Motivations for Microsoft LinkedIn Purchase

April 13, 2017

We thought the purchase was related to Microsoft’s in-context, real-time search within an Office application. However, according to BackChannel’s article, “Now We Know Why Microsoft Bought LinkedIn,” it’s all about boosting the company’s reputation. Writer Jessi Hempel takes us back to 2014, when CEO Satya Nadella was elevated to his current position. She reminds of the fiscal trouble Microsoft was having at the time, then continues:

It also had a lousy reputation, particularly in Silicon Valley, where camaraderie and collaboration are hallmarks of tech’s evolution and every major player enjoys frenemy status with its adversaries. Microsoft wasn’t a company that partnered with outsiders. It scorned the open-source community and looked down its nose at tech upstarts. In a public conversation with Marc Andreessen in October 2014, investor Peter Thiel called Microsoft a bet ‘against technological innovation.’

The write-up goes on to detail ways Nadella has turned the company around financially. According to Hempel, the LinkedIn purchase, and the installation of its founder Reid Hoffman on the board, are in an effort to boost Microsoft’s reputation. Hembel observes:

As a board member, Hoffman will be Microsoft’s ambassador in the Valley. Among a core group of constituents for whom Microsoft may not factor into conversation, Hoffman will work to raise its profile. The trickle-down effect has the potential to be tremendous as Microsoft competes for partners and talent.

See the article for more information on the relationship between the Nietzsche-quoting Nadella and the charismatic tech genius Hoffman, as well as changes Microsoft has been making to boost both its reputation and its bottom line.

Cynthia Murrell, April 13, 2017

Whose Message Is It Anyway?

April 11, 2017

Instant messaging service provider WhatsApp is in a quandary. While privacy of its users is of utmost importance to them, where do they draw the line if it’s a question of national security?

In an editorial published in The Telegraph titled WhatsApp Accused of Giving Terrorists ‘a Secret Place to Hide’ as It Refuses to Hand over London Attacker’s Messages, the writer says:“The Government was considering legislation to force online firms to take down extremist material, but said it was time for the companies to “recognise that they have a responsibility” to get their own house in order.

Apps like WhatsApp offer end-to-end encryption for messages sent using its network. This makes it impossible (?) for anyone to intercept and read them, even technicians at WhatsApp. On numerous occasions, WhatsApp, owned by Facebook, has come under fire for protecting its user privacy. In this particular incident, the London attacker Ajao used WhatsApp to send message to someone. While Soctland Yard wants access to the messages sent by the terrorist, WhatsApp says its hands are tied.

The editorial also says that social media networks are no more tech companies, rather they are turning into publishing companies thus the onus is on them to ensure the radical materials are also removed from their networks. Who ultimately will win the battle remains to be seen, but right now, WhatsApp seems to have the edge.

Vishal Ingole, April 11, 2017

Dataminr Presented to Foreign Buyers Through Illegal Means

April 4, 2017

One thing that any company wants is more profit.  Companies generate more profit by selling their products and services to more clients.  Dataminr wanted to add more clients to their roster and a former Hillary Clinton wanted to use his political connections to get more clients for Dataminr of the foreign variety.  The Verge has the scoop on how this happened in, “Leaked Emails Reveal How Dataminr Was Pitched To Foreign Governments.”

Dataminr is a company specializing in analyzing Twitter data and turning it into actionable data sets in real-time.  The Clinton aide’s personal company, Beacon Global Strategies, arranged to meet with at least six embassies and pitch Dataminr’s services.  All of this came to light when classified emails were leaked to the public on DCLeaks.com:

The leaked emails shed light on the largely unregulated world of international lobbying in Washington, where “strategic advisors,” “consultants,” and lawyers use their US government experience to benefit clients and themselves, while avoiding public scrutiny both at home and overseas.

Beacon isn’t registered to lobby in Washington. The firm reportedly works for defense contractors and cybersecurity companies, but it hasn’t made its client list public, citing non-disclosure agreements. Beacon’s relationship with Dataminr has not been previously reported.

The aide sold Dataminr’s services in a way that suggest they could be used for surveillance.  Beacon even described Dataminr as a way to find an individual’s digital footprint.  Twitter’s development agreement forbids third parties from selling user data if it will be used for surveillance.  But Twitter owns a 5% stake in Dataminr and allows them direct access to their data firehose.

It sounds like some back alley dealing took place.  The ultimate goal for the Clinton aide was to make money and possibly funnel that back into his company or get a kickback from Dataminr.  It is illegal for a company to act in this manner, says the US Lobbying Disclosure Act, but there are loopholes to skirt around it.

This is once again more proof that while a tool can be used for good, it can also be used in a harmful manner.  It begs the question, though, that if people leave their personal information all over the Internet, is it not free for the taking?

Whitney Grace, April 4, 2017

Beta-Stage Video Sharing Platform BitChute Tosses Gauntlet at YouTube

March 28, 2017

The article on ITWire titled BitChute: The First Serious YouTube Competitor? touts the new video sharing platform, BitChute. Never heard of it? Don’t feel bad, neither has anyone else, it is still in the beta stage. But the article argues that BitChute’s peer-to-peer technology may make it a serious threat to YouTube. YouTube has always had the upper hand when it came to centralized servers, especially since being acquired by Google, due to its enormous resources. The article explains how BitChute may challenge YouTube,

An example of the peer-to-peer model being used to scale up online is the creation of Skype in 2003. By 2012, Skype, the first Internet telephony application to use peer-to-peer technology, had carved out a market share of more than 30%. Not only does BitChute use different technology, its principles are clearly outlined in its FAQ, in which it is revealed that the website’s existence is in response to YouTube’s failure to cater to independent content creators.

BitChute broadcasts its disruptive intentions in the FAQs, setting up a David and Goliath archetype. YouTube’s strike system, which goes by the honor code more than anything else, alongside its history of demonetization of advertisements, plays directly into the hands of a company like BitChute. The startup calls for freedom of expression, decentralization, and customized pairings for monetization.

Chelsea Kerwin, March 28, 2017

The Human Effort Behind AI Successes

March 14, 2017

An article at Recode, “Watson Claims to Predict Cancer, but Who Trained It To Think,” reminds us that even the most successful AI software was trained by humans, using data collected and input by humans. We have developed high hopes for AI, expecting it to help us cure disease, make our roads safer, and put criminals behind bars, among other worthy endeavors. However, we must not overlook the datasets upon which these systems are built, and the human labor used to create them. Writer (and CEO of DaaS firm Captricity) Kuang Chen points out:

The emergence of large and highly accurate datasets have allowed deep learning to ‘train’ algorithms to recognize patterns in digital representations of sounds, images and other data that have led to remarkable breakthroughs, ones that outperform previous approaches in almost every application area. For example, self-driving cars rely on massive amounts of data collected over several years from efforts like Google’s people-powered street canvassing, which provides the ability to ‘see’ roads (and was started to power services like Google Maps). The photos we upload and collectively tag as Facebook users have led to algorithms that can ‘see’ faces. And even Google’s 411 audio directory service from a decade ago was suspected to be an effort to crowdsource data to train a computer to ‘hear’ about businesses and their locations.

Watson’s promise to help detect cancer also depends on data: decades of doctor notes containing cancer patient outcomes. However, Watson cannot read handwriting. In order to access the data trapped in the historical doctor reports, researchers must have had to employ an army of people to painstakingly type and re-type (for accuracy) the data into computers in order to train Watson.

Chen notes that more and more workers in regulated industries, like healthcare, are mining for gold in their paper archives—manually inputting the valuable data hidden among the dusty pages. That is a lot of data entry. The article closes with a call for us all to remember this caveat: when considering each new and exciting potential application of AI, ask where the training data is coming from.

Cynthia Murrell, March 14, 2017

Chipping Away at Social Content with Pictures

February 27, 2017

Analytics are catching up to content. In a recent ZDNet article, Digimind Partners with Ditto to Add Image Recognition to Social Media Monitoring, we are reminded images reign supreme on social media. Between Pinterest, Snapchat and Instagram, messages are often conveyed through images as opposed to text. Capitalizing on this, an intelligence software company Digimind has announced a partnership with Ditto Labs to introduce image-recognition technology into their social media monitoring software called Digimind Social. We learned,

 “The Ditto integration lets brands identify the use of their logos across Twitter no matter the item or context. The detected images are then collected and processed on Digimind Social in the same way textual references, articles, or social media postings are analysed. Logos that are small, obscured, upside down, or in cluttered image montages are recognised. Object and scene recognition means that brands can position their products exactly where there customers are using them. Sentiment is measured by the amount of people in the image and counts how many of them are smiling. It even identifies objects such as bags, cars, car logos, or shoes.”

 It was only a matter of time before these types of features emerged in social media monitoring. For years now, images have been shown to increase engagement even on platforms that began focused more on text. Will we see more watermarked logos on images? More creative ways to visually identify brands? Both are likely and we will be watching to see what transpires.

 Megan Feil, February 27, 2017

 

Finding Meaning in Snapchat Images, One Billion at a Time

February 27, 2017

The article on InfoQ titled Amazon Introduces Rekognition for Image Analysis explores the managed service aimed at the explosive image market. According to research cited in the article, over 1 billion photos are taken every single day on Snapchat alone, compared to the 80 billion total taken in the year 2000. Rekognition’s deep learning power is focused on identifying meaning in visual content. The article states,

The capabilities that Rekognition provides include Object and Scene detection, Facial Analysis, Face Comparison and Facial Recognition. While Amazon Rekognition is a new public service, it has a proven track record. Jeff Barr, chief evangelist at AWS, explains: Powered by deep learning and built by our Computer Vision team over the course of many years, this fully-managed service already analyzes billions of images daily. It has been trained on thousands of objects and scenes. Rekognition was designed from the get-go to run at scale.

The facial analysis features include markers for image quality, facial landmarks like facial hair and open eyes, and sentiment expressed (smiling = happy.) The face comparison feature includes a similarity score that estimates the likelihood of two pictures being of the same person. Perhaps the most useful feature is object and scene detection, which Amazon believes will help users find specific moments by searching for certain objects. The use cases also span vacation rental markets and travel sites, which can now tag images with key terms for improved classifications.

Chelsea Kerwin, February 27, 2017

Mobile App Usage on the Rise from 34% of Consumer Time in 2013 to 50% in 2016

February 24, 2017

Bad news, Google. The article titled Smartphone Apps Now Account for Half the Time Americans Spend Online on TechCrunch reveals that mobile applications are still on the rise. Throw in tablet apps and the total almost hits 60%. Google is already working to maintain relevancy with its In Apps feature for Androids, which searches inside apps themselves. The article explains,

This shift towards apps is exactly why Google has been working to integrate the “web of apps” into its search engine, and to make surfacing the information hidden in apps something its Google Search app is capable of handling.  Our app usage has grown not only because of the ubiquity of smartphones, but also other factors – like faster speeds provided by 4G LTE networks, and smartphones with larger screens that make sitting at a desktop less of a necessity.

What apps are taking up the most of our time? Just the ones you would expect, such as Facebook, Messenger, YouTube, and Google Maps. But Pokemon Go is the little app that could, edging out Snapchat and Pinterest in the ranking of the top 15 mobile apps. According to a report from Senor Tower, Pokemon Go has gone beyond 180 million daily downloads. The growth of consumer time spent on apps is expected to keep growing, but comScore reassuringly states that desktops will also remain a key part of consumer’s lives for many years to come.

Chelsea Kerwin, February 24, 2017

 

Upgraded Social Media Monitoring

February 20, 2017

Analytics are catching up to content. In a recent ZDNet article, Digimind partners with Ditto to add image recognition to social media monitoring, we are reminded images reign supreme on social media. Between Pinterest, Snapchat and Instagram, messages are often conveyed through images as opposed to text. Capitalizing on this, and intelligence software company Digimind has announced a partnership with Ditto Labs to introduce image-recognition technology into their social media monitoring software called Digimind Social. We learned,

The Ditto integration lets brands identify the use of their logos across Twitter no matter the item or context. The detected images are then collected and processed on Digimind Social in the same way textual references, articles, or social media postings are analysed. Logos that are small, obscured, upside down, or in cluttered image montages are recognised. Object and scene recognition means that brands can position their products exactly where there customers are using them. Sentiment is measured by the amount of people in the image and counts how many of them are smiling. It even identifies objects such as bags, cars, car logos, or shoes.

It was only a matter of time before these types of features emerged in social media monitoring. For years now, images have been shown to increase engagement even on platforms that began focused more on text. Will we see more watermarked logos on images? More creative ways to visually identify brands? Both are likely and we will be watching to see what transpires.

Megan Feil, February 20, 2017

 

Penn State Research Team Uses Big Data to Explore Crime Rates

February 2, 2017

The article on E&T titled Social Media and Taxi Data Improve Crime Pattern Picture delves into a fascinating study that uses big data involving taxi routes and social media location labels from sites like Foursquare to discover a correlation between taxis, locations of interest, and crime. The study was executed by Penn State researchers who are looking for a more useful way to estimate crime rates rather than the traditional approach targeting demographics and geographic data only. The article explains,

The researchers say that the analysis of crime statistics that encompass population, poverty, disadvantage index and ethnic diversity can provide more accurate estimates of crime rates … the team’s approach likens taxi routes to internet hyperlinks, connecting different communities with each other… One surprising discovery is that the data suggests areas with nightclubs tend to experience lower crime rates – at least in Chicago.  The explanation may be that it reflects people’s choices to be there.

This research will be especially useful to city planners interested in how certain spaces are being used, and whether people want to go to those spaces. But the researcher Jessie Li, an assistant professor of information sciences, explained that while the correlation is clear, the underlying cause is not yet known.

Chelsea Kerwin, February 2, 2017

 

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