Data Science newsletter – October 6, 2016

It was 1982, and a group of computer science graduate students at Carnegie Mellon University in Pittsburgh, Pennsylvania, was thirsty for more than knowledge: some wanted a Coca Cola. But the researchers were frustrated. The Coke machine was on the third floor of the university’s Wean Hall, and oftentimes they’d venture up to the dispenser only to find it empty, or worse, full of warm soda. So the scientists connected the machine to the university’s computer network. By checking online, thirsty researchers could ensure the machine was stocked with cold bottles before visiting. This turned out to be more than an achievement in efficient caffeine delivery; it’s thought to be one of the first noncomputer objects to go online.

The infrastructure of the internet exists in real places and governments are awakening to the idea that they can use that fact to regulate what we do online. Increasingly, each country wants to control the internet in the same way they do financial transactions, immigration, or the postal system. If your data crosses enters their territory they want to be able to inspect it, count it, and—not yet, but maybe someday—tax it.

Future radio-astronomy observatories will need deep learning to find worthwhile signals in their otherwise unmanageable amounts of data; gravitational-wave detectors will use it to understand and eliminate the tiniest sources of noise; and publishers will use it to scour and tag millions of research papers and books. Eventually, some researchers believe, computers equipped with deep learning may even display imagination and creativity. “You would just throw data at this machine, and it would come back with the laws of nature,” says Jean-Roch Vlimant, a physicist at the California Institute of Technology in Pasadena.

But such advances would make the black-box problem all the more acute. Exactly how is the machine finding those worthwhile signals, for example? And how can anyone be sure that it’s right? How far should people be willing to trust deep learning?

Data science and analytics are growing at an exponential rate, as the ability to collect, track and interpret data continues to accelerate. Data scientists are trying to deploy this massive data haul to formulate more effective public policy, according to participants at Bloomberg’s annual Data for Good Exchange (D4GX) conference, which took place on Sunday, September 25, 2016 at Bloomberg HQ in New York City.

You may not realize it, but every website you visit is created, literally, the moment you arrive. Each element of the page — the pictures, the ads, the text, the comments — live on computers in different places and are sent to your device when you request them.

That means that it’s easy for companies to create different web pages for different people. Sometimes that customization is helpful, such as when you see search results for restaurants near you. Sometimes it can be creepy, such as when ads follow you around from website to website. And sometimes customization can cost you money, research has shown.

This article is an intervention in the debate on big data. It seeks to show, first, that behind the wager to make sociology more relevant to the digital there lies a coherent if essentially unstated vision and a whole stance which are more a symptom of the current world than a resolute endeavour to think that world through; hence the conclusion that the perspective prevailing in the debate lacks both the theoretical grip and the practical impulse to initiate a much needed renewal of social theory and sociology. Second, and more importantly, the article expounds an alternative view and shows by thus doing that other possibilities of engaging the digital can be pursued. The article is therefore an invitation to widen the debate on big data and the digital and a call for a more combative social theory. [full text]

When I look at where computing is heading, I see how machine learning and artificial intelligence are unlocking capabilities that were unthinkable only a few years ago. This means that the power of the software — the “smarts” — really matter for hardware more than ever before. The last 10 years have been about building a world that is mobile-first, turning our phones into remote controls for our lives. But in the next 10 years, we will shift to a world that is AI-first, a world where computing becomes universally available — be it at home, at work, in the car, or on the go — and interacting with all of these surfaces becomes much more natural and intuitive, and above all, more intelligent.

This is why we built the Google Assistant.

While technologically advanced, Samsung has plans for Viv that may not be as far-reaching as Apple’s for Siri, or Google’s strategy for its much-improved Assistant. Instead, the Korean hardware giant plans to take a more device-centric approach that aims to “revolutionize how users interact with our devices and appliances.”

“The Environmental Protection Agency (EPA) is offering up to $40,000 to two communities as part of their Smart City Air Challenge in order to help the communities create and implement plans to deploy air quality sensors and share the subsequent data.” Deadline for submissions is Friday, October 28.

New Haven We Robot fosters conversations between the people designing, building, and deploying robots and the people who design or influence the legal and social structures in which robots will operate. Deadline for submissions is Thursday, November 3.

“Today we’re excited to announce R Notebooks, which add a powerful notebook authoring engine to R Markdown. Notebook interfaces for data analysis have compelling advantages including the close association of code and output and the ability to intersperse narrative with computation. Notebooks are also an excellent tool for teaching and a convenient way to share analyses.”

… I need to create a page that displays the latest activity from the users, so I just need to get the data from the REST endpoint and display it in some sort of filterable table, and update it if anything changes in the server. I was thinking maybe using jQuery to fetch and display the data?

-Oh my god no, no one uses jQuery anymore. You should try learning React, it’s 2016.

According to a recent McKinsey Global Survey of executives from a range of industries regarding their analytics activities, 86 percent of them say their organizations have been “at best only somewhat effective in meeting the primary objective of their data and analytics programs.” Moreover, one-quarter say they’ve been “ineffective.” Forrester has reported that “while 74 percent of enterprise architects aspire to be data-driven, only 29 percent say their firms are good at translating the resulting analytics into measurable business outcomes.”

We believe a key part of the problem stems from misplaced big data priorities. There’s a logical hierarchy of needs for developing a big data strategy, and it is one we would prescribe for virtually any organization: first, define the business problem you are trying to solve. Second, find the data you need. And third, assess or create the infrastructural needs for the project.

“VIA SMS Group writes the decision algorithms in the R programming language. Before using ScienceOps, VIA SMS had to rewrite these algorithms from R to the server-side language of PHP in order to deploy models into their web and mobile apps.”

nteract is an open-source, desktop-based, interactive computing application. Interactive computing applications allow individuals to create documents that contain executable code, textual content, and images and convey a computational narrative.