NYU Data Science newsletter – August 28, 2015

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for August 28, 2015

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Data Science News



How Historically and Contextually Rich Data Leads to Better Insights

Crimson Hexagon


from August 20, 2015

Crimson Hexagon’s unique approach to data access, storage and usage.

 

duecredit/duecredit · GitHub

GitHub, duecredit


from August 27, 2015

Automated collection and reporting of citations for used software/methods/datasets

 

The Difference Between Machine Learning and Statistics

Galvanize


from August 26, 2015

At a glance, machine learning and statistics seem to be very similar, but many people fail to stress the importance of the difference between these two disciplines. Machine learning and statistics share the same goals—they both focus on data modeling—but their methods are affected by their cultural differences. In order to empower collaboration and knowledge creation, it’s very important to understand the fundamental underlying differences that reflect in the cultural profile of these two disciplines. To gain a deeper understanding of these differences, we need to take a step back and look at their historical roots.

 

Reproducibility Project: Psychology Wiki

Open Science Framework, Wiki


from August 27, 2015

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered designs and original materials when available. Replication effects (Mr = .197, SD = .257) were half the magnitude of original effects (Mr = .403, SD = .188), representing a substantial decline. Ninety-seven percent of original studies had significant results (p < .05). Thirty-six percent of replications had significant results; 47% of original effect sizes were in the 95% confidence interval of the replication effect size; 39% of effects were subjectively rated to have replicated the original result; and, if no bias in original results is assumed, combining original and replication results left 68% with significant effects. Correlational tests suggest that replication success was better predicted by the strength of original evidence than by characteristics of the original and replication teams.

 

Duncan Watts’ AMA «

Sociology Job Market Rumors


from August 27, 2015

We thank Duncan Watts for participating in SJMR’s AMA series. Dr. Watts is a prominent network sociologist and public figure. He is currently a principal researcher at Microsoft Research. Before that, he was a professor of sociology at Columbia University. We are grateful to Duncan for his insightful and frank responses, which are postedbelow. You can learn more about his research by visiting his website.

 

Yann LeCun – More on Project M. | Facebook

Facebook, Yann LeCun


from August 26, 2015

Today we’re beginning to test a new service called M. M is a personal digital assistant inside of Messenger that completes tasks and finds information on your behalf. It’s powered by artificial intelligence that’s trained and supervised by people.

Unlike other AI-based services in the market, M can actually complete tasks on your behalf.

 

The big question: how will Artificial Intelligence advance in the next 20 years?

Wired UK


from May 05, 2015

Marian Bartlett, Co-founder and lead scientist, Emotient

“We underestimate how hard vision is because our brains do it so well. The human brain has evolved for millions of years to be able to make sense of light patterns. Until ten years ago, getting a computer to recognise even simple objects in natural images has been elusive. Now, with advances in machine learning — many of which are inspired by our understanding of the brain — computers can recognise something as nuanced as facial expressions. This will transform the way we interact with machines. In the next 20 years it will be commonplace for devices to respond to non-verbal signals such as attention and mood.”

And more expert answers like this …

 

Fashion Metric Builds a Strong Data Science Team in Austin

SiliconHills


from August 27, 2015

Fashion Metric is working to solve the problem of ill-fitting clothes by employing data science.

The startup created software, which it calls a virtual tailor, to help online shoppers find clothes that fit by calculating their detailed body measures. It does this by simply asking a few questions online and then processing that information through its proprietary algorithm and databases to come up with the correct size and fit.

 

Can Data Science Save The New York Times?

Medium, NYU Center for Data Science


from August 24, 2015

On how Chris Wiggins is using data science to bring the New York Times into the digital age

 

Microsoft Uses Reprogrammable Chips to Put Computing Power Behind Artificial Neural Networks

MIT Technology Review


from August 25, 2015

A new approach to powering AI software could produce artificial neural networks of “unprecedented size,” says Microsoft.

 

Bridging the divide: Business users and machine learning experts

O'Reilly Radar, Ben Lorica


from August 27, 2015

During the latest episode of the O’Reilly Data Show podcast, I sat down with Alice Zheng, one of Strata + Hadoop World’s most popular speakers. She has a gift for explaining complex topics to a broad audience, through presentations and in writing. We talked about her background, techniques for evaluating machine learning models, how much math data scientists need to know, and the art of interacting with business users. [audio, 45:40]

 

Rethinking Data: How to Create a Holistic View of Students

KQED, MindShift


from August 26, 2015

For at least a decade now, the driving force behind education reform has been data. We talk about collecting data, analyzing data, and making data-driven decisions. All of this data can certainly be useful, helping us notice patterns we might not have seen without aggregating our numbers in some way, looking for gaps and dips and spikes, allowing us to figure out where we are strong and where we need help. In terms of certain academic behaviors, we can quantify student learning to some extent and improve our practice as a result.

 
CDS News



Political Polarization on Twitter Depends on the Issue

Association for Psychological Science


from August 27, 2015

Twitter offers a public platform for people to post and share all sorts of content, from the serious to the ridiculous. While people tend to share political information with those who have similar ideological preferences, new research from NYU’s Social Media and Political Participation Lab demonstrates that Twitter is more than just an “echo chamber.”

This is an illustration of various political symbols coming out of a megaphone.The research is published in Psychological Science, a journal of the Association for Psychological Science.

 

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