Google Brain, the search giant's machine learning arm, is setting up a new group to see if it can teach AI to make its own, original works of art. The company, named Magenta, will be announced more officially at the beginning of June, but was referenced to in a talk given by Douglas Eck, a Google Brain researcher, at Moogfest. This new group has been founded specifically to find out if computers can actually create their own works of art. Whether that be more traditional pictures, videos or even music, Magenta wants to find out if it's possible. The aim may sound familiar to other Google Brain projects, but its direction is completely different.

China plans to expand its artificial intelligence (AI) products market to over US 15 billion by speeding up the manufacturing of products like robots, home appliances and mobile phones as part of efforts to develop new technologies to reorient its sluggish economy. China will speed up the development of its artificial intelligence sector and create a market worth more than 100 billion yuan (US 15.26 billion) over the next three years, the National Development and Reform Commission said in a statement. By 2018, China shall build platforms for fundamental AI resources and innovation and make breakthroughs on basic core technology, the three-year implementation programme for'Internet Plus' artificial intelligence said. Under the plan, China will cultivate and develop emerging artificial intelligence industries, promote innovation in intelligent products and enhance the intelligence level of terminal products. The programme will involve key projects such as intelligent home appliances, smart automobiles, intelligent unmanned systems, intelligent wearable devices and robots.

This IR Magazine interview explores how artificial intelligence, including machine learning and natural language processing, is affecting investor relations. Darrell Heaps, founder and CEO of Q4, a market intelligence and investor engagement firm based in Toronto, is joined by Adam Frederick, Q4's senior vice president, intelligence. Frederick also covered the topic in a recent webinar with Matt Danziger, director of IR at LinkedIn. Look for part two of the video interview, in which Heaps and Frederick discuss the consumerization of IR teamwork.

Dr. Abdalla Kablan is an entrepreneur and academic specialising in machine intelligence, big data analytics and computational Finance. He is the founder of Scheduit – a business oriented social network that uses artificial intelligence and machine learning to match relevant business professionals with each other. Abdalla is also a lecturer and researcher of Computational Finance at the University of Malta. He obtained his PhD degree in Computational Finance from University of Essex, UK where he was a candidate at the Centre for Computational Finance and Economic Agents (CCFEA). His PhD thesis title was (The Use of Fuzzy Logic Applications for High frequency Trading).

Despite the altogether likely Robot Apocalypse that's looming, it's fun to pretend that we -- the humans -- are in control of the machines. As robotics bleed further into the mainstream with autonomous cars and low-cost Arduino kits, we're seeing an influx of laboratories designed to incubate ever more astonishing things. Though most sit on the campuses of universities, many technology firms are investing in labs of their own. Below, we've rounded up five that are changing the world in the here and now. Enjoy, and keep your head on a swivel.

Goodbye to the days when you had to be affiliated with a university or research institute in order to access awesome, cutting-edge scientific research. GotScience.org is a digital publication that delivers comprehensible science to the public--for FREE. GotScience.org is a project of Science Connected, a nonprofit organization dedicated to increasing public understanding of science. In our work to increase public understanding of science, we uphold the highest possible standards of scientific and journalistic integrity. We do not sensationalize, cherry-pick, or misrepresent the research reports.

This story was delivered to BI Intelligence IoT Briefing subscribers. To learn more and subscribe, please click here. Switzerland-based agricultural tech Gamaya announced yesterday that it had raised 3.2 million in funding to combine drones and artificial intelligence to improve farmers' yields, according to Fortune. Gamaya was spun out of the Swiss Federal Institute of Technology, a research university in Switzerland, and received funding from Nestle's Chairman, Peter Brabeck-Letmathe, Swiss venture capital firm VI Partners, and others. The startup uses drones equipped with hyperspectral cameras that can capture changes in water and fertilizer use, crop yields, and pests.

Siri, Cortana, and Google Now have all ceded some of the spotlight to new technologies (such as the Ambient User Experience we discussed yesterday), but at the end of the day, Virtual Personal Assistants (VPAs) such as the three listed above, will lead the way to Artificial Intelligence. AI may seem like a pipe dream but here's the shocker: we're already living in a world of AI. A Virtual Personal Assistant like Siri is an AI – just a very limited one. We tend to think of AI as C-3PO, or Sonny from I, Robot, but those are just advanced (fictionalized) versions of AI. This is an AI like Siri or Chess Wiz cum Cognitive Chef IBM's Watson.

The Naive Bayes classifier is a simple classifier that is often used as a baseline for comparison with more complex classifiers. We will use the famous MNIST data set (pre-processed via PCA and normalized [TODO]) for this tutorial, so our class labels are {0, 1, …, 9}. If you're like me, you may have found this notation a little confusing at first. We can read the left side P(C X) as "the probability that the class is C given the data X". We can read the right side P(X C) as "the probability that the data X belongs to the class C". (this is called the "likelihood") And we can compute the probability that the class 0 given the data, probability that the class 1 given the data, etc. just by computing the probability of the data for each class (how well the data fits a model of each class).

Posted by Kaz Sato, Staff Developer Advocate, Google and Ray Sakai, Product Manager, Reactive Inc. At GCP NEXT 2016, the biggest Google Cloud Platform event held this year in San Francisco, Jeff Dean, Google Senior Fellow, presented the Cloud Vision API with Cloud Vision Explorer. This amazing demo is now available for anyone and we warmly invite you to give it a try. To recap, Cloud Vision API is an image analysis service that's part of Cloud Platform. It enables you to understand the content of images by encapsulating powerful machine learning models in an easy-to-use REST API.