A few months back, we asked the question as to what MongoDB wanted to be when it grows up. The answers to that question impact quite a lot of organizations given the popularity of the database now ranked fifth by db-Engines. With current 3.x generation, the company that made its mark with developers as a simple, quick to implement database has been building in more enterprise-grade robustness. With the new 3.4 release, announced this week, Mongo is beefing up its capabilities for managing data placement, database consistency, and extensibility. Enterprise applications power the heart of business productivity, but they are traditionally difficult to implement, upgrade, and innovate.

Knowing why a machine is making a certain decision becomes especially important in, say, critical situations involving autonomous vehicles. As humans grapple with ethical questions about what artificial intelligence should do in life-and-death situations, researchers at MIT have devised a way for machines to explain their decisions. The method, outlined in a new paper, could be as important for the adoption of artificial intelligence technologies as actual breakthroughs in AI enabled by deep learning and neural networks. As MIT points out, while neural networks can be trained to excel at a specified task, such as classifying data, researchers still don't understand why some models work and others don't. Neural nets are effectively black boxes.

Solving the world's biggest problems requires creativity and perseverance -- and innovators around the globe are taking up the challenge. From a device that alerts medical workers when to re-bandage a wound to a wheelchair specifically designed for people in developing nations, these inventions are making a massive difference for those who need it most. While certainly not an exhaustive list, here are nine ingenious innovations that made an impact in October. An estimated 2.3 billion people worldwide live with poor vision due to refractive errors, with many living in low-income regions without access to regular health care. The Folding Phoropter, a cost-effective eye-screening tool, hopes to make essential eye health more accessible to these populations.

Can your financial institution's fraud detection system learn, reason and adapt to new and emerging cyberthreats? Can it identify fraudulent behavior within your account simply by analyzing interactions and patterns? In this day and age, people can access their bank accounts anywhere, anytime. We need strong, agile and efficient fraud detection systems to keep financial institutions and their customers safe. Mobile functionality and safety are among customers' top concerns when it comes to online banking -- so IBM Security Trusteer is releasing new cognitive fraud detection and behavioral biometric functionality that accomplishes just that. This enhanced functionality adds even more strength to an already robust security platform without impacting user experience.

Classification Learner lets you perform common supervised learning tasks such as interactively exploring your data, selecting features, specifying validation schemes, training models, and assessing results. You can export classification models to the MATLAB workspace, or generate MATLAB code to integrate models into applications. Choose your country to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .

Real-time machine learning has access to a continuous flow of transactional data, but what it really needs in order to be effective is a continuous flow of labeled transactional data, and accurate labeling introduces latency. During this year, I heard and read a lot about real-time machine learning. People usually provide this appealing business scenario when discussing credit card fraud detection systems. They say that they can continuously update credit card fraud detection model in real-time (See "What is Apache Spark?", "…real-time use cases…" and "Real time machine learning"). It looks fantastic but not realistic to me.

NEC Corporation, one of Japan's biggest IT providers, says it has built an AI that can rapidly search CCTV footage and spot a specific person out of a million or more faces. The application – snappily titled NeoFace Image data mining – can find wanted criminals, missing kids, and so on, all from video surveillance. We're told "when searching video where roughly one million individual instances of facial data appear, the software is capable of conducting searches within approximately 10 seconds." In other words, you can feed 24 hours of CCTV into NeoFace, and it could identify, say, a million faces in the video frames. Then when you need to find a sought-after person, the software will take just seconds to scan the database and locate them.

IBM has acquired XPS, a Watson-based AI chatbot for personal shopping, from software firm Fluid. Expert Personal Shopper (XPS) uses natural language technology to interact with customers while they shop. IBM invested in Fluid in 2014, as part of a $100m fund it established to contribute to Watson-based businesses and applications. 'In addition to retail, we believe, XPS can be leveraged and applied to the digital properties for brands across a variety of industries' – PAUL PAPAS Watson is IBM's signature artificial intelligence (AI) platform and the cornerstone of the company's future in AI-based business processes and services. The XPS solution and several key members of the XPS team will become part of IBM iX (Interactive Experience).

Microsoft unveils Concept Graph: 'It's time AI learned some common sense' Adobe shows glimpse of future at MAX conference, and it's in A.I. Sinequa's Cognitive Search and Analytics Platform Certified for Cloudera Enterprise to Provide ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.