NYU Data Science newsletter – March 29, 2016

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for March 29, 2016

GROUP CURATION: N/A

 
Data Science News



Big data is overhauling credit scores

Dataconomy


from March 28, 2016

Brands utilising big data are cultivating an ‘insight economy’ where every business move is mapped out with pinpoint accuracy thanks to the internet of things (objects that send and receive data) building a connected world. Businesses are leaping at the chance to embrace ‘cognitive computing’, a process where coding, tools and data are combined to achieve artificial intelligence (AI), reasoning and learning. Now, more than ever, analysts and data scientists in the financial services have the capacity to deliver concise, data-driven predictions based purely on data and performance, and unleashing lucrative returns. But, that’s not happening.

 

The future of Biostatistics

Medium, Biostatistics; Dimitris Rizopoulos and Jeff Leek


from March 24, 2016

Starting in January we took over as co-editors of the journal Biostatistics. We are really excited about the opportunity to give back to our community and help shape the future of our field. … we still think that now is an even more exciting time for the field of biostatistics and for the journal. Health and biomedical data have never been more abundant. Statisticians are playing key roles in the discussions of how to model big data from sequencers and brain scans. Cleaning and processing data remains one of the largest open challenges for biostatistics and data science. As we examine electronic health records and data from wearable computing, the modeling of longitudinal and survival data have never been more important or more difficult.

 

One Year In, the “On This Day” Feature Exemplifies How A.I. Is Changing Facebook

Inverse


from March 24, 2016

One year ago today, Facebook released its “On This Day” feature. Inverse spoke with Facebook’s Computer Vision Research Lead Manohar Paluri about how artificial intelligence, machine learning, and computer vision make this feature more meaningful — and how these areas of research and development will continue to improve the Facebook experience in years to come.

 

Silicon Valley Looks to Artificial Intelligence for the Next Big Thing – The New York Times

The New York Times, Bits blog


from March 27, 2016

As the oracles of Silicon Valley debate whether the latest tech boom is sliding toward bust, there is already talk about what will drive the industry’s next growth spurt.

The way we use computing is changing, toward a boom (and, if history is any guide, a bubble) in collecting oceans of data in so-called cloud computing centers, then analyzing the information to build new businesses.

The terms most often associated with this are “machine learning” and “artificial intelligence,” or “A.I.” And the creations spawned by this market could affect things ranging from globe-spanning computer systems to how you pay at the cafeteria.

“There is going to be a boom for design companies, because there’s going to be so much information people have to work through quickly,” said Diane B. Greene, the head of Google Compute Engine, one of the companies hoping to steer an A.I. boom. “Just teaching companies how to use A.I. will be a big business.”

 

These unlucky people have names that break computers

BBC Future


from March 25, 2016

A few people have names that can utterly confuse the websites they visit, and it makes their life online quite the headache. Why does it happen?

 

Top talent leaves Google startup Verily under divisive CEO

STAT


from March 28, 2016

Google’s brash attempt to revolutionize medicine as it did the Internet is facing turbulence, and many leaders who launched its life sciences startup have quit, STAT has found.

Former employees pointed to one overriding reason for the exodus from Verily Life Sciences: the challenge of working with CEO Andrew Conrad.

 

Studying the science of science

Science, Articles


from March 28, 2016

In theory, the scientific method works like this: Researchers ask a question, construct a hypothesis, collect data, evaluate their results, and—ta da!—the world gains valuable scientific insights. In practice, of course, it doesn’t always work that way, and some scientists are taking it upon themselves to go beyond their core research areas to study where the system can go wrong.

Ferric Fang, a professor of laboratory medicine and microbiology at the University of Washington, Seattle, added retractions and scientific misconduct to his research agenda because of his experience as editor-in-chief of the journal Infection and Immunity. In 2010, 3 years after he had taken the helm, the journal retracted five papers by researcher Naoki Mori after they were found to contain manipulated images of various types of protein and DNA gels. In that process, Fang says, “it became apparent to me that the current system was not really well-equipped to recognize skillful misconduct.” In the following months, other journals that published Mori’s papers found similar issues. In the end, more than 30 of Mori’s papers were retracted. “That was the end of my period of naiveté,” Fang says.

 

4 Ways Big Data Is Changing SEO

Inc.com, AJ Agrawal


from March 28, 2016

Content is Becoming Data At An Exponential Rate

In a general sense, content is nothing more than published information. But as Google emerged as a major curator of big data, it started thinking about content as quantifiable entities.

By turning content into data, search engines are able to easily analyze it and get better at delivering the relevant answers people are looking for.

 

The State of Artificial Intelligence Technology, Per Nvidia’s CEO

Fortune, Tech


from March 22, 2016

As industry embraces AI, computers aren’t the only ones that have to learn new tricks. Fortune’s Andrew Nusca talks with Nvidia CEO Jen-Hsun Huang.

Fortune: What’s the current status of artificial intelligence?

Jen-Hsun Huang: 2015 was a big year. Artificial intelligence is moving into the commercial world. AI has been worked on for many years, largely in research. Various aspects of commercial use of AI, otherwise known as machine learning, is used for advertising and web searches and things like that. It wasn’t until the last few years that AI could do things that people can’t do. Several milestones were achieved in 2015 in particular that made it possible for us to use it in all kinds of areas.

 
Events



Big Data and Sustainable Development



As the world decides upon the indicators to measure progress towards the Sustainable Development Goals, there is an urgent need to mobilize data to hold governments and decision-makers accountable. With dire constraints to finite natural resources, rising populations and levels of inequality, how can we tap into big data to track progress and modify the status quo? What kinds of data and analytical tools will be relevant to the 2030 Sustainable Development Agenda? What trends and practices may be useful and what are the greatest foreseeable obstacles which lie ahead?

Panelists:
Nicole Barberis
Senior Data Scientist, Bloomberg LLP;
Louis Coppola
Co-Founder, Executive Vice President, Governance & Accountability Institute;
Kirk Borne
Principal Data Scientist, Booz Allen Hamilton, PhD Astrophysicist;
Justine Dowden
Data Analyst Center for Sustainable Development, Columbia University;

Thursday, April 14, at 6 p.m., NYU Kaufman Management Center (44 W 4th Street, Room M2-60). Please RSVP.

 

Fast Forward Labs Data Leadership Conference



This half-day conference will explore the theme of how to manage great data teams inside of organizations. You’ll see how different organizations in several industries think about hiring, structuring teams, prioritizing projects, operationalizing results, and driving excellence for their data capabilities. You’ll also have a chance to network with data leaders from a rich variety of backgrounds and industries.

Thursday, Apr 28, at 2:30 p.m., at Civic Hall (156 5th Avenue). Cost is $249.

 

ICLR 2016



International Conference on Learning Representations (ICLR) is an annual conference sponsored by the Computational and Biological Learning Society.

May 2 – 4, at the Caribe Hilton in San Juan, Puerto Rico

 
Tools & Resources



Theano 0.8.0 : Python Package Index

Python Software Foundation


from March 21, 2016

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays. … We recommend that everybody update to this version.

 

How to do Data Science | Machine Learning Blog

TechNet Blogs, Machine Learning blog; Brandon Rohre


from March 28, 2016

 

IBM Better Data Science Through Open Source Design

Spark Technology Center


from March 23, 2016

… The Apache Zeppelin notebook is an open source data analytics tool that emphasizes data visualization and has native integration with Spark. It was created in 2013 by Moon Soo Lee and his co-founder at NFLabs, Sejun Ra. As adoption of Zeppelin grew over the year, they made the decision to move it under the Apache Software Foundation and take it global. The popularity of Zeppelin has been growing ever since.

Why Open-Source Design Is Crucial

As a whole, data science tools do not offer the best user experience. The field of data science is growing and evolving at an accelerated pace and the need for better tools has never been more urgent.

 

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