NYU Data Science newsletter – May 26, 2016

GROUP CURATION: N/A

Contextual factors, such as the race of participants in an experiment or the geography of where the experiment was run, can reduce the likelihood of replicating psychological studies, a team of New York University researchers has found. Their work, which appears in the journal Proceedings of the National Academy of Sciences, analyzed papers examined by the Reproducibility Project in an effort to identify potential challenges to replicating scientific scholarship.

Also, in context and reproducibility:

Context matters when replicating experiments, argues study (May 23, Retraction Watch)

Contextual sensitivity in scientific reproducibility (May 23, Proceedings of the National Academy of Sciences; Jay J. Van Bavel, Peter Mende-Siedlecki, William J. Brady, and Diego A. Reinero)

Should the government regulate artificial intelligence? That was the central question of the first White House workshop on the legal and governance implications of AI, held in Seattle on Tuesday.

“We are observing issues around AI and machine learning popping up all over the government,” said Ed Felten, White House deputy chief technology officer. “We are nowhere near the point of broadly regulating AI … but the challenge is how to ensure AI remains safe, controllable, and predictable as it gets smarter.”

I have started gathering user stories from humanists to give us a better sense of what they do and how (or whether) our kind of skills training might help. Two of these are summarized below; there’s a lot to think about, but the most important observation is that DH really is different in important ways from what Software and Data Carpentry have been doing so far.

She is between the ages of 25–34. Or she’s under 32. She is a millen­nial. She’s inferred married. But she uses her phone like a single lady. She completed high school. She votes, but she’s not regis­tered in a party.

The jury is currently in deliberations over whether Android’s use of the declaring code, and the structure, sequence, and organization of 37 Java API packages, was fair use.

The precedent that APIs are copyrightable was already set by the Federal Circuit in 2014. But the US Copyright Act doesn’t protect purely functional things. It doesn’t extend to any “process, system, method of operation.” If it seems to you like APIs are so purely functional that they should be covered under that latter doctrine, you’re not alone.

Also, in Oracle vs. Google:

Google beats Oracle—Android makes “fair use” of Java APIs (May 26, Ars Technica, Joe Mullin)

Any harm done to humanity by computers will stem from our directives and our failure to anticipate impending societal change that looms over billions of U.S. and international workers. We’ve already witnessed the type of damage computers can inflict with the stock market “flash crashes’” resulting from unexpected feedback interactions between high-frequency automatic trading systems. Flawed programming logic can also lead to disasters.

Hillary Clinton is doubling down on data. Earlier this month the campaign advertised three dozen analytics, digital, and engineering openings, including one for an e-mail writer. Her campaign is looking to build on the digital engagement strategy devised by the Obama team, customizing the messages e-mailed to the estimated 8.6 million people on its list to make them as personal as possible.

Intelligent machines won’t be ruling the world anytime soon – but what happens when they turn you down for a loan, crash your car or discriminate against you because of your race or gender?

On one level, the answer is simple: “It depends,” says Bryant Walker Smith, a law professor at the University of South Carolina who specializes in the issues raised by autonomous vehicles.

Video available of People & Robotics seminar at UC-Berkeley, on motivation behind the 100-Year Study on AI.

Today, digital humanists are applying advanced computational tools to a wide range of disciplines, including literature, history, and urban studies. They are learning programming languages, generating dynamic three-dimensional (3D) re-creations of historic city spaces, developing new academic publishing platforms, and producing scholarship.

The breadth of the field has led to something of an identity crisis.

Also, in digital humanities:

The Science of Culture? Social Computing, Digital Humanities and Cultural Analytics (May 23, CA: Journal of Cultural Analytics; Lev Manovich)

What Digital Humanists Do (May 25, Software Carpentry)

Astronomers today signed an unprecedented contract to build the world’s largest ground-based optical and infrared telescope. In a ceremony at the headquarters of the European Southern Observatory (ESO) in Garching, Germany, ESO Director General Tim de Zeeuw inked the record deal—worth €400 million—with three Italian engineering firms. They will build the structure that will hold the huge 39-meter mirror of the European Extremely Large Telescope (E-ELT), as well as the domed building that will enclose it.

The theme for VISAP’16, the IEEE VIS 2016 Arts Program, is Metamorphoses. We encourage artists, designers, and researchers to think about transformation as a fundamental component of the pipeline from raw data to meaning.

Baltimore, MD Sunday-Friday, October 23–28, for IEEE VIS 2016.

Deadline for VISAP submissions is Friday, June 24.

A joint effort created by five international statistical societies—American Statistical Association, International Biometric Society, International Statistical Institute, Institute of Mathematical Statistics, and the Royal Statistical Society—the International Prize in Statistics also raises awareness of the invaluable role that statistics, data analysis, probability, and the understanding of uncertainty play in science, technology, human welfare, and the overall advancement of society.

Deadline for submissions is Monday, August 15.

We are building a novel end-to-end system called ModelDB to manage ML models. ModelDB clients automatically track machine learning models in their native environments (e.g., scikit-learn, spark.ml), the ModelDB backend introduces a common layer of abstractions to represent models and pipelines, and the ModelDB frontend allows visual exploration and analyses via a web-based interface. Figure 1 shows the high-level architecture of ModelDB.

Last year we announced the introduction of our new distributed stream computation system, Heron. Today we are excited to announce that we are open sourcing Heron under the permissive Apache v2.0 license. Heron is a proven, production-ready, real-time stream processing engine, which has been powering all of Twitter’s real-time analytics for over two years. Prior to Heron, we used Apache Storm, which we open sourced in 2011. Heron features a wide array of architectural improvements and is backward compatible with the Storm ecosystem for seamless adoption.