NYU Data Science newsletter – June 1, 2015

GROUP CURATION: N/A

DARPA’s Short-range Wide-field-of-view Extremely agile Electronically steered Photonic EmitteR (SWEEPER) program has successfully integrated breakthrough non-mechanical optical scanning technology onto a microchip. Freed from the traditional scanning architecture of gimbaled mounts, lenses and servos, SWEEPER technology has demonstrated that it can sweep a laser back and forth more than 100,000 times per second, 10,000 times faster than current state-of-the-art mechanical systems.

SWEEPER technology can also steer a laser precisely across a 51-degree arc, the widest field of view yet achieved by a chip-scale optical scanning system. These accomplishments could open the door to a new class of miniaturized, extremely low-cost, robust laser-scanning technologies for LIDAR and other uses. Applications can include autonomous vehicle technology.

Whether by accident or design, the details of Google’s plans for artificial intelligence (AI) have been elusive. In some cases, there’s no real mystery, just nothing all that exciting to talk about. AI technology is the foundation of the company’s search engine, and the most obvious reason for Google’s high-profile, $400M acquisition of DeepMind in 2014 is to use the UK firm’s expertise in deep learning—a subset of AI research, but more on that later—to bolster that core capability. But the Googleplex has absorbed other bright minds from the field of AI, as well as some of the most buzzed-about companies in robotics, with only some of that collective braintrust officially allocated to driverless cars, delivery drones or other publicly announced robotics or AI-related projects. What, exactly, are Google’s AI experts up to?

In a word: food.

We present a perspective on an approach to data sharing in scientific publications we have been developing in our group. The essence of the approach is that data can be embedded in a human-readable and machine-addressable way within the traditional publishing environment. We show this by example for both computational and experimental data. We articulate a need for new authoring tools to facilitate data sharing, and we discuss the tools we have been developing for this purpose. With these tools, data generation, analysis, and manuscript preparation can be deeply integrated, resulting in easier and better data sharing in scientific publications.

… In their ground floor office on a bland-looking block in Farringdon, a team of about 60 engineers from MIT, Goldman Sachs and Google sit at $40 desks writing code that could dramatically reshape not only how we play games but how large organizations make decisions.

Worlds Adrift, launching this autumn, will be the first public application of Improbable’s technology.

Meanwhile, Samsung has talked to the company about running simulations for its Internet of Things devices, and economists from Oxford are using it to run models of the U.K. housing market.

Four leading researchers (Stuart Russell, Sabine Hauert, Russ Altman, Manuela Veloso) share their concerns and solutions for reducing societal risks from intelligent machines.

A single, standard format to identify resources such as model organisms and reagents used in published experiments is catching on with researchers and journals, its originators claim. The biosciences would benefit from a universal way of citing research materials, they say.

An article published before peer review at F1000Research describes the early fortunes of the Research Resource Identifier (RRID) — a citation format used in papers for referencing antibodies, model organisms and software. The RRID was launched in February 2014 as a pilot study sponsored by the US National Institutes of Health (NIH) in Bethesda, Maryland, and has since made its way into hundreds of papers, says Anita Bandrowski at the University of California, San Diego, who is coordinating the project.

A mere four weeks since releasing the first beta of version 2, the REST API team has returned to announce the second beta of version 2 is available. Adding more than forty enchancements and bugfixes, WP REST API: 2.0 Beta 2 “You Finally Made a Monkey Out of Me” is available for download on Github.

Using high-precision genetic tests to differentiate the thousands of bacteria that make up the human microbiome, researchers at NYU Langone Medical Center suggest that they have found a possible — and potentially surprising — root cause of the increased frequency of certain eye infections among contact lens wearers.

In a study report on their work to be presented at the annual meeting of the American Society for Microbiology on May 31 in New Orleans, NYU Langone researchers say they have identified a diverse set of microorganisms in the eyes of daily contact lens wearers that more closely resembles the group of microorganisms of their eyelid skin than the bacterial grouping typically found in the eyes of non-wearers.