NYU Data Science newsletter – October 7, 2015

GROUP CURATION: N/A

“Machine learning connects supercomputing to consumers; its impact will be felt everywhere.”

So began Pradeep Dubey, an Intel Fellow and director of the Intel Parallel Computing Lab as he outlined key facets of deep learning, a special form of machine learning, and how new technology from the chipmaker is aimed at enabling a brilliant future for new applications that spans the full gamut of HPC, enterprise, and consumer end users.

The Data-Pop Alliance recently released a “working draft” of a white-paper I co-authored: Beyond Data Literacy: Reinventing Community Engagement and Empowerment in the Age of Data. The paper is a collaboration with folks there, and at Internews, and attempts to put the nascent term “data literacy” in historical context and project forward to future uses and the role of data in culture and community.

Research studies show that social media may be valuable tools in the disease surveillance toolkit used for improving public health professionals’ ability to detect disease outbreaks faster than traditional methods and to enhance outbreak response. A social media work group, consisting of surveillance practitioners, academic researchers, and other subject matter experts convened by the International Society for Disease Surveillance, conducted a systematic primary literature review using the PRISMA framework to identify research, published through February 2013, answering either of the following questions:

Can social media be integrated into disease surveillance practice and outbreak management to support and improve public health?

Can social media be used to effectively target populations, specifically vulnerable populations, to test an intervention and interact with a community to improve health outcomes?

UW CSE alum and soon-to-be faculty member Kurtis Heimerl (UW CSE B.S. ’07, UC Berkeley Ph.D. ’13) co-founded startup company Endaga to help under-served communities in remote areas of the world to build small-scale, independent cellular networks that they own and run themselves. … Now, thanks to Facebook, Kurtis and the Endaga team are starting a new chapter as they work to improve connectivity for people everywhere.

… So it’s safe to ask, before this great wave of artificial intelligence arrives and we become fully integrated with it, shouldn’t we strive to change our inherited mental models and let go of antiquated thinking patterns?

This question was in essence posed to Ray Kurzweil at a session during the 2015 Graduate Studies Program at Singularity University. [video, 3:10]

It’s my pleasure today to announce the first class of Mozilla Fellows for Science. The folks chosen are representative of the change we want to see in the community, championing openness, collaboration, and mentorship in science. Over the next ten months, the Fellows will work on projects to help their local communities engage with open data, open source software and teach forward to their peers. They will also receive training and support from Mozilla to hone their skills around open source, participatory learning, and data sharing.

Data has dollar value. Gasoline has horsepower. Extracting both takes some care.

Data: Fuel

We start with data, the gasoline of data science. Although we hear the phrase “big data” a lot, quantity isn’t everything. If it were, we would just fill up with barrels of crude oil. Just like the fuel you put in your car, data needs to be clean, concentrated, and of the right type.

The bottom line: Without good data, nothing produces any value at all.

… Today, time-based structures, flowing data—in streams, feeds, blogs—increasingly dominate the Web. Flow has become the basic organizing principle of the cybersphere. The trend is widely understood, but its implications aren’t.

A simple genetic test can stop a severe drug reaction that causes people’s skin to peel off in sheets. Why isn’t it more commonly used?

Random sample of Twitter images from 2013 labeled by GoogLeNet deep learning model as web sites and texts. We call refer to these images as “image-texts.” The category includes screen shots of text chats, other types of texts and other kinds of non-photographic images. We found that the frequencies of these images are correlated with well-being responses from Gallup surveys, and also median housing prices, incomes, and education levels.

When David Lipton, a promising economist, was finishing his graduate work at Harvard in the early 1980s, he faced one of those potentially life-changing choices. He had one job offer from the International Monetary Fund in Washington, the multinational institution that for 70 years has served as a lender of last resort and dispenser of orthodox economic advice to countries that get into financial trouble. There was also an offer of a teaching job from the University of Virginia. Unsure of which path to take, he turned for advice to an intellectually restless and charismatic assistant professor, a Frenchman named Olivier Blanchard. … As one colleague put it, Blanchard “changed the way the Fund looked at the world and the way the world looked at the Fund.” In the process, he helped the IMF pull the global economy back from the brink of another Great Depression.