Machines may not be known for their EQ (emotional quotient), but a new collaboration between Cogito Corp. and the U.S. Military Academy is on track to change our perceptions. The art of negotiation, which may have once been a distinctly human skill set, is being enhanced by Cogito, a company that specializes in real-time emotional intelligence solutions. Cadets at West Point will use this technology to improve their own tactics when it comes to striking a bargain and gaining crucial information. "We believe that the ability to effectively negotiate is a critical skill that will serve military personnel well in numerous situations over the course of their careers," said Col. James Ness, Ph.D. Director: Engineering Psychology Program, West Point. "Cogito's behavioral analytics technology will systematically analyze communication patterns within negotiating sessions and provide insight into the cadet's psychological state. This technology will provide an unbiased assessment of how each cadet is being perceived by the other party. It will deliver insights into how they can modify their behavior to improve negotiation outcomes."

Machine learning technology is poised to move from niche data analytics applications to mainstream enterprise big data campaigns over the next two years, a recent vendor survey suggests. SoftServe, a software and application development specialist based in Austin, Texas, reports that 62 percent of the medium and large organizations it polled in April said they expect to roll out machine learning tools for business analytics by 2018. That majority said real-time data analysis was the most promising big data opportunity. The survey authors argue that artificial intelligence-based technologies like machine learning are moving beyond the "hype cycle" as enterprise look to automate analytics capabilities ranging from business intelligence to security. The goal is to demonstrate that that cyber defenses can be automated as more infrastructure is networked via an Internet of Things.)

Extracting value from a universe of data, analysing sentiment around company names (equities) or about anything else (macro), is a complex journey and we are only about 5% down that road. The parameters are evolving by which an ever-expanding data set, including the likes of Twitter, pictures, text, video is processed; relying on experts versus the wisdom of the crowd; sentiment derived from a "bag of words", as opposed to structured linguistic analysis. Last week's Unicom conference, AI, Machine Learning and Sentiment Analysis Applied to Finance (July 14) brought together a group of experts in this area. Professor Gautum Mitra, OptiRisk Systems introduced Elijah DePalma and James Cantarella, Thomson Reuters; Pierce Crosby, StockTwits; Anders Bally, Sentifi; Peter Hafez, RavenPack; Stephen Morse, Twitter. DePalma differed somewhat from the others because the Thomson Reuters sentiment engine uses only accredited Reuters news data, rather than raw social media chatter.

Companies large and small are embracing machine learning in a bid to make better use of their data. Last week, eBay Inc. led the charge by acquiring SalesPredict Ltd., an Israeli startup that uses the technology help marketers gain a better understanding of their customers. Its cloud service can analyze a company's sales records to identify the most valuable type of leads and then single out the accounts that match the description in its CRM system. Moreover, SalesPredict says that the data produced by its technology also makes it easier to identify new high-value prospects and optimize advertising campaigns. No terms were disclosed for the acquisition, but eBay did divulge that the startup's team will join its Israeli engineering team to help with product development efforts.

The Rio Olympics 2016 is something everyone is looking forward to. But the Olympics have been a frontier for individuals flexing their physical muscles and achieving greatness. Well, it's time for it to move over to the Cyber Grand Challenge (CGC) is here. Individuals wanting to showcase their brain muscles in a competition could change how we defend against cyber attacks. The challenge organized by the Defense Advanced Research Projects Agency (DARPA) of the United States where the seven finalists will battle it out with the Artificial Intelligence system to detect flaws and scan networks for exploits.

Chief Operating Officer Rachel Cooke Rachel is responsible for business operations including overseeing client services, research events and project management. Prior to joining Brandon Hall Group, Rachel was the Chief Operating Officer Co-founder of AC Growth. Rachel has over 15 years of experience in sales, marketing, business development, and sales performance management. Prior to AC Growth, she held several senior management roles and was on the leadership team at Bersin & Associates, a pioneer analyst firm in e-learning and now industry leading HR and talent Research Company. In her Senior Director role, Rachel developed the strategy and led the commercial execution of the solution provider vertical, and grew the vertical into the company's largest market segment.

When Tesla Motors CEO Elon Musk proclaims that artificial intelligence is "our biggest existential threat," it makes headlines worldwide. But what goes unreported is that the very search engines people used to find Musk's comments are themselves an example of how AI has subtly but forcefully become a part of everyday, real-world life. When it comes to a discussion of AI, it helps to have a sense of history--as well as a sense of humor. Thanks to premonitory proclamations by Musk, Microsoft's Bill Gates, Cambridge's Stephen Hawking and other prominent technologists, AI has become a popular topic again, after a 20-year cooling-off period. It's tempting to assume that the "dire warnings" about AI being a threat to mankind were mostly tongue-in-cheek, but the end result is that just as it happened in the 1980s and '90s, the hype over AI is again outpacing the reality (virtual and otherwise). The first question that needs to be answered though is: Whatever happened to AI and why did it go underground for so many years?

BERLIN (Reuters) - Germany plans new legislation to require manufacturers of cars equipped with an autopilot function to install a black box to help determine responsibility in the event of an accident, transport ministry sources told Reuters on Monday. The fatal crash of a Tesla Motors Inc Model S car in its Autopilot mode has increased the pressure on industry executives and regulators to ensure that automated driving technology can be deployed safely. Under the proposal from Transport Minister Alexander Dobrindt, drivers will not have to pay attention to traffic or concentrate on steering, but must remain seated at the wheel so they can intervene in the event of an emergency. Manufacturers will also be required to install a black box that records when the autopilot system was active, when the driver drove and when the system requested that the driver take over, according to the proposals. The draft is due to be sent to other ministries for approval this summer, a transport ministry spokesman said.

Deep Learning Methods Within Video An End Game Application – We'll explore the use cases of using deep learning to drive higher video views. The coming Valhalla of Deep Learning is being realized in visual object recognition and image classification within video. Mobile video has and continues to transform the way video is being distributed and consumed. Mobile video advertising is the fastest growing segment projected to account for 25 billion worth of ad spend by 2021. Deep Learning and artificial intelligence are also growing within the very same companies who are jockeying for your cognitive attention.

On 21 June, the Law Society hosted a conference on the topic of artificial intelligence (AI) and machine learning in law. Artificial intelligence and machine learning technology have made significant progress in recent years. Arguably, legal and ethical thinking about the societal implications could now be lagging behind this technical progress. We explored how machine learning is currently being used, how it may be used in the future and what the legal implications could be. The discussion will also examine the potential liability and regulatory considerations in relation to machine learning, and the role of the law and lawyers in this rapidly evolving area.