NYU Data Science newsletter – June 28, 2016

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

GROUP CURATION: N/A

 
Data Science News



Rogue Machine Intelligence and A New Kind of Hedge Fund

Medium, Numerai


from June 21, 2016

At Numerai, we are building the first interface between machine intelligence and global capital. It is a hedge fund built by a community of anonymous data scientists. And it’s working.

Anonymous users like NCVSAI constantly upload new predictions based on their machine learning models. These predictions are ensembled to control the capital in our hedge fund. Since we launched 7 months ago, 1.9 billion equity price predictions have been submitted to Numerai. This number is growing by 50 million per day.

 

The Superhuman Lawyer: Artificial Intelligence and the Future of the Profession

Medium, Marlet Edwards


from June 27, 2016

Learn how two North Carolina companies are collaborating to bring artificial intelligence into the legal profession.

 

DARPA is looking to make huge strides in machine learning

Computerworld


from June 24, 2016

When New York University researchers wanted to model block-by-block traffic flow data for the city, it took 60 person-months of work by data scientists to prepare the data for use and an additional 30 person-months to develop the model.

The Defense Advanced Research Projects Agency (DARPA) wants to change that.

It’s proposing research into “automated model discovery systems” that would allow a subject matter expert with no data science expertise to create a model.

 

5 Startups Building Artificial Intelligence Chips – Nanalyze

Nanalyze


from June 25, 2016

The actual way you architect a chip can be optimized for specific artificial intelligence tasks like image recognition, voice recognition, or big data analysis of any kind. In the case of deep learning, you use artificial neural networks which simulate the behavior of the brain by creating simulated neurons. Here are 5 companies that are building chips and hardware solutions that promise to optimize artificial intelligence tasks.

 

StoryBot – A Facebook Messenger Bot Experiment

Cornell Tech, The Foundry blog, Adrian Vatchinsky


from June 27, 2016

Hot on the heels of the numerous bot-related announcements made this year by the tech giants, I wanted to get my hands dirty with making a chat-bot of my own. The result is StoryBot, a Facebook messenger bot whose purpose is to mediate a simple writing game in which you are paired up with another anonymous person and take alternating turns writing a story. As as player, you have no direct means of communicating with the other person and are also provided with a writing prompt should you choose to follow it.

The high level goal of the project was to go through a product cycle from ideation and development through iteration and testing and finally launching with a smidget of marketing. The project specific goal was to explore the application of bots for purposes different than ordering stuff, scheduling meetings, or looking up the weather which dominate the current bot scene.

 

Structure and inference in annotated networks

Nature Communications, M. E. J. Newman and Aaron Clauset


from June 16, 2016

For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this ‘metadata’ can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains. [full text]

 

Brexit: voter turnout by age

Financial Times, John Burn-Murdoch


from June 24, 2016


Between the UK’s census and other official national statistics, data is available on the demographic and socio-economic characteristics of most or all of these areas, allowing us to identify common factors among regions which voted one way or the other.

 

Six former commissioners say FDA should be independent agency

STAT


from June 25, 2016

Give the FDA independence and elevate its status. That’s the message from six former commissioners who led the FDA for a combined 32 years.

The FDA is currently part of the Department of Health and Human Services (HHS). Making it a Cabinet-level organization or finding another way to give it more autonomy would be a step in the right direction for public health, the former commissioners argued.

 

The new fintech generation: don’t fight the banks, embrace them

Lending Times


from June 21, 2016

Akouba Credit is a software company muscling in the fintech sector by providing a complete platform for the traditional brick and mortar banks to take their process online.

The early pioneers in fintech lending have all focussed on “bankless” lending. LC, Prosper, FundingCircle, OnDeck etc are all direct competitors of banks. Their technologies and proprietary algorithms have revolutionized the borrower experience, eliminating hassles and moving the entire credit appraisal process online.

 

What’s It Like to Be the CEO of the Smartest Company in the World?

San Diego Magazine


from June 24, 2016

What’s it like to be the CEO of the Smartest Company in the World? We didn’t have to travel far to find out. It’s a quick trip up to the UTC-area campus of genetic sequencing giant Illumina, where outgoing CEO Jay Flatley reflected on nearly two decades of leadership that took the company from 40 employees to 4,000, from very little revenue to more than $2 billion in earnings, and from a very small, niche scientific and medical research market to the boundless clinical space. That means regular people. We all now live in a world where knowing our genetic makeup, down to the individual chromosomes, is not only possible, but affordable. It will be soon be the norm. The data will detect cancer and disease early enough to cure it. Even prevent it. So when it took weeks to nail down time with Flatley, we had to understand. He, and his team, are literally curing cancer.

 

How Manufacturing Giants Are Building The Massive Industrial Internet Of Things

ARC


from June 24, 2016

Over the next 10 years, investment in the Industrial Internet of Things is predicted to be hundreds of billions of dollars. Companies such as General Electric and Hitachi have already started pouring significant amounts of money into dedicated research and software platforms to make the most of the opportunities available.

 

Statistics is Dead – Long Live Data Science…

Data Science Central, Lee Baker


from June 23, 2016

I keep hearing Data Scientists say that ‘Statistics is Dead’, and they even have big debates about it attended by the good and great of Data Science. Interestingly, there seem to be very few actual statisticians at these debates.

So why do Data Scientists think that stats is dead? Where does the notion that there is no longer any need for statistical analysis come from? And are they right?

 
Deadlines



Y Combinator – New Cities

deadline: subsection?

Some existing cities will get bigger and there’s important work being done by smart people to improve them. We also think it’s possible to do amazing things given a blank slate. Our goal is to design the best possible city given the constraints of existing laws. … We’ve begun research and are now forming a team to work on it full-time. We need people with strong interests and bold ideas in architecture, ecology, economics, politics, technology, urban planning, and much more.

Deadline to apply is Saturday, July 30.

 

Call for Papers — Visual Data as Accountability, Resistance, and Surveillance

deadline: subsection?

As technological developments far outpace empirical research on—and legal regulation of—visual data, this special paper symposium in Law & Social Inquiry will provide an opportunity to highlight new empirical work with connections to law and policy, serve as a venue to build theory about a rapidly changing subject, and showcase research relevant to a variety of stakeholders—including lawyers, judges, law enforcement, legislators and policymakers, activists and civil and human rights organizations, technologists, and academics in a variety of fields.

Deadline for abstracts is Saturday, August 10.

 
Tools & Resources



7 awesome data science newsletters to keep you informed

Dataquest.io


from June 24, 2016

In a fast-paced and rapidly growing industry like data science, keeping up is essential. Knowing what is trending is essential in helping you know what new tools to learn, to help you get a job, and much more. At the same time, there is so much content out there that it can be hard to know what to read and easy to be overwhelmed.

The solution is to turn to email newsletters, which can help you keep a handle on the latest news, tools and tutorials. These great newsletters give you everything you need to know to keep up with the world of data science. Their creators put in the hard work so that you don’t have to.

 

10 tips to handle the media as an academic researcher

KevinMD blog


from June 26, 2016

As a researcher, there may come a time when you interact with the media. It may make you cringe; for traditional research publications, we have the protection of editing, and feedback from mentors and colleagues. Interviews feel much more risky: Questions are unpredictable, and there is seldom an opportunity to polish the product before it goes into the wild. Yet, interacting with the media offers an opportunity to garner attention for your research team and share ideas.

What can you expect?

 

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