Summary of “Against an Increasingly User-Hostile Web”

Now, we the architects of the modern web – web designers, UX designers, developers, creative directors, social media managers, data scientists, product managers, start-up people, strategists – are destroying it.
In the guise of user-centered design, we’re building an increasingly user-hostile web.
If you’re a regular web user, consider this an appeal to demand a better web, one that respects you instead of abusing and exploiting you.
The Web was Born Open: a very brief history of the web The Modern Web: the disturbing state of the web today Track the Trackers, an Experiment: with whom websites are sharing your information Gated Communities: recentralization and closed platforms The Way Forward: open tools, technologies and services for a better web The Web was Born Open.
The web as we know it was born of a vision to create an open system that brought people and ideas together, with documents that “May reside on any computer supported by that web”.
Advances in the hyper-text transfer protocol, network infrastructure, web browsers and standards, consumer Internet access, accessible hosting and blogging platforms led to a massive democratization and adoption of the web.
Since so many websites report to the same third parties, these companies can essentially have your web history on file as you go from link-to-link, website to website.
If you want to protect yourself from predatory web marketing companies and defend the open web, there a few things you can do today at an individual level.

The orginal article.

Summary of “Where the STEM Jobs Are”

In a recent analysis, Edward Lazowska, a professor of computer science at the University of Washington, focused on the Bureau of Labor Statistics employment forecasts in STEM categories.
In the decade ending in 2024, 73 percent of STEM job growth will be in computer occupations, but only 3 percent will be in the physical sciences and 3 percent in the life sciences.
A working grasp of the principles of science and math should be essential knowledge for all Americans, said Michael S. Teitelbaum, an expert on science education and policy.
Unemployment rates for STEM majors may be low, but not all of those with undergraduate degrees end up in their field of study – only 13 percent in life sciences and 17 percent in physical sciences, according to a 2013 National Science Foundation survey.
Computer science is the only STEM field where more than half of graduates are employed in their field.
Insight Data Science Fellows Program, which has offices in New York, Boston, Seattle and Palo Alto, Calif., began its first training program five years ago and now has 900 alumni working at companies like Facebook, LinkedIn, Airbnb, Amazon and Microsoft.
Dr. Faham joined the seven-week Insight Data Science Fellows program in 2015.
Data science is distinctly different from neuroscience, Dr. Das said, but some of the tools she employs, like a machine-learning technique called artificial neural networks, do take their inspiration from the brain.

The orginal article.

Summary of “Against an Increasingly User-Hostile Web”

Now, we the architects of the modern web – web designers, UX designers, developers, creative directors, social media managers, data scientists, product managers, start-up people, strategists – are destroying it.
In the guise of user-centered design, we’re building an increasingly user-hostile web.
If you’re a regular web user, consider this an appeal to demand a better web, one that respects you instead of abusing and exploiting you.
The Web was Born Open: a very brief history of the web The Modern Web: the disturbing state of the web today Track the Trackers, an Experiment: with whom websites are sharing your information Gated Communities: recentralization and closed platforms The Way Forward: open tools, technologies and services for a better web The Web was Born Open.
The web as we know it was born of a vision to create an open system that brought people and ideas together, with documents that “May reside on any computer supported by that web”.
Advances in the hyper-text transfer protocol, network infrastructure, web browsers and standards, consumer Internet access, accessible hosting and blogging platforms led to a massive democratization and adoption of the web.
Since so many websites report to the same third parties, these companies can essentially have your web history on file as you go from link-to-link, website to website.
If you want to protect yourself from predatory web marketing companies and defend the open web, there a few things you can do today at an individual level.

The orginal article.

Summary of “How Strava, The App For Athletes, Became An App For Cities”

That includes major cities like Seattle and entire statewide groups like Florida’s Department of Transportation, but also smaller and rural areas like Rapides Parish in central Louisiana, which used Metro data to get its first comprehensive look at how people in the region bike and walk, resulting in its first bicycle and pedestrian plan.
According to Drew Robb, ‎an Infrastructure and Data Engineer who joined Strava five years ago, the new visualization required mapping 1 billion activities, or 13 trillion data points-roughly 10 terabytes of data.
Created alongside the Metro team, the map is a reflection of the way Strava has created a de facto census of walkers and riders around the world-and how, with the right data design, that census can be incredibly useful.
To understand why Metro has grown so quickly, it helps to know how planners typically collect data about walkers and cyclists.
While most transportation departments have a fairly fine-grained understanding of how cars or buses operate on their streets, walkers and cyclists are a very difficult group on which to collect granular, network-scale data.
Devaney points to Seattle as an example of a city that has taken this into account; the city compares its manual counter data to Strava’s data to observe the correlation between the two types of data.
The kind of data that Strava collects about its users isn’t so different from the data that developers or tech companies hope to one day glean from people who work and live in so-called “Smart cities,” where sensors and cameras collect big data about how people move in their cities.
Strava itself maintains a robust guide to privacy functionality-a solid user-centered UX practice that many apps don’t offer-that clearly spells out how to opt-out of sharing data with Metro, too.

The orginal article.

Summary of “These 8 Excel Timesavers Will Make You a Spreadsheet Speed Demon”

If you’ve spent any time operating a computer, you’ve encountered Microsoft Excel.
Excel has become synonymous with spreadsheets as a whole, the way Kleenex defined the facial tissue industry, and is truly the unicorn of the category.
Unlike a unicorn, almost everyone has spent some time with Excel.
Being familiar with Excel is almost a requirement in business.
Excel is genuinely one of the most popular pieces of software on the market today, but so many professionals still don’t fully understand its capabilities.
Many people dedicate a significant amount of time to learning Excel, hoping they can wrangle this majestic unicorn and make it do their bidding, which is no easy feat.
If you’re like me, any tips that can help you get the most out of Excel are like gold; I can’t get enough.
All of the processes are simple, so even those who’ve just gotten their feet wet in Excel can master these quickly.

The orginal article.

Summary of “We Asked Men and Women to Wear Sensors at Work. They Act the Same but Are Treated Very Differently”

Which raises the question: Do women and men act all that differently? We realized that there’s little to no concrete data on women’s behavior in the office.
We went in with a few hypotheses about why fewer women ended up in senior positions than men: Perhaps women had fewer mentors, less face time with managers, or weren’t as proactive as men in talking to senior leadership.
As we analyzed our data, we found almost no perceptible differences in the behavior of men and women.
Women had the same number of contacts as men, they spent as much time with senior leadership, and they allocated their time similarly to men in the same role.
In email, meeting, and face-to-face data, we found that both men and women were roughly two steps, or social connections, away from senior management.
Our analysis suggests that the difference in promotion rates between men and women in this company was due not to their behavior but to how they were treated.
If men and women are equal stakeholders in a family, they should presumably be leaving the workforce at the same rate.
To tailor a solution to a company’s specific problems, you need to seek data to answer fundamental questions such as “When are women dropping out?” and “Are women acting differently than men in the office?” and “What about our company culture has limited women’s growth?” When organizations implement a solution, they need to measure the outcomes of both behavior and advancement in the office.

The orginal article.

Summary of “Change Management Is Becoming Increasingly Data-Driven. Companies Aren’t Ready”

Data science is becoming a reality for change management, and although it may not have arrived yet, it is time for organizations to get ready.
The companies best positioned to change in the next decade will be the ones that set themselves up well now, by collecting the right kind of data and investing in their analytics capacity.
Although predictive models for change management are still a ways off, organizations can get themselves on the right path by adopting the right tools and capturing the right data.
These tools have obvious relevance to change management and can help answer questions like: Is a change being equally well received across locations? Are some managers better than others at delivering messages to employees?
Waggl.com goes further, creating an ongoing conversation with employees about a change effort, allowing change managers to tie this dialogue to the progress of initiatives they are undertaking.
These tools can already have a big impact on change programs, but the data stream they create could be even more important as we learn to build predictive models of change.
Change managers can also look beyond the confines of the enterprise for insight about the impact of change programs.
If every change leader and team member underwent psychometric testing and evaluation before the project, this data would become variables to include as you search for a causal model on what leads to successful change projects.

The orginal article.

Summary of “Exclusive: Ideo’s Plan To Stage An AI Revolution”

Today, the design consultancy Ideo announced that it has acquired Datascope, a Chicago-based data science company.
“The things we designed were relatively dumb and all of the intelligence in the relationship between us and the artifact came from the human being. Algorithms and technology are taking on their own intelligence. That’s a fundamentally new design problem. We’re designing relationships now as opposed to designing artifacts. How does the traditional discipline of design, the new discipline of data science, and the new technologies of machine learning come together to form these relationships?”.
Algorithms have always been designed, but most often by engineers and computer programmers, not by designers who have been trained in the traditional sense to respond to the needs of people.
With the acquisition, Ideo plans to further integrate data scientists into its project teams; for example, data scientists working side by side with design researchers, engineers, and interaction designers.
As a design consultancy, is using data to improve the work it performs for clients, Datascope, as a company of engineers, sees ample opportunity for learning how to better design the practice of data science, from what type of raw data is collected, how it’s collected, how algorithms are designed, and so on.
“We’ve been talking from the perspective of design, and how influences design, but this is the edge of data science, too,” Stringer says.
“When you see these negative examples of AI and data science in the press, we see them as opportunities for human-centered design,” Stringer says.
So far human-centeredness hasn’t been able to rid technology of bias, and some designers believe “Design thinking” has reached the end of its usefulness for solving systemic problems, like racial inequality.

The orginal article.

Summary of “Numerai’s Master Plan”

Numeraire Q2|17The reason we created our own cryptocurrency is because it connects directly to the first part of our master plan.
In the staking tournament, data scientists stake Numeraire on their predictions to express their confidence that their model will perform well on live data.
Including the value of the Numeraire rewards, Numerai has paid out millions of dollars, and is now the most well paying data science tournament in the world.
A data scientists on Numerai recently uploaded the 1 millionth prediction set.
Finally, Numeraire is also the beginning of part 4 of the master plan, which is to decentralize Numerai.
Numerai needs to use the predictions from our data scientists live in our hedge fund without having access to any of the algorithms that built the models.
A data scientist could build a server that automatically downloads new data from Numerai, trains a machine learning algorithm, stakes Numeraire on the set of predictions, and repeats this process forever earning more and more money and NMR for the data scientist, and adding more and more intelligence to Numerai’s hedge fund.
Compute will create an entirely new use case for Numeraire which is directly related to improving machine learning models, increasing engagement, and achieving the goals of the master plan.

The orginal article.

Summary of “Mobile phone companies appear to be providing your number and location to anyone who pays”

He found a pair of websites which, if visited from a mobile data connection, report back in no time with numerous details: full name, billing zip code, current location, and more.
Why bother with a text-based one time password if a service can verify you’re you by querying your mobile provider? It’s at least a reasonable possibility.
The main issue is that with all the legitimate mobile change events fraudsters get in For example, if you download a mobile banking app today, the bank is not sure if it is you on your new phone or someone acting as you – the fraudster only needs your bank password.
As Neustrom found out, mobile providers don’t appear to be working very hard to verify that consent.
Both sites provide demos of their functionality, pinging mobile providers for data and presenting it to you.
Where’s the text or email from the mobile provider asking you for verification? It seems that this kind of request could be made fraudulently by many means, since the providers don’t verify them in any way other than a few programmatic ones.
Without rigorous consent standards, mobile companies may as well be selling the data indiscriminately the same way they were before advocacy groups took them to task for it.
I’ve asked T-Mobile, AT&T, and Verizon whether they participate in this kind of program, providing subscriber details to anyone who pays – and who, in turn, may provide to to others.

The orginal article.