Information contained on this page is provided by an independent third-party content provider. If you are affiliated with this page and would like it removed please contact pressreleases@franklyinc.com Addition of enterprise-level AI capabilities furthers Genpact's ability to drive digital transformation at speed and scale for clients Genpact (NYSE: G), a global professional services firm focused on delivering digital transformation for clients, has signed a definitive agreement to acquire Rage Frameworks, a leader in knowledge-based automation technology and services providing Artificial Intelligence (AI) for the Enterprise. Terms of the deal are not disclosed. As part of its strategy to drive both digital-led innovation and digital-enabled intelligent operations for its clients, Genpact is investing in leading technologies, such as AI, that are transforming the way companies in many industries compete. Genpact will embed Rage's AI in business operations and apply it to complex enterprise issues to allow clients to generate insights and drive decisions and action, at a scale and speed that humans alone could not achieve.

Artificial intelligence enables virtual agents to learn by observation. That adaptive ability is what a Swedish bank is counting on to serve its customers.

The National Grid has confirmed that it is in the "earliest stages" of discussions exploring the use of Artificial Intelligence (AI), which could potentially maximise the use of renewable energy by predicting peaks in demand across the UK. The National Grid, which operates and owns the infrastructure that transports electricity across the UK, has seen its ability in balancing and stabilising the grid challenged in recent years as intermittent renewables such as solar and wind have been fed into the energy mix. While the introduction of renewables into the mix forms a key role in both national and European legislation to decarbonise the grid, concerns have been raised as to the National Grid's ability to deal with fluctuating wind and solar resources, which can sometimes produce more energy than the system can cope with. Energy storage and demand response initiatives, whereby businesses either store surplus energy or increase or reduce energy consumption based on demand, are being incorporated by the National Grid, which is now "exploring what opportunities" AI could offer to balance the situation. The National Grid revealed that it is in discussions with the UK-based AI company DeepMind about introducing new technologies to help balance the grid and improve the use of renewables.

What is machine learning and how does it work? You've probably heard the buzz. The age of artificial intelligence has arrived. But that doesn't mean it's easy to wrap your mind around. Let's break down the basics of artificial intelligence, bots, and machine learning. Besides, there's nothing that will impact marketing more in the next five to ten years than artificial intelligence.

Germany reportedly intends to acquire the Northrop Grumman MQ-4C Triton high-altitude surveillance drone, according to a story in Sueddeutsche Zeitung. In 2013, Germany cancelled a similar program to acquire Northrop Grumman's RQ-4 Global Hawk, a surveillance drone on which the newer Triton is based, due to cost overruns. The Triton is a large, long-endurance system that was originally developed for maritime surveillance by the U.S. Navy. The U.S. Army released a report outlining its strategy for obtaining and using unmanned ground vehicles. The Robotics and Autonomous Systems strategy outlines short, medium, and long-term goals for the service's ground robot programs.

Intel announced plans to acquire Israel-based Mobileye, a developer of vision technology used in autonomous driving applications, for $15.3 billion. Mobileye share prices jumped from $47 to $61 (the tender offering price is $63.54) on the news, a 30% premium. The purchase marks the largest acquisition of an Israeli hi-tech company ever. This transaction jumpstarts Intel's efforts to enter the emerging autonomous driving marketplace, an arena much different than Intel's present business model. The process to design and bring a chip to market involves multiple levels of safety checks and approvals as well as incorporation into car company design plans – a process that often takes 4 to 5 years – which is why it makes sense to acquire a company already versed in those activities.

Intelligent algorithms are starting to perceive sights and sounds like human beings. Androids are taking more anthropomorphic forms, powered by actuators wrapped silicone and latex skins. Even these skins are becoming increasingly lifelike. Earlier this year, researchers created an artificial material that's twice as sensitive as human skin. And this month, a team of Oxford professors proposed a provocative idea -- grow human tissue on humanoid robots.

We head to Austin now for the annual South by Southwest Conference in this week's All Tech Considered. CORNISH: Now, South by Southwest is known for the music, but running alongside the shows are panels that bring leaders across industries together to discuss what's cutting edge. And one emerging technology being talked about a lot is artificial intelligence. For more on that, NPR's Laura Sydell joins us from Austin. CORNISH: To begin, obviously, people are talking about AI across the tech industry.

The health sector is the most vulnerable, most targeted, and resoundingly least equipped to defend against hackers who are pummeling healthcare organizations with ransomware attacks. This ransomware epidemic will only become more severe and costly as the infection volume in 2017 will trump infections in 2016. In this post, entitled "How to Crush the Health Sector's Ransomware Pandemic: The Machine Learning Based Artificial Intelligence Revolution Starts Now!", the Institute for Critical Infrastructure Technology discusses the power of machine based artificial intelligence as a potent cybersecurity defense layer to defend against hyper-evolving APTs and malware. This paper was authored by James Scott, Sr. Fellow, ICIT

Pentaho helps data scientists and engineers easily prepare and blend traditional sources like ERP, EAM and big data sources like sensors and social media. Pentaho also accelerates the notoriously difficult and costly task of feature engineering by automating data onboarding, data transformation and data validation in an easy-to-use drag and drop environment. Model training, tuning and testing -- Data scientists often apply trial and error to strike the right balance of complexity, performance and accuracy in their models. With integrations for languages like R and Python, and for machine learning packages like Spark MLlib and Weka, Pentaho allows data scientists to seamlessly train, tune, build and test models faster. Model deployment and operationalization -- a completely trained, tuned and tested machine learning model still needs to be deployed.