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Data Science News
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Tweet of the Week
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Twitter, Stef Lewandowski
from September 22, 2016
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A Diversity of Genomes
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Harvard Medical School
from September 21, 2016
A study of hundreds of new genomes from across the globe has yielded insights into modern human genetic diversity and ancient population dynamics, including compelling evidence that essentially all non-Africans today descend from a single migration out of Africa.
The multinational research effort, led by Harvard Medical School geneticists and published Sept. 21 in Nature, also suggests that no single genetic change or small group of changes is likely to explain the substantial transformations in human culture and cognition seen in the last 50,000 years.
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What is hardcore data science—in practice?
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O'Reilly Radar, Mikio Braun
from September 22, 2016
The anatomy of an architecture to bring data science into production.
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Jason Furman, Obama’s Chief Economic Adviser, on Artificial Intelligence
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The Atlantic, Jason Furman
from September 21, 2016
President Obama’s chief economist argues that, with the right policies, artificial intelligence can be boon to the labor market, not a threat.
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P2P insurance firm Lemonade launches out of stealth, powered by chatbots, morals, and big bucks
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VentureBeat, Paul Sawers
from September 21, 2016
The company’s platform is built around a number of core founding principles. At the root is a desire to remove friction from the insurance process, cutting bureaucracy, time needed, and costs. But Lemonade is also setting out to combat existing models through an annual “giveback,” where it donates unclaimed money to good causes. Through the app, users select a cause that they care about, and this cause-creation process generates virtual groups of like-minded people — or “peers.”
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Etsy buys Blackbird Technologies to bring AI to its search
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TechCrunch, Ingrid Lunden
from September 19, 2016
The popular handmade-goods site Etsy announced it has acquired a startup called Blackbird Technologies, which developed algorithms for natural language processing, image recognition and analytics — similar to those used by Amazon and Google for product and other searches — and then “democratized” them to be used by any company of any size. At Etsy, the tech will be used to improve its own search features.
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Johns Hopkins Bloomberg School of Public Health Awarded $95 Million NIH Grant
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Johns Hopkins Bloomberg School of Public Health
from September 21, 2016
The National Institutes of Health today announced that the Johns Hopkins Bloomberg School of Public Health, along with the research firm RTI International, will receive a seven-year, $95 million grant to analyze the data from its new Environmental Influences on Child Health Outcomes (ECHO) program, an initiative designed to understand how the environment influences health beginning in the womb.
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Show and Tell: image captioning open sourced in TensorFlow
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Google Research Blog, Chris Shallue
from September 22, 2016
Open source release “contains significant improvements to the computer vision component of the captioning system, is much faster to train, and produces more detailed and accurate descriptions” Git repo at the bottom of the blog linked here.
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Events
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Partnering For Cures medical research conference
New York, NY Sunday-Tuesday, 13-15 November 2016 [$$$$]
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Deadlines
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Big data conference: Strata + Hadoop World, March 13 – 16, 2017
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deadline: Conference
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San Jose, CA The deadline for speaker proposals is Friday, 30 September 2016.
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CFP: “Countercultures of Data” – Special Issue of Philosophy & Technology
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deadline: Journal/Publishing
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Deadline for paper submissions is 24 October 2016.
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Tools & Resources
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Global Historical Daily Weather Data in BigQuery
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Google Cloud Platform Blog, Lak Lakshmanan
from September 22, 2016
“From 80,000+ stations in 180 countries, spans decades and has been quality-checked to ensure that it’s temporally and spatially consistent.”
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Interpreting and Visualizing Neural Networks for Text Processing
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Civis Analytics, Melissa Roemmele
from September 22, 2016
We applied a neural network to the task of predicting numerical ratings for text-based consumer reviews, training the model to learn the ratings directly from the words in each review. It turns out that our model had decent prediction accuracy, but this wasn’t our objective, especially since we already knew the ratings for these reviews. Instead, we wanted to understand why those particular ratings were assigned. Interpreting what a neural network has learned from data is a separate endeavor from applying it to make predictions. In this post, we’ll explore some strategies for bringing the inside of a neural network to light, using our ratings prediction model to demonstrate.
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Careers
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Full-time positions outside academia |
Data Scientist – Stack Overflow
Stack Overflow; New York, NY
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Tenured and tenure track faculty positions |
Assistant Professor in Digital Humanities
Penn Arts & Sciences, University of Pennsylvania; Philadelphia, PA
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Tenure-Track Assistant Professor of English – Renaissance Drama
Carnegie Mellon University; Pittsburgh, PA
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Full-time, non-tenured academic positions |
Data Acquisition Manager
Institute for the Interdisciplinary Study of Decision Making, New York University; New York, NY
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