Artificial intelligence programs have beaten chess masters and TV quiz show champions. Two exchange operators have announced plans to launch artificial intelligence tools for market surveillance in the coming months and officials at a Wall Street regulator tell Reuters they are not far behind. Executives are hoping computers with humanoid wit can help mere mortals catch misbehavior more quickly. The software could, for instance, scrub chat-room messages to detect dubious bragging or back slapping around the time of a big trade. It could also more quickly unravel complex issues, like "layering," where orders are rapidly sent to exchanges and then canceled to artificially move a stock price.

YOKOHAMA – The top executive overseeing connected vehicles at Nissan and Renault believes the benefits of developing a common technology for connectivity within the Japanese and French auto partnership outweigh the risks of sustaining huge damage from a possible defect or cyberattack. Ogi Redzic, senior vice president at the Renault-Nissan Alliance, oversees such connected services, including staying connected with family, navigating best routes and remote control of vehicles -- an area where all the world's automakers are trying to gain an edge. Redzic, formerly of Motorola and Nokia, was tapped for the post at the alliance earlier this year. He told reporters Tuesday at Nissan's Yokohama headquarters that the alliance has begun hiring 300 people to build such software applications for future cars, in addition to 300 employees at Nissan Motor Co. and 300 at Renault SA already working on the effort. Nissan-Renault hopes to lead in connected vehicles, in addition to green cars and self-driving cars, the two other increasingly vital auto technologies.

You might well have been responsible for one of the biggest-ever attacks on the internet last week. And you're probably going to do it again. A huge network of webcams and other internet of things devices is being built that can be used as perhaps the biggest cyberweapon ever created. And those people who own the devices probably don't even know that they're infected with the malicious code that allows them to be used that way. To be involved in the attack, you wouldn't need to do more than buy a common webcam – perhaps for keeping an eye on your house, or a pet that lives in it – or any other connected home or internet of things device.

The ability to think is one of the things that marks us out as human, "I think therefore I am," as the philosopher René Descartes put it. But what if machines were able to mimic human thought processes? This is the basic premise behind cognitive computing. In order to replicate human thought processes, cognitive computing uses techniques like pattern recognition, data mining and natural language processing. The ultimate goal is to be able to make computer systems that are capable of solving complex problems without needing human assistance to prompt them in the right direction.

Customer satisfaction is the bread and butter of every customer service team. We all want to make our customers happy, and satisfaction becomes both the reason we show up to work and barometer by which we measure our success. Every interaction offers a business the chance to leave a customer feeling good, helping to build a relationship for the longer term. Yet some customer interactions have a mercurial quality and end up in a different place than they began. If you knew what direction customer interactions were heading, you'd be better-equipped to handle them successfully, able to deploy specific tools and resources to ensure a positive outcome.

I have tried to exhibit credit scoring case studies with German Credit Data. This article includes detail programming of predictive modeling 1. Univariate And Bi-Variate Analysis 2. Information Value and Weight Evidence to access prediction power of variables 3. Multivariate Analysis and Dimension Reduction using Variable Clustering 4. Different Machine Learning Techniques and their performance evaluation using ROC, AUC and KS The basic difference of traditional modeling and machine learning is that "in traditional modeling we intend to setup a modelimg framework and try to establish relationships while in machine learning we allow the model to learn from the data by understanding the hidden patterns". Hence the first one requires analyst to have solid understanding of statistical techniques and business knowledge while the later one is more complex in nature and computational intensive, hence requires higher computation power of the systems and analyst needs to be tech savvy. Kindly note that while traditional techniques perform well on small to large amount of data, machine learning will certainly learn better on high-dimensional and complex data such as BigData setup. If you want to do more experiments and not sure where to get a problem definition or data to machine learning, you may explore the online machine learning repository here http://archive.ics.uci.edu/ml/.

If you start feeling a cold or flu coming on this season, you will be able to reach out to IBM's artificial intelligence-fueled Watson to find some answers to your sniffly, coughy questions. GlaxoSmithKline, (GSK) the world's sixth-largest pharmaceutical company, is teaming up with IBM to use Watson to better connect with customers. The London-based company plans to start using Watson Ads in November, enabling people to ask questions by voice or text right through GSK's online ads. Jason Andree, senior brand manager of the Cough and Cold division of GlaxoSmithKline North America, will announce the move at the start of the IBM World of Watson conference in Las Vegas this week. The event, which focuses on changing business through cognitive computing, analytics and big data, runs from today to Thursday.

Almost half of UK grocery retail directors say replenishment is still driven by gut feel, according to research by Blue Yonder, which supplies predictive applications of retail. It said that interviews with 750 grocery managers and directors in the USA, UK, Germany and France, showed that despite a rise in accurate machine learning algorithms for automated replenishment and demand planning, 46 per cent of surveyed directors in the UK say replenishment is still an entirely manual process. Some 85 per cent of respondents identified automation as a key tool for making the fast decisions needed to meet customer demand. The research also identified that 31 per cent of directors in the UK feel there are now too many decisions to be made manually, with the same number stating that gut feel is slowing them down. The research also found that 62 per cent of UK directors say they have invested in replenishment optimisation in the last two years; 31 per cent say they will be investing further in replenishment optimisation in the next two years.

The dream of thinking machines goes back centuries, at least to Gottfried Wilhelm Leibniz, in the 17th century. Leibniz (right) helped invent mechanical calculators, independently of Isaac Newton developed the integral calculus, and had a lifelong fascination with reducing thinking to calculation. His Mathesis Universalis was a vision of universal science made possible by a mathematical language more precise than natural languages, like English. The Limits of Modern AI: A Story In the 18th Century the Enlightenment philosopher and proto-psychologist Étienne Bonnot de Condillac imagined a statue outwardly appearing like a man and also with what he called "the inward organization." In an example of supreme armchair speculation, Condillac imagined pouring facts--bits of knowledge--into its head, wondering when intelligence would emerge. Condillac's musings drew inspiration from the early mechanical philosophy of Thomas Hobbes, who had famously declared that thinking was nothing but ...

The small drone, with its six whirring rotors, swept past the replica of a Middle Eastern village and closed in on a mosque-like structure, its camera scanning for targets. No humans were remotely piloting the drone, which was nothing more than a machine that could be bought on Amazon. But armed with advanced artificial intelligence software, it had been transformed into a robot that could find and identify the half-dozen men carrying replicas of AK-47s around the village and pretending to be insurgents. As the drone descended slightly, a purple rectangle flickered on a video feed that was being relayed to engineers monitoring the test. The drone had locked onto a man obscured in the shadows, a display of hunting prowess that offered an eerie preview of how the Pentagon plans to transform warfare. Almost unnoticed outside defense circles, the Pentagon has put artificial intelligence at the center of its strategy to maintain the United States' position as the world's dominant military power.