NYU Data Science newsletter – August 10, 2016

GROUP CURATION: N/A

… The 49ers have entered into a deal with Orig3n to reward fans for making genetic contributions to populate their ever-growing database that informs pharmaceutical research. In fact, Orig3n has two uses for the gene samples they collect: the first is to build up LifeCapsule, which the company claims is the world’s largest blood cell repository dedicated to supporting regenerative medicine. That’s the primary focus of the 49ers deal.

The second use is for LifeProfiles, where consumers can learn more about their own genetic profiles in the quest to know their own propensity for greatness (by, for instance, comparing their sample to those from the 49ers bench). These tests offer the following claim: “the SUPERHERO assessment decodes secret information in your DNA, giving you insights into where your super-powers lie.”. They tell you, with some sheen of scientific specificity, whether the footnote on your cape should reflect your extreme intelligence, strength or speed.

Brain.fm is a freemium auditory program designed to help people either focus, relax or sleep using AI-generated music. It’s users include students, insomniacs and athletes. Heavily steeped in scientific research, its creators have a history in making niche audio brainwave software for psychologists and researchers and their work includes patents on auditory brainwave stimulation and memory.

I spoke to Brain.fm co-founder Adam Hewett, client and Olympic athlete Robby Smith, and Junaid Kalmadi, who looks after business development for Brain.fm. Smith is using Brain.fm as part of his preparations for the 2016 Rio Olympics.

By now, many of us have burned a few precious seconds at the checkout line waiting for a fancy new chip-and-pin card to confirm your purchase of groceries. A small price to pay, we’re assured, for much better protection from data theft.

A downside to that additional security: The hackers have moved on.

Today, according to cybersecurity specialists, criminals hoping to scoop up valuable personal data are increasingly targeting health care companies — from local doctor’s offices to major health insurers.

We’re a group of research scientists and engineers that work on the Google Brain team. Our group’s mission is to make intelligent machines, and to use them to improve people’s lives. For the last five years, we’ve conducted research and built systems to advance this mission.

The Office of Management and Budget is easing open federal computer code for inspection. The OMB revealed its finalized requirements for the Federal Source Code policy on Monday, which demand federal projects make at least 20 percent of their computer code open source. What’s more, agencies will be expected to share all internally-developed code with one another.”

A roboticist and crucial member of the team that created Google’s self-driving car is leaving the company, the latest in a string of departures by important technologists working on the autonomous car project.

Chris Urmson, a Carnegie Mellon University research scientist, joined Google in 2009 to help create the then-secret effort. He took over leadership of the team after Sebastian Thrun, the Stanford computer scientist and founder of Google X laboratory, left in 2013.

Environmental social scientist Jampel Dell’Angelo and filmmaker Matteo Dell’Angelo recently co-directed a documentary film of Elinor Ostrom’s last research project. Working Together documents the challenges and successes of interdisciplinary research on smallholder climate adaptation and community water governance in semi-arid areas. The study found that involvement of all the river basin actors in a participatory way reduced social conflicts while providing more sustainable water allocation in the region. The film features water competition and governance in Kenya, which is a country that is innovative among developing countries for participatory water governance reforms.

A postdoctoral research fellow at the National Socio-Environmental Synthesis Center (SESYNC), Jampel Dell’Angelo conducted over nine months of fieldwork on Mount Kenya as postdoctoral researcher at the Ostrom Workshop and coordinator of an interdisciplinary team on the U.S. National Science Foundation research project awarded to Elinor Ostrom. [video, 18:10]

Intel is buying artificial intelligence startup Nervana Systems to boost the chip giant’s prowess in the data center and capabilities around the rising area of machine learning.

The move, which will reportedly cost Intel in the neighborhood of $350 million, brings the chip giant expertise in deep learning, a subset of machine learning which is used to train software. All of this is needed as connected devices generate more and more data that must be crunched to gain insights or for computers to make decisions.

A scientist can spend several months, in many cases even years, strenuously investigating a single research question, with the ultimate goal of making a contribution—little or big—to the progress of human knowledge.

Succeeding in this hard task requires specialized, years-long training, intuition, creativity, in-depth knowledge of current and past theories and, most of all— lots of perseverance.

As a member of the scientific community, I can say that, sometimes, finding an interesting and novel result is just as hard as convincing your colleagues that your work actually is novel and interesting. That is, the work would deserve publication in a scientific journal.

Machines that truly understand language would be incredibly useful. But we don’t know how to build them.

… What drew you to the Center for Data Science at NYU?

There’s a level of excitement in being at a place where such a diverse set of research backgrounds are united by an interest in data-driven and data-inspired research. That sort of environment creates potential for collaborations across fields that would, otherwise, potentially not interact.

As artificial intelligence gains ground, designers will need to adapt. Here’s how to get started.

Your friends and family don’t share [your] addiction. When you sit down and knock out a few hundred lines of code and finish up–satisfied–they don’t feel the same satisfaction.

As developers, it can be difficult to find that balance of work and life. Relationships are a key part of being happy, so it’s important we understand how to maintain and grow them.

In this blog post, I present Raymond Yeh and Chen Chen et al.’s paper Semantic Image Inpainting with Perceptual and Contextual Losses, which was just posted on arXiv on July 26, 2016. This paper shows how to use deep learning for image completion with a DCGAN. This blog post is meant for a general technical audience with some deeper portions for people with a machine learning background.