In this series of exploratory blog posts, we explore the relationship between recurrent neural networks (RNNs) and IoT data. The article is written by Ajit Jaokar, Dr Paul Katsande and Dr Vinay Mehendiratta as part of the Data Science for Internet of Things practitioners course.

Microsoft releases giant Q&A dataset to help researchers build AI tools that ... Microsoft gives away 100000 questions and answers to advance virtual assistants like Siri, Alexa ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

It's a phrase that often pops up in discussions about the safety and ethics of artificial intelligence. How can scientists create AI with goals and values that align with those of the people it interacts with? Very simple robots with very constrained tasks do not need goals or values at all. Although the Roomba's designers know you want a clean floor, Roomba doesn't: it simply executes a procedure that the Roomba's designers predict will work--most of the time. If your kitten leaves a messy pile on the carpet, Roomba will dutifully smear it all over the living room.

Microsoft is trying to help create machines that can have conversations by releasing a new set of data for free. The data, called the Microsoft Machine Reading Comprehension dataset (MS MARCO) is a bundle of 100,000 English queries along with corresponding answers. It's supposed to help people build artificial intelligence systems that can understand human written language. The company is opening up its dataset in the hope that Microsoft can work with other organizations on making machines better at reading comprehension, said Rangan Majumder, program manager for the Microsoft Partner Group, in a blog post on Friday. The queries in MS MARCO are based on anonymized questions that were submitted to Microsoft's Bing search engine and Cortana virtual assistant.

Momentarily, robots can only construct narratives from uncomplicated, quantitative information because humans have only developed early forms of AI termed narrow intelligence, intelligence equivalent or better than human intelligence at solving very specific lower level tasks, but in the future "robo reporters" may become advanced enough to tell personal, emotional, complicated stories. In other words, AI in its current capacity is capable of deciphering easily interpretable information (e.g., data-driven stories like the statistical analysis of a football game). Naturally, in this form, AI is incapable of composing qualitative, complex stories comparable to first hand experiences or your favorite blog or novel but eventually, algorithms will outperform human writers and be more cost effective. Content marketers will simply plug in basic guidelines and install software programs and AI will take care of the rest.

Most of us already know that adopting new cloud applications can boost a business's productivity by enabling organizations to be more agile and ready to change course in our fast-moving and connected digital world. But the rapid adoption of cloud apps and services also brings with it profound security threats, including visibility and control challenges that aren't present in traditional on-premises environments. At the same time, the cloud - because of its interconnected, flexible and adaptable nature - can also provide new possibilities for addressing cloud security problems. By leveraging the power of the cloud with a data science and machine learning cloud-based solution, security and risk professionals can solve many of the traditional security challenges found in popular apps like Office 365, Google Drive, Salesforce and Box. In her session at 19th Cloud Expo, Deena Thomchick, Senior Director of Cloud Security at Symantec, detailed how cloud-based data science, machine learning, computational analysis and intelligent algorithms can work together to help to deliver truly intelligent and responsive security and compliance for the cloud.

As we look to a New Year, it is hard to say what 2017 has in store thanks to the level of uncertainty that 2016 leaves in its wake. One thing for certain, however, is that 2017 is set to be an eventful and thrilling ride for FinTech with many factors and potential obstacles on the horizon which still challenge the sector. In today's society people are effectively running their whole lives from their mobiles: reading the news, online banking, heating their home and maintaining relationships. Payments are a part of that so it is expected that the numbers to climb further next year. Smartphone sales are reaching saturation point in developed markets, and the growth of technology adoption today are now firmly fixated on emerging and developing nations.

Daniel Tran is an IBM Co-op Student working as a Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly developed cloud environment. He has worked with various components that deal with Big Data, including Hadoop, Pig, Hive, HBase, MapReduce & YARN, Sqoop, Oozie, and Phoenix. He has also worked on separate courses involving Machine Learning.

Google's DeepMind is teaming up with the makers of the StarCraft video game to train its artificial intelligence systems. The AI systems "playing" the game will need to learn strategies similar to those that humans need in the real world, DeepMind said. Its ultimate aim is to develop artificial intelligence that could solve any problem. It has previously taught algorithms to play a range of Atari computer games. StarCraft II, made by developer Blizzard, is a real-time strategy game in which players control one of three warring factions - humans, the insect-like Zerg, or aliens known as the Protoss.

Mathematicians have long wondered at the logical structure of Johann Sebastian Bach's music, attempting to distill it into fractals or understand his "rational and consistent system" of composition. In particular, Bach's chorale harmonizations are ripe for systems like artificial intelligence to understand: They always consist of four parts (a melody and three harmonies), are short in duration (about a minute long), and are based off simple melodies that were popular in Lutheran hymns. These consistent traits allowed a deep neural network built by Sony's Computer Science Laboratories to break down the patterns found between 352 of Bach's chorales and generate new harmonies. The machine's goal isn't to make an original melody, but instead generate the thee supporting harmonies around a supplied melody. It writes each harmony separately, its goal to predict which note Bach himself would write given the preceding note, the notes adjacent in the melody and other harmonies, and the beat the note lands on. Its prediction is made by looking at what Bach did in similar situations, the 352 chorales used to train the AI.