Internet access is increasingly being recognized as a necessity like food, water, and shelter -- and Google doesn't want to leave anybody out. Before leaving office, President Barack Obama supported the notion that the internet should be easily accesible, while Canada's government went as far as officially declaring broadband connections "necessary to the quality of life" of its citizens. SEE ALSO: Google's Waymo self-driving platform shows big improvements That's why Google has been hard at work on a project to connect people living in underserved rural and remote regions with the rest of the world online. Project Loon, which first launched in 2013 as part of the Google X (now just X), has been sending smart balloons up into the stratosphere for four years now with the aim of creating a floating, balloon-powered LTE network. Today, Project Loon's top brass announced a major breakthrough.

As organizations struggle with finding the delicate balance of cybersecurity and customer convenience, Mordecai Rosen of CA Technologies says behavioral analytics and machine learning will help. Rosen is senior vice president and general manager for the cybersecurity business at CA Technologies. He joined CA in August 2015 when it acquired Xceedium, where he was the COO. He has more than 25 years of technology experience, including founding and leading an early stage venture management firm. Rosen formerly served as senior vice president of corporate development and strategy at NetSec, a leading managed security services provider acquired by MCI/Verizon.

The world of Artificial Intelligence today is still centered around the aspiration for machines to understand: from virtual assistants capable of anticipating our needs and helping us drafting emails or handling our complex schedule, to self-driving cars, and personalized medicine. These are just some examples of how machines need to acquire, demonstrate, and apply understanding. Today's investments in public research and in the private sector are directed at cracking the nut of machines that understand. But the breadth of techniques goes well beyond what it did even ten years ago. In light of today's relevance of Cognitive Artificial Intelligence, we are happy to announce the Cognitive Artificial Intelligence Meetup #CAIM ("Kay-im").

Although artificial intelligence (as a set of technologies, not in the sense of mimicking human intelligence) is here since a long time in many forms and ways, it's a term that quite some people, certainly IT vendors, don't like to use that much anymore – but artificial intelligence is real, for your business too. Instead of talking about artificial intelligence (AI) many describe the current wave of AI innovation and acceleration with – admittedly somewhat differently positioned – terms and concepts such as cognitive computing or focus on several real-life applications of artificial intelligence that often start with words such as "smart", "intelligent", "predictive" and, indeed, "cognitive", depending on the exact application – and vendor. Despite the term issues, artificial intelligence is essential for and in, among others, information management, medicine/healthcare, data analysis, digital transformation, security (cybersecurity and others), various consumer applications, scientific advances, FinTech, predictive systems and so much more. There are many reasons why several vendors doubt using the term artificial intelligence for AI solutions/innovations and often package them in another term (trust us, we've been there). Artificial intelligence (AI) is a term that has somewhat of a negative connotation in general perception but also in the perception of technology leaders and firms. One major issue is that artificial intelligence – which is really a broad concept/reality, covering many technologies and realities – has become like a thing we talk about and also seem to need to have an opinion/feeling about, with thanks to, among others, popular culture.

"Ambiguity is pervasive" – true to its definition, as increasingly data getting generated, system connectivity reaching its peak, data and outcome are diverging. IT systems are evolving from "BIG DATA" to "BIGGER DATA" systems. Not all of this data is structured and easily consumable, thus challenge is posed by nexus of technology & "Data Greed". Having said this, fact is that future is found in ambiguity and chaos. We will never have complete and perfect information or a full understanding of data, system, experts, people, process and "partially hidden" technology.

If you play video games, imagine how much you sometimes empathize with the character you're controlling. You may even forget the separation between the two of you, experiencing the world as that character. Consider whether your control in these moments is different, both at a high level and in tiny movements, than what the character would do if you had simply written down a set of rules for it to act by. In that difference lies the promise of this method for developing character AI. In psychology research methodology, there's a broad consensus that if you want to know what someone would do in a situation, you don't ask them what they would do.

Facial recognition technology is something that most of us take for granted. We've casually noticed that our smartphones now organize photos by people, or that Facebook somehow always knows the right friends to tag. But until recently, most people haven't realized that this technology is less of a "cool trick" and will actually significantly shape the way we do business in the next five to ten years. The technology is already being tested out in many different industries for vastly different purposes. For example, security scanners at the airport use it to allow e-passport holders to clear customs more easily; as facial recognition improves, Customs and Border Protection will be able to weed out travelers with fake passports more easily.

As advancements in Quantum Computing and Artificial Intelligence are always popular media topics, I thought it'd be interesting to take a look at how the two are converging and what such a convergence might mean for cybersecurity. Today's traditional computers operate by processing information as "bits." A bit can be either a "1" or a "0." Even massive data-crunching super computers such as the US Department of Energy's Titan Cray XK7 operate in this fashion. These computers explore potential solutions to problems in a methodical, sequential manner.

Security is a broad term, and in industry and government there are a myriad of "security" contexts on a variety of levels – from the individual to nation-wide. Artificial intelligence and machine learning technologies are being applied and developed across this spectrum. While many of these technologies have the potential and have greatly benefited society (helping reduce credit card fraud, for example), the evolving social contexts and applications of these technologies often leave more questions than answers – in terms of rules, regulations and moral judgments – in their wake. Artificial intelligence and security were – in many ways – made for each other, and the modern approaches of machine learning seem to be arriving just in time to fill in the gaps of previous rule-based data security systems. The purpose of this article is to shed light on current trends and applications, in industry and government, at the intersection of artificial intelligence and the security field.

We are dealing with plethora of data and information in the world today and expectation is to predict and forecast how we can gain competitive advantage based on the information that we have, to act in advance. We look forward to define and furnish various methods based on our gut feel, past historical data, simple mathematical averages, and many more to get an incredibly precise prediction. With advanced analytics and data science, we develop "always-on" forecasting models which enable our clients to take their decisions effectively. From intuition to traditional algorithms to machine learning, phases have been evolving over a period. Processes 2 & 3 are iterative and 6 & 7 are iterative. We try to look for answers to various questions in the process – is the goal / business objective descriptive or predictive in nature?