NYU Data Science newsletter – May 25, 2015

NYU Data Science Newsletter features journalism, research papers, events, tools/software, and jobs for May 25, 2015

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Data Science News



Why are Eight Bits Enough for Deep Neural Networks? « Pete Warden’s blog

Pete Warden's blog


from May 23, 2015

Deep learning is a very weird technology. It evolved over decades on a very different track than the mainstream of AI, kept alive by the efforts of a handful of believers. When I started using it a few years ago, it reminded me of the first time I played with an iPhone – it felt like I’d been handed something that had been sent back to us from the future, or alien technology.

One of the consequences of that is that my engineering intuitions about it are often wrong. When I came across im2col, the memory redundancy seemed crazy, based on my experience with image processing, but it turns out it’s an efficient way to tackle the problem. While there are more complex approaches that can yield better results, they’re not the ones my graphics background would have predicted.

 

‘Deep Web Search’ May Help Scientists

Jet Propulsion Laboratory


from May 22, 2015

When you do a simple Web search on a topic, the results that pop up aren’t the whole story. The Internet contains a vast trove of information — sometimes called the “Deep Web” — that isn’t indexed by search engines: information that would be useful for tracking criminals, terrorist activities, sex trafficking and the spread of diseases. Scientists could also use it to search for images and data from spacecraft.

 

New ‘deep learning’ technique enables robot mastery of skills via trial and error

UC Berkeley


from May 21, 2015

UC Berkeley researchers have developed algorithms that enable robots to learn motor tasks through trial and error using a process that more closely approximates the way humans learn, marking a major milestone in the field of artificial intelligence.

They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks — putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more — without pre-programmed details about its surroundings.

 

Artificial intelligence joins hunt for human–animal diseases : Nature News & Comment

Nature News & Comment


from May 18, 2015

Lyme disease, Ebola and malaria all developed in animals before making the leap to infect humans. Predicting when such a ‘zoonotic’ disease will spark an outbreak remains difficult, but a new study suggests that artificial intelligence could give these efforts a boost.

A computer model that incorporates machine learning can pinpoint, with 90% accuracy, rodent species that are known to harbour pathogens that can spread to humans, researchers report this week in the Proceedings of the National Academy of Sciences. The model also identified more than 150 species that are likely to be disease reservoirs but have yet to be confirmed as such.

 

Inside Obama’s plan to use open data to curb police brutality – life – 22 May 2015 – New Scientist

New Scientist


from May 22, 2015

Camden, New Jersey, isn’t where you might expect to find the future of policing emerging. It was renowned as one of the most violent cities in the US for decades. Two years ago, struggling with crime and running out of money, the city scrapped its police department entirely.

But now Camden is at the centre of an ambitious new scheme. The White House launched the Police Data Initiative there this week, tasking Camden and 20 other US cities with using data analysis to understand and change police behaviour in an attempt to heal the broken relationship between them and the public.

 

Chelsea Clinton Shares 20-Year Data on Women’s Progress, Problems | Xconomy

Xconomy


from May 22, 2015

Putting a number on gender inequality issues can bring attention to the scale of such matters.

During Internet Week New York, Chelsea Clinton presented data and findings from a report by the No Ceilings project, which gave a snapshot of progress made in support of women around the world. The report also identified key problems that need to be addressed.

 

Google a step closer to developing machines with human-like intelligence

The Guardian


from May 21, 2015

Computers will have developed “common sense” within a decade and we could be counting them among our friends not long afterwards, one of the world’s leading AI scientists has predicted.

Professor Geoff Hinton, who was hired by Google two years ago to help develop intelligent operating systems, said that the company is on the brink of developing algorithms with the capacity for logic, natural conversation and even flirtation.

 

Wiki Surveys: Open and Quantifiable Social Data Collection

PLOS One


from May 20, 2015

In the social sciences, there is a longstanding tension between data collection methods that facilitate quantification and those that are open to unanticipated information. Advances in technology now enable new, hybrid methods that combine some of the benefits of both approaches. Drawing inspiration from online information aggregation systems like Wikipedia and from traditional survey research, we propose a new class of research instruments called wiki surveys. Just as Wikipedia evolves over time based on contributions from participants, we envision an evolving survey driven by contributions from respondents. We develop three general principles that underlie wiki surveys: they should be greedy, collaborative, and adaptive. Building on these principles, we develop methods for data collection and data analysis for one type of wiki survey, a pairwise wiki survey. Using two proof-of-concept case studies involving our free and open-source website www.allourideas.org, we show that pairwise wiki surveys can yield insights that would be difficult to obtain with other methods.

 
Events



The Art and (Data) Science of Data Visualization – Metis: Data Science (New York, NY)- Meetup



Join us at Metis for a panel discussion and presentation that brings together leaders in the field of Data Visualization. Our speakers for the evening will be Kevin Quealy, Graphics Editor at The New York Times, Annelie Berner, Member of the New Museum’s NEWINC and instructor at Rutgers College of Art and Design, and Enrico Bertini, Co-Creator of Data Visualization podcast Data Stories and Assistant Professor at the NYU Polytechnic School of Engineering. Moderating our panel will be Dr. Naomi B. Robbins, Chair of the Statistical Graphics Section of the American Statistical Association.
 

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