NYU Data Science newsletter – January 27, 2016

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for January 27, 2016

GROUP CURATION: N/A

 
Data Science News



Without a data-driven culture, big data projects will fail

TechTarget, SearchCIO


from January 26, 2016

Tara Paider, associate vice president of IT architecture at Nationwide Insurance based in Columbus, Ohio, has some advice for data experts eager for big data project success: One of the biggest reasons big data projects fail is neither the technology nor the quantity of the data. It’s people.

Case in point: A regular part of the job for Nationwide’s insurance agents is to make sure customers don’t jump ship when their premiums are due to go up. Working from a list of premiums expected to change in the next 30 days, agents pick up the phone and connect with their best customers to explain the changes. Data from a new customer data analytics program, however, found that doing so sometimes had a negative effect: Rather than help agents hold on to customers whose premiums were changing, the calls actually caused attrition, Paider said.

 

AI Benchmark Will ask Computers to Make Sense of the World

MIT Technology Review


from January 26, 2016

A new database will gauge progress in artificial intelligence, as computers try to grasp what’s going on in scenes shown in photographs.

 

How Microsoft Plans to Beat Google and Facebook to the Next Tech Breakthrough

Bloomberg Business


from January 25, 2016

At a Microsoft executive retreat during his first month as chief executive officer, Satya Nadella saw a research project that captured his attention. The demonstration in February 2014 used speech recognition and artificial intelligence to translate a live conversation into another language. Nadella told the team he wanted the tool combined with Skype and ready in time to show off at his first public speech three months later.

This is not how Microsoft typically works. As Nadella, a 24-year veteran of the company, would have known, the process of turning a Microsoft Research project into a product would often happen slowly, if at all. That’s partly by design. The company’s research group was set up in isolation from the product teams to allow researchers to envision the future without worrying about how their inventions will make money or fit into the company’s mission.

But Nadella’s tight deadline left executives with no time to debate the separation of church and state. “We did not have a formal team working on this when he made that statement,” said Lilian Rincon, the Skype group program manager. So they assembled one and immediately went to work on what would eventually become Skype Translator.

 

Approaching big data from a business perspective

O'Reilly Media, Ben Lorica


from January 25, 2016

In this episode of the O’Reilly Podcast, O’Reilly’s Ben Lorica sat down with Ben Sharma, CEO and co-founder of Zaloni, an organization that provides enterprise data lake management solutions. They discussed real-time data processing, the changing nature of data, sentiment analysis, and the trend toward combining cloud with on-prem infrastructures. [audio, 28:13]

 

What Does the Big Data Job Industry Look Like in 2016?

SmartData Collective, Rick Delgado


from January 26, 2016

With the recession well behind us, hiring trends across all industries are favoring skilled employees. The same—and more—can be said for those who are experts in big data. Data Quality Director, Software Engineer, Platform Software Engineer, Database Engineer, Big Data Platform Engineer, Security Analyst, Management Analysts and Information Systems Developer jobs are all positions that require proficiency in big data. It’s clear that big data will become “bigger” data in the upcoming years.

Here are eight things big data experts can look forward to in 2016.

 

Roundup: #aas227 #hackaas Hack Day | astrobites

Meredith Rawls, astrobites blog


from January 23, 2016

As the 227th American Astronomical Society meeting drew to a close (see highlights from Day 1, Day 2, Day 3, and Day 4), a group of at least 50 attendees spent “Day 4” working on small projects fondly called hacks. Thanks to sponsorship from LSST and Northrup Grumman, the industrious hackers were well-caffeinated and fed so we could devote time and energy to working in groups on one-day projects.

The Hack Day began at 10am with pitches. Anybody with a project idea was welcome to briefly speak and try to convince others to work with them. Only some ideas panned out, but the enthusiasm was palpable. It’s not every day you get a full room of astronomers and affiliates eager to spend hours working on fun and useful projects to benefit the community.

 

How Big Data Is Quietly Fighting Diseases and Illnesses

Dataconomy


from January 18, 2016

As Big Data becomes more and more integral to healthcare, everyone on the food chain will begin to see changes. Governments, universities, businesses, everyone has a stake in the future of health care. In recent years, Big Data has already offered up amazing proof of its applications, by helping stop the spread of Ebola, and combating the very common and devastating condition, Sepsis. It also may emerge as a surprising competitor in the world of social debate by spreading evidence-based knowledge on health.

 

The Rise of the Artificially Intelligent Hedge Fund

WIRED, Business


from January 25, 2016

Last week, Ben Goertzel and his company, Aidyia, turned on a hedge fund that makes all stock trades using artificial intelligence—no human intervention required. “If we all die,” says Goertzel, a longtime AI guru and the company’s chief scientist, “it would keep trading.”

He means this literally. Goertzel and other humans built the system, of course, and they’ll continue to modify it as needed. But their creation identifies and executes trades entirely on its own, drawing on multiple forms of AI, including one inspired by genetic evolution and another based on probabilistic logic. Each day, after analyzing everything from market prices and volumes to macroeconomic data and corporate accounting documents, these AI engines make their own market predictions and then “vote” on the best course of action.

 

Researchers Are Pushing Back Against Elsevier’s Open-Access Publishing Fees

The Atlantic, Jane C. Hu


from January 26, 2016

Publishing an open-access paper in a journal can be prohibitively expensive. Some researchers are drumming up support for a movement to change that.

 
Events



Coming Up! 2nd NIAC Workshop on Urban Science & Engineering | Urban@UW



Advances in computing, communications, and sensor technologies offer the possibility of dramatically advancing our understanding of the behavior of cities, as well as fuel hopes that such knowledge can help enhance a city’s efficiency, sustainability, resiliency, and livability.

There is no registration fee for the event. However, as seating is limited, attendees are asked to register as soon as possible.

Tuesday-Wednesday, February 2-3, NHS Hall, Center for Urban Horticulture
University of Washington

 

JuliaCon 2016: Boston, MA.



The third Julia conference will take place June 21st-25th, 2016 at the Massachusetts Institute of Technology in Cambridge, Massachusetts. Expect cutting-edge technical talks, hands-on workshops, a chance to rub shoulders with Julia’s creators, and a weekend in a city known for its historical significance and colonial architecture. Looking forward to seeing you there!

Deadline for Call for Proposals is Friday, March 18.

 
Deadlines



New York Machine Learning Deadlines

deadline: subsection?

Helpful, long list of NYC-area Machine Learning-related paper [and conference] deadlines that may interest [starting with New York ML Symposium on Friday, January 29].

 
CDS News



MedidataVoice: Is Machine Learning the Next Big Thing In Healthcare?

Forbes, BrandVoice


from January 25, 2016

Electronic Health Record (EHRs) systems are now used in 80% of doctors offices and contain a rich source of patient data available to innovate and improve healthcare.

A team at New York University’s Courant Institute of Mathematical Sciences developed algorithms and a system to extract EHR data to faster diagnose patients and provide a thorough understanding of the patient’s health.

NYU’s David Sontag, an assistant professor of data science and computer science, researches how machine learning and EHRs can bring changes and innovations in healthcare. His team at NYU has already forged ahead and created a complex system using EHR data that develops clinical state predictions about patients.

 
Tools & Resources



NIMBLE: Programming Statistical Algorithms for Graphical (Hierarchical) Models | Berkeley Institute for Data Science

Berkeley Institute for Data Science, Moore-Sloan Data Science Environment


from January 25, 2016

The NIMBLE project is a jointly collaborative effort between the departments of Statistics; Computer Science; and Environmental Science, Policy and Management of UC Berkeley. NIMBLE stands for Numerical Inference for statistical Models using Bayesian and Likelihood Estimation. The key idea behind NIMBLE is to combine flexible hierarchical model specification with a system for programming statistical algorithms that can adapt to model structures. NIMBLE makes it possible to implement, distribute, and programmatically control and modify statistical algorithms, which can be applied to any stochastic model defined as a directed acyclic graph. The NIMBLE system provides a flexible language for declaring a wide range of hierarchical models, a framework for defining algorithms that operate on this representation of models, and a compiler for generating equivalent C++—all from within the R environment.

 

Hypothetical Outcome Plots: Experiencing the Uncertain

Medium, UW Interactive Data Lab


from January 26, 2016

If you are like most people, including many data analysts, interpreting visualizations of uncertainty feels hard and abstract. This article describes Hypothetical Outcome Plots (HOPs), a promising approach to visualizing uncertain data for general audiences and analysts alike. Rather than showing a continuous probability distribution, HOPs visualize a set of draws from a distribution, where each draw is shown as a new plot in either a small multiples or animated form. HOPs enable a user to experience uncertainty in terms of countable events, just like we experience probability in our day to day lives.

 

Leave a Comment

Your email address will not be published.