NYU Data Science newsletter – February 4, 2016

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A look at the process from The Mill shows how they put together the latest video for the Chemical Brothers directed by Dom&Nic, using 3D scanning and rendering to produce that ‘how the hell did they do that convincingly’ transparent one shot effect. [video, 4:06]

At first glance, Kia’s Super Bowl advertisement is standard big-game fare: a well-known celebrity, Christopher Walken, will try to hawk a midsize sedan to more than a hundred million viewers hooked to their televisions.

But for its seventh Super Bowl appearance, the auto maker is hoping to add some extra star power by employing some unlikely assistance. IBM’s Watson will help identify “social media influencers” who can buoy Kia’s message before and during the 60-second spot.

Answers by Pedro Domingos, Professor at the University of Washington and author of The Master Algorithm, on Quora.

Q: What do you think of the machine learning research that is happening in industry vs. academia?

A:Academic research is more theoretical and long-term; industrial research is more applied and short-term. If you want to have impact in a few months’ time, industry is the place for you. If you want to work on the deep problems and have a shot at very high impact, go to academia. These days I often hear things like “Why do machine learning in academia when industry has way more resources and way more people working on the same problems?” I think this is a red herring. Researchers in industry are under constant pressure, explicit on implicit, to contribute to the company’s bottom line, and that’s understandable. But solving the deep problems is more important than ever, precisely because of how pervasive machine learning has become, and academia is the best place to do it.

This past week I went to the Rework Deep Learning conference. It was a good two days of talks by both top researchers in DL and companies applying DL. I made summary notes for the talks divided them by ‘Research’ and ‘Companies’, within these two categories they are simply in order of who talked first in the following sections. There was also a Q&A with Andrew Ng which I stuffed under ‘Research’.

The research talks were an excellent line up. All presented on recent work, though if you keep up with the literature, they should be familiar. I won’t mention any in particular here because I think their summaries are all worth reading if you are not yet familiar.

Also: Team Rework provides its Storify summary of the conference.

The Ebola virus disease epidemic in West Africa is the largest on record, responsible for over 28,599 cases and more than 11,299 deaths. Genome sequencing in viral outbreaks is desirable to characterize the infectious agent and determine its evolutionary rate. Genome sequencing also allows the identification of signatures of host adaptation, identification and monitoring of diagnostic targets, and characterization of responses to vaccines and treatments. The Ebola virus (EBOV) genome substitution rate in the Makona strain has been estimated at between 0.87?×?10?3 and 1.42?×?10?3 mutations per site per year. This is equivalent to 16–27 mutations in each genome, meaning that sequences diverge rapidly enough to identify distinct sub-lineages during a prolonged epidemic. Genome sequencing provides a high-resolution view of pathogen evolution and is increasingly sought after for outbreak surveillance. Sequence data may be used to guide control measures, but only if the results are generated quickly enough to inform interventions8. Genomic surveillance during the epidemic has been sporadic owing to a lack of local sequencing capacity coupled with practical difficulties transporting samples to remote sequencing facilities. To address this problem, here we devise a genomic surveillance system that utilizes a novel nanopore DNA sequencing instrument. In April 2015 this system was transported in standard airline luggage to Guinea and used for real-time genomic surveillance of the ongoing epidemic.

… Institutes across Stanford support similarly interdisciplinary approaches to solving many of the grand challenges we face today in environmental research, security, economic policy and energy. Technology like virtual reality (above) is being applied to environmental research, questions of empathy, and athletics.

We’ve collected many of these stories and videos of boundary-crossing research on a new website that chronicles the results of venturing outside departmental silos.

The IK Prize celebrates digital creativity in all its forms. Awarded annually for an idea that proposes an innovative application of digital technology, the winning project will enable the public to experience art on display at Tate Britain and on our website in exciting new ways.

Tate invites entries from creative practitioners around the world in response to the subject of artificial intelligence.

Deadline for submissions is Sunday, February 7.

The Computer Science and Engineering (CSE) department, part of the NYU Tandon School of Engineering in New York University (NYU), and the Center for Data Science (CDS) at NYU invite applications for a joint, open rank, tenure-track or tenured faculty. NYU is one of the top private universities in the United States, and the Tandon School of Engineering has the distinct history of having been known previously as Brooklyn Poly and the NYU Polytechnic School of Engineering.

Deadline for applications is Tuesday, March 15.

Microsoft has launched an online Cortana Analytics Suite challenge as part of the ImagineCup “Hello Cloud” contest.

ImagineCup, one of Microsoft’s premier student technology programs, provides a variety of challenges and opportunities for young aspirants to skill up. The Cortana Analytics and ImagineCup teams are partnering with each other to bring to you this opportunity to show-off your data science and app development skills.

Deadline for submissions is Wednesday, April 27.

To connect with NYU Center for Data Science, log in or sign up at Facebook.

Last fall, the Center for Data Science was thrilled to welcome BetterWorks for one of our career information sessions. BetterWorks is a startup trying to improve the work management and performance evaluation processes in corporate offices. If you are a working professional, chances are you might have used one of their performance evaluation tools, which keep track of your yearly goals, such as sales targets or product efficiency quotas.

I’ve been thinking quite a bit about how people learn d3.js, I don’t mean technical mastery of a useful library, I mean learning the craft enabled by a new tool. Creating data visualizations with d3.js puts the practitioner in the unique position of controlling the data, code and design all at once. With d3 in your tool belt you look at datasets in a different way, you know you can create custom tools for communication with interactive interfaces. You can bend time to your will. You can explan things that are really hard to explain with words.

So how does one acquire these powers? I asked some of the most skilled practitioners I know how they went about learning d3 and an interesting pattern emerged: start (small) projects with an idea and no idea how to implement it, and then try to implement it.

World Fertility Data 2015 presents data on age-specific fertility rates, total fertility and mean age at childbearing for 201 countries or areas of the world. The database includes data available as of November 2015 and covers the time period from 1950 to the present. Data for the time period before 1950 have been included as well, if readily available, but no systematic attempt was made to collect data prior to 1950 for all countries. The time series are available for download in Excel workbooks and are presented in online charts.