e-Xploration

We will look at the abundant use of data visualization in this work, which is published in print as an 80-page .pdf file. The report links back to the companion website, which offers lots of additional materials (such as videos) and a much more interactive experience (such as the Data Explorer). The website is a great example of the benefits of modern layout, with annotations, footnotes, references and figures broken out in a second column next to the main text.

One of the main ways to understand risks is to quantify it in two dimensions, namely its likelihood and its impact, say on a scale from 1 (min) to 5 (max). Each risk can then be visualized by its position in the square spanned by those two dimensions. Often risk mitigation is prioritized by the product of these two factors. In other words, the further right and/or top a risk, the more important it becomes to prepare for or mitigate it.

This work is based on a comprehensive survey of more than 1000 experts worldwide on a range of 50 risks across 5 broad categories. Each of these categories is assigned a color, which is then used consistently throughout the report. Based on the survey results the report uses some basic visualizations, such as a list of the top 5 risks by likelihood and impact, respectively.

WRESTLING OVER SECRET ORDERS

The initial reports about Prism included an internal NSA slide listing the dates that each of nine companies began allowing Prism data collection, starting with Microsoft in 2007 and Yahoo in 2008. The other companies include Apple, AOL and PalTalk as well as YouTube and Skype, which are owned by Google and Microsoft respectively.

Sources familiar with the conversations between the government and the Internet companies say there are frequent disagreements over how to handle specific requests.

Only one company, Yahoo, is known to have taken the highly unusual step of appealing an order from the Foreign Intelligence Surveillance Court. The company argued in 2008 that the order violated the Fourth Amendment protection against unreasonable searches and seizures.

But U.S. District Judge Bruce Selya, who headed the FISA court's Court of Review, ruled the data collection program did not run afoul of the Bill of Rights.

Selya's ruling was published in redacted form, only the second time such a decision had ever been made public. A Justice Department spokesman said it was published at the court's behest, but the executive branch would have had to approve the waiving of secrecy rules.

Two days after that, according to the leaked NSA slides, Google joined the Prism data-collection effort.

"When Yahoo lost that case, it dissuaded everyone else from going to court," a person at another company told Reuters.

"A provider seeing that decision erases the doubt about whether a judge would approve this process," said a former lawyer for Yahoo.

Twitter, which has positioned itself as a hard-line defender of free speech and customer privacy, is still not participating in Prism. But people familiar with talks between the tech companies and the government said it will likely be forced to comply.

In Twitter's case, as in that of some other companies, the objections have ostensibly been about the technological difficulty in complying with orders and the format in which the information will be shared, people familiar with the situation say.

Ever since Facebook introduced the Share button, users have found it easy to reshare photos and other content they see on Facebook with their friends. In fact, sometimes photos are so widely shared that they generate cascades of tens to hundreds of thousands of reshares. To better understand how these large resharing cascades are generated, we used anonymized data to look in depth at two very popular albeit very different photos from the last year.

On November 6, 2012, shortly after the U.S. presidential election was called in favor of Barack Obama, his official Facebook page posted a photo of him hugging the First Lady, Michelle Obama, with the caption "Four more years." This triggered a cascade of over 600,000 reshares, leading to this becoming the most liked photo on Facebook. Half of these reshares were by users directly following the Obama page, while the other half were users who saw it because of a reshare by another user or Facebook page. We refer to this photo as the Obama Victory Photo (OVP).

A photo doesn't have to be posted by a very popular page in order to trigger a large cascade. On January 17, 2013, a young Norwegian man by the name of Petter Kverneng posted a photo following the format of other "Million Likes" memes  (MLM). The essence of this meme is that someone claims that they will have some sought after request granted (usually receiving a pet puppy or kitten) if their photo is liked one million times. In Mr. Kverneng's photo he is shown holding up a sign saying that his friend would have sex with him if the photo gets 1 million likes. The intent was not serious, as Mr. Kverneng later told ABC News:  “it started as a joke, and it ended as a joke. Me and Catherine are just friends.”  Nevertheless, the photo received a million likes in less than a day, producing a cascade of reshares, a subset of which is visualized below.

Si bien cada vez son más numerosas las organizaciones que optan por implantar redes sociales empresariales destinadas al uso interno, por parte de sus empleados, las conversaciones sobre temas relacionados con el  trabajo pueden realizarse a través de otras redes: los medios sociales digitales «públicos». Por tanto, parece indispensable analizar las prácticas colaborativas en el ámbito del trabajo, en función de las nuevas escalas y de las nuevas modalidades que ofrecen aquellas plataformas que atraviesan las esferas de las actividades privadas y públicas de los empleados. Así, además de considerar la plataforma de microblogging Twitter, que es especialmente utilizada por algunos trabajadores con el propósito de difundir conocimientos tácitos o formales, se analizan también las hipótesis y los puntos de reflexión que están en la base de sus prácticas laborales. Si bien Twitter no ha sido concebida en sus orígenes como una plataforma de conversación ni de colaboración, esta herramienta de microblogging se inmiscuye de manera informal, en el día a día de los trabajadores, permitiéndoles comunicarse, en tiempo real, con un público que excede las fronteras de la organización en la que trabajan, a través de un canal público que no necesariamente ha sido autorizado, reconocido ni controlado por aquella. De esta forma, los comentarios y las interacciones que suscitan estas publicaciones pueden dar lugar al nacimiento de redes informales de colaboración. En el presente artículo se identifican y se caracterizan las distintas modalidades de prácticas informales, sobre el trabajo, y de colaboración digital a través de Twitter. Los ejemplos que aquí se presentan permiten ilustrar el lugar que, espontáneamente, han concedido a Twitter los empleados que voluntariamente han decidido integrarla en sus prácticas profesionales. En este sentido, se distinguen cuatro tipos de usos: los relativos al ámbito laboral, la apertura y transmisión de aspectos de su trabajo, la difusión de intenciones y las preguntas dirigidas a las masas. 

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Vers une conceptualisation des affordances collaboratives de Twitter

D’un point de vue théorique, les chercheurs s’inscrivant dans une perspective de la pratique (Corradi, Gherardi et Verzelloni, 2008) – et plus particulièrement ceux qui adoptent les approches de la sociomatérialité  – envisagent les pratiques de travail en tant que configurations sociomatérielles devant être étudiées comme telles (Orlikowski et Scott, 2008) et considèrent que les changements organisationnels et technologiques sont mutuellement constitutifs et inter-reliés (Leonardi, 2009). En ce sens, le concept d’affordance (Gibson, 1979) a été mobilisé par Leonardi pour décrire les contraintes et possibilités inhérentes aux propriétés matérielles des outils numériques dans les organisations (Leonardi, 2010). Treem et Leonardi (2012) proposent d’ailleurs de développer une perspective « relationnelle » des affordances pour décrire la relation entre organisation et médias sociaux.  Celle-ci permet de ne pas se borner à décrire seulement les fonctionnalités des outils et les usages qu’elles permettent pour se tourner vers les actes communicationnels rendus possibles par les médias sociaux. L’application du concept d’affordance avait d’ailleurs été habilement illustrée par Zammuto et al. (2007) qui ont traité de l’influence de la technologie sur les processus organisationnels de façon non-déterministe, en montrant comment certains agencements sociomatériels offrent de nouvelles possibilités « d’actions organisantes ».

"Les prochaines étapes de notre recherche seront consacrées à l’exploration et au raffinement de ces (...)

Dans le même ordre d’idée, nous croyons que les usages informels que nous avons décrits tirent profit des spécificités matérielles et communicationnelles de la plateforme de microblogage étudiée et favorisent la mise en lumière de ses affordances collaboratives. Nous souhaitons ici apporter notre modeste contribution au développement du concept d’affordance en soulignant plus particulièrement les potentialités collaboratives de Twitter.  Le tableau qui suit reprend les exemples relatés dans cet article pour permettre ultérieurement le développement d’une réflexion sur les liens entre ces différentes dimensions et propose quelques hypothèses relatives aux conséquences organisationnelles possibles.7

- See more at: http://ticetsociete.revues.org/1330#sthash.KAZ4Gnms.dpuf

Kurzweil was attracted not just by Google’s computing resources but also by the startling progress the company has made in a branch of AI called deep learning. Deep-learning software attempts to mimic the activity in layers of neurons in the neocortex, the wrinkly 80 percent of the brain where thinking occurs. The software learns, in a very real sense, to recognize patterns in digital representations of sounds, images, and other data.

The basic idea—that software can simulate the neocortex’s large array of neurons in an artificial “neural network”—is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, computer scientists can now model many more layers of virtual neurons than ever before.

With this greater depth, they are producing remarkable advances in speech and image recognition. Last June, a Google deep-learning system that had been shown 10 million images from YouTube videos proved almost twice as good as any previous image recognition effort at identifying objects such as cats. Google also used the technology to cut the error rate on speech recognition in its latest Android mobile software. In October, Microsoft chief research officer Rick Rashid wowed attendees at a lecture in China with a demonstration of speech software that transcribed his spoken words into English text with an error rate of 7 percent, translated them into Chinese-language text, and then simulated his own voice uttering them in Mandarin. That same month, a team of three graduate students and two professors won a contest held by Merck to identify molecules that could lead to new drugs. The group used deep learning to zero in on the molecules most likely to bind to their targets.

In the US, the Central Intelligence Agency (CIA) and other non-military spy agencies will get $48bn next year. The UK is spending around $3.6bn on its spy agencies. And China's spending on "internal security" was $111bn last year.

Security services, and the private contractors they use, are trawling for data which would, in theory, make us safer. But the idea that the likes of Google, Facebook, Microsoft and Apple gave backdoor access to the NSA to snoop on users' data, has concerned many people.

They all deny it, but still, it begs the question of what your data is really worth - given that most people simply hand over their photos, personal information, and documents to such websites without giving it a second thought.

The data industry in the US alone is worth $300bn a year and it employs some three million people. That is coupled with the online sales industry, which generates $10tn per year.

All of this is managed by the more than 2,000 so-called 'data broker companies'. Acxiom Corp is the biggest - with a profit of $77mn in 2011, coming from the largest commercial consumer database with 500 million active users worldwide.

Theodore Berger, a biomedical engineer and neuroscientist at the University of Southern California in Los Angeles, envisions a day in the not too distant future when a patient with severe memory loss can get help from an electronic implant. In people whose brains have suffered damage from Alzheimer’s, stroke, or injury, disrupted neuronal networks often prevent long-term memories from forming. For more than two decades, Berger has designed silicon chips to mimic the signal processing that those neurons do when they’re functioning properly—the work that allows us to recall experiences and knowledge for more than a minute. Ultimately, Berger wants to restore the ability to create long-term memories by implanting chips like these in the brain.

The idea is so audacious and so far outside the mainstream of neuroscience that many of his colleagues, says Berger, think of him as being just this side of crazy. “They told me I was nuts a long time ago,” he says with a laugh, sitting in a conference room that abuts one of his labs. But given the success of recent experiments carried out by his group and several close collaborators, Berger is shedding the loony label and increasingly taking on the role of a visionary pioneer.

Derived from the Latin Privatus, meaning “withdraw from public life,” the notion of privacy has been foundational to the development of our diverse societies, forming the basis for individuals' rights such as free speech and religious freedom1. Despite its importance, privacy has mainly relied on informal protection mechanisms. For instance, tracking individuals' movements has been historically difficult, making them de-facto private. For centuries, information technologies have challenged these informal protection mechanisms. In 1086, William I of England commissioned the creation of the Doomsday book, a written record of major property holdings in England containing individual information collected for tax and draft purposes2. In the late 19th century, de-facto privacy was similarly threatened by photographs and yellow journalism. This resulted in one of the first publications advocating privacy in the U.S. in which Samuel Warren and Louis Brandeis argued that privacy law must evolve in response to technological changes3.

Modern information technologies such as the Internet and mobile phones, however, magnify the uniqueness of individuals, further enhancing the traditional challenges to privacy. Mobility data is among the most sensitive data currently being collected. Mobility data contains the approximate whereabouts of individuals and can be used to reconstruct individuals' movements across space and time. Individual mobility traces T [Fig. 1A–B] have been used in the past for research purposes4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 and to provide personalized services to users19. A list of potentially sensitive professional and personal information that could be inferred about an individual knowing only his mobility trace was published recently by the Electronic Frontier Foundation20. These include the movements of a competitor sales force, attendance of a particular church or an individual's presence in a motel or at an abortion clinic.

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Our dataset contains 15 months of mobility data for 1.5 M people, a significant and representative part of the population of a small European country, and roughly the same number of users as the location-based service Foursquare®31. Just as with smartphone applications or electronic payments, the mobile phone operator records the interactions of the user with his phone. This creates a comparable longitudinally sparse and discrete database [Fig. 3]. On average, 114 interactions per user per month for the nearly 6500 antennas are recorded. Antennas in our database are distributed throughout the country and serve, on average, ~ 2000 inhabitants each, covering areas ranging from 0.15 km2 in cities to 15 km2 in rural areas. The number of antennas is strongly correlated with population density (R2 = .6426) [Fig. 3C]. The same is expected from businesses, places in location-based social networks, or WiFi hotspots.