NYU Data Science newsletter – March 18, 2016

GROUP CURATION: N/A

The paper highlights the absence of a dialogue around the significant legal risks posed by the collection, use, and international transfer of personally identifiable data and humanitarian information, and the grey areas around assumptions of public good. The paper calls for a critical discussion around the experimental nature of data modeling in emergency response due to mismanagement of information has been largely emphasized to protect the contours of human rights.

[Penn State’s Peter] Wilf has worked with a computational neuroscientist from Brown University to program computer software to do what the human eye cannot: identify families of leaves, in mere milliseconds. The software, which Wilf and his colleagues describe in detail in a recent issue of Proceedings of the National Academy of Sciences, combines computer vision and machine learning algorithms to identify patterns in leaves, linking them to families of leaves they potentially evolved from with 72 percent accuracy. In doing so, Wilf has designed a user-friendly solution to a once-laborious aspect of paleobotany. The program, he says, “is going to really change how we understand plant evolution.”

… behind the scenes a more pedestrian drama was playing out. Executives at Google parent Alphabet Inc., absorbed with making sure all the various companies under its corporate umbrella have plans to generate real revenue, concluded that Boston Dynamics isn’t likely to produce a marketable product in the next few years and have put the unit up for sale, according to two people familiar with the company’s plans.

If Facebook announces the “Messenger Bot Store” at F8, as many predict, it would be arguably the most consequential event for the tech industry since Apple announced the App Store and iPhone SDK in March 2008.

Even Steve Jobs could not have foreseen the impact of what he described as “a new application that lets users browse, search, purchase and download third party applications directly onto their iPhone”.

DeepMind cofounder Mustafa Suleyman gave a rare insight into the work he and his team are doing within Google during a machine learning conference in London in June 2015.

… When researchers are done with their data they can share their work with the push of a button, Karen Adolph, professor of psychology at New York University and director of the Databrary, said.

Having been open to the research community for approximately two years now, the Databrary has simplified the process of collaboration for researchers, [Penn State psychology professor Rick] Gilmore said.

“For me personally, Databrary has been a great way to collaborate with some colleges who are in different locations,” Gilmore said.

… (mostly) benign examples that we encounter every day hide the truth about what large-scale government and corporate data collection means and where it’s being used. Predictions about you (and millions of other strangers) are starting to deeply shape your life. Your career, your love life, major decisions about your health and well-being, and even if you end up in jail, are now being governed in no small part by the digital bread crumbs you’ve left behind—many of which you don’t even know you’ve dropped in the first place.

NYU—long at the forefront of that multidisciplinary approach—is now the home of the Center for Cyber Security (CCS), a collaboration between the Tandon School of Engineering; NYU School of Law; Steinhardt School of Culture, Education, and Human Development; and other NYU schools and departments. (The new research center—which has physical locations in New York and Abu Dhabi as well as a network of scholars and practitioners in Europe, Asia, and the Middle East, among other locales—had a precursor in the university’s Center for Interdisciplinary Studies in Security and Privacy.)

The methods many data scientists currently use to coordinate operations are often ad hoc and rely on making full, redundant copies of an entire dataset in their individual workspaces. This not only wastes storage, but also woefully restricts collaborations: users cannot easily share patches or modifications to datasets, users cannot easily track which versions of a dataset were used for certain experiments, and there is no easy way to determine who is using particular versions of a dataset. … To remedy this problem, a team of ISTC researchers has introduced DataHub, a hosted, collaborative and on-line analytics platform that allows users to easily upload, modify, query, and share datasets.

Also, similarly: Databrary helps researchers collaborate by sharing video and data (Penn State University, Daily Collegian, March 16)

“What is the relationship like between your team and the data scientists?” This is, without a doubt, the question I’m most frequently asked when conducting interviews for data platform engineers. It’s a fine question – one that, given the state of engineering jobs in the data space, is essential to ask as part of doing due diligence in evaluating new opportunities. I’m always happy to answer. But I wish I didn’t have to, because this a question that is motivated by skepticism and fear.

Why is that? If you read the recruiting propaganda of data science and algorithm development departments in the valley, you might be convinced that the relationship between data scientists and engineers is highly collaborative, organic, and creative. Just like peas and carrots.

However, it’s not a well kept secret that this is seldom the case. Most shops foster a relationship between engineers and scientists that lies somewhere in the spectrum between non-existent1 and highly dysfunctional.