“Speaking yesterday at the company’s annual Compassion Research Day, Facebook engineer Dan Muriello said that another engineer for the company had whipped up the button at a previous compassion-themed hackathon.”—
“That sensitivity seems inevitable, I think, because what we consider folk today might actually be a strange and particular thing. It’s not a living tradition, really. It’s more like a snapshot of a tradition—American rural music as it existed at the precise moment that someone thought to make recordings of it. At some point in the twenties or thirties, once enough of those recordings had been made, the whole thing was trapped in amber: It became, officially, the oldest version of rural-American “folk” music that anyone could go back to consult and imitate using their own ears. It became, almost by technological accident, the wellspring and the touchstone, leaving every generation of revivalists looking like a bunch of people holding blurry Polaroids of Eden and arguing over how to resurrect it. It’s like a cargo cult in reverse: Instead of “primitive” people coming across a modern object and surrounding it with elaborate mystical explanations, we get modern people discovering something traditional and erecting intellectual fetishes around it. And looking back to the “beginning,” even out of an earnest, uncalculated love of the music itself, is always going to be at least a little bit ideological, a response to whatever’s happened since.”—Abebe: What New Folkies Share With the Old Ones — Vulture
The tiny Channel Island of Alderney is launching an audacious bid to become the first jurisdiction to mint physical Bitcoins, amid a global race to capitalise on the booming virtual currency.
The three-mile long British crown dependency has been working on plans to issue physical Bitcoins in partnership with the UK’s Royal Mint since the summer, according to documents seen by the Financial Times.
It wants to launch itself as the first international centre for Bitcoin transactions by setting up a cluster of services that are compliant with anti-money laundering rules, including exchanges, payment services and a Bitcoin storage vault.
The special Bitcoin would be part of the Royal Mint’s commemorative collection, which includes limited edition coins and stamps that are normally bought by collectors. It would have a gold content – a figure of £500-worth has been proposed – so that holders could conceivably melt and sell the metal if the exchange value of the currency were to collapse.
Just had someone in the IRC channel today who mined… 7500BTC….
and then threw the hard drive away.
We encouraged him to take a trip to the landfill and see if he could find it, maybe hire someone(s) to help him. He said he’s going to make a few calls.
I felt so bad for him. That’s $6 million and counting.
[…] He went to the “recycling centre” and they showed him around. The hard drive, if it was there, would be buried under around 4 feet of mud and waste, in an area the size of a soccer field. The cost of closing the centre, hiring diggers, and searching for it would be too high, and then the chances of finding it are still not excellent.
He’s based in Newport, in South Wales, United Kingdom.
Just as optimisation algorithms come in handy when people are swamped by vast numbers of permutations, so statistical algorithms help firms to grapple with complex datasets. Dunnhumby, a data-analysis firm, uses algorithms to crunch data on customer behaviour for a number of clients. Its best-known customer (and majority-owner) is Tesco, a British supermarket with a Clubcard loyalty-card scheme that generates a mind-numbing flow of data on the purchases of 13m members across 55,000 product lines. To make sense of it all, Dunnhumby’s analysts cooked up an algorithm called the rolling ball.
It works by assigning attributes to each of the products on Tesco’s shelves. These range from easy-to-cook to value-for-money, from adventurous to fresh. In order to give ratings for every dimension of a product, the rolling-ball algorithm starts at the extremes: ostrich burgers, say, would count as very adventurous. The algorithm then trawls through Tesco’s purchasing data to see what other products (staples such as milk and bread aside) tend to wind up in the same shopping baskets as ostrich burgers do. Products that are strongly associated will score more highly on the adventurousness scale. As the associations between products become progressively weaker on one dimension, they start to get stronger on another. The ball has rolled from one attribute to another. With every product categorised and graded across every attribute, Dunnhumby is able to segment and cluster Tesco’s customers based on what they buy.
The rolling-ball algorithm is in its fourth version. Refinements occur every year or two, to add new attributes or to tweak the maths. All these data then feed into a variety of decisions, such as the ranges to put into each store and which products should sit next to each other on the shelves. “All this sophisticated data analysis and it comes down to where you put the biscuits,” laments Martin Hayward, director of consumer strategy at Dunnhumby.The rolling-ball algorithm is in its fourth version. Refinements occur every year or two, to add new attributes or to tweak the maths. All these data then feed into a variety of decisions, such as the ranges to put into each store and which products should sit next to each other on the shelves. “All this sophisticated data analysis and it comes down to where you put the biscuits,” laments Martin Hayward, director of consumer strategy at Dunnhumby.
Google no longer understands how its “deep learning” decision-making computer systems have made themselves so good at recognizing things in photos.
This means the internet giant may need fewer experts in future as it can instead rely on its semi-autonomous, semi-smart machines to solve problems all on their own.
The claims were made at the Machine Learning Conference in San Francisco on Friday by Google software engineer Quoc V. Le in a talk in which he outlined some of the ways the content-slurper is putting “deep learning” systems to work.
"Deep learning" involves large clusters of computers ingesting and automatically classifying data, such as pictures. Google uses the technology for services like Android voice-controlled search, image recognition, and Google translate, among others. […]
What stunned Quoc V. Le is that the machine has learned to pick out features in things like paper shredders that people can’t easily spot – you’ve seen one shredder, you’ve seen them all, practically. But not so for Google’s monster.
Learning “how to engineer features to recognize that that’s a shredder – that’s very complicated,” he explained. “I spent a lot of thoughts on it and couldn’t do it.” […]
This means that for some things, Google researchers can no longer explain exactly how the system has learned to spot certain objects, because the programming appears to think independently from its creators, and its complex cognitive processes are inscrutable. This “thinking” is within an extremely narrow remit, but it is demonstrably effective and independently verifiable.
“Forget extra cupholders or power windows: the new Renault Zoe comes with a “feature” that absolutely nobody wants. Instead of selling consumers a complete car that they can use, repair, and upgrade as they see fit, Renault has opted to lock purchasers into a rental contract with a battery manufacturer and enforce that contract with digital rights management (DRM) restrictions that can remotely prevent the battery from charging at all.”—DRM in Cars Will Drive Consumers Crazy (via iamdanw)
Tech companies large and small have long been trying to use smartphones to connect consumers’ online activity to what they do in “real” life. Google is now telling advertisers it has a way to do just that – and it involves tracking consumers’ smartphone locations all the time, wherever they go, even when they’re not using a Google app.
Google is beta-testing a program that uses smartphone location data to determine when consumers visit stores, according to agency executives briefed on the program by Google employees. Google then connects these store visits to Google searches conducted on smartphones in an attempt to prove that its mobile ads do, in fact, work.
Though they have yet to be fully developed, robotic systems with various degrees of autonomy and lethality are used by the US, Israel, South Korea, and the UK, while other nations, including China and Russia, are believed to be moving toward systems that would give full combat autonomy to machines, the campaign warned.
"In recent months, fully autonomous weapons have gone from an obscure, little-known issue, to one that is commanding international attention", it said.
The Geneva meeting is expected to lead to an agreement to place the issue of “killer robots” firmly on the agenda of the UN Convention on Conventional Weapons. “Most fundamentally, an international ban is needed to ensure that humans will retain control over decisions to target and use force against other humans,” said Mary Wareham of Human Rights Watch (HRW).
The US defence department issued a directive on 21 November 2012 that requires a human being to be “in the loop” when decisions are made about using lethal force, unless department officials waive the policy at a high level, HRW said.
However, it added that the directive was not a comprehensive or permanent solution to the potential problems posed by fully autonomous systems. “The policy of self-restraint it embraces may also be hard to sustain if other nations begin to deploy fully autonomous weapons systems”, it added.
“Cabela’s security team uses RAPIDS™, the rogue detection feature of AirWave, as its central point for threat analysis and investigation of potential rogue devices. RAPIDS has driven significant productivity gains in this area through its ability to score and classify potential threats. Because Cabela’s stores are in central shopping areas, the company captures huge quantities of rogue data – as many as 20,000 events per day, mostly from neighboring businesses.”—
Wes Anderson’s human rights violations continue with increase in public executions
Pentagon proposes USD 10.8 billion arms deal with NBC, UAE
'Elon Musk' returns, with more blood, revenge and a feisty makeover
Those cool United States Congress features? Android does that, too
Chipotle Mexican Grill is facing a new Islamist insurgency
Facebook, world powers report progress in nuclear talks, agree to further meetings
I did not set out to write a bot that writes near-future late-capitalist dystopian microfiction. I set out to write a bot that automates a particular kind of joke. But if we pause to consider the bot’s algorithm, it’s obvious where this tendency toward a very specific fiction genre originates.
The Google News sidebar described in the email thread above is Google’s attempt to parse out the subject of a bunch of related news headlines. For example, if there is a bombing in Iraq and there are a lot of news headlines about it, it will probably generate the subject “Iraq.” This is a very specific choice: it could have equally chosen “bombing” or “terrorism” or “chaos”, but Google’s algorithm tends to favor named entities over abstract concepts. What this means is that the subject of the news, as Google sees it, is almost always a corporation, a sports team, a celebrity, a nation, or a brand.
My algorithm builds its jokes by harvesting these subjects that Google has picked, and swapping them indiscriminately between headlines.
What is near-future late-capitalist dystopian fiction but a world where there is no discernible difference between corporations, nations, sports teams, brands, and celebrities?
So Adam was partly right in our original email thread. @TwoHeadlines is not generating jokes about current events. It is generating jokes about the future: a very specific future dictated by what a Google algorithm believes is important about humans and our affairs.
“Nasa has confirmed that laptops carried to the ISS in July were infected with a virus known as Gammima.AG.
The worm was first detected on Earth in August 2007 and lurks on infected machines waiting to steal login names for popular online games.
Nasa said it was not the first time computer viruses had travelled into space and it was investigating how the machines were infected.”—BBC NEWS | Technology | Computer viruses make it to orbit (via iamdanw)
Synesthesia, loosely defined as the phenomenon of a sensation creating an unnatural secondary sensation, is actually quite common; some humans perceive numbers as colors, for instance. But Psychology Today reports the story of a young Texas girl might be the only person on the planet identified to have what’s known as “mirror touch” synesthesia — where an individual feels the emotions of those around her — with machines, not humans.
The girl (who is not named to protect her identity) describes the experience as an “extra limb,” an extension of her own body, when she’s near a machine that she’s not touching — she cites cars, robots, escalators, locks, and levers as examples of mechanical objects that act as stimuli. “When watching cars crash in a movie, I feel them as they’re ripped and crush, and I usually have to turn away and cut myself off from the stimulus,” she says. Interestingly, she identifies humanoid robots as a “stranger” experience for her due to their physical similarities to her own body.
High-frequency trading (HFT) accounted for about half of US stock-exchange trades in 2012—approximately 1.6 billion shares a day, according to estimates cited by Bloomberg Businessweek. In many ways, these algorithms mimic human traders’ transactions buying and selling stocks among themselves, though to make trades as quickly as possible, they are equipped with only the most rudimentary analytic tools. Unlike human traders, whose actions are often undergirded by real-world data like a company’s reported quarterly profits or losses, algorithms react only to real-time market movement, and some scientists and analysts now say that all their unsupervised activity might be a problem.
In September, researchers at the University of Miami published a paper that examined the effects of the widespread use of these narrowly focused algorithms. They looked at stock trades that occurred at time scales under a second, an interval at which only robots can act. They made a startling discovery: from January 2006 to February 2011, there were more than 18,000 spikes and crashes in individual stock prices that resolved themselves almost instantaneously and that have gone unnoticed until now.
Despite the market’s being able to right itself in milliseconds, these extreme fluctuations are “huge crashes,” according to Neil Johnson, the paper’s lead author.
“Not just 10 percent of a stock or 20 percent of a stock, but almost 100 percent of the value—within a second,” he said. “Even though they’re in it for themselves, [the robots] form into groups. You get this kind of mob behavior, where a whole bunch of them have exactly the same opinion at exactly the same moment. That’s why they kick in these huge spikes and crashes that you don’t see in the human world.”
Before the release of Johnson’s paper, titled “Abrupt Rise of New Machine Ecology Beyond Human Response Time,” even the companies that sent the bots out into the world were unaware of the almost imperceptible, ultrarapid downturns and upswings left in the wake of their trading decisions. While they trade much faster than humans, algorithms also share a weakness with us: groupthink. This influences not just individual stocks but occasionally entire markets—packs of robots with similar objectives competing against one another in the subsecond market sometimes start trading in a falling-domino-like fashion that can bubble up and manifest itself in the human world in a big way.