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.

Summary of “Our entire credit bureau system is broken”

The hack exposed Social Security numbers, birthdays, and, in some cases, even credit cards.
It’s bad to have your Social Security number and birthday stolen because criminals can use that information to apply for credit in your name.
So why are we still using a credit system that relies on breachable data?
The credit bureau system is broken, and it’s been broken for a long time.
So along the way, credit bureaus have become an identity service, too.
The three big credit bureaus – Equifax, TransUnion, and Experian – see their customers as the businesses checking people out, not the people themselves.
There have been a few regulations aimed at fixing that – most notably the Fair Credit Reporting Act – but it’s still an extremely clunky system, and the average consumer has little awareness or control over their own profile.
Thursday’s breach should wake us up to how fundamentally broken this system is, and how urgently we need to replace it.

The orginal article.

Summary of “Email and Calendar Data Are Helping Firms Understand How Employees Work”

These sorts of analyses are helping EY, where some of us work, by working with Microsoft Workplace Analytics to help clients predict the likelihood of retaining key talent following an acquisition and to develop strategies to maximize retention.
Using email and calendar data, we can identify patterns around who is engaging with whom, which parts of the organization are under stress, and which individuals are most active in reaching across company boundaries.
Using data science to predict how people in companies are changing may sound futuristic.
These sorts of analyses are helping EY, where some of us work, by working with Microsoft Workplace Analytics to help clients to predict the likelihood of retaining key talent following an acquisition and to develop strategies to maximize retention.
Understandably, there may be privacy concerns about examining an individual’s email or calendar, even in a work context.
We used an analysis of anonymized email and calendar data to predict what impact the number of direct reports a manager had on the ability of specific teams to collaborate.
We will always need professional change managers to interpret this data and to design the right sorts of ways to work with employees during transformation or external emergencies, such as the travel ban.
What these data science tools can do is make our responses faster and more targeted and tell us what worked in a faster, more reliable, and less invasive way than was previously achievable.

The orginal article.

Summary of “Business models will drive the future of autonomous vehicles”

Over the last year, we have seen many groundbreaking announcements regarding autonomous cars, from companies like Ford promoting its autonomous vehicle leader to the position of CEO, to Tesla’s NHSTA investigation showing a 40 percent decrease in accidents with Autopilot enabled and Audi beginning mass-market sales of a “Level 3” autonomous car.
How will autonomous cars make ethical decisions, as in the case of the “Trolley problem”? How will cities, streets and parking change? What will happen to the millions of people employed as ridesharing drivers or long-haul truck drivers? What is the right package of sensors to drive autonomous vehicles?
We believe that many of the open questions about autonomous vehicles will be answered not just by technological innovation but by the emerging business models around autonomous vehicles.
If regulators decide to tax autonomous vehicles based on miles traveled within a city, there will be different incentives for vehicles to stay close by to maximize trips and minimize costs.
There is no better indicator for how companies will make decisions across many technology, business and societal questions than their underlying business models and profit motives.
Now, automakers and technology companies are all in a race to build the software that will drive autonomous vehicles, but it’s unclear how those companies will monetize their software.
It is likely that several models will emerge for monetizing the operating system layer for vehicles, and these models will deeply impact how different companies invest in R&D, marketing, lobbying and operations.
At the same time, autonomous vehicles will be generating terabytes of data each day from cameras, radar, lidar, sonar, GPS and other sensors that can be used to further improve the cars’ driving models, a city’s traffic planning or a ridesharing company’s route optimization algorithms.

The orginal article.

Summary of “We need to nationalise Google, Facebook and Amazon. Here’s why”

Ello’s rapid rise and fall is symptomatic of our contemporary digital world and the monopoly-style power accruing to the 21st century’s new “Platform” companies, such as Facebook, Google and Amazon.
The platform – an infrastructure that connects two or more groups and enables them to interact – is crucial to these companies’ power.
Platforms, as spaces in which two or more groups interact, provide what is in effect an oil rig for data.
Every interaction on a platform becomes another data point that can be captured and fed into an algorithm.
At the heart of platform capitalism is a drive to extract more data in order to survive.
Facebook is a master at using all sorts of behavioural techniques to foster addictions to its service: how many of us scroll absentmindedly through Facebook, barely aware of it?
Others have simply bought up smaller companies: Facebook has swallowed Instagram, WhatsApp, and Oculus, while investing in drone-based internet, e-commerce and payment services.
All the dynamics of platforms are amplified once AI enters the equation: the insatiable appetite for data, and the winner-takes-all momentum of network effects.

The orginal article.

Summary of “To Survive in Tough Times, Restaurants Turn to Data-Mining”

Newer companies now aspire to eliminate the need for translation, to create an analytics program that integrates all aspects of a restaurant’s operations into one system, with one password, in real time with mobile access, said Shu Chowdhury, the chief executive of a start-up called Salido, based in SoHo.
These new tools make a paradoxical promise: that they can take restaurants back to the good old days, before the business grew so big.
“The goal,” Mr. Oberholtzer said, “Is to leverage the technology to do what we would do if we had one little restaurant and we were there all the time and knew every customer by name.”
Mr. Oberholtzer and his two partners opened the first of two dozen cafeteria-style restaurants in Culver City, Calif., in 2006, and plan to open an equal number in the Northeast by 2020.
In June 2015, the online reservation service OpenTable, which represents 43,000 restaurants worldwide, started to provide customized recommendations, just as Netflix and Amazon suggest programs or products based on a customer’s history.
If data can help a customer find a restaurant, it can also help a restaurant find its customers.
The basic point-of-sale part of the program is already in use in New York at Made Nice, the new fast-casual restaurant from the chef Daniel Humm and Will Guidara, owners of Eleven Madison Park and the NoMad, and at Jean-George Vongerichten’s ABC Kitchen.
The sheer glut of new restaurant data systems can be overwhelming, even to those who embrace them.

The orginal article.

Summary of “Here’s What Hackers Don’t Want You to Know”

Segmented networks can protect sensitive information even when a hacker penetrates the exterior of the network.
In the new zero-trust network, there is a network of firewalls within the network, and security measures are taken to protect each one of them.
According to an NIST/Forrester report, “The Zero Trust Model is simple: cybersecurity professionals must stop trusting packets as if they were people. Instead, they must eliminate the idea of a trusted network and an untrusted network. In Zero Trust, all network traffic is untrusted.” This includes both insider and outsider data access, which should be treated as suspect and secured.
“According to the Department of Homeland Security,”Proper network segmentation is a very effective security mechanism to prevent an intruder from propagating exploits or laterally moving around an internal network.
On a poorly segmented network, intruders are able to extend their impact to control critical devices or gain access to sensitive data and intellectual property.
“Segregation separates network segments based on role and functionality. A securely segregated network can contain malicious occurrences, reducing the impact from intruders, in the event that they have gained a foothold somewhere inside the network.”
Your CSO has to ensure that everyone who has access to the network only has access to what they need and nothing more.
You have to have someone whose dedicated job is to maintain the security in your network.

The orginal article.

Summary of “3 Ways Companies Are Building a Business Around AI”

Many other companies, including Microsoft and Amazon, also already offer AI tools which, like Google Cloud, where I work, will be sold online as cloud computing services.
AI typically works by crunching very large amounts of data to figure out telltale patterns, then testing provisional patterns against similar data it hasn’t yet processed.
“There were lots of pictures in big agricultural universities, but no one had the information well-tagged. Seed companies had pictures too, but no one had pictures of healthy corn, corn with early NCLB, corn with advanced NCLB.”.
TalkIQ is a company that monitors sales and customer service phone calls, turns the talk into text, and then scans the words in real time for keywords and patterns that predict whether a company is headed for a good outcome – a new sale, a happy customer.
The company got its start after Jack Abraham, a former eBay executive and entrepreneur, founded ZenReach, a Phoenix company that connects online and offline commerce, in part through extensive call centers.
“Why does one rep close 50% of his calls, while the other gets 25%?”. The data from those calls could improve performance at ZenReach, he realized, but could also be the training set for a new business that served other companies.
The product went into commercial release in January, and according to Abraham now has 27 companies paying for the service.
Blinker has filed for patents on a number of the things it does, but the company’s founder and chief executive thinks his real edge is his 44 years in the business of car dealerships.

The orginal article.

Summary of “Silicon Valley siphons our data like oil. But the deepest drilling has just begun”

Amazon isn’t abandoning online retail for brick-and-mortar.
To increase profits, Silicon Valley must extract more data.
Amazon is going to show the industry how to monitor more moments: by making corporate surveillance as deeply embedded in our physical environment as it is in our virtual one.
This data holds valuable lessons about your personality and your preferences – lessons that Amazon will use to sell you more stuff, online and off.
Putting a listening device in your living room is an excellent way for Amazon to learn more about you.
Amazon is likely to face some resistance as it colonizes more of our lives.
It’s worth considering what further concessions will come to feel normal in the next 20 years, as Silicon Valley is forced to dig deeper into our lives for data.
Companies like Google and Facebook and Amazon dominate the digital sphere – you can’t avoid them.

The orginal article.

Summary of “Winner-takes all effects in autonomous cars”

There are now several dozen companies trying to make the technology for autonomous cars, across OEMs, their traditional suppliers, existing major tech companies and startups.
Rather, the place to look is not within the cars directly but still further up the stack – in the autonomous software that enables a car to move down a road without hitting anything, in the city-wide optimisation and routing that mean we might automate all cars as a system, not just each individual car, and in the on-demand fleets of ‘robo-taxis’ that will ride on all of this.
On-demand robo-taxi fleets will dynamically pre-position their cars, and both these and quite possibly all other cars will co-ordinate their routes in real time for maximum efficiency, perhaps across fleets, to avoid, for example, all cars picking the same route at the same time.
Clearly, some people hope there will be leverage across layers, or perhaps bundling – Tesla says that it plans to forbid people from using its autonomous cars with any on-demand service other than its own.
You survey the road in advance, process all the data at leisure, build a model of the street and then put it onto any car that’s going to drive down the road. The autonomous car doesn’t now have to process all that data and spot the turning or traffic light against all the other clutter in real-time at 65 miles an hour – instead it knows where to look for the traffic light, and it can take sightings of key landmarks against the model to localise itself on the road at any given time.
The more cars you sell the better all of your cars are – the definition of a network effect.
Just as for maps, the more cars you sell the better all of your cars are – the definition of a network effect.
The network effects – the winner-takes-all effects – are in data: in driving data and in maps.

The orginal article.