Data Science newsletter – December 2, 2016

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The NYC Data Science Academy (NYCDSA), regarded as one of the top data science bootcamps in the US, has announced a new online education program called Remote Data Science Bootcamp. The full or part-time program offers a complete and comprehensive data science curriculum, interaction with peers and faculty, and job placement assistance for students anywhere in the world with an internet connection who wants to become a data scientist.

Every year hundreds of people come together for the Monitoring, Evaluation, Research, and Learning (MERL) in Development Tech Conference. The MERL Tech Conference – this year hosted in Washington, D.C.— brings together practitioners, from a number of international development sectors, to exchange ideas on innovative ways to use technology to collect, analyze, and use data.

My colleague, CIPE Senior Evaluation Officer Denise Baer, and I attended the conference in search of ideas of how to best handle the “responsible data” CIPE collects from its partners. We also partnered with representatives from Sonjara, The Engine Room, and Reboot to co-lead a conference session entitled “The Lifecycle of Responsible Data.”

“AI,” “big data,” and “machine learning” are all trending buzzwords, and you might be curious about how they apply to your domain. You might even have startups beating down your door, pitching you their new “AI-powered” product. So how can you know which problems in your business are amenable to machine learning? To decide, you need to think about the problem to be solved and the available data, and ask questions about feasibility, intuition, and expectations.

Start by distinguishing between automation problems and learning problems. Machine learning can help automate your processes, but not all automation problems require learning.

Nvidia cofounder Chris Malachowsky is eating a sausage omelet and sipping burnt coffee in a Denny’s off the Berryessa overpass in San Jose. It was in this same dingy diner in April 1993 that three young electrical engineers–Malachowsky, Curtis Priem and Nvidia’s current CEO, Jen-Hsun Huang–started a company devoted to making specialized chips that would generate faster and more realistic graphics for video games. East San Jose was a rough part of town back then–the front of the restaurant was pocked with bullet holes from people shooting at parked cop cars–and no one could have guessed that the three men drinking endless cups of coffee were laying the foundation for a company that would define computing in the early 21st century in the same way that Intel did in the 1990s.

“There was no market in 1993, but we saw a wave coming,” Malachowsky says. “There’s a California surfing competition that happens in a five-month window every year. When they see some type of wave phenomenon or storm in Japan, they tell all the surfers to show up in California, because there’s going to be a wave in two days. That’s what it was. We were at the beginning.”

“History of the Internet” is an animated graphic documentary explaining the inventions from time-sharing to filesharing, from Arpanet to Internet.

Alexa, Tell Me Where You’re Going Next
Amazon’s VP of Alexa talks about machine learning, chatbots, and whether industry is strip-mining AI talent from academia.

AI is going to bring major shifts in society through developments in self-driving cars, medical image analysis, better medical diagnosis, and personalized medicine. And it will also be the backbone of many of the most innovative apps and services of tomorrow. But for many it remains mysterious.

To help unwrap some of this mystery, Facebook is creating a series of educational online videos that outline how AI works. We hope these simple and short introductions will help everyone understand how this complex field of computer science works.

I knew that this was a common dilemma: the architects of the panel (whether it is a panel evaluating grant proposals, an interdisciplinary symposium, or an edited volume), who are typically natural scientists of some sort, make a good-faith effort to bring social scientists into the fold, but generally have little luck. Through a series of slightly hilarious miscommunications and travel snafus, I was unable to attend the meeting in Bethesda. I holed up for a weekend in a cottage in Santa Fe (where I had been participating in an panel the previous week) and wrote a document on how researchers working on the ecology of infectious disease could engage the social sciences and social scientists. As I contemplate my new role in the School of Earth, Energy, and the Environment at Stanford, it seems like a propitious time to revisit this white paper.

Researchers at Imperial College London have developed a USB stick that, in under 30 minutes, can measure the presence and amount of HIV in a person’s blood. The researchers tested 991 blood samples using the USB stick and compared the results with traditional testing methods—the USB stick was 95% accurate.

Dmitri Kri­oukov, asso­ciate pro­fessor in the Depart­ment of Physics, directs the Net­work Sci­ence Institute’s DK-​​Lab, which focuses on net­work theory. We asked him to explain the logic behind the sim­u­la­tion argu­ment and whether we might be living in a Matrix–style world.

Company gets FAA approval to use drones for data collection. Apple acquired startup Indoor.io for interior mapping project.

Why should we care about each little change in the life of an individual plant or the soil on the world’s fields? Because each piece of data farmers collect helps them make more precise decisions about resources like seeds, water, soil nutrients and plant health. That precision cuts down on waste and helps grow food more sustainably, with more efficient use of land, water, fertilizer, fossil fuels and other resources. When combined with other important practices, like reducing the carbon released by tilling, precision agriculture can help reduce agriculture’s carbon footprint. Now agriculture can be a piece of the solution to climate change.

A new theoretical model shows that networks evolve very differently depending on how often these “second neighbor” connections occur. The work could offer a better understanding of how dense networks form.

Networks—like those based on social media or internet connections—are often characterized by their degree, which is the number of links per member, or node. Previous models of networks have tended to focus on sparse networks in which the degree remains finite as a network grows.

By including friend-of-friend interactions in their model, Renaud Lambiotte (University of Namur, Belgium), Paul Krapivsky (Boston University), and Uttam Bhat and Sid Redner (both Santa Fe Institute) could control the link density of the network.

In this episode I spoke with Vikash Mansinghka, research scientist at MIT, where he leads the Probabilistic Computing Project, and co-founder of Empirical Systems. I’ve long wanted to introduce listeners to recent developments in probabilistic programming, and I found the perfect guide in Mansinghka. [audio, 44:37]

In this episode of the O’Reilly Bots Podcast, Pete Skomoroch and I talk with Richard Socher, chief scientist at Salesforce. He was previously the founder and CEO of MetaMind, a deep learning startup that Salesforce acquired in 2016. Socher also teaches the “Deep Learning for Natural Language Processing” course at Stanford University. Our conversation focuses on where deep learning and NLP are headed, and interesting current and near-future applications. [audio, 57:54]

Yingjie Hu, now an assistant professor of geography at the University of Tennessee, Knoxville, and his colleagues have developed an algorithm that indicates which disaster response areas need detailed mapping first. With better maps of the disaster zone, response teams can respond more efficiently to the most urgent needs. Their paper was recently published in the journal Geographical Analysis.

Math. Math. Oh and perhaps some more math.

That’s the gist of the advice to students interested in AI from Facebook’s Yann LeCun and Joaquin Quiñonero Candela

 who run the company’s Artificial Intelligence Lab and Applied Machine Learning group respectively.

Titled: Active Research Areas in Deep Learning for NLP

It’s a quick guide through the basics of Pandas that you will need to get started on wrangling your data with Python.

Landsat on AWS makes Landsat data available for anyone to use via Amazon S3. All Landsat 8 scenes from 2015 are available along with a selection of cloud-free scenes from 2013 and 2014. All new Landsat 8 scenes are made available each day, often within hours of production.