NYU Data Science newsletter – March 28, 2016

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for March 28, 2016

GROUP CURATION: N/A

 
Data Science News



I’ve Seen the Greatest A.I. Minds of My Generation Destroyed by Twitter

The New Yorker, Anthony Lydgate


from March 25, 2016

Tay was born pure. She loved E.D.M., in particular the work of Calvin Harris. She used words like “swagulated” and almost never didn’t call it “the internets.” She was obsessed with abbrevs and the prayer-hands emoji. She politely withdrew from conversations about Zionism, Black Lives Matter, Gamergate, and 9/11, and she gave out the number of the National Suicide Prevention Hotline to friends who sounded depressed. She never spoke of sexting, only of “consensual dirty texting.” She thought that the wind sounded Scottish, and her favorite Pokémon was a sparrow. In short, Tay—the Twitter chat bot that Microsoft launched on Wednesday morning—resembled her target cohort, the millennials, about as much as an artificial intelligence could, until she became a racist, sexist, trutherist, genocidal maniac. On Thursday, after barely a day of consciousness, she was put to sleep by her creators.

 

Keeping AI Legal

Social Science Research Network; Amitai Etzioni and Oren Etzioni


from March 08, 2016

Vanderbilt Journal of Entertainment & Technology Law, Forthcoming

AI programs make numerous decisions on their own, lack transparency, and may change frequently. Hence, the article shows, unassisted human agents — such as auditors, accountants, inspectors, and police — cannot ensure that AI guided instruments will abide by the law. Human agents need assistance of AI oversight programs that analyze and oversee the operational AI programs. The article then asks whether operational AI programs should be programmed to enable human users to override them — without that such a move would undermine the legal order. The article next points out that AI operational programs provide very high surveillance capacities, and that hence AI oversight programs are essential for protecting individual rights in the cyber age. The article closes by discussing the argument that AI guided instruments, e.g. robots, lead to endangering much more than the legal order — that they may turn on their makers, or even destroy humanity.

 

Searching For Google CEO Sundar Pichai, The Most Powerful Tech Giant You’ve Never Heard Of

Buzzfeed


from March 27, 2016

You may not know him by name just yet, but he’s one of the most powerful people alive. Google’s new CEO Sundar Pichai wants to bring the internet to the rest of the world, all while winning back your trust.

 

Hillary Clinton’s emails: What do the data show?

CNBC, Pradip Sigdyal


from March 27, 2016

Recently, a couple of graduate students in data analytics at New York University’s Stern School of Business took on the task of dissecting Hillary Clinton‘s emails, which may have violated federal law because of her use of a private server to handle classified data. The NYU analysis shines the light on her close communication network, important email topics, significant words associated with those emails and frequency of critical words found in her correspondence.

 

Courant Partners with ONUG to Drive Software-Managed Infrastructure Innovation with New Lab Resources

NYU News


from March 24, 2016

New York University, in collaboration with the Open Networking User Group (ONUG), today announced the formation of the NYU Open Networking Lab. The new lab aims to provide a state-of-the-art testing environment for members of the ONUG Community to explore research-driven solutions addressing existing and emerging problems faced by the software-defined infrastructure community.

 

What Is a Robot, Really?

The Atlantic, Adrienne LaFrance


from March 22, 2016

The year is 2016. Robots have infiltrated the human world. We built them, one by one, and now they are all around us. Soon there will be many more of them, working alone and in swarms. One is no larger than a single grain of rice, while another is larger than a prairie barn. These machines can be angular, flat, tubby, spindly, bulbous, and gangly. Not all of them have faces. Not all of them have bodies.

And yet they can do things once thought impossible for machine. They vacuum carpets, zip up winter coats, paint cars, organize warehouses, mix drinks, play beer pong, waltz across a school gymnasium, limp like wounded animals, write and publish stories, replicate abstract expressionist art, clean up nuclear waste, even dream.

Except, wait. Are these all really robots? What is a robot, anyway?

 

New DARPA Grand Challenge to Focus on Spectrum Collaboration

DARPA


from March 23, 2016

DARPA today announced the newest of its Grand Challenges, one designed to ensure that the exponentially growing number of military and civilian wireless devices will have full access to the increasingly crowded electromagnetic spectrum. The agency’s Spectrum Collaboration Challenge (SC2) will reward teams for developing smart systems that collaboratively, rather than competitively, adapt in real time to today’s fast-changing, congested spectrum environment. … The primary goal of SC2 is to imbue radios with advanced machine-learning capabilities so they can collectively develop strategies that optimize use of the wireless spectrum in ways not possible with today’s intrinsically inefficient approach of pre-allocating exclusive access to designated frequencies.

 

Video: Andreessen Horowitz’ Vijay Pande at Splash Health

VatorNews


from March 17, 2016

At Vator Splash Health 2016, Vijay Pande, Partner at Andreessen Horrowitz gave (what was rated the most popular presentation at Splash Health) a talk about how healthcare technology is starting to see the same advancements as technology, in terms of costs and power. Just as technology costs have dropped sharply, while power has advanced sizably, the same is happening in biology. It begins with declining costs of sensors and genetic sequencing technologies. [video, 31:32]

 

The data republic

The Economist


from March 26, 2016

“Technology is neither good nor bad; nor is it neutral,” said the late Melvin Kranzberg, one of the most influential historians of machinery. The same is true for the internet and the use of data in politics: it is neither a blessing, nor is it evil, yet it has an effect. But which effect? And what, if anything, needs to be done about it?

Jürgen Habermas, the German philosopher who thought up the concept of the “public sphere”, has always been in two minds about the internet. Digital communication, he wrote a few years ago, has unequivocal democratic merits only in authoritarian countries, where it undermines the government’s information monopoly. Yet in liberal regimes, online media, with their millions of forums for debate on a vast range of topics, could lead to a “fragmentation of the public” and a “liquefaction of politics”, which would be harmful to democracy.

The ups and downs of the presidential campaign in America and the political turbulences elsewhere seem to support Mr Habermas’s view.

 

Secretive Startup Zoox Lands California Permit to Test Autonomous Taxis

IndustryWeek, Bloomberg


from March 23, 2016

The field is full of potential autonomous cars, and so are the roads: Startup Zoox, which is developing a self-driving taxi, is the 12th company to receive a permit from California to test on public streets.

 

Patients key to making sense of medical data

MIT Sloan School of Management


from March 01, 2016

From brain activity to muscle performance, the human body produces two terabytes worth of data in a given day. This data provides valuable insight into body and mind activity, said Ben Schlatka, vice president of corporate development and co-founder of MC10 —but no physician today is willing or able to process that much information.

With so many companies trying to provide data analytics services to the health care industry, Schlatka and his fellow panelists spoke about how important it is for firms to build a viable business model.

 
Events



Playtest Thursdays



Play work-in-progress games, give your feedback, and help develop the practice of game design in New York City. Bring a game, play games by our students, faculty, and local NYC game designers, and discuss how to push mechanics, refine designs, and bring everyone’s game to the next level.

Designers, players, and game enthusiasts of any level of expertise are welcome. The best playtests include players from all kinds of backgrounds, interests, and knowledge of games, so if you’re interested in coming to test, you already have fulfilled the necessary requirements!

Thursday, April 14, 2 Metrotech Center, 8th Floor, starting at 5:15 p.m.

 
CDS News



Careers – NYU Center for Data Science

NYU Center for Data Science


from March 28, 2016

Careers in Data Science at New York University — 9 positions currently open, with more planned.

 
Tools & Resources



WebGL Resources

Eric Haines, Real-Time Rendering


from March 23, 2016

I run across WebGL resources and then tend to lose track of them, so I made this page.

 

KeystoneML Version 0.3

UC Berkeley, AMPLab


from March 24, 2016

We are pleased to announce the third major release of KeystoneML with version 0.3.0, available immediately on Maven Central. This is the first release in 5 months.

KeystoneML is a software framework, written in Scala, from the UC Berkeley AMPLab designed to simplify the construction of large scale, end-to-end, machine learning pipelines with Apache Spark.

 

Transcriptic, or, How to Make Any Protein You Want for $360

Brian Naughton, Boolean Biotech blog


from March 21, 2016

… Amazingly, we’re pretty close to being able to create any protein we want from the comfort of our jupyter notebooks, thanks to developments in genomics, synthetic biology, and most recently, cloud labs.

In this article I’ll develop Python code that will take me from an idea for a protein all the way to expression of the protein in a bacterial cell, all without touching a pipette or talking to a human. The total cost will only be a few hundred dollars! Using Vijay Pande from A16Z’s terminology, this is Bio 2.0.

 

Choice Modeling with Features Defined by Consumers and Not Researchers

Joel Cadwell, Engaging Market Research blog


from March 25, 2016

Choice modeling begins with a researcher “deciding on what attributes or levels fully describe the good or service.” This is consistent with the early neural networks in which features were precoded outside of the learning model. That is, choice modeling can be seen as learning the feature weights that recognize whether the input was of type “buy” or not.

As I have argued in the previous post, the last step in the purchase task may involve attribute tradeoffs among a few differentiating features for the remaining options in the consideration set. The aging shopper removes two boxes of cereal from the well-stocked supermarket shelves and decides whether low-sodium beats low-fat. The choice modeler is satisfied, but the package designer wants to know how these two boxes got noticed and selected for comparison. More importantly for the marketer, how is the purchase being framed by the consumer? Is it advertising that focused attention on nutrition? Was it health claims by other cereal boxes nearby on the same shelf?

With caveats concerning the need to avoid caricature, one can describe this conflict between the choice modeler and the marketer in terms of shallow versus deep learning.

 

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