NYU Data Science newsletter – May 20, 2015

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CSE graduate student Ricardo Martin Brualla and professor Steve Seitz, in collaboration with David Gallup of Google, have pioneered a new method for creating time-lapse videos of popular or historically significant landmarks from the millions of photos posted online.

Today, the President is in Camden to talk about the promising progress that city is making in enhancing community policing. Last December, President Obama launched the Task Force on 21st Century Policing to better understand specific policing challenges and help communities identify actions they can take to improve law enforcement and enhance community engagement. Since that time, we have seen law enforcement agencies around the country working harder than ever to make the promise of community policing real.

… Can big data, open data, and real- time measurements of everything get us to such a thing as “evidence based urban design”, fact-based city planning, and an approach that thinks long-term instead of in election cycles? In other words, can Smart Cities technology, i.e. the transition from analog to digital, bring about a more holistic, system-based and scientific approach to city planning? In light of the lopsided wealth patterns of US cities, the question also becomes: Can a more scientific, data based approach address issues of equity?

The answers vary depending on whom one asks. Big business, regular citizens, or professionals. The views range from dream to nightmare.

… In today’s big data world, the focus is all about building “smart applications.” The intelligence in those apps, more often than not, doesn’t come from adding programmatic responses to the code–it comes from allowing the software itself to recognize what’s happening in the real world, how it’s different from what happened yesterday, and adjust its response accordingly.

Computers are learning to think, read, and write, says Bloomberg Beta investor Shivon Zilis. “They’re also picking up human sensory function, with the ability to see and hear (arguably to touch, taste, and smell, though those have been of a lesser focus),” she writes on her blog. “Machine intelligence technologies cut across a vast array of problem types (from classification and clustering to natural language processing and computer vision) and methods (from support vector machines to deep belief networks). All of these technologies are reflected on this landscape.”

Mad Street Den, a one-year-old artificial intelligence startup based in Chennai, started by husband and wife duo who returned to India from Silicon Valley last year has closed a $1.5 million seed round to bring technology developed in the labs to consumers in a useful way.

Anand is a Neuroscientist turned Neuromorphic Engineer from Stanford, who was a part of the team that built Neurogrid. Ashwini is a designer turned Mobile Innovation Lead at Intel Labs, driving cutting edge mobile research and working with a team of engineers, scientists, designers and social scientists to bring machine learning, sensing, image processing systems to life in the form of consumer centric products.

HIV infections continue to rise in a new generation of young, gay, bisexual and other men who have sex with men (YMSM) despite three decades of HIV prevention as well as recent availability of biomedical technologies to prevent infection. In the U.S., it is estimated that 63% of incident HIV infections in 2010 were among YMSM despite the fact that they represent a very small portion of the population. Given this heightened risk for HIV seroconversion among YMSM, researchers at New York University’s Center for Health, Identity, Behavior & Prevention Studies (CHIBPS) sought to identify the factors associated with incident HIV infection among a cohort of racially/ethnically and socioeconomically diverse YMSM.

Salil Mehta examines Microsoft’s viral “How old do I look?” site, the limits of its age recognition, possible algorithms, and implications for Big Data analysis.

New technologies for monitoring and manipulating the nervous system promise exciting biology but pose challenges for analysis and computation. Solutions can be found in the form of modern approaches to distributed computing, machine learning, and interactive visualization. But embracing these new technologies will require a cultural shift: away from independent efforts and proprietary methods and toward an open source and collaborative neuroscience.