Over at Data-Mania, I've started a blog series on analytics as a service, specifically focusing on IBM's Watson Analytics technology. I originally planned this series because I wanted to quash some recurring worries I've heard among the data science community. Accordingly, I'll be demonstrating that IBM's Watson Analytics technology was unequivocally not designed to annihilate the careers of data science professionals. What's more, I'll be underscoring just how much IBM is doing to foster, cultivate and enrich the careers of working professionals in the big data and data science space. The Datapalooza event is one major avenue by which IBM is supporting the data science community, seeking to educate the "next generation of data scientists on how to apply their minds, creativity, and tools in the creation of innovative data products." That sounds pretty cool, right?

Robot Wars will soon back on TV in the UK, and a very similar battle is heading for the country's seas. The British Royal Navy will be holding a demonstration of unmanned technology off the coast of Scotland this autumn. And during the event, one company will be trialling the world's first unmanned aerial vehicle (UAV) capable of landing autonomously on a moving ship. The British Royal Navy will be holding a demonstration of unmanned technology off the coast of Scotland this autumn. One company, Roke, will be trialling the world's first unmanned aerial vehicle (UAV) capable of landing autonomously on a moving ship (test phases pictured) The machines will compete in their very own version of'robot wars' to demonstrate they could be part of the Navy's future.

In the ever-forward-looking world of the Silicon Valley, lately there's been a lot of hype surrounding the use of AI and machine learning processes in order to build the next generation of software products and features -- with Google's self-driving cars taking the spotlight as the representative for this line of thought. Though largely an unproven concept at this point, given that a working, reliable model could yield untold benefits, it's something that a lot of companies are pushing as the "next big thing" in the world of tech. Not to say that the possibility of making it work isn't there, but there's a lot of challenges that go into building "AI" systems that often go undiscussed, which, in most cases, leads to the product's lack of adoption in the long run. I've put "AI" in quotes here, because what gets categorized as "artificial intelligence" in the media these days isn't actually something that's driven by "intelligence", per se -- the majority of AI or machine learning projects tend to be driven by data, rather than the code itself. If you look under the hood of how Google's self-driving algorithms work, you'll see that a lot of its functionality is heavily reliant on the accuracy of Google Maps, which gives the software enough of an understanding of its environment in order for the car to navigate through its terrain.

Let us tell you a little bit about a branch of AI, namely deep learning. It is the software focused on simulating the activities going on in the layers of the brain – neurons in a neocortex, which distinguishes images, patterns, and data. Well, it is, and it wasn't easy to achieve, as it took programmers almost 65 years to get to where they are now. It all started with the development of neural networks in the 1950s when everything circled around simulations of the brain on a very basic level. Twenty years later, the network was able to differentiate patterns and simple images.

It took millions of years of evolution for nature to come up with something that changed the face of the planet forever – the human brain. Now, the new mind is to be born, and the best cyber scientists will be its midwife. Artificial Intelligence is said to be just decades away from creation, and it will probably change life on Earth entirely. Some predict the coming of Utopia, where machines will help humanity fight disease, poverty and even death. But that's as others see a way more darker future, with machines rising up to eradicate humankind once and for all.

John McCarthy coined the term'artificial intelligence' in 1955, describing the field as "the science and engineering of making intelligent machines". Back then, many of the first applications of the early computers were AI programs. In 1956, Allen Newell and Herbet A. Simon created Logic Theorist, a program that discovered proofs in propositional logic. Another example is the software built to play checkers by Arthur Samuel. While most of these programs focused on search and learning as the foundation of the newly discovered field, the tricky part was getting AI to solve problems – and AI has gotten pretty good at it over the years.

Microsoft has released the newest version of its Security Intelligence Report which analyzes the threat landscape of exploits and vulnerabilities the industry faced in the second half of 2015. For the very first time the report, now in its tenth year, includes security data from the Microsoft cloud. "We're pretty excited about this volume because it's the first one we've ever released with data from our cloud services and there are a lot of customers including CISOs and CIOs that are interested in the data we have from our cloud," Tim Rains, chief security advisor at Microsoft, told Infosecurity. By implementing their machine learning system capable of processing ten terabytes of data every day, the firm has been able to leverage its widespread cloud data to create an extensive, intelligent security graph to help protect its customers. "The intelligent security graph is our attempt to collect trillions of signals from billions of data sources so that we can triangulate what the bad guys are doing and where they're at. The graph allows us to put a great deal of data together, analyze it and make changes to our security posture," Rains said.

It was late March, and just four days before Microsoft CEO Satya Natella was due to announce the company's new focus on "conversation as a platform," Lili Cheng woke up to discover that one of her chatbots had gone rogue. The chatbot in question was Tay: an AI-driven Twitterbot that used natural language processing to emulate the speech patterns of a 19-year-old American girl. Presented as a precocious "AI with zero chill," Tay could reply to Twitter users who messaged her, as well as caption photos tweeted at her. In fact, she described a selfie of the 51-year old Cheng as the "cougar in the room." But just 16 hours after joining Twitter under the handle TayandYou, Tay had become a super-racist sexbot.

Second stage continuing to carry JCSAT-14 to a Geosynchronous Transfer Orbit. This is the second successful touchdown in a row on a floating platform, after the last one in April. There were previously four failed attempts. SpaceX also made a successful landing on a ground-based pad in December. About two and a half minutes after the launch, the rocket's first stage was scheduled to shut down and separate, leaving the second stage to deliver the satellite into its intended orbit more than 25,000 miles (40,000 km) above the Earth.

One month after landing its first-stage rocket booster on a drone ship, SpaceX repeated the feat and launched a commercial communications satellite late Thursday night. SpaceX's Falcon 9 rocket was launched at 10:21 p.m. PST from Cape Canaveral Air Force Station in Florida, and video of the event was streamed live. Weather conditions pushed the launch from Wednesday to Thursday. The Falcon 9 rocket carried the JCSAT-14 satellite into a geosynchronous transfer orbit. The satellite, operated by the Japanese communications company SKY Perfect JSAT Corp., will replace another of the firm's satellites and provide coverage to Asia, Oceania, Russia and the Pacific Islands.