Data Science newsletter – October 13, 2016

“As anyone who follows election polling can tell you, when you survey 1,000 people, the margin of error is plus or minus three percentage points. This roughly means that 95 percent of the time, the survey estimate should be within three percentage points of the true answer.”

“If 54 percent of people support Hillary Clinton, the survey estimate might be as high as 57 percent or as low as 51 percent, but it is unlikely to be 49 percent. This truism of modern polling, heralded as one of the great success stories of statistics, is included in textbooks and taught in college classes, including our own.”

“But the real-world margin of error of election polls is not three percentage points. It is about twice as big.”

Welcome to “R Views”, the new R Community blog from RStudio. For this first post, I sat down with J.J. Allaire, RStudio’s founder and CEO, to discuss RStudio’s history, its mission and JJ’s vision for its future. In a short time, we touched on a wide range of subjects including RStudio’s business, the growth of the R language, the importance of the R Consortium to the R Community and J.J.’s advice to anyone coming to R for the first time.

Facebook, Twitter, and Instagram supplied police in Ferguson and Baltimore with data that was used to track minorities, according to an investigation by the American Civil Liberties Union. But such snooping is increasingly inevitable, unless our use of social media changes.

Elite teams from seven universities won finalist slots in the ninth annual Embedded Security Challenge (ESC). The oldest and largest hardware hacking competition in the world, ESC is also the most difficult event at New York University Tandon School of Engineering’s annual Cyber Security Awareness Week (CSAW) games for students.

[David] Wild has seen the program explode from just an idea to more than 550 students since January 2015. … The program has proven popular with students thanks to flexible degree options. Students can complete their Master of Science in data science on the Bloomington campus, exclusively online, or a through a hybrid of completing half of the degree online and half in residence. They also can earn an online certificate.

For clinical purposes, next-generation sequencing (NGS) has all but replaced its methodological predecessor, Sanger sequencing. It is faster. It is cheaper. But is next-gen sequencing alone sensitive and specific enough to catch every difficult-to-detect, disease-associated variant while avoiding false-positives?

“There is significant debate within the diagnostics community regarding the necessity of confirming NGS variant calls by Sanger sequencing, considering that numerous laboratories report having 100% specificity from the NGS data alone,” Ambry Genetics Chief Executive Officer Aaron Elliott and colleagues wrote in a study published last week (October 6) in The Journal of Molecular Diagnostics.

The basic issue is that human law talks about human activities, and (unlike, say, for the mechanics of particles) we don’t have a general formalism for describing human activities. When it comes to talking about money, for example, we often can be precise. And as a result, it’s pretty easy to write a very formal contract for paying a subscription, or determining how an option on a publicly traded stock should work.

But what about all the things that typical legal contracts deal with? Well, clearly we have one way to write legal contracts: just use natural language (like English). It’s often very stylized natural language, because it’s trying to be as precise as possible. But ultimately it’s never going to be precise. At the lowest level it’s always going to depend on the meanings of words, which for natural language are effectively defined just by the practice and experience of the users of the language.

A recent Stanford University report told us not to fear artificial intelligence. What it didn’t tell us was whether we should fear its legal implications.

In her new role, Professor van der Schaar will continue to conduct research alongside teaching and mentoring duties for graduate research students and junior colleagues. She will also interact and collaborate with Man AHL and its team of machine learning and data science researchers.

China’s Huawei announced that it would form a research partnership with UC Berkeley focused on AI, and fund it to the initial tune of $1 million.

Professor Philip Torr joins the company, which wants to introduce artificial intelligence and machine learning techniques to the autonomous vehicle industry, as CSA in a co-founding role. He leads the University of Oxford’s Torr Vision group, a global, state-of-the-art research team comprising 25 post-doctoral and PhD students doing pioneering work in the field of machine learning for computer vision.

A new report from the Administration focuses on the opportunities, considerations, and challenges of Artificial Intelligence (AI).

The National Science Foundation has begun filling in the details of its national plan for big data research. Less than a year ago the NSF introduced funding for four regional hubs focused on data science innovation, led by consortia of research universities. Now, the federal agency has announced $11 million in planning activities and awards to 10 “big data spokes” projects in areas identified as important by each of those hubs. Similar to the hubs, the spokes are expected to serve in a “convening and coordinating” role; however, while the hubs extend broadly into multiple fields, each spoke will reflect a specific mission.

For example, one spoke will work on developing a new standardized modular data-sharing license and a platform that makes data sharing easier and helps to enforce the license. Partners on that project include researchers from a team of institutions — Brown University, MIT and Drexel — along with publishers Elsevier and Thomson Reuters; technology companies Intel, Microsoft Research and Oracle; as well as Rhode Island Hospital.

Google’s sister company Sidewalk Labs wants to insert itself into America’s urban transportation infrastructure. Here are the documents that show how it could happen.

Deadline for women-run start-ups to submit applications is Monday 21 November 2016.

Deadline to submit proposals is Friday, 9 December 2016.

Deadline to apply is Wednesday, January 18, 2017.

New York University’s Center for Data Science is at the cutting edge of fields with revolutionary implications such as machine learning, natural language processing, computer vision and intelligent machines.

Because computing speed is critical to accelerating experimentation and advancing research, the center’s Computational Intelligence, Learning, Vision and Robotics (CILVR) lab recently acquired a NVIDIA DGX-1 AI supercomputer to fuel this work like never before.

Julia 0.5 is a pivotal release: improvements to functional programming, comprehensions, generators, arrays, strings, and more.

Just 358 lines of code to make Python speak SQL

We’ve used the npm client successfully at Facebook for years, but as the size of our codebase and the number of engineers grew, we ran into problems with consistency, security, and performance. After trying to solve for each issue as it came up, we set out to build a new solution to help us manage our dependencies more reliably. The product of that work is called Yarn — a fast, reliable, and secure alternative npm client.

individual talks with links

At the KDD 2016 conference last October, a team from Microsoft presented a tutorial on Scalable R on Spark, and made all of the materials available on Github.