Summary of “Your Data is Being Manipulated”

At this moment, AI is at the center of every business conversation.
Governments, and researchers are obsessed with data.
We are currently seeing an evolution in how data is being manipulated.
If we believe that data can and should be used to inform people and fuel technology, we need to start building the infrastructure necessary to limit the corruption and abuse of that data - and grapple with how biased and problematic data might work its way into technology and, through that, into the foundations of our society.
In short, I think we need to reconsider what security looks like in a data-driven world.
Part 1: Gaming the SystemLike search engines, social media introduced a whole new target for manipulation.
This attracted all sorts of people, from social media marketers to state actors.
The economic and political incentives are obvious, but alongside these powerful actors, there are also a whole host of people with less-than-obvious intentions coordinating attacks on these systems.

The orginal article.

Summary of “The thinking behind Snapchat's sports and weather filters”

Snapchat isn’t a resource many turn to for weather and sports scores, but it’s spending increasing amounts of money on licensing deals to give users such information.
Michael Pachter, managing director of stock research at Wedbush Securities, estimated that the weather data cost Snap at most about $48 million a year based on assumptions of a 25-cent-per-user monthly fee and usage by 10% of people on Snapchat.
Domenic Venuto, who oversees Weather Channel apps and Weather Co.’s consumer partnerships, said he couldn’t resist the opportunity to connect with Snapchat’s young audience when Snap reached out.
Depending on factors such as location and time, Snapchat users can decorate their images with multi-day forecasts, expected arrival times during Uber trips and the score of a sports event they’re attending.
More recently, Snapchat introduced bitmoji-based filters that change by the time of day and day of week.
The 2-year-old deal with Stats took Snapchat filters to a new level, giving fans the ability to adorn photos with an up-to-the-minute virtual scoreboard.
Stats’ Kirkorsky said Snapchat does have room to be an informational resource for sports, and he expects Snap to license scores for international sports such as cricket while getting more detailed statistics for NFL, MLB and other U.S. sports.
Using software to scrutinize consumer photos and videos could help Weather Co. make more accurate predictions about air quality and storm paths, Venuto said, whether they’re from satellites, webcams or Snapchat.

The orginal article.

Summary of “An Ambitious Chinese Startup Wants To Know Everything about Your Body”

The devices will help gather, analyze, and display a crush of health data he wants to collect about himself-and, he hopes, from millions of others.
ICX wants to capture more data about your body than has ever before been possible.
“AI is how we can take all of this information and tell you things that you don’t know about your health,” says Wang.
Now Imagu’s engineers are working with counterparts at ICX to create what they call a “Virtual health brain” that will interpret the thousands of data points ICX wants to collect on each customer.
In the back of ICX’s HQ is Wang’s office, a comfortable niche with deep leather chairs and a private conference room-a business setting that is a long way from where Wang started, as an academic researcher sequencing DNA at Beijing University in the late 1990s.
Soon after his departure from BGI, Wang formed ICX, knowing he would do something with AI and health.
PatientsLikeMe, which runs a service where thousands of members discuss their various chronic diseases in online forums and provide metrics about their health and the progression of their disease, had already shown the value of careful health tracking by individuals.
They’re pursuing everything from mental health and drug discovery to lifestyle management, virtual assistants, hospital management, and medical imaging and diagnostics.

The orginal article.

Summary of “Do Tech Companies Really Need All That User Data?”

Most search engines capture user data, including IP addresses and other data that can identify a user across multiple visits.
This data then allows search companies to improve their algorithms and to personalize results for the user.
To determine whether storage of users’ personal data improves search results, Chiou and Tucker looked at how search results from Bing and Yahoo differed before and after changes in the European Commission’s rules on data retention.
In 2010 Microsoft changed its policy, and began deleting IP addresses associated with searches on Bing after six months and all data points intended to identify a user across visits after 18 months.
Even if the long-term storage of large amounts of historical data isn’t an advantage, other aspects of data collection might still benefit incumbents.
Research by Microsoft has found that user data can yield better search results.
If massive data troves are required for any decent AI search solution, then it’s likely that the industry will be dominated by existing tech behemoths, who have the capabilities to gather and analyze that much data.
Historical data may be less valuable in informing search results than fresher data, they note, and a considerable fraction of searches are so uncommon that collecting sufficient data might be impossible, even for larger companies.

The orginal article.

Summary of “The Amazing Ways Coca Cola Uses Artificial Intelligence And Big Data To Drive Success”

The Coca Cola Company is the world’s largest beverage company selling more than 500 brands of soft drink to customers in over 200 countries.
Every single day the world consumes more that 1.9 billion servings of their drinks including brands like Coca Cola as well as Fanta, Sprite, Dasani, Powerade, Schweppes, Minute Maid and others.
Coca Cola was one of the first globally-recognized brands outside of the IT market to speak about Big Data, when in 2012 their chief big data officer, Esat Sezer, said “Social media, mobile applications, cloud computing and e-commerce are combining to give companies like Coca-Cola an unprecedented toolset to change the way they approach IT. Behind all this, big data gives you the intelligence to cap it all off.”
Coca Cola is known to have ploughed extensive research and development resources into artificial intelligence to ensure it is squeezing every drop of insight it can from the data it collects.
Fruits of this research were unveiled earlier this year when it was announced that the decision to launch Cherry Sprite as a new flavor was based on monitoring data collected from the latest generation of self-service soft drinks fountains, which allow customers to mix their own drinks.
As the machines allow customers to add their own choice from a range of flavor “Shots” to their drinks while they are mixed, this meant they were able to pick the most popular combinations and launch it as a ready-made, canned drink.
Coca Cola is also looking to follow the lead of tech giants by developing something similar to their “Virtual assistant” AI bots such as Alexa and Siri.
The AI will reside in vending machines, allowing greater personalization – for example, users will be able to order their favorite blend from any vending machine, with the machine mixing it to their individual preference.

The orginal article.

Summary of “How to navigate the coming A.I. hypestorm”

Here’s what you need to know about every way-cool and-or way-creepy machine learning study that has ever been or will ever be published: Anything that can be represented in some fashion by patterns within data – any abstract-able thing that exists in the objective world, from online restaurant reviews to geopolitics – can be “Predicted” by machine learning models given sufficient historical data.
How does this study in particular go about deriving gayness from images of faces? Pretty much the same way most machine learning algorithms do any other visual recognition tasks.
Generally, to train a machine learning model – the thing that’s eventually tasked with making predictions from previously unseen observations – we take these giant matrices and add our own labels to them.
The researchers behind the current paper sourced the images used to train their machine learning model from an unnamed US dating website where members advertised their sexual orientation by specifying the gender of the partners they were seeking.
I want to pause here to emphasize how unmagical the training phase of a machine learning algorithm is.
I want to pause here to emphasize how unmagical the training phase of a machine learning algorithm is because it is endlessly black boxed in reporting about way-cool machine learning studies like this one.
Once you have values for the a’s and b’s and c’s, the coefficients or relative importance of each feature, you can go out and find some new data that describes observations your machine learning model hasn’t seen before and that is so-far unlabeled.
A machine learning model basically just looks like the first equation above, except now we have some good values for the coefficients.

The orginal article.

Summary of “The Amazing Ways Coca Cola Uses Artificial Intelligence And Big Data To Drive Success”

The Coca Cola Company is the world’s largest beverage company selling more than 500 brands of soft drink to customers in over 200 countries.
Every single day the world consumes more that 1.9 billion servings of their drinks including brands like Coca Cola as well as Fanta, Sprite, Dasani, Powerade, Schweppes, Minute Maid and others.
Coca Cola was one of the first globally-recognized brands outside of the IT market to speak about Big Data, when in 2012 their chief big data officer, Esat Sezer, said “Social media, mobile applications, cloud computing and e-commerce are combining to give companies like Coca-Cola an unprecedented toolset to change the way they approach IT. Behind all this, big data gives you the intelligence to cap it all off.”
Coca Cola is known to have ploughed extensive research and development resources into artificial intelligence to ensure it is squeezing every drop of insight it can from the data it collects.
Fruits of this research were unveiled earlier this year when it was announced that the decision to launch Cherry Sprite as a new flavor was based on monitoring data collected from the latest generation of self-service soft drinks fountains, which allow customers to mix their own drinks.
As the machines allow customers to add their own choice from a range of flavor “Shots” to their drinks while they are mixed, this meant they were able to pick the most popular combinations and launch it as a ready-made, canned drink.
Coca Cola is also looking to follow the lead of tech giants by developing something similar to their “Virtual assistant” AI bots such as Alexa and Siri.
The AI will reside in vending machines, allowing greater personalization – for example, users will be able to order their favorite blend from any vending machine, with the machine mixing it to their individual preference.

The orginal article.

Summary of “Facebook Faces a New World as Officials Rein In a Wild Web”

The scale of the Chinese government’s use of Facebook to communicate abroad offers a notable sign of Beijing’s understanding of Facebook’s power to mold public opinion.
They were finalizing plans, more than two years in the making, for WhatsApp, the messaging app Facebook had bought in 2014, to start sharing data on its one billion users with its new parent company.
A month after the new data-sharing deal started in August 2016, German privacy officials ordered WhatsApp to stop passing data on its 36 million local users to Facebook, claiming people did not have enough say over how it would be used.
The goal of European regulators, officials said, is to give users greater control over the data from social media posts, online searches and purchases that Facebook and other tech giants rely on to monitor our online habits.
As a tech company whose ad business requires harvesting digital information, Facebook has often underestimated the deep emotions that European officials and citizens have tied into the collection of such details.
On Sept. 12, Spain’s privacy agency fined the company 1.2 million euros for not giving people sufficient control over their data when Facebook collected it from third-party websites.
“Facebook simply can’t stick to a one-size-fits-all product around the world,” said Max Schrems, an Austrian lawyer who has been a Facebook critic after filing the case that eventually overturned the 15-year-old data deal.
“I prefer using Facebook because that’s where my customers are. The first thing people want to do when they buy a smartphone is to open a Facebook account.”

The orginal article.

Summary of “Only 3% of Companies’ Data Meets Basic Quality Standards”

Most managers know, anecdotally at least, that poor quality data is troublesome.
Bad data wastes time, increases costs, weakens decision making, angers customers, and makes it more difficult to execute any sort of data strategy.
The method is widely applicable and relatively simple: We instruct managers to assemble 10-15 critical data attributes for the last 100 units of work completed by their departments – essentially 100 data records.
This number, which can range from 0 to 100, represents the percent of data created correctly – their Data Quality Score.
No manager can claim that his area is functioning properly in the face of data quality issues.
Still, most find a good first approximation in the “Rule of ten,” which states that “It costs ten times as much to complete a unit of work when the data are flawed in any way as it does when they are perfect.” For instance, suppose you have 100 things to do and each costs a $1 when the data are perfect.
Bad data is a lens into bad work, and our results provide powerful evidence that most data is bad. Unless you have strong evidence to the contrary, managers must conclude that bad data is adversely affecting their work.
While some data quality issues are unfathomably complex, many yield quickly and produce outsize gains.

The orginal article.

Summary of “Comcast said he used too much data-so he opted to live without home Internet”

According to Comcast, Weaver had used up his “Courtesy months” in which a customer is allowed to exceed the data cap without penalty and would have to pay overage charges going forward unless he limited his usage or bought unlimited data.
As we detailed in a feature last year, Comcast doesn’t have a meter in each customer’s home to measure data usage.
Though Comcast lets customers check Comcast’s measurement totals online, it doesn’t provide any way for customers to verify whether the meter readings are accurate.
Comcast has admitted mistakes in some cases, but it’s nearly impossible for regular customers to challenge Comcast’s data usage claims.
Comcast CEO Brian Roberts has claimed that Internet data is just like electricity and gasoline and that customers who use more should pay more.
Weaver was using his own modem, an Arris Surfboard SB6190, but he had previously used that modem with Comcast for more than two years without any similar problems, he said.
After going a while without a home Internet service, Weaver decided to stick with just having mobile access.
Still, he recognizes that he might need a home Internet connection for work and that Comcast might end up being his only viable option.

The orginal article.