NYU Data Science newsletter – November 16, 2015

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In data science, a pattern of scandals has emerged. Volkswagen’s gaming of emissions data is the latest example.

In July, the CEO of Whole Foods Markets issued a mea culpa after the supermarket was found to have manipulated product data, over-stating the weight of pre-packaged produce and meats. Over the summer, controversy engulfed Ashley Madison, the social network for married people seeking other partners, as hackers managed to extract a huge amount of private data from the company’s servers. General Motors was also revealed to have hidden information about a faulty ignition switch that has been linked to over one hundred deaths.

While top managers take the fall for these scandals, none of the dubious activities could have happened without the active participation of technical teams. Besides engineers, software developers, and product managers, the burgeoning community of data scientists are also complicit in developing the concepts, algorithms, and software to enable the deception.

I often hear from friends who have an idea for an app, but aren’t software engineers. They want to know how they make progress without having to learn a whole new set of technical skills or fund a development team. They know I’ve worked at Apple and Google, and built my own app for Jetpac, so they’re hoping I can offer some guidance.

Happily there’s actually a lot you can do before you have to dive deep into engineering, so here’s my step by step guide. This based on the process Cathrine, Julian, Chris and I followed at Jetpac, so it’s actually the same process I recommend even if you do have engineers!

I started using Scala 5 years ago instead of Java after starting (but never quite finishing) to read the first edition of Programming in Scala. First only present in tests, my first attempts at Scala code would quite soon invade small utility classes and before I knew it take over all of my projects.

There are many rants about Scala out there. This isn’t one of them. I’m not here to complain, but rather to applaud.

Astronomers have discovered some of the oldest stars in the galaxy, whose chemical composition and movements could tell us what the Universe was like soon after the Big Bang.

The world’s most sensitive detector for dark matter — the mysterious stuff thought to make up 85% of matter in the Universe — was inaugurated on 11 November under the Gran Sasso mountain in central Italy.

If the experiment, called XENON1T, finds dark matter, it will enter the history books. Meanwhile a failure to do so, say many theorists, would go a long way to ruling out a popular candidate for the elusive substance — a type of weakly interacting massive particle (WIMP) predicted by supersymmetry, an elegant extension to the standard model of particle physics.

From Hawaii’s flurry of hurricanes, to record high sea ice in Antarctica, and a heat wave that cooked the Australian Open like shrimp on a barbie, 2014 saw some wild weather. How much of that was tied to climate change is what scientists around the world tried to answer in the Bulletin of the American Meteorological Society’s annual attribution report, which was published Thursday.

What they discovered was that the clearest impacts of warming could be found in heat-related events, from heat waves on land to unusually hot ocean waters. Other events, like droughts in East Africa and the Middle East, California’s intense wildfires, and winter storms that continually swept across the eastern U.S., were harder to pinpoint. In part this is because such events are inherently complex, with a multitude of factors influencing them.

According to Chris Elias, president of the Global Development Program at the Bill & Melinda Gates Foundation, increasing the availability and use of data is a public health intervention in its own right. Mobile phone can be a simple yet powerful tool to collect such data through rapid-turnaround, nationally representative surveys that measure household well-being and health program performance.

The Performance Monitoring and Accountability 2020 (PMA2020) project of the Johns Hopkins Bloomberg School of Public Health is using mobile phones to support its data collection activities across Africa and Asia. The project has now received a $25 million grant from the Gates Foundation. PMA2020 data is informing policies, programs and tracking family planning progress in 12 geographies, by providing decision-makers with high-quality, more cost-effective data every 6 to 12 months.

Whether seeking prestige or profit, New York’s top universities are schooling new entrepreneurs in the art of growing a company.

If you’ve ever tried to hold a conversation with a chatbot like CleverBot, you know how quickly the conversation turns to nonsense, no matter how hard you try to keep it together.

But now, a research team led by Bruno Golosio, assistant professor of applied physics at Università di Sassari in Italy, has taken a significant step toward improving human-to-computer conversation. Golosio and colleagues built an artificial neural network, called ANNABELL, that aims to emulate the large-scale structure of human working memory in the brain — and its ability to hold a conversation is eerily human-like.

The public research university is the source of so much that makes an innovation ecosystem run: talent, technology, fresh ideas, and a conduit for research funding. But where federal dollars fall short, Amazon is stepping in with a new program of small grants to back students, researchers, and others pursuing “bold, risky, globally impactful projects.”

The Toyota Research Institute Inc. will straddle two coasts, with headquarters in Silicon Valley near Stanford University and a second facility in Cambridge, Mass., near the Massachusetts Institute of Technology. Toyota is also investing an additional $50 million each in Stanford and MIT to establish joint artificial intelligence research centers.

35 percent of all trial results for 15 drugs that were approved by the Food and Drug Administration in 2012 were not disclosed. Additionally, nearly 30 percent of the trials conducted for those drugs failed to meet legal disclosure requirements.

“This confirms that pharmaceutical companies often fall below legal and ethical standards,” said Jennifer Miller, an assistant professor in the division of medical ethics at the New York University School of Medicine and a co-author of the study, which looked at 318 clinical trials involving almost 100,000 participants.