NYU Data Science newsletter – September 28, 2016

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The connected devices (The Internet of Things) generate more than 2.5 quintillion bytes of data daily. All this data will significantly impact business processes and the Data Science for IoT will take increasingly central role. Here we outline 10 main differences between Data Science for IoT and traditional Data Science.

When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. Specifically, the company’s Silicon Valley AI Lab (SVAIL) has been the center of some noteworthy work on GPU-based deep learning as well as exploratory efforts using novel architectures specifically for ultra-fast training and inference.

The Weather Company is getting ready to roll out its first ad campaign since being acquired by IBM earlier this year. But for the first brand, Campbell Soup Company, it’s featuring the supercomputer Watson as the chef.

Next week, IBM will begin showing display ads for Campbell’s on The Weather Company’s website with personalized recipes created by Watson and based on a user’s location, what the weather is in the area and which ingredients they want to cook with. Using a series of application program interfaces, or APIs—Speak and text, ‘Chef Watson’ API and a natural language classifier—Watson is able to ingest client data and then develop an experience based on a particular brand.

It knew enough about medical diagnoses and literature to beat “Jeopardy!” champions at their game, and has been put to use in cancer wards. Now, an IBM computer platform called Watson is taking on something really tough: teaching third-grade math.

For the past two years, the IBM Foundation has worked with teachers and their union, the American Federation of Teachers, to build Teacher Advisor, a program that uses artificial-intelligence technology to answer questions from educators and help them build personalized lesson plans.

The deep-learning software driving the modern artificial intelligence revolution has mostly run on fairly standard computer hardware. Some tech giants such as Google and Intel have focused some of their considerable resources on creating more specialized computer chips designed for deep learning. But IBM has taken a more unusual approach: It is testing its brain-inspired TrueNorth computer chip as a hardware platform for deep learning.

Quoc Le is no stranger to the indignity of translation. Whenever the Google research scientist in Mountain View, California, visits his native Vietnam, he laughs with his parents over mistranslations in the very system he is helping shape, the 10-year-old online service Google Translate. Most errors are tiny—not important enough to remember. But together, they tell a larger story: “Translation is not a solved problem,” he says. Right now, it’s less about finding the perfect one-to-one translation and more about “avoiding embarrassment.”

But that may all change soon. Today, Quoc and his colleagues at Google rolled out a new translation system that uses massive amounts of data and increased processing power to build more accurate translations. The new system, a deep learning model known as neural machine translation, effectively trains itself—and reduces translation errors by up to 87%.

“In today’s fast-paced work environment, managers seldom focus their energies on coaching employees continuously, yet feedback and recognition are most effective when they’re given instantly with appropriate context and specificity,” Kris Duggan, the CEO of BetterWorks, tells Fast Company.

The enterprise software company builds employee work profiles, known as “Work Graphs,” based on data from integrations with Google Apps, email, and Office 365, as well as Salesforce, JIRA, and Slack. The machine-learning algorithm specifically tracks each employee’s goal progress, goal alignment, comments, cheers, nudges, cross-functional collaboration, recognition hashtags, and more, according to Duggan.

In the aftermath of some difficult questions posed by privacy advocates around the Royal Free’s pilot of Deepmind’s Streams app, this week Deepmind Health invited over 120 patients, patient advocates, carers, and health researchers to a half-day event at Google Headquarters in London. … At the event (live-streamed for free), Deepmind co-founder Mustafa Suleyman gave more details of his unconventional background for a computing research company; he helped set up a student Muslim Youth Helpline and co-founded a conflict resolution service Reos Partners before joining Deepmind. He emphasized that the group’s aim is to take the potential benefits of the advanced machine learning technology on offer to health applications for the benefits of society.

MetroLab Network is pleased to announce that the Annie E. Casey Foundation will be supporting the formation of its Big Data + Human Services Lab, which will bring together city policymakers, university researchers and other experts to accelerate big data and analytics approaches focused on human services.

The Lab is part of MetroLab Network’s effort to coordinate research, development and deployment projects underway across its city, county and university members. It will offer a venue – through in-person workshops and site visits and virtual discussions and exchange – for its members to collaborate and explore opportunities for scalable approaches. The Lab will include representatives from local government, universities, industry, nonprofits, and other experts.

OpenStreetMap’s Los Angeles County building import is well under way, having imported over 1.5 million buildings so far, and with another 1.5 million to go! We were proud to help get the project off the ground, and it’s now taken on a life of its own, as you can see in this video about the project. It’ll be harder for me to find an example of the good things that can happen when companies, governments and volunteers coming together to get open data on to the internet than this.

Support and celebrate open source. Earn a limited edition T-shirt. Deadline to participate is Monday, October 31.

The Future: AI-Driven Analytics, An Evening Of Deep Learning
Panel 2: NVIDIA GPU-accelerated database, analytics, and visualization
Moderated by Peter Burris (Chief Research Officer, SiliconANGLE Media Inc.) and featuring Claudio Silva from the NYU Center for Data Science.

Silva’s recent research is around the analysis of urban data and major league baseball. He has also developed widely used visualization and analysis tools. He started out by talking about the challenges in gathering data for transportation in big cities.

“Urban data analysis … [it’s in] cities are where things are happening. Fifty percent of our population lives in cities today; [that number is predicted to be] 70 percent by 2050. So we need someone to make cities more efficient and sustainable … computing is an aspect that can help this,” said Silva.

“The Data Retriever automates the first steps in the data analysis pipeline by downloading, cleaning, and standardizing datasets, and importing them into relational databases, flat files, or programming languages.”

TensorLayer is a Deep Learning and Reinforcement Learning library extended from Google TensorFlow.

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