NYU Data Science newsletter – May 13, 2016

GROUP CURATION: N/A

Beginning January 2014, Psychological Science gave authors the opportunity to signal open data and materials if they qualified for badges that accompanied published articles. Before badges, less than 3% of Psychological Science articles reported open data. After badges, 23% reported open data, with an accelerating trend; 39% reported open data in the first half of 2015, an increase of more than an order of magnitude from baseline. There was no change over time in the low rates of data sharing among comparison journals. Moreover, reporting openness does not guarantee openness. When badges were earned, reportedly available data were more likely to be actually available, correct, usable, and complete than when badges were not earned. Open materials also increased to a weaker degree, and there was more variability among comparison journals. Badges are simple, effective signals to promote open practices and improve preservation of data and materials by using independent repositories.

I’d like to introduce you to Emil Kirkegaard, a self-described “polymath” at the University of Aarhus who has neatly managed to tie every single way to be irresponsible and unethical in academic publishing into a single research project. This is going to be a bit long, so here’s a TL;DR: linguistics grad student with no identifiable background in sociology or social computing doxes 70,000 people so he can switch from publishing pseudoscientific racism to publishing pseudoscientific homophobia in the vanity journal that he runs.

Yeah, it’s just as bad as it sounds.

A key question that arises is that if Palantir is having trouble trying to scale the business, why might that be? The Buzzfeed article doesn’t contain any answers but in this post I will attempt to speculate.

The real message from the Buzzfeed article goes beyond just Palantir and is highly relevant to the data science world. It ultimately comes down to the question of what is the value of data analysis?, and secondarily, how do you communicate that value?

Microsoft had a strong presence at the recently concluded Hannover Messe 2016. We had a chance to interact with visitors of many backgrounds and from all over the world – a great experience for us and the rest of our team. Many business decision makers, data scientists, IT professionals, researchers and students were in attendance. There was a very high level of interest around intelligent solutions involving machine learning in areas such as forecasting, predictive maintenance and energy optimization and many others, including some very unique solutions to problems in the world of audio and video.

The Defense Department’s attempt to incorporate Silicon Valley innovation now is entering its second phase, which is defined by major organizational changes. The Defense Innovation Unit-Experimental, or DIUx, that was established in the California technology hub last year will be joined by an equivalent office in Boston as part of a new nationwide thrust. Known as DIUx 2.0, this new approach will feature broader focus and expanding funding.

France Córdova, the director of the National Science Foundation in Arlington, Virginia, has unveiled a research agenda intended to shape the agency’s next few decades and win over the next U.S. president and Congress.

The nine big ideas (see list below) illustrate how increased support for the type of basic research that NSF funds could help answer pressing societal problems, she says, ranging from how humans interact with technology to how climate change in the polar regions will impact the global economy, environment, and culture.

As chips become smaller and more powerful, and as new ways to accelerate deep learning are discovered, it’s not just large data centers that can run the “artificial intelligence” in your devices, but also small embedded chips can be put into anything from IoT devices to self-driving cars.

A “mini track” at the Hawaii International Conference on System Sciences, (HICSS-50)
January 4-7, 2017, Hilton Waikoloa Village, Hawaii

We solicit papers on how human learning takes place via interactive and social processes enabled or supported by digital and social media. We seek to bridge disciplines and research communities between system and learning sciences, so within this scope a broad range of research questions, learning settings, and theoretical and methodological traditions will be considered. Contributions may include new design approaches, theoretical perspectives, learning analytic techniques, policy implications and/or other research results relating to the relationship between digital and social media and learning.

Deadline for papers is Wednesday, June 15.

As data scientists, we inhabit an ever-changing landscape of languages, packages, and frameworks. Given that it seems like something new pops up every day, it can be easy to succumb to tool fatigue. If this sounds familiar, you may have missed the increasing popularity of Linux containers in the DevOps world, in particular the rise of Docker. In the talk, I showcase Docker’s many benefits to the data scientist, from making data science code and environments more portable and shareable to making the transition from development to production more seamless to giving data scientists a common basis for collaborating with software engineers. [video, 42:49]

At Google, we spend a lot of time thinking about how computer systems can read and understand human language in order to process it in intelligent ways. Today, we are excited to share the fruits of our research with the broader community by releasing SyntaxNet, an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding (NLU) systems. Our release includes all the code needed to train new SyntaxNet models on your own data, as well as Parsey McParseface, an English parser that we have trained for you and that you can use to analyze English text.

We currently use OCaml in an advanced financial derivatives risk management application delivered through the Bloomberg Terminal. Because Bloomberg heavily uses JavaScript to deliver much of what makes the Bloomberg Terminal so unique, we decided to research and prototype a novel way to integrate OCaml in the JavaScript ecosystem.

The result is BuckleScript – which provides a unique backend for OCaml. We believe it has a lot to offer both the OCaml and JavaScript communities, and we are excited about sharing this project with the open source community.