They often have to navigate, with limited resources, a stormy market made of customers, competitors, and regulators, and the interactions between all these actors make finding answers to business questions a complex process. But recently, machines have demonstrated their abilities to help shine some light on this chaos and provide, if not direct answers, context clues that help guide executives in using AI to to handle business problems. In this article, we delve into examples of how natural language processing (NLP) business applications can be applied at scale to address 5 pressing business questions. NLP is used by computers to manipulate human language, whether to extract meaning, generate text, or for any other purpose. The interaction computer-language is categorized according to the task that needs to be accomplished: summarizing a long document, translating between two human languages, or detecting spam email are all examples of tasks that today can be decently accomplished by a machine.

The information security industry's rush to adopt machine learning will help businesses burn US$96 billion on big data, intelligence, and analytics by 2021, says research house ABI . The report by lead number cruncher Dimitrios Pavlakis claims User and Entity Behavior Analytics (UEBA) and "deep learning algorithm designs" will be widely adopted by security companies as they collectively put big data to work detecting threats. The former machine learning technology, UEBA, is correlation on steroids, capable of detecting anomalies that can indicate if staff logins have been compromised and are being tested across the enterprise network. It can learn the activities and services most typical of a user to generate alerts when something anomalous occurs, like login attempts to odd network shares. Vendors are buying up across the space including Splunk's buy of Caspida, and Arksight selling Securonix.

Machine learning and cognitive computing have emerged as two of the hottest topics in digital marketing during this year, and look set to dominate much of the discussion in 2017. Yet many marketers are only just coming to an appreciation of technologies that have been under development for more than a decade. In large measure, that is because practical applications for cognitive computing through platforms like IBM's Watson have emerged only in recent years. These issues were covered in detail at a recent series of senior executive round tables hosted by IBM and ADMA in Sydney and Melbourne. In simple terms, think of Watson as a very large computer system that sits in the cloud and has advanced cognitive capabilities -- including the ability to ingest data, learn from that data and then provide answers by interacting in a variety of ways with people and systems.

The history of Artificial Intelligence is long, but it's only been recently that technology companies and markets have begun to get excited about it… Why? After a few decades of exploration of symbolic AI methods, the field shifted toward statistical approaches, that have as of late started working in a broad array of tasks due to the explosion of data and computing power, this in turn has led to machine learning and, most importantly, enabled deep learning. This is great news for the tech industry. The downside is that there aren't enough data scientists that understand deep learning. For those who do, there is a huge demand for their services.

The world is always moving in different directions towards an innovative future. IBM worked with Daybreaker, "an early morning dance movement in 15 cities around the world," to plan a special dance party. The genius super computer, Watson, that took down two all-star Jeopardy champs in 2011 was used to plan the first "cognitive dance party" in San Francisco. The computer is widely used for daily tasks in lung cancer treatment, financial advising and it even assists veterans return to civilian life. To understand the artificial intelligence even more, check out the video at the bottom of the article made by IBM. Watson is truly something amazing and the technology will surely advance the world.

The artificial intelligence research behind bots_alive promises to usher in lifelike robots at affordable price points. Artificial intelligence is all the rage in robotics these days, and for good reason: Properly implemented, it has the potential to program'bots on the fly. But those toys and others react in predictable ways to changing contexts and situations. One startup, though, purports to have developed an algorithm capable of generating entirely new behaviors dynamically. It's called bots_alive, and it's the brainchild of Brad Knox.

It is often talked about as the ultimate prize of artificial intelligence: a machine that can think like a human. But human minds are only one example of the kinds of minds on earth. So what are those other minds like? How do they work and how can we understand them? Suppose we do create human level cognition in artificial intelligence (AI), does that widen the'space of possible minds' to include AI alongside humans and animals?

Researchers have developed an Artificial Intelligence (AI) bot that beat professional players in a 20-day poker tournament. The bot, named Libratus, beat four of the World's best Heads-Up No-Limit Texas Hold'Em poker players at a casino in Pittsburgh, Pennsylvania. Libratus won the tournament after 120,000 hands, winning with a lead of $1.7 million in virtual poker chips. Professor of computer science Tuomas Sandholm said: 'The best AI's ability to do strategic reasoning with imperfect information has now surpassed that of the best humans' The AI was developed at Carnegie Mellon University (CMU) by Professor Tuomas Sandholm and his PhD student Noam Brown. They said that the AI's victory wasn't just a matter of luck - it was statistically significant.

A top US general responsible for shaping the military of the future has revealed that robots could make up a quarter of the army within the next few decades. General Robert Cone said last year that the hi-tech machines will fight alongside soldiers on the battlefield by 2030 or 2040, in a bid to make the army'a smaller, more lethal, deployable and agile force'. He said modern health and safety concerns designed to keeping soldiers safe - adding armour and protection to vehicles and personnel - has caused the army to lose some of its lethal edge, but hinted that robots could revitalize its killing force. The US Army has a long-running mission to create autonomous robots - machines capable of making decisions, in real time, on the front line to changing circumstances. More than a decade of war in Afghanistan and Iraq has allowed the public to see the huge resources already dedicated to unmanned systems; from the high-flying Global Hawk to the lethal Predator drones to hand-launched Raven.

Evergage, the real-time personalization platform company, revealed new enhancements to its machine learning personalization solution, Evergage Recommend. Since its introduction, Evergage Recommend has provided digital marketers with flexibility in building, deploying, testing, and optimizing 1:1 product or content recommendations across websites, mobile apps, emails, and onsite search. But now, marketers have the power to create and deploy machine learning algorithms that incorporate recommendations seamlessly into every aspect of each visitor's overall experience. Take, for example, an online department store that sells thousands of products from different brands and across scores of categories. With Evergage's deep behavioral tracking capabilities and the new enhancements to Evergage Recommend, the department store is able to understand affinities and offer personalized navigation for each and every shopper – customizing the homepage and navigation menu to prioritize the specific categories (or brands, styles, etc.) that are of greatest interest to each visitor. So, a mom with a young son might see images, offers, and menu items for women's shoes, dresses and boys' clothing on the home page, while a single male customer might see men's shirts, jackets, and shoes.