NYU Data Science newsletter – June 23, 2016

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The development of magnetic resonance imaging (MRI) techniques has defined modern neuroimaging. Since its inception, tens of thousands of studies using techniques such as functional MRI and diffusion weighted imaging have allowed for the non-invasive study of the brain. Despite the fact that MRI is routinely used to obtain data for neuroscience research, there has been no widely adopted standard for organizing and describing the data collected in an imaging experiment. This renders sharing and reusing data (within or between labs) difficult if not impossible and unnecessarily complicates the application of automatic pipelines and quality assurance protocols. To solve this problem, we have developed the Brain Imaging Data Structure (BIDS), a standard for organizing and describing MRI datasets. The BIDS standard uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.

The Defense Advanced Research Projects Agency has launched its Data-Driven Discovery of Models program that aims to automate aspects of data science to help non-experts construct their own empirical models.

DARPA said Friday D3M looks to address a data science expertise gap the agency says is reflected by lack of results for predictive questions among popular search engines.

Ashton Kutcher as a television morning show host? Aaron Paul as an advertising spokesman for hybrid automobiles?

Those are two of the ideas suggested by a new analytics tool unveiled by the Nielsen company Wednesday, one that it believes can provide scientific rigor to decisions on how to deploy talent.

Each year we identify 50 companies that are “smart” in the way they create new opportunities. Some of this year’s stars are large companies, like Amazon and Alphabet, that are using digital technologies to redefine industries. Others are wrestling with technological changes: companies like Microsoft, Bosch, Toyota, and Intel. Also on the list are ambitious startups like 23andMe, a pioneer in consumer-accessible DNA testing; 24M, a reinventor of battery technology; and Didi Chuxing, a four-year-old ride-hailing app that’s beating Uber in the Chinese market. Still, despite the excitement of recent advances in such fields as artificial intelligence and genomic medicine, technology has failed to energize the overall economy.

Could machines become so intelligent and powerful they pose a threat to human life, or even humanity as a whole?

It’s a question that has become fashionable in some parts of Silicon Valley in recent years, despite being more or less irreconcilable with the simple robots and glitchy virtual assistants of today (see “AI Doomsayer Says His Ideas Are Catching On”). Some experts in artificial intelligence believe speculation about the dangers of future, super-intelligent software is harming the field.

Now Google, a company heavily invested in artificial intelligence, is trying to carve out a middle way.

The city of Boston has been moving ahead rapidly in recent years to make its operations more data driven. With the hiring of its first chief data officer, Andrew Therriault, the city is hoping to accelerate this process.

Therriault comes to the city from the political realm. After doing consulting work for a few years he moved to the Democratic National Committee in 2014 where he helped the party standardize many of the data-driven processes pioneered by the Obama campaign in 2012. He spoke with SearchBusinessAnalytics about how he plans to leverage that experience to improve the efficiency and effectiveness of city government.

Sales of HPC systems were a lot brisker in 2015 than anticipated, and according to the latest prognostications from the market researchers at IDC presented from the International Supercomputing Conference in Frankfurt, Germany this week, growth in the HPC sector will continue to outpace that of the overall IT market for many years to come.

Large-scale brain-like machines with human-like abilities to solve problems could become a reality, now that researchers have invented microscopic gadgets that mimic the connections between neurons in the human brain better than any previous devices.

The new research could lead to better robots, self-driving cars, data mining, medical diagnosis, stock-trading analysis and “other smart human-interactive systems and machines in the future,” said Tae-Woo Lee, a materials scientistat the Pohang University of Science and Technology in Korea and senior author of the study.

Artificial Intelligence is about to transform management from an art into a combination of art and science. Not because we’ll be taking commands from science fiction’s robot overlords, but because specialized AI will allow us to apply data science to our human interactions at work in a way that earlier theorists like Peter Drucker could only imagine.

Also in technology management:

50 Smartest Companies 2016 (June 21, MIT Technology Review)

Baidu Is Using Its Own Data to Measure China’s Economy (June 21, Bloomberg)

What makes a great software engineer? (June 21, Andew J. Ko, Bits and Behavior blog)

A recently published survey by the Evans Data Corporation shed light on a growing trend in the world of technology development. It showed the high-profile shift in focus of data-driven corporations towards artificial intelligence, robotics, and the Internet of Things.

This survey, which included 1,441 developers, found that of all the industries impacted by big data analytics, the Internet of Things was ranked at the top with 15.1% followed closely by telecommunications and professional scientific services at 10% each.

Among data mining app developers, robotics, automobiles, and entertainment were being focused on by over half of those surveyed. The sensors used in IoT technologies offer data mining applications valuable insights that go beyond the data traditional systems could obtain.

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. With 107,785 question-answer pairs on 536 articles, SQuAD is significantly larger than previous reading comprehension datasets.

Streaming data can be used as a long-term auditable history when you choose a messaging system with persistence, but is this approach practical in terms of the cost of storing years of data at scale? The answer is “yes”, particularly because of the way topic partitions are handled in MapR Streams. Here’s how it works.

Although text often contains highly valuable data for companies, extracting meaningful data from it can be a challenge. The field of text analytics utilizes natural language processing to extract meaningful structured data from text, and often includes areas such as sentiment analysis, entity recognition and linking, and text clustering.

The Microsoft Text Analytics API is one of the Cognitive Services that can help you turn unstructured text into meaningful insights.