Emerging Technologies Like Advanced Analytics, Machine Learning and Internet of Things Help Revolutionize Public Sector Agencies, Accenture Report Finds Survey results show meeting customer expectations is one of the lowest ranked priorities ARLINGTON, Va.; Nov. 15, 2016 – Advanced analytics and other emerging technologies are revolutionizing the way governments and public service agencies are trying to address citizen demands, helping to overcome persistent challenges such as regulatory compliance, outdated legacy IT infrastructures and organizational cultures, according to a new research report from Accenture. The report, Emerging Technologies in Public Service, examines the adoption of emerging technologies across agencies with the most direct interaction with citizens or the greatest responsibility for citizen-facing services: health and social services, policing/justice, revenue, border services, pension / social security and administration. As part of the report, Accenture surveyed nearly 800 public service technology professionals across nine countries to identify emerging technologies being implemented or piloted. These technologies include advanced analytics/ predictive modeling, the Internet of Things, intelligent process automation, video analytics, biometrics/ identity analytics, machine learning, and natural language processing/ generation. The survey found that while more than two-thirds (70 percent) of public sector agencies are evaluating the potential of emerging technologies, only a small percentage (25 percent) is moving beyond the pilot phase to full implementation.

At the recent Spark & Machine Learning Meetup in Brussels, François Garillot of Skymind delivered a lightning talk called "DeepLearning4J and Spark: Successes and Challenges." Specifically, François offered a tour of the DeepLearning4J architecture intermingled with applications. He went over the main blocks of this deep learning solution for the JVM that includes GPU acceleration, a custom n-dimensional array library, a parallelized data-loading swiss army tool, deep learning and reinforcement learning libraries--all with an easy-access interface.

Diane Greene, who leads Google's cloud business, announced the team at an event at the company's facilities in San Francisco. The group will be led by Fei-Fei Li, an artificial intelligence professor at Stanford University, and researcher Jia Li. "What really attracted these two people to come and be in Google Cloud is a chance to democratize machine learning and artificial intelligence," Greene said.

Google says its Translate app now spits back more natural translations. Google said Tuesday that it has vastly improved its Google Translate app, available on phones and the web. The search giant said it's now incorporating "neural machine translation" into the software, which translates whole sentences at a time, instead of breaking the text down to smaller chunks and translating those pieces. That means translations come out more natural, with better syntax and grammar. "It has improved more in one single leap than in 10 years combined," said Barak Turovsky, the product lead for Google Translate, during a press event at Google's San Francisco office. The new translation system is coming to eight of the 103 languages supported by the app.

Clearly there are ways modern technology could improve these processes, and one group is hoping smarter AI could help voters make candidate decisions that better reflect their own goals and priorities. Researchers at Harvard and Carnegie Mellon University have been hard at work devising better methods for collective decision-making, using cutting-edge developments in artificial intelligence and machine learning. The researchers behind the effort, led by computer scientist Prof. Ariel Procaccia, stumbled upon the idea while working on decision making for software agents. The "aha" moment came when he realized the same toolset that could be used to help AI make better decisions could be leveraged to help groups of people make better decisions as well.

This is a game built with machine learning. You draw, and a neural network tries to guess what you're drawing. But the more you play with it, the more it will learn. We made this as an example of how you can use machine learning in fun ways. Watch the video below to learn about how it works, and check out more A.I. Experiments here.

My name is Victoria, and I'll be your conference operator today. Welcome you to the NVIDIA Financial Results Conference Call. All lines have been placed on mute. After the speakers' remarks there will be a question-and-answer period. I will now turn the call over to Arnab Chanda, Vice President of Investor Relations at NVIDIA. You may begin your conference. With me on the call today from NVIDIA are Jen-Hsun Huang, President and Chief Executive Officer, and Colette Kress, Executive Vice President and Chief Financial Officer. I'd like to remind you that our call is being webcast live on NVIDIA's Investor Relations website. You can hear a replay by telephone until the 17 November, 2016. The webcast will be available for replay up until next quarter's conference call to discuss Q4 financial results. The content of today's call is NVIDIA's property. It cannot be reproduced or transcribed without our prior written consent. During this call, we may make forward-looking statements based on current expectations. These forward-looking statements are subject to a number of significant risks and uncertainties and our actual results may differ materially. For a discussion of factors that could affect our future financial results and business, please refer to the disclosure in today's earnings release, our most recent Form 10-K and 10-Q, and the reports that we may file on Form 8-K with the Securities and Exchange Commission. All of our statements are made as of today, the 10th of November, 2016 based on information currently available to us. Except as required by law, we assume no obligation to update any such statements. During this call, we will discuss non-GAAP financial measures. You can find a reconciliation of these non-GAAP financial measures to GAAP financial measures in our CFO commentary which is posted on our website. Revenue reached a record in the third quarter, exceeding $2 billion for the first time. Driving this was success in our Pascal-based gaming platform and growth in our datacenter platform, reflecting the role of NVIDIA's GPU as the engine of AI computing.

Nataliia Dmytriievska was 15 years old and enveloped by flames when she first heard the call of outer space. A year earlier her boyfriend had taught her the basics of poi, a Maori dance in which performers swing flaming, tethered weights to describe bright geometric shapes in the dark. Despite the burns and bruises she earned, Dmytriievska was a determined pupil. She would practice for hours each day, drawing flowers and other outlines around her body using dummy weights, before attempting the same perilous tricks using fire. Although money was never the primary motivation – "I simply love the fire; there is something magical when you feel like it's in your control," she said – after a few years Dmytriievska turned semi-professional. She joined a circus troupe in her home city of Kiev, Ukraine to help support her university studies. In June 2007, the troupe began rehearsals for an interpretation of Edgar Allan Poe's poem, The Raven. As the backing music sounded out for the first time – a pipe organ, played rhythmically, as if calling people to worship, soon joined by galloping guitars and a furious drumbeat – Dmytriievska took to the stage. But her mind was not on the performance. As soon as she finished the routine she left the stage, walked up to her friend on the mixing desk and asked: "Where is that music from?" Eve Online: how a virtual world went to the edge of apocalypse and back. The track, he said, came from Eve Online, a science-fiction video game. It is, he explained, a game set in a vast galaxy comprised of tens of thousands of stars and planets, and inhabited by half a million or so people from around the world, who explore and do battle together daily via the internet.

Google's work in machine learning and artificial intelligence is often interesting, but it can be a bit academic. People like to get their hands on these things -- as much as you can, anyway, with something intangible. To that end, Google is collecting a bunch of little demonstrations of this emerging category of tech in its AI Experiments showcase. The idea is simply to let people fool around with example of machine learning, or download the code themselves to see how it works. Giorgio Cam (best on mobile), which identifies objects seen by the device's camera and hypes them up in a rhyming fashion.

"Algorithm" might be one of the most popular terms that almost no one understands. Not many people have PhDs in data science, and even those experts don't always know what's happening. "It's not clear even from a technical perspective that every aspect of AI algorithms can be understood by humans," says Guruduth Banavar, IBM's chief science officer for cognitive computing, which is what IBM calls AI. Artificial intelligence is making decisions by reviewing people's medical tests in hospitals, credit histories in banking, job applications in some HR systems, even criminal risk factors in the justice system. Yet it's not always clear how the computers are thinking.