Data Science newsletter – August 28, 2017

Newsletter features journalism, research papers, events, tools/software, and jobs for August 28, 2017

GROUP CURATION: N/A

 
 
Data Science News



Tweet of the Week

Twitter, Eric Holthaus


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Charles River Analytics-Led Team to Help DARPA Build Virtual Data Scientist Assistant | ExecutiveBiz

ExecutiveBiz, Scott Nicholson


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Charles River Analytics will lead a team comprised of Win-Vector and Oregon State University to develop a virtual data scientist assistant that will help users answer real-world analytical questions under a four-year, $2.8 million contract from the Defense Advanced Research Projects Agency.


AI Crushes the 2017 Microsoft Hackathon

Edgy Labs, Brett Forsberg


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The 2017 Microsoft Hackathon was the biggest private hackathon event in the world thus far with some 18,000 participants. It continued to bring the company insights into what is trending and important both internally and within the larger technology industry.

The 2017 Hackathon more than doubled the number of team projects from the 2014 event with 4,760 total team projects throughout the event.

What’s even more impressive is that 48% of the 2017 Hackathon projects were centered around artificial intelligence, a startling increase from 35% at last year’s hackathon.


ORNL researchers turn to deep learning to solve science’s big data problem

Oak Ridge National Laboratory


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A team of researchers from Oak Ridge National Laboratory has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis.

The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world’s most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL’s Thomas Potok, includes Robert Patton, Chris Symons, Steven Young and Catherine Schuman.


I tried HireVue’s AI-powered job interview platform

Business Insider, Richard Feloni


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HireVue uses a combination of proprietary voice recognition software and licensed facial recognition software in tandem with a ranking algorithm to determine which candidates most resemble the ideal candidate. The ideal candidate is a composite of traits triggered by body language, tone, and key words gathered from analyses of the existing best members of a particular role.

After the algorithm lets the recruiter know which candidates are at the top of the heap, the recruiter can then choose to spend more time going through the answers of these particular applicants and determine who should move onto the next round, usually for an in-person interview.


Amazon Has Developed an AI Fashion Designer

MIT Technology Review, Will Knight


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Researchers at the e-commerce juggernaut are currently working on several machine-learning systems that could help provide an edge when it comes to spotting, reacting to, and perhaps even shaping the latest fashion trends. The effort points to ways in which Amazon and other companies could try to improve the tracking of trends in other areas of retail—making recommendations based on products popping up in social-media posts, for instance. And it could help the company expand its clothing business or even dominate the area.

“There’s been a whole move from companies like Amazon trying to understand how fashion develops in the world,” says Kavita Bala, a professor at Cornell University who took part in a workshop on machine learning and fashion organized by Amazon last week. “This is completely changing the industry.”


Why AI visionary Andrew Ng teaches humans to teach computers

Associated Press, Ryan Nakashima


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Andrew Ng has led teams at Google and Baidu that have gone on to create self-learning computer programs used by hundreds of millions of people, including email spam filters and touch-screen keyboards that make typing easier by predicting what you might want to say next.

As a way to get machines to learn without supervision, he has trained them to recognize cats in YouTube videos without being told what cats were. And he revolutionized this field, known as artificial intelligence, by adopting graphics chips meant for video games.


NSF Announces $17.7M in Awards for Cross-disciplinary Data Sciences Projects

HPC Wire


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NSF announced it will provide $17.7 million in funding for 12 Transdisciplinary Research in Principles of Data Science (TRIPODS) projects, “which will bring together the statistics, mathematics and theoretical computer science communities to develop the foundations of data science.” Conducted at 14 institutions in 11 states, these projects will promote long-term research and training activities in data science that transcend disciplinary boundaries.


Drones will watch Australian beaches for sharks with AI help

Engadget, Jon Fingas


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Humans aren’t particularly good at spotting sharks using aerial data. At best, they’ll accurately pinpoint sharks 30 percent of the time — not very helpful for swimmers worried about stepping into the water. Australia, however, is about to get a more reliable way of spotting these undersea predators. As of September, Little Ripper drones will monitor some Australian beaches for signs of sharks, and pass along their imagery to an AI system that can identify sharks in real-time with 90 percent accuracy. Humans will still run the software (someone has to verify the results), but this highly automated system could be quick and reliable enough to save lives.

The detection AI is a quintessential machine learning system. The team trains the system to both look for sharks based on aerial videos as well as distinguish them from other life on the water. That approach doesn’t just help it identify sharks, though. It can also flag dolphins, whales and other sea creatures of interest, which could give researchers an additional way to track populations.


What I Learned at Gerrymandering Summer Camp

WIREd, Business, Issie Lapowsky


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They’re participants in a weeklong summer camp of sorts for adults focused on how math and technology can be used to make electoral maps more fair, and to convince judges and juries when they’re not. Gerrymandering, they believe, allows politicians to choose their voters, not the other way around. This event is the first of many planned by the unfortunately named Metric Geometry and Gerrymandering Group at Tufts. You can think of the hackathon as the arts and crafts part of the week—a chance for the geeks to get their hands dirty. Attendees had to apply to this session; just 14 made the cut.


Mega-collaborations for scientific discovery

symmetry magazine, Leah Poffenberger


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Sometimes it takes lot of people working together to make discovery possible. More than 7000 scientists, engineers and technicians worked on designing and constructing the Large Hadron Collider at CERN, and thousands of scientists now run each of the LHC’s four major experiments.

Not many experiments garner such numbers. On August 15, the Deep Underground Neutrino Experiment (DUNE) became the latest member of the exclusive clique of particle physics experiments with more than a thousand collaborators.


Data Visualization of the Week

Twitter, National Weather Service


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Events



Crisis Code: Humanitarian Protection in the Digital Age

swissnex


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San Francisco, CA A two-day conference organized by swissnex San Francisco exploring how crisis-affected populations can be safeguarded from emerging cyber-threats. September 27-28 at Pier 17, Suite 800. [$$]


Genomic Innovation – At the New York Genome Center Fall 2017

New York Genome Center, NYU, Columbia University


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New York, NY Genomic Innovation is a project-oriented university course for New York area graduate students. Available to NYU and Columbia students. Course begins on Thursday, September 7.


4S 2017 Boston – Annual Meeting of the Society for Social Studies of Science (4S)

Society for Social Studies of Science


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Boston, MA August 30-September 2. [$$$]


First Annual ArTech Cre8-a-th0n

RIT MAGIC Center


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Rochester, NY September 16-17, organized by RIT MAGIC Center. Attendees from RIT and the Rochester community will work on creative challenges that demonstrate the expressive power of technology when combined with art and design. [free, registration required]


Second Annual Innovations in Imaging for Life Sciences Symposium

University of Washington


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Seattle, WA Wednesday, August 30, starting at 8:30 a.m., University of Washington, Magnuson Health Sciences Center K. [free, registration required]

 
Deadlines



Call for Papers – Workshop on Knowledge Extraction from Games

New Orleans, LA Workshop takes place at AAAI-18 on February 2 or February 3. Deadline for submissions is October 13.
 
Tools & Resources



noWorkflow: a Tool for Collecting, Analyzing, and Managing Provenance from Python Scripts

Proceedings of the VLDB Endowment; João Felipe Pimentel, Leonardo Murta, Vanessa Braganholo, Juliana Freire:


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We present noWorkflow, an open-source tool that system-
atically and transparently collects provenance from Python
scripts, including data about the script execution and how
the script evolves over time. During the demo, we will show
how noWorkflow collects and manages provenance, as well as
how it supports the analysis of computational experiments.


Google’s Tensorflow team open-sources speech recognition dataset for DIY AI

VentureBeat, Khari Johnson


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“Google researchers open-sourced a dataset today to give DIY makers interested in artificial intelligence more tools to create basic voice commands for a range of smart devices. Created by the TensorFlow and AIY teams at Google, the Speech Commands dataset is a collection of 65,000 utterances of 30 words for the training and inference of AI models.”


Using ReproZip for Reproducibility and Library Services

LIS Scholarship Preprint Archive; Vicky Steeves Remi Rampin Fernando Chirigati


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“In this paper, we present ReproZip, an open source tool to help overcome the technical difficulties involved in preserving and replicating research, applications, databases, software, and more. We examine the current use cases of ReproZip, ranging from digital humanities to machine learning. We also explore potential library use cases for ReproZip, particularly in digital libraries and archives, liaison librarianship, and other library services.”


Open Access VIS

Steve Haroz


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A collection of open access visualization research at the VIS 2017 conference.


htmldate: extract the date of HTML documents

GitHub – adbar


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Seamless extraction/scraping of dates in web pages. htmldate provides following ways to date documents, based on HTML parsing.

 
Careers


Postdocs

Post-doc in biology



University of Gothenburg; Gothenburg, Sweden

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