Based on the disturbing number of successful data breaches over the past few years, it's pretty evident that organizations are being overwhelmed by the growing number of threats.

Booz Allen Hamilton and Conversable are teaming up to tackle what could be government's next big move to improve customer service -- chatbots. Booz Allen announced Monday that the two companies have a "joint strategic focus to advance, test and deliver world-class automated interactive messaging, tools, services and experiences that fundamentally enhance and transform customer care in all channels." "The federal government is uniquely positioned and motivated to seize the opportunity and deliver on the promise for self-service," Michael Isman, vice president in Booz Allen's Strategic Innovation Group, told FedScoop. The companies plan to leverage advances in artificial intelligence, machine learning and natural language processing to advance the capabilities of chatbots -- automated software programs developed to converse with people, usually via messaging. "The idea is -- we believe that the gains [in artificial intelligence, machine learning, automated next-gen analytics, crowdsourcing] can be most readily realized through the deployment of chatbots," Isman said, "to improve client, customer and stakeholder access, listening, responsiveness, [and] service delivery."

This past spring, Google began feeding its natural language algorithm thousands of romance novels in an effort to humanize its "conversational tone." The move did so much to fire the collective comic imagination that the ensuing hilarity muffled any serious commentary on its symbolic importance. The jokes, as they say, practically wrote themselves. But, after several decades devoted to task-specific "smart" technologies (GPS, search engine optimization, data mining), Google's decision points to a recovered interest among the titans of technology in a fully anthropic "general" intelligence, the kind dramatized in recent films such as Her (2013) and Ex Machina (2015). Amusing though it may be, the appeal to romance novels suggests that Silicon Valley is daring to dream big once again. The desire to automate solutions to human problems, from locomotion (the wheel) to mnemonics (the stylus), is as old as society itself.

An anonymous reader writes: At the Code Conference on Wednesday, Bill Gates balanced his fears of artificial intelligence with praise. He talked about two of the challenges AI will pose: a loss of existing jobs, and making sure humans remain in control of super-intelligent machines. Gates, as well as many other experts in the field, predict there will be an excess of labor resources as robots and AI systems take over. He plans to talk with others about ideas to combat the threat of AI controlling humans, specifically noting work being done at Stanford. Even with such threats, Gates called AI the "holy grail" as he envisions a future "with machines that are capable and more capable than human intelligence."

We don't understand AI because we don't understand intelligence

It has only been in recent years that we've come to see artificial intelligence as a reality and not something out of a science fiction story. Even now, we're still struggling to come up with an adequate definition of the term "artificial intelligence" which isn't surprising when you consider that after thousands of years, humans can't even decide on a definition for "intelligence." Put one way, artificial intelligence is a term given to computer systems that attempt to simulate human intelligence and learning. But even that definition is too big to wrap your head around. To simplify it, you can break artificial intelligence into two categories: Strong (or broad) AI, and weak (or narrow) AI.

This is also known in leading AI research circles as Artificial Narrow Intelligence or simply ANI. Google's autonomous car is an ANI system, so are aircraft flying systems, search engine technologies, stock market systems, Japan's industrial and home robotics or Google's AlphaGO which recently beat Grandmaster Lee Sedol at the game of Go. Tech will gain this ability by acquiring the ability to read, comprehend and derive meaning intelligently from existing big data. This is Artificial General Intelligence or simply AGI.

This is part of a series on Artificial Intelligence. If you are catching it for the first time I'd recommend that you start here where I introduce the idea and provide some instrumental background. In the last article, I talked about the usefulness of thinking about artificial intelligence in its chapters. True, you could start biting this elephant anywhere and anyhow. The phases approach is just my recommended way of understanding, with better clarity, the goals and ultimate intentions of AI.

"Alone we can do so little and together we can do much" - a phrase from Helen Keller during 50's is a reflection of achievements and successful stories in real life scenarios from decades. Same thing applies with most of the cases from innovation with big impacts and with advanced technologies world. The machine Learning domain is also in the same race to make predictions and classification in a more accurate way using so called ensemble method and it is proved that ensemble modeling offers one of the most convincing way to build highly accurate predictive models. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Typically, an ensemble model is a supervised learning technique for combining multiple weak learners or models to produce a strong learner with the concept of Bagging and Boosting for data sampling.

Imagine the difference between a buffalo stampede and a cheeseburger. Both are tasty sources of protein. The difference lies in their requisite culinary tools. Predictive Analytics (PA) is the buffalo stampede of quantitative research: data is big, fast, and shaggy. Interactive Analytics (IA) is a cheeseburger: structured, convenient, and easy to grill.