NYU Data Science newsletter – November 17, 2015

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MIT researchers (Youssef Marzouk, Patrick Conrad, Natesh Pillai) have developed a new algorithm that vastly reduces the computation of virtually any computational model.

rOpenSci is a developer collective originally formed in 2011 by graduate students and post-docs from ecology and evolutionary biology to collaborate on building software tools to facilitate a more open and synthetic approach in the face of transformative rise of large and heterogeneous data. Born on the internet (the collective only began through chance discussions over social media), we have grown into a widely recognized effort that supports an ecosystem of some 45 software packages, engages scores of collaborators, has taught dozens of workshops around the world, and has secured over $480,000 in grant support. As young scientists working in an academic context largely without direct support for our efforts, we have first hand experience with most of the the technical and social challenges WSSSPE seeks to address. In this paper we provide an experience report which describes our approach and success in building an effective and diverse community.

Deep Learning waves have lapped at the shores of computational linguistics for several years now, but 2015 seems like the year when the full force of the tsunami hit the
major Natural Language Processing (NLP) conferences. However, some pundits are predicting that the final damage will be even worse. Accompanying ICML 2015 in Lille, France, there was another, almost as big, event: the 2015 Deep Learning Workshop. The workshop ended with a panel discussion, and at it, Neil Lawrence said, “NLP is kind of like a rabbit in the headlights of the Deep Learning machine, waiting to be flattened.” Now that is a remark that the computational linguistics community has to take seriously!

Perversely, academic research penalises expert software developers for providing the tools on which the research relies. Time for a change, says Simon Hettrick of the Software Sustainability Institute.

While Tesla’s new hands-free driving is drawing a lot of interest this week, it’s the technology behind-the-scenes of the company’s newly-enabled autopilot service that should be getting more attention.

At an event on Wednesday Tesla’s CEO Elon Musk explained that the company’s new autopilot service is constantly learning and improving thanks to machine learning algorithms, the car’s wireless connection, and detailed mapping and sensor data that Tesla collects.

The Macaulay Library at the Cornell Lab of Ornithology is the oldest and largest scientific archive of natural sound and video recordings in the world – and it’s about to get much, much bigger. A $7.5 million gift from the Macaulay Family Foundation will open the digital floodgates, allowing the public to contribute images, video and sound recordings.

“This is a huge step toward integrating all of the lab’s rich media, and it will keep us at the cutting edge of the digital age as we begin our next 100 years,” said John W. Fitzpatrick, the Louis Agassiz Fuertes Director of the lab and professor of ecology and evolutionary biology.

When Google open sourced its artificial intelligence engine last week—freely sharing the code with the world at large—Lukas Biewald didn’t see it as a triumph of the free software movement. He saw it as a triumph of data.

That’s how you’d expect him to see it. He’s the CEO of the San Francisco startup CrowdFlower, which helps online companies like Twitter juggle massive amounts of data. But after spending time at the Stanford AI Lab, he knows artificial intelligence. And his point is a good one.

‘What they’re not opening up is their data. They would never do that.’ Lukas Biewald, CrowdFlower

In open sourcing the TensorFlow AI engine, Biewald says, Google showed that, when it comes to AI, the real value lies not so much in the software or the algorithms as in the data needed to make it all smarter.

Autonomous vehicles might remain an expensive novelty, or they might utterly transform society. Either way, they have much to teach us about how to look at the cities we live in.

An effort to get people better boots may say much about the future of artificial intelligence in the business world.

The company doing the work, called Sentient, based in San Francisco, has used a version of advanced A.I. to build a visual search service for Shoes.com, an online footwear company based in Vancouver, Canada. The service, available on its Canadian site, is to go live on Tuesday.

While it is — at least for the moment — limited to retail, over the long haul the technology could demonstrate how important it is for companies to be sitting on vast warehouses of information.

The potential of artificial intelligence and advanced robotics to perform tasks once reserved for humans is no longer reserved for spectacular demonstrations by the likes of IBM’s Watson, Rethink Robotics’ Baxter, DeepMind, or Google’s driverless car. Just head to an airport: automated check-in kiosks now dominate many airlines’ ticketing areas. Pilots actively steer aircraft for just three to seven minutes of many flights, with autopilot guiding the rest of the journey. Passport-control processes at some airports can place more emphasis on scanning document bar codes than on observing incoming passengers.

What will be the impact of automation efforts like these, multiplied many times across different sectors of the economy? Can we look forward to vast improvements in productivity, freedom from boring work, and improved quality of life? Should we fear threats to jobs, disruptions to organizations, and strains on the social fabric?

On Nov. 3, the American Association for the Advancement of Science (AAAS) announced a new fellowship program focused on community engagement in the science community. This makes me curious.

The new venture, called the AAAS Community Engagement Fellows program, was launched with support from the Alfred P. Sloan Foundation. The goal, according to a AAAS news release, is to “professionalize and institutionalize the role of community engagement managers in the scientific community” and “provide training and professional development for up to 18 fellows.”

But what does AAAS mean by “community engagement”? Why is it important to researchers? How can a small class of fellows make a difference? And what will the fellowship actually entail?

The Network Science PhD program is a pioneering interdisciplinary program that provides the tools and concepts aimed at understanding the structure and dynamics of networks arising from the interplay of human behavior, socio-technical infrastructures, information diffusion and biological agents.

The priority application deadline for Fall 2016 is February 1, 2016.