The geographic expansion raised probably the most excitement: Google has committed to add two additional data centers this year to the current 3, following by yet another 10 new datacenters in 2017. This will be an impressive geographical coverage ramp up in two years, and will act as genuine competition against the other big providers. It looks like Google has a well cooked "recipe" for building cloud data centers, and in fact they have not kept this as a secret to themselves, but released it for public consumption with all the standard best practises, along with other papers such as its highly scalable network load balancer design. While the platform is geographically expanding, the focus is also on technology innovations and the Google team are releasing Machine Learning and Big Data offerings one after the other at a fast pace. Nowadays, Machine Learning (ML) is playing a key role in all aspects of IT, including the operating data centers (according to Google). Machine Learning services have been around for a while: Google's Prediction API is available since 2011 and was probably among the first ones to publish ML services in the cloud.

China's search engine giant Baidu launched a project to bring scientists and sci-fi writers to collaborate on imaginative research on Friday. The project, named the Verne Institute after French writer Jules Verne, aims to blend wild imagination and solid science to bring more possibilities, Zhang Yaqin, president of Baidu, told Xinhua in an email interview on Friday. Verne famously said "Anything one man can imagine, other men can make real." Baidu chose to launch the project on April Fool's Day for a sense of contrast to underline the reality of it, Liu Chun with the company's marketing department said. Bridging science and sci-fi may be new in China but is a common way of collaboration elsewhere in the world, Zhang said.

Vast amounts of data and computing power are becoming available to companies and citizens. Both can be leveraged to take informed decisions and automate tasks. Intelligent automation is possible with a set of tecniques going under the name of "Machine Learning", which is an applied branch of Artificial Intelligence. Machine learning models are able to abstract statistical patterns from data and offer quick responses in tasks like prediction, classification, regression, clustering and recommendation. On the 16th of March I joined the RomaJS MeetUp group and introduced them to the basic concepts of machine learning and the possibilities offered to Javascript practitioners.

Machine Learning and Functional Programming are both very hot topics these days; they are also both rather intimidating for the beginner. In this workshop, we'll take a 100% hands-on approach, and learn practical ideas from Machine Learning, by tackling real-world problems and implementing solutions in F#, in a functional style. In the process, you will see that once you get beyond the jargon, F# and Machine Learning are actually not all that complicated – and fit beautifully together. So if you are curious about what Machine Learning is about, and want to sharpen your developer skills, come with your laptop and… let's hack together!

Seal Software announced today that its first meetup session on advanced concepts in Machine Learning (ML) and Natural Language Processing (NLP) was a huge success, with additional sessions being scheduled to meet high attendance demand. The meetup, led by Seal's ML team based in Gothenburg, Sweden, was designed to assemble some of the best minds in ML, describe and explore the newest techniques in the field, and help cultivate the next generation of advancements in this area. Interest in the meetup was also driven by Seal's strong ties to Chalmers University in Gothenburg, a leading technology center in the advancement of data science. The meetup was met with overwhelmingly high demand, with more than twice the number of registrants that could be accommodated. Because of this, Seal plans to schedule additional sessions in order to open the discussion to all community members interested in advanced technology.

On March 23rd, 2016, Microsoft released a chunk of artificial intelligence onto the Internet. Dubbed "Tay," this was a bot1 designed to chat with real human beings, simulating a 19-year-old female, learning from those humans how to act more human. On March 24th, less than 24 hours later, Microsoft put Tay to sleep. She was spewing neo-Nazi, xenophobic, racist tweets. Apparently, Tay had been learning from the wrong humans--those who had chosen to teach her.

Quoc Le, a software engineer at Google Brain, is one such human. Google Brain focuses on "deep learning," a part of artificial intelligence. Think of it as a sophisticated type of machine learning, which is the science of getting computers to learn from data. Deep learning uses multiple layers of algorithms, called neural networks, to process images, text and sentiments quickly and efficiently. The idea is for machines to eventually be able to make decisions as humans do.

Today's robots are far smarter and more capable than the clumsy and awkward robots of the 1980s and 1990s. And although we have a long way to go before autonomous robots reach the sophistication of TV science fiction, we do seem to be getting closer. Engineers, entrepreneurs and academics from the US and UK met this week to think out loud about the past, present and future of robotics during a conference hosted by the British Consulate General, a short walk from the Massachusetts Institute of Technology (MIT) in Cambridge. The robotics and artificial intelligence sectors will be a 2.6 trillion industry by 2020, according to the consulate, with robots playing an ever-larger role in healthcare, military and autonomous systems such as self-driving cars, unmanned aerial vehicles and surface/underwater drones. I went to the conference to investigate a simple question: are the robots we're researching, designing and building today secure against hackers?

SOMETIMES it is perceived as a figment of the far future. But artificial intelligence (AI) is today's great obsession in Silicon Valley. Last year technology companies spent 8.5 billion on deals and investments in artificial intelligence, four times more than in 2010. Nearly all of the world's technology giants, including Google, Microsoft, Facebook, Amazon and Baidu, are competing fiercely to hire the best AI experts, snap up start-ups and pour money into research. The technology has not always been so popular.

Delivered as a cloud platform, the EverString integration enables marketers to analyze and validate their entire addressable market of prospects, then proactively select and target the optimal audience based on data from their Microsoft Dynamics CRM Online and more than 20,000 external signals. EverString's predictive marketing technology leverages applied data science, machine learning, and artificial intelligence to find and analyze patterns in the pipeline of all prospects and customers in a user's instance of Microsoft Dynamics CRM Online. This model of the ideal customer is then applied against the entire prospect database so the EverString priority score can be assigned and surfaced.