OpenAI, the artificial intelligence research non-profit backed by Tesla's Elon Musk, Y Combinator's Sam Altman, a Donald Trump fan called Peter Thiel, and numerous other tech luminaries, is partnering with Microsoft to tackle the next set of challenges in the still-nascent field. OpenAI will also make Microsoft Azure its preferred cloud platform, in part because of its existing support for AI workloads with the help of Azure Batch and Azure Machine Learning, as well as Microsoft's work on its recently rebranded Cognitive Toolkit. Microsoft also offers developers access to a high-powered GPU-centric virtual machine for these kind of machine learning workloads. These N-Series machines are still in beta, but OpenAI has been an early adopter of them and Microsoft says they will become generally available in December. Amazon already offers a similar kind of GPU-focused virtual machine, though oddly enough, Google has lagged behind and -- at least for the time being -- doesn't offer this kind of machine type yet.

The day before the election, as millions of Americans were feeling confident that the vast majority of the country shared their opinions, a pair of researchers at the University of Southern California Information Sciences Institute published a paper that looked closely at something many of us ignored: the provenance of political tweets. Where do they come from? How many are, in reality, made by humans? And if not, who is designing these crude straw-bots? Analyzing Twitter during three televised debates, they discovered that 20% of all political tweets were made by bots.

Microsoft knows that Minecraft can get kids into programming, and it's banking on that strategy again this year. It just teamed up with Code.org to introduce the Minecraft Hour of Code Designer, a tutorial that teaches young newcomers (6 years old and up) how to create a simple game. The Designer uses a drag-and-drop interface to illustrate familiar code concepts, such as object-oriented programming and loops, while letting imaginations run wild in Minecraft's blocky universe. You can make chickens that drop gold, and otherwise set rules that are as logical or ludicrous as you'd like. The tutorial is available right now in 10 languages, and it'll be available in 50 languages by the time Computer Science Education Week kicks off on December 5th.

Google released an update for Google Play Music, its music-streaming service. With this update, the service will use machine learning to present a user with personalized music recommendations based on "signals" such as their location, current activity and the weather. For instance, a selection of workout music may appear when a user walks into a gym. Google said users can opt in to receive these personalized recommendations. In addition, the app's home screen has been updated to provide users with additional personalized content.

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self driving cars. In Figure 1 above, a ConvNet is able to recognize scenes and the system is able to suggest relevant tags such as'bridge', 'railway' and'tennis' while Figure 2 shows an example of ConvNets being used for recognizing everyday objects, humans and animals. Lately, ConvNets have been effective in several Natural Language Processing tasks (such as sentence classification) as well. ConvNets, therefore, are an important tool for most machine learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be an intimidating experience.

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.