence, given that a particular French word or phrase is in the corresponding translation. It also used Hansard to estimate probabilities for the way words and phrases are shuffled around within translated sentences.

Using this statistical model, the computer could take a new French sentence — one it had never seen before — and figure out the most likely corresponding English sentence. And that would be the program’s translation.

When I first heard about this approach, it sounded ludicrous. This statistical model throws away nearly everything we know about language. There’s no concept of subjects, predicates or objects, none of what we usually think of as the structure of language. And the models don’t try to figure out anything about the meaning (whatever that is) of the sentence either.

Despite all this, the IBM team found this approach worked much better than systems based on sophisticated linguistic concepts. Indeed, their system was so successful that the best modern systems for language translation — systems like Google Translate — are based on similar ideas.

Statistical models are helpful for more than just computer translation. There are many problems involving language for which statistical models work better than those based on traditional linguistic ideas. For example, the best modern computer speech-recognition systems are based on statistical models of human language. And online search engines use statistical models to understand search queries and find the best responses.

Many traditionally trained linguists view these statistical models skeptically. Consider the following comments by the great linguist Noam Chomsky:

There’s a lot of work which tries to do sophisticated statistical analysis, … without any concern for the actual structure of language, as far as I’m aware that only achieves success in a very odd sense of success. … It interprets success as approximating unanalyzed data. … Well that’s a notion of success which is I think novel, I don’t know of anything like it in the history of science.

Chomsky compares the approach to a statistical model of insect behavior. Given enough video of swarming bees, for example, researchers might devise a statistical model that allows them to predict what the bees might do next. But in Chomsky’s opinion it doesn’t impart any true understanding of why the bees dance in the way that they do.

Related stories are playing out across science, not just in linguistics. In mathematics, for example, it is becoming more and more common for problems to be settled using computer-generated proofs. An early example occurred in 1976, when Kenneth Appel and Wolfgang Haken proved the four-color theorem, the conjecture that every map can be colored using four colors in such a way that no two adjacent regions have the same color. Their computer proof was greeted with controversy. It was too long for a human being to check, much less understand in detail. Some mathematicians objected that the theorem couldn’t be considered truly proved until there was a proof that human beings could understand.

Today, the proofs of many important theorems have no known human-accessible form. Sometimes the computer is merely doing grunt work — calculations, for example. But as time goes on, computers are making more conceptually significant contributions to proofs. One well-known mathematician, Doron Zeilberger of Rutgers University in New Jersey, has gone so far as to include his computer (which he has named Shalosh B. Ekhad) as a co-author of his research work.

Not all mathematicians are happy about this. In an echo of Chomsky’s doubts, the Fields Medal-winning mathematician Pierre Delignesaid: “I don’t believe in a proof done by a computer. In a way, I am very egocentric. I believe in a proof if I understand it, if it’s clear.”

On the surface, statistical translation and computer-assisted proofs seem different. But the two have something important in common. In mathematics, a proof isn’t just a justification for a result. It’s actually a kind of explanation of why a result is true. So computer-assisted proofs are, arguably, computer-generated explanations of mathematical theorems. Similarly, in computer translation the statistical models provide circumstantial explanations of translations. In the simplest case, they tell us that bonjour should be translated as hello because the computer has observed that it has nearly always been translated that way in the past.

Thus, we can view both statistical translation and computer-assisted proofs as instances of a much more general phenomenon: the rise of computer-assisted explanation. Such explanations are becoming increasingly important, not just in linguistics and mathematics, but in nearly all areas of human knowledge.

But as smart skeptics like Chomsky and Deligne (and critics in other fields) have pointed out, these explanations can be unsatisfying. . .

Continue reading.

Written by LeisureGuy

26 July 2015 at 9:39 am

Posted in Math, Technology

Interesting point: The virtual (on-line) classroom is also the surveilled classroom

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Everything is permanently recorded, and wide-ranging discussions are a rich source of damning comments taken out of context. More here.

Written by LeisureGuy

23 July 2015 at 9:27 am

Posted in Education, Technology

How hackers can take control of your car as you drive it

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One inevitably thinks of the totally mysterious single-car crash that killed Michael Hastings, a reporter who aroused the ire of the US national security state (military, CIA, NSA, et al.). He was working a story and when he was alone in his car in LA, the car accelerated to a very high speed and crashed. Even at the time there was speculation that Hastings himself was not responsible but a victim of having his car hacked by some entity with the resources to do such a thing.

Andy Greenberg reports in Wired (and there’s a video at the link):

I WAS DRIVING 70 mph on the edge of downtown St. Louis when the exploit began to take hold.

Though I hadn’t touched the dashboard, the vents in the Jeep Cherokee started blasting cold air at the maximum setting, chilling the sweat on my back through the in-seat climate control system. Next the radio switched to the local hip hop station and began blaring Skee-lo at full volume. I spun the control knob left and hit the power button, to no avail. Then the windshield wipers turned on, and wiper fluid blurred the glass.

As I tried to cope with all this, a picture of the two hackers performing these stunts appeared on the car’s digital display: Charlie Miller and Chris Valasek, wearing their trademark track suits. A nice touch, I thought.

The Jeep’s strange behavior wasn’t entirely unexpected. I’d come to St. Louis to be Miller and Valasek’s digital crash-test dummy, a willing subject on whom they could test the car-hacking research they’d been doing over the past year. The result of their work was a hacking technique—what the security industry calls a zero-day exploit—that can target Jeep Cherokees and give the attacker wireless control, via the Internet, to any of thousands of vehicles. Their code is an automaker’s nightmare: software that lets hackers send commands through the Jeep’s entertainment system to its dashboard functions, steering, brakes, and transmission, all from a laptop that may be across the country.

To better simulate the experience of driving a vehicle while it’s being hijacked by an invisible, virtual force, Miller and Valasek refused to tell me ahead of time what kinds of attacks they planned to launch from Miller’s laptop in his house 10 miles west. Instead, they merely assured me that they wouldn’t do anything life-threatening. Then they told me to drive the Jeep onto the highway. “Remember, Andy,” Miller had said through my iPhone’s speaker just before I pulled onto the Interstate 64 on-ramp, “no matter what happens, don’t panic.”1

As the two hackers remotely toyed with the air-conditioning, radio, and windshield wipers, I mentally congratulated myself on my courage under pressure. That’s when they cut the transmission.

Immediately my accelerator stopped working. As I frantically pressed the pedal and watched the RPMs climb, the Jeep lost half its speed, then slowed to a crawl. This occurred just as I reached a long overpass, with no shoulder to offer an escape. The experiment had ceased to be fun.

At that point, the interstate began to slope upward, so the Jeep lost more momentum and barely crept forward. Cars lined up behind my bumper before passing me, honking. I could see an 18-wheeler approaching in my rearview mirror. I hoped its driver saw me, too, and could tell I was paralyzed on the highway.

“You’re doomed!” Valasek shouted, but I couldn’t make out his heckling over the blast of the radio, now pumping Kanye West. The semi loomed in the mirror, bearing down on my immobilized Jeep.

I followed Miller’s advice: I didn’t panic. I did, however, drop any semblance of bravery, grab my iPhone with a clammy fist, and beg the hackers to make it stop.

This wasn’t the first time Miller and Valasek had put me behind the wheel of a compromised car. . .

Continue reading. It’s a lengthy article and there’s much more detail.

Certainly would explain the Hastings crash. His article in Rolling Stone on Gen. Chrystal really pissed off Special Forces.

Written by LeisureGuy

22 July 2015 at 9:55 am

Posted in Technology

The undermining of the hyperlink

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Marketing and business is all about control in general and more specifically about controlling you, the consumer. Once you control the consumer, you’ve got it made, so naturally enough much money, research, and thought goes into controlling you (the consumer).

And some things you don’t notice if they change gradually. <Cue story of frog in slowly heated water: apocryphal but useful. It works only if the frog has been pithed—is brain dead—but an amazing proportion of consumers fall into that category.>

Hossein Derakhshan describes what happened to the hyperlink while he was away:

Seven months ago, I sat down at the small table in the kitchen of my 1960s apartment, nestled on the top floor of a building in a vibrant central neighbourhood of Tehran, and I did something I had done thousands of times previously. I opened my laptop and posted to my new blog. This, though, was the first time in six years. And it nearly broke my heart.

A few weeks earlier, I’d been abruptly pardoned and freed from Evin prison in northern Tehran. I had been expecting to spend most of my life in those cells: In November 2008, I’d been sentenced to nearly 20 years in jail, mostly for things I’d written on my blog.

But the moment, when it came, was unexpected. I smoked a cigarette in the kitchen with one of my fellow inmates, and came back to the room I shared with a dozen other men. We were sharing a cup of tea when the voice of the floor announcer — another prisoner — filled all the rooms and corridors. In his flat voice, he announced in Persian: “Dear fellow inmates, the bird of luck has once again sat on one fellow inmate’s shoulders. Mr. Hossein Derakhshan, as of this moment, you are free.”

That evening was the first time that I went out of those doors as a free man. Everything felt new: The chill autumn breeze, the traffic noise from a nearby bridge, the smell, the colors of the city I had lived in for most of my life.

Around me, I noticed a very different Tehran from the one I’d been used to. An influx of new, shamelessly luxurious condos had replaced the charming little houses I was familiar with. New roads, new highways, hordes of invasive SUVs. Large billboards with advertisements for Swiss-made watches and Korean flat screen TVs. Women in colorful scarves and manteaus, men with dyed hair and beards, and hundreds of charming cafes with hip western music and female staff. They were the kinds of changes that creep up on people; the kind you only really notice once normal life gets taken away from you.

Two weeks later, I began writing again. Some friends agreed to let me start a blog as part of their arts magazine. I called it Ketabkhan — it means book-reader in Persian.

Six years was a long time to be in jail, but it’s an entire era online. Writing on the internet itself had not changed, but reading — or, at least, getting things read — had altered dramatically. I’d been told how essential social networks had become while I’d been gone, and so I knew one thing: If I wanted to lure people to see my writing, I had to use social media now.

So I tried to post a link to one of my stories on Facebook. Turns out Facebook didn’t care much. It ended up looking like a boring classified ad. No description. No image. Nothing. It got three likes. Three! That was it.

It became clear to me, right there, that things had changed. I was not equipped to play on this new turf — all my investment and effort had burned up. I was devastated.

Blogs were gold and bloggers were rock stars back in 2008 when I was arrested. At that point, and despite the fact the state was blocking access to my blog from inside Iran, I had an audience of around 20,000 people every day. Everybody I linked to would face a sudden and serious jump in traffic: I could empower or embarrass anyone I wanted.

People used to carefully read my posts and leave lots of relevant comments, and even many of those who strongly disagreed with me still came to read. Other blogs linked to mine to discuss what I was saying. I felt like a king.

The iPhone was a little over a year old by then, but smartphones were still mostly used to make phone calls and send short messages, handle emails, and surf the web. There were no real apps, certainly not how we think of them today. There was no Instagram, no SnapChat, no Viber, no WhatsApp.

Instead, there was the web, and on the web, there were blogs: the best place to find alternative thoughts, news and analysis. They were my life.

It had all started with 9/11. I was in Toronto, and my father had just arrived from Tehran for a visit. We were having breakfast when the second plane hit the World Trade Center. I was puzzled and confused and, looking for insights and explanations, I came across blogs. Once I read a few, I thought: This is it, I should start one, and encourage all Iranians to start blogging as well. So, using Notepad on Windows, I started experimenting. Soon I ended up writing on, using Blogger’s publishing platform before Google bought it.

Then, on November 5, 2001, I published a step-to-step guide on how to start a blog. That sparked something that was later called a blogging revolution: Soon, hundreds and thousands of Iranians made it one of the top 5 nations by the number of blogs, and I was proud to have a role in this unprecedented democratization of writing.

Those days, I used to keep a list of all blogs in Persian and, for a while, I was the first person any new blogger in Iran would contact, so they could get on the list. That’s why they called me “the blogfather” in my mid-twenties — it was a silly nickname, but at least it hinted at how much I cared.

Every morning, from my small apartment in downtown Toronto, I opened my computer and took care of the new blogs, helping them gain exposure and audience. It was a diverse crowd — from exiled authors and journalists, female diarists, and technology experts, to local journalists, politicians, clerics, and war veterans — and I always encouraged even more. I invited more religious, and pro-Islamic Republic men and women, people who lived inside Iran, to join and start writing.

The breadth of what was available those days amazed us all. It was partly why I promoted blogging so seriously. I’d left Iran in late 2000 to experience living in the West, and was scared that I was missing all the rapidly emerging trends at home. But reading Iranian blogs in Toronto was the closest experience I could have to sitting in a shared taxi in Tehran and listening to collective conversations between the talkative driver and random passengers.

There’s a story in the Quran that I thought about a lot during my first eight months in solitary confinement. In it, a group of persecuted Christians find refuge in a cave. They, and a dog they have with them, fall into a deep sleep. They wake up under the impression that they’ve taken a nap: In fact, it’s 300 years later. One version of the story tells of how one of them goes out to buy food — and I can only imagine how hungry they must’ve been after 300 years — and discovers that his money is obsolete now, a museum item. That’s when he realizes how long they have actually been absent.

The hyperlink was my currency six years ago. Stemming from the idea of thehypertext, the hyperlink provided a diversity and decentralisation that the real world lacked. The hyperlink represented the open, interconnected spirit of the world wide web — a vision that started with its inventor, Tim Berners-Lee. The hyperlink was a way to abandon centralization — all the links, lines and hierarchies — and replace them with something more distributed, a system of nodes and networks.

Blogs gave form to that spirit of decentralization: They were windows into lives you’d rarely know much about; bridges that connected different lives to each other and thereby changed them. Blogs were cafes where people exchanged diverse ideas on any and every topic you could possibly be interested in. They were Tehran’s taxicabs writ large.

Since I got out of jail, though, I’ve realized how much the hyperlink has been devalued, almost made obsolete. . .

Continue reading.

Written by LeisureGuy

19 July 2015 at 10:24 am

The end of capitalism has begun

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Interesting article by Paul Mason in The Guardian. It makes sense: a society based on the simple idea that anything that increases profits is good has too narrow a foundation to long endure.

The red flags and marching songs of Syriza during the Greek crisis, plus the expectation that the banks would be nationalised, revived briefly a 20th-century dream: the forced destruction of the market from above. For much of the 20th century this was how the left conceived the first stage of an economy beyond capitalism. The force would be applied by the working class, either at the ballot box or on the barricades. The lever would be the state. The opportunity would come through frequent episodes of economic collapse.

Instead over the past 25 years it has been the left’s project that has collapsed. The market destroyed the plan; individualism replaced collectivism and solidarity; the hugely expanded workforce of the world looks like a “proletariat”, but no longer thinks or behaves as it once did.

If you lived through all this, and disliked capitalism, it was traumatic. But in the process technology has created a new route out, which the remnants of the old left – and all other forces influenced by it – have either to embrace or die. Capitalism, it turns out, will not be abolished by forced-march techniques. It will be abolished by creating something more dynamic that exists, at first, almost unseen within the old system, but which will break through, reshaping the economy around new values and behaviours. I call this postcapitalism.

As with the end of feudalism 500 years ago, capitalism’s replacement by postcapitalism will be accelerated by external shocks and shaped by the emergence of a new kind of human being. And it has started.

Postcapitalism is possible because of three major changes information technology has brought about in the past 25 years. First, it has reduced the need for work, blurred the edges between work and free time and loosened the relationship between work and wages. The coming wave of automation, currently stalled because our social infrastructure cannot bear the consequences, will hugely diminish the amount of work needed – not just to subsist but to provide a decent life for all.

Second, information is corroding the market’s ability to form prices correctly. That is because markets are based on scarcity while information is abundant. The system’s defence mechanism is to form monopolies – the giant tech companies – on a scale not seen in the past 200 years, yet they cannot last. By building business models and share valuations based on the capture and privatisation of all socially produced information, such firms are constructing a fragile corporate edifice at odds with the most basic need of humanity, which is to use ideas freely.

Third, we’re seeing the spontaneous rise of collaborative production: goods, services and organisations are appearing that no longer respond to the dictates of the market and the managerial hierarchy. The biggest information product in the world – Wikipedia – is made by volunteers for free, abolishing the encyclopedia business and depriving the advertising industry of an estimated $3bn a year in revenue.

Almost unnoticed, in the niches and hollows of the market system, whole swaths of economic life are beginning to move to a different rhythm. Parallel currencies, time banks, cooperatives and self-managed spaces have proliferated, barely noticed by the economics profession, and often as a direct result of the shattering of the old structures in the post-2008 crisis.

You only find this new economy if you look hard for it. In Greece, . . .

Continue reading.

Written by LeisureGuy

17 July 2015 at 1:35 pm

The New Laws of Explosive Networks

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Jennifer Ouellette has a very interesting article in Quanta:

Last week, United Airlines grounded nearly 5,000 flights when its computer system crashed. The culprit: a faulty network router. Later on the same morning, another computer glitch halted trading on the New York Stock Exchange for over three hours.

Some saw the sinister hand of a hacker in these outages, but they are far more likely to be a coincidence, an intrinsic feature of the system rather than a bug. Networks go down all the time, a consequence of unprecedented levels of interconnection. Disruptions can occur even in the most robust networks, whether these are power grids, global financial markets, or your favorite social network. As the former Atlanticreporter Alexis Madrigal observed when a computer error shut down the Nasdaq stock exchange in 2013, “When things work in new ways, they break in new ways.”

A fresh new understanding of such systems — the way they grow, and how they break — has arisen from the physics of coffee.

Researchers usually think of network connectivity as happening in a slow, continuous manner, similar to the way water moves through freshly ground coffee beans, slowly saturating all the granules to become coffee in the container below. However, over the past few years, researchers have discovered that in special cases, connectivity might emerge with a bang, not a whimper, via a phenomenon they have dubbed “explosive percolation.”

This new understanding of how über-connectivity emerges, which wasdescribed earlier this month in the journal Nature Physics, is the first step toward identifying warning signs that may occur when such systems go awry — for example, when power grids begin to fail, or when an infectious disease starts to mushroom into a global pandemic. Explosive percolation may help create effective intervention strategies to control that behavior and, perhaps, avoid catastrophic consequences.

An Explosive Twist

Traditional mathematical models of percolation, which date back to the 1940s, view the process as a smooth, continuous transition. “We think of percolation as water flowing through the ground,” said Robert Ziff, a physicist at the University of Michigan who has been studying phase transitions for the past 30 years. “It’s a formation of long-range connectivity in the system.”

The formation of connectivity can be understood as a phase transition, the process whereby water freezes into ice or boils away into vapor.

Phase transitions are ubiquitous in nature, and they also provide a handy model for how individual nodes in a random network gradually link together, one by one, via short-range connections over time. When the number of connections reaches a critical threshold, a phase shift causes the largest cluster of nodes to grow rapidly, and über-connectivity results. (Seen this way, the percolation process that gives rise to your morning cup of joe is an example of a phase transition. Hot water passes through roasted beans and shifts into a new state — coffee.)

Explosive percolation works a bit differently. The notion arose during a workshop in 2000 at the Fields Institute for Research in Mathematical Sciences in Toronto. Dimitris Achlioptas, a computer scientist at the University of California, Santa Cruz, proposed a possible means for delaying a phase transition into a densely connected network, by merging the traditional notion of percolation with an optimization strategy known as the power of two choices. Instead of just letting two random nodes connect (or not), you consider two pairs of random nodes, and decide which pair you prefer to connect. Your choice is based on predetermined criteria — for instance, you might select whichever pair has the fewest pre-existing connections to other nodes.

Because a random system would normally favor those nodes with the most pre-existing connections, this forced choice introduces a bias into the network — an intervention that alters its typical behavior. In 2009, Achlioptas, Raissa D’Souza, a physicist at the University of California, Davis, and Joel Spencer, a mathematician at New York University’s Courant Institute of Mathematical Sciences, found that tweaking the traditional percolation model in this way dramatically changes the nature of the resulting phase transition. Instead of arising from a slow, steady continuous march toward greater and greater connectivity, connections emerge globally all at once throughout the system in a kind of explosion — hence the moniker “explosive percolation.”

The concept has exploded in its own right, spawning countless papers over the past six years. Many of the papers debate whether this new model constitutes a truly discontinuous phase transition. Indeed, in 2011 researchers showed that for the particular model analyzed in the original 2009 study, explosive transitions only happen if the network is finite. While networks such as the Internet have at most about a billion nodes, phase transitions are most commonly associated with materials, which are intricate lattices of so many molecules (approximately 1023 or more) that the systems are effectively infinite. Once extended to a truly infinite system, explosive percolations appear to lose some of their boom.

Yet D’Souza and her cohorts have not been idle either. They have uncovered many other percolation models that do yield truly abrupt transitions. These new models share a key feature, according to D’Souza. In traditional percolation, nodes and pairs of nodes are chosen at random to form connections, but the likelihood of two clusters merging is proportional to their size. Once a large cluster has formed, it dominates the system, absorbing any smaller clusters that might otherwise merge and grow.

However, in the explosive models, the network grows, but the growth of the large cluster is suppressed. This allows many large but disconnected clusters to grow, until the system hits the critical threshold where adding just one or two extra links triggers an instantaneous switch to über-connectivity. All the large clusters combine at once in a single violent merger.

A New Paradigm for Control

D’Souza wants to learn how to better control complex networks. Connectivity is a double-edged sword, according to her. . .

Continue reading.

Written by LeisureGuy

14 July 2015 at 10:41 am

Posted in Science, Technology

Robots Are Wandering Through Oil Pipelines That Have Never Been Inspected

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Of course oil companies don’t inspect their pipelines—what’s the worst that could happen? — Ah, well. Perhaps that’s why they’re now using robots to inspect the lines from the inside. Jordan Pearson reports in Motherboard:

Oil and natural gas are flowing through nearly 1 million kilometres of aging pipelineacross Canada at high pressure, and some of the pipes have never been inspected for defects—ever.

Robots could change this, but industry observers are skeptical about the possibility of mechanical safety technicians making a positive change in the often reckless oil industry.

Oil pipes are inspected by defect-detecting devices called “smart pigs,” named for the ultrasonic or magnetic sensors they carry. Uninspected lines are typically secondary pipes welded on to the main line at pumping stations. These lines are referred to as “unpiggable” because their geometry or small size makes them impossible for smart pigs to traverse.

“There are definitely pipelines in Canada that have not been inspected,” said Greg Zinter, manager of pipeline integrity assessment at inspection tech company Applus RTD. Seventy percent of TransCanada’s rural pipeline system is made up of small diameter pipe, according to a presentation last year, and at the time only 26 percent of it had been pigged.

The condition of these pipes is essentially unknown, and so they’re often considered a safety hazard. In 2014, after a spate of ruptures in TransCanada’s pipeline system, Canada’s National Energy Board (NEB) ordered the company to reduce the pressure in unpiggable lines deemed to be high risk—based on factors like their proximity to a populated area, and the type of protective coating used on the pipe—to 20 percent of their normal levels.One company is proposing a possible solution. Diakont, a Russian company with offices in San Diego and Italy, built a robot that makes unpiggable lines piggable by rumbling along on tank-like treads connected to actuators that allow them to morph to the contours of the pipe.

A smart pig is normally carried through a main pipe by the flowing oil or gas itself. They tend not to do so well with 90 degree turns. Diakont’s robot can handle these obstacles because its form-fitting treads give it enough traction to pull off Spiderman-level stunts like climbing vertically.


The main issue with going with the flow is that the pigs can’t stop to inspect anything that looks sketchy. “The problem is that you’re not in control of where you are in the pipe,” said Brian Carlson, director of pipeline services for Diakont. “If you see an anomaly, you’re essentially just flying past it. Our system is very deliberate in the way it drives through the pipe, so you can stop and analyze anything as you’re going through.”

The robot carries a video camera, ultrasonic sensors, and a laser scanner that measures the anomalies it detects. The robot sends the data back to HQ in real time through a tethered line. This means that it can do a decent job of inspecting a portion of a pipe, but not a whole line.

Diakont’s robot was used last year to inspect a part of the Trans-Alaskan Pipeline System after a 2011 leak shut down the entire pipeline for days, prompting federal regulators to order the company that manages the pipeline to replace hazardous pipes. According to Carlson, Diakont has customers across the US and Russia, but none in Canada. . .

Continue reading.

Written by LeisureGuy

12 July 2015 at 9:15 am


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