Data Science newsletter – May 12, 2017

Newsletter features journalism, research papers, events, tools/software, and jobs for May 12, 2017

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Data Science News



Internet of things made simple: One sensor package does work of many

Carnegie Mellon University, School of Computer Science


from

Ubiquitous sensors seem almost synonymous with the internet of things (IoT), but some Carnegie Mellon University researchers say ubiquitous sensing — with a single, general purpose sensor for each room — may be better.

The plug-in sensor package they’ve developed monitors multiple phenomena in a room, including things such as sounds, vibration, light, heat, electromagnetic noise and temperature. With help from machine learning techniques, this suite of sensors can determine whether a faucet’s left or right spigot is running, if the microwave door is open or how many paper towels have been dispensed.


CIOs face painful new round of shiny object syndrome

The Enterprisers Project, Seth Earley


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CIOs and technology leaders have it tough these days. Change is happening at a faster rate; agile, born-digital competitors are threatening to upend longstanding business models; and digital capabilities increasingly depend less on the tools themselves and more on rethinking the business process and customer value proposition. When business leaders come to IT with an ask, the things they want are not always aligned with what they really need.

Shiny new technologies such as cognitive computing, AI, bots, the Internet of Things (IoT), personalization, and machine learning have their intrinsic conceptual appeal, but novelty should not be confused with business value. Nevertheless, management may read an article about technologies that are not fully baked or that are not cost effective at the present level of industry maturity, and think “we need this.” Or they hear a vendor pitch that, though not actually an outright lie, promotes “aspirational functionality” that is not realistic at present (OK, they are lying).


Bloomberg data scientists bring real-world experience to New York City universities

Tech at Bloomberg


from

“Institutions of higher learning often tap experts with real-world experience for part-time teaching positions. This spring, two Bloomberg data scientists in New York City are serving as professors, leading-graduate level courses in machine learning and data science. From the Office of the CTO, David Rosenberg is teaching “Machine Learning & Computational Statistics” at New York University’s Center for Data Science, while Gary Kazantsev, head of the Machine Learning Group in Bloomberg’s Engineering organization, is co-teaching a class at Cornell Tech titled “Data Science in the Wild.”


Lighthouse is an Andy Rubin-backed smart security camera that identifies people and pets

The Verge, Ashley Carman


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The team at Lighthouse, a startup out of Android co-founder Andy Rubin’s Playground accelerator, doesn’t see its new hardware product as a home security camera. Instead, they see it as an “interactive assistant.” But Lighthouse, at least at first, will definitely be perceived as another new entrant in the smart camera market.

The device, unveiled for the first time today, sits in the home just like a Nest Cam to monitor what’s going on indoors. That’s where the overlap with Nest ends, however. Lighthouse incorporates deep learning and 3D-sensing technology to determine who is in the home, where they are inside, and if that’s a normal occurrence or not. The camera pairs with a companion iOS / Android app over Wi-Fi, so users can determine remotely whether an intruder is in their house. More innocuously, Lighthouse can also determine whether a dog’s been walked and send alerts when kids get home.


The Obama Foundation has hired Glenn Otis Brown as chief digital officer and will tackle the internet’s echo-chamber problem

Quartz, Gideon Lichfield


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What will Barack Obama do next? The question has hovered in the air ever since Donald Trump’s victory made clear that much of what Obama had done as president could be summarily dismantled. Now more clues are emerging.

The Obama Foundation, which last week unveiled plans for its new headquarters on Chicago’s South Side, has hired Glenn Otis Brown, a veteran of Twitter and Google, as its chief digital officer. His task, say foundation officials, is to build a team that, among other things, will study “the problems of digital media in the 21st century”—the filter bubbles and audience fragmentation that have made it almost impossible for people from different political stripes to hold cogent debates around an agreed set of facts.


Are Pop Lyrics Getting More Repetitive?

The Pudding, Colin Morris


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In 1977, the great computer scientist Donald Knuth published a paper called The Complexity of Songs, which is basically one long joke about the repetitive lyrics of newfangled music (example quote: “the advent of modern drugs has led to demands for still less memory, and the ultimate improvement of Theorem 1 has consequently just been announced”).

I’m going to try to test this hypothesis with data. I’ll be analyzing the repetitiveness of a dataset of 15,000 songs that charted on the Billboard Hot 100 between 1958 and 2017.


AI, Labor, and the Parable of the Horse

John Horton


from

Today I attended the 5th year anniversary celebration for MSR NYC. There was a great group of speakers and panelists—I’m super impressed by what MSR has accomplished. One topic that came up at several points during the day was the labor market effects of technological developments—particularly that powerful AI might displace many workers.

Economists have traditionally been sanguine about the effects of technological change on the labor market, viewing widespread technological unemployment as unlikely. This perspective is based on the historical experience of substantial technological change not having persistent disemployment effects. However, it has been pointed out that we have one vivid example where this optimism has not been warranted—what I call the parable of the horse.

The story is that the internal combustion engine came along and horses saw their marginal product decline below the cost of their feed and so horses disappeared, at least in the “labor” market.


Confronting a Nightmare for Democracy

Medium, David Carroll and Justin Hendrix


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With the help of researchers in Europe, we learned Cambridge Analytica was subject to laws that have no parallel in the US. After submitting a request for personal data, a Cambridge Analytica voter profile was delivered and publicly exposed for the first time. Does this prove that our voter data is stored and processed in the UK? Couldn’t our request have been denied if personal data had not left US territory? Americans don’t have a basic right to request and view our own voter data profiles and ideology models, a right that citizens enjoy in the UK and other European nations. We are concerned as to whether SCL/CA have complied with the UK Data Protection Act of 1998 and have instructed solicitors in the UK to write to them, with a view to court action.


Trump Finally Signed His Long Awaited Executive Order On Cybersecurity

BuzzFeed News, Sheera Frenkel


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The order calls for broad reviews of the federal government’s online vulnerabilities and creates standards for cybersecurity practices across various government agencies.


The next big step for AI is getting out of the cloud and onto your phone

Quartz, Dave Gershgorn


from

You want AI on your phone. It’s faster, more secure, you can use it regardless of the availability of cell service or wi-fi, and perhaps just as importantly, you can look down in your hand at that five-inch device wrought from sand and silicon with the full knowledge that dozens, if not hundreds, of virtual machine brains are making decisions inside just for you.

Luckily, tech companies that develop the services you use every day, like Google and Facebook, also want this to happen, and have been developing AI that takes up less space and runs faster, which is optimal for mobile devices.


Nvidia To Train 100,000 Developers In ‘Deep Learning’ AI To Bolster Healthcare Research

Forbes, Lee Bell


from

Despite the huge demand for expertise in the AI field for developers, there is not enough experts to fill the demand, so more projects like the above are difficult to get off the ground.

This is why Nvidia has revealed that it will train 100,000 developers this year through something it’s calling the Deep Learning Institute (DLI); to provides developers, data scientists and researchers with practical training on the use of the latest AI tools and technology. The DLI will offer 14 different labs and train more than 2,000 developers on the applied use of AI, a tenfold increase over last year.

 
Deadlines



CfP | Mediated Populisms

New York, NY NYU’s Department of Media, Culture, and Communications invites you to participate in the annual Neil Postman Conference, to be held October 6, 2017. The topic this year is “Mediated Populisms,” and the keynote speaker will be Zeynep Tufekci. Deadline for paper submissions is May 15.

PyOhio 2017 Call for Proposals

Columbus, OH PyOhio 2017, the annual Python programming conference for Ohio and the surrounding region, will take place Saturday, July 29th, and Sunday, July 30th, 2017 at The Ohio State University in Columbus, Ohio. Deadline for proposals is May 25.
 
Tools & Resources



Scaling Airbnb’s Experimentation Platform

Medium, Airbnb Engineering & Data Science


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At Airbnb, we are constantly iterating on the user experience and product features. This can include changes to the look and feel of the website or native apps, optimizations for our smart pricing and search ranking algorithms, or even targeting the right content and timing for our email campaigns. For the majority of this work, we leverage our internal A/B Testing platform, the Experimentation Reporting Framework (ERF), to validate our hypotheses and quantify the impact of our work. Read about the basics of ERF and our philosophy on interpreting experiments.


A Guide to Receptive Field Arithmetic for Convolutional Neural Networks

Medium, Synced


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1.Receptive Field and Feature Map Visualization

The receptive field is defined as the region in the input space that a particular CNN’s feature is looking at (i.e. be affected by).


Analyzing 10 years of startup news with Machine Learning

MonkeyLearn Blog, Bruno Stecanella


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This is the final part in a series where we use machine learning and natural language processing to analyze articles published in tech news sites in order to gain insights about the state of the startup industry.

 
Careers


Full-time, non-tenured academic positions

Research Scientist – Applied Statistics/Biometrics



CSIRO Data61; Canberra, Australia

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