While artificial intelligence is making waves globally, Cognitive Business Intelligence (BI) is the next stage of machine learning to design and analyze unstructured data, video, images and human language, say experts. According to them, we are generating data but is this data being analyzed to create insights which could help in running businesses more effectively is the real concern for businesses. "This potential can be leveraged using Business Intelligence. Using BI, we can understand what really the data means," said Nikhilesh Tiwari, Co-Founder, Helical Insight, the world's first open source Business Intelligence (BI) framework. At the moment, less than 0.5 percent of all data is ever analyzed and used globally.

Contrary to fictional portrayals of humans being herded like animals by Artificially Intelligent (AI) machines, realistic concerns about artificial intelligence are far more benign. Still they are no less important and conversations have to begin looking at perhaps setting some ground rules. That's why the big players are stepping up. Researchers and scientists from large tech companies Google, Amazon, Microsoft, IBM, and Facebook have been meeting and discussing the future implications of AI for humans. While no hard details on the group's policies, objectives, or even it's name have come out, insiders have stated the group's intentions: to ensure that A.I. research is focused on benefiting people, not hurting them.

All of the above is part of working with NLP and machine learning. If you don't mind these steps, then you're ready to get to work.

Using data analytics to understand hospital emergency department overcrowding and wait times, two researchers have developed a methodology to predict future ER demand. Hospitals that use their analysis could use the results to reduce wait times for patients by as much as 15 percent, the researchers contend. The methodology uses machine learning technology to assess data on known patterns of ER activity, say Carri Chan, associate professor of business at Columbia Business School, and Kuang Xu, an assistant professor at Stanford Graduate School of Business. Their approach takes into account factors such as time of day, general level of severity, holidays, weather patterns, bad air quality, flu season and special events, to predict how many walk-in patients will come during a certain time period. That data then can help providers determine when to begin diverting them to their primary care physician, an urgent care facility or another hospital, as well as when to start diverting ambulances to other facilities.

Google DeepMind claims to have significantly improved computer-generated speech with its AI technology, paving the way forward for sophisticated talking machines like those seen in sci-fi films like „Her" and „Ex-Machina."

We all become accustomed to the tone and pattern of human speech at an early age, and any deviations from what we have come to accept as "normal" are immediately recognizable. That's why it has been so difficult to develop text-to-speech (TTS) that sounds authentically human. Google's DeepMind AI research arm has turned its machine learning model on the problem, and the resulting "WaveNet" platform has produced some amazing (and slightly creepy) results. Google and other companies have made huge advances in making human speech understandable by machines, but making the reply sound realistic has proven more challenging. Most TTS systems are based on so-called concatenative technologies.

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data--in this case, the insurance company's customer records--and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

"AI ...surely will be a trend at least on the size of big data. It almost certainly will be a trend on the size of mobile. It might be a trend on the size of the internet. And maybe, just maybe, it'll be a trend on the size of software; that the software before machine intelligence and after will be two worlds that are very different from each other." It's undeniable that artificial intelligence (AI) is one of tech's hottest topics and a trend that is permeating every part of our our world.

Artificial intelligence (AI) has already transformed our lives -- from the autonomous cars on the roads to the robotic vacuums and smart thermostats in our homes. Over the next 15 years, AI technologies will continue to make inroads in nearly every area of our lives, from education to entertainment, health care to security. The question is, are we ready? Do we have the answers to the legal and ethical quandaries that will certainly arise from the increasing integration of AI into our daily lives? Are we even asking the right questions? Now, a panel of academics and industry thinkers has looked ahead to 2030 to forecast how advances in AI might affect life in a typical North American city and spark discussion about how to ensure the safe, fair, and beneficial development of these rapidly developing technologies.

The terms "machine learning" and "artificial intelligence" (AI) conjure up feelings that are equal parts fear and fascination. Until recently, the prospect of a piece of software making human-like decisions resided safely in the far-fetched expectations of 1960s-era computer scientists or the plot lines of science fiction novels. Today, however, after decades of unmet expectations, we finally have AI systems that are beginning to influence our lives in tangible ways. Voice recognition systems like Amazon's Echo and Apple's Siri, and once-unimaginable fantasies like self-driving cars, are on the market for consumers, with more exciting life-like systems to come. We have also seen a few early signs of robotic autonomy that makes us feel uneasy, like the Russian robot that learned how to escape the lab!