UBS Card Center, which processes roughly 25 percent of all credit cards in Switzerland, has won the Security Innovation of the Year award at the Retail Banker International Awards, presented in London. UBS Card Center's fraud team used the the latest artificial intelligence and machine learning capabilities in the FICO Falcon Platform to stop 84 percent more fraudulent transactions last year than in 2015. The need to optimise costs in the face of fierce competition meant UBS Card Center had to keep fraud write-offs to the very minimum. They were facing new fraud attack volumes but needed to uphold the highest standards for customer experience and satisfaction. This required the use of machine learning to minimize consumer interruptions while investigating more potential cases of fraud, all without adding staff.

Justification theory is a unifying framework for semantics of non-monotonic logics. It is built on the notion of a justification, which intuitively is a graph that explains the truth value of certain facts in a structure. Knowledge representation languages covered by justification theory include logic programs, argumentation frameworks, inductive definitions, and nested inductive and coinductive definitions. In addition, justifications are also used for implementation purposes. They are used to compute unfounded sets in modern ASP solvers, can be used to check for relevance of atoms in complete search algorithms, and recent lazy grounding algorithms are built on top of them. In this extended abstract, we lay out possible extensions to justification theory.

King Digital Entertainment's'Candy Crush Saga' is seen being played on an Apple iPad Mini. A federal lawmaker wants to introduce legislation that would ban "pay to win" practices and "loot boxes" from all video games. In a statement released Wednesday, Sen. Josh Hawley, a Republican representing Missouri, said video games offering these systems are preying on user addiction, particularly among children. "When a game is designed for kids, game developers shouldn't be allowed to monetize addiction," said Hawley in a statement. "And when kids play games designed for adults, they should be walled off from compulsive microtransactions."

Following warnings from the FTC, both Apple and Google have removed several dating apps from their platforms that they say allowed children to join. According to a letter sent by the FTC to Ukraine-based Wildec LLC, which owns FastMeet, Meet24 and Meet4U, the trio of dating apps allowed children as young as 12 to participate in the service and communicate with adults. In an FTC investigation of the app, the organization says they were also able to identify and confirm multiple children within the service using a built-in age filter that allows users to search by age. A trio of dating apps was removed by Google and Apple for allowing children under 13-years-old to participate. The FTC says the apps have already been used by sexual predators.

Member States also think the possibility of setting "minimum rules [at EU level] concerning the definition of criminal offences and sanctions" should be examined for environmental crimes, trafficking in cultural goods, trafficking in human organs and the manipulation of elections.

Fox News Flash top headlines for May 6 are here. Check out what's clicking on Foxnews.com A Kentucky man has been charged with murder for fatally punching his 1-year-old son in the head after becoming angry over losing a video game, authorities said Sunday. Anthony Trice, 26, was watching the baby Friday when he grew enraged over losing the game, threw his controller and struck the infant in the head, the Louisville Metro Police Department said. Trice tried to comfort the baby, carrying him into the kitchen, but dropped him, Louisville station WAVE-TV reported.

The following is a devastating truth about a biased machine learning program that happened in real life. It is safe to say that the following is an example of the reasons why racism still exists. It's what I'd like to start with to show you how important it is to fix any bias in your AI program. Developed by a private company called Equivant (formerly Northpointe). Compas is a machine learning algorithm that predicts the defendant's likelihoods to commit crimes, it has been shown that it makes biased predictions about who is more likely to recommit crimes.

Since the introduction of GDPR and the success it brought about on a global standard, the EU has set its eyes on regulating Artificial Intelligence. The aim is to build trust by establishing policies and creating AI tools which ultimately will result in exporting such policies on a global level as it did with GDPR. Such proposition would benefit the EU as it would give it a competitive advantage. An expert group was set up by the European Commission which is comprised of 52 independent experts from academia, industry and civil society. A set of Guidelines has been put forward and the pilot phase will start this summer.

When I first became focused on the military and existential concerns of AI in 2012, there was only a small handful of publications and organizations focused on the ethical concerns of AI. MIRI, the Future of Humanity Institute, the Institute for Ethics and Emerging Technologies, and the personal blogs of Ben Goertzel and Nick Bostrom was most of my reading at the time. These limited sources focused mostly on the consequences of artificial general intelligence (i.e. By 2014, artificial intelligence made its way firmly onto the radar of almost everyone in the tech world. New startups began (by 2015) ubiquitously including "machine learning" in their pitch decks, and 3-4-year-old startups were re-branding themselves around the value proposition of "AI." Not until later 2016 did the AI ethics wave make it into the mainstream beyond the level of Elon Musk's tweets. By 2017, some business conferences began having breakout sessions around AI ethics – mostly the practical day-to-day concerns (privacy, security, transparency).

In e-commerce, product content, especially product images have a significant influence on a customer's journey from product discovery to evaluation and finally, purchase decision. Since many e-commerce retailers sell items from other third-party marketplace sellers besides their own, the content published by both internal and external content creators needs to be monitored and enriched, wherever possible. Despite guidelines and warnings, product listings that contain offensive and non-compliant images continue to enter catalogs. Offensive and non-compliant content can include a wide range of objects, logos, and banners conveying violent, sexually explicit, racist, or promotional messages. Such images can severely damage the customer experience, lead to legal issues, and erode the company brand. In this paper, we present a machine learning driven offensive and non-compliant image detection system for extremely large e-commerce catalogs. This system proactively detects and removes such content before they are published to the customer-facing website. This paper delves into the unique challenges of applying machine learning to real-world data from retail domain with hundreds of millions of product images. We demonstrate how we resolve the issue of non-compliant content that appears across tens of thousands of product categories. We also describe how we deal with the sheer variety in which each single non-compliant scenario appears. This paper showcases a number of practical yet unique approaches such as representative training data creation that are critical to solve an extremely rarely occurring problem. In summary, our system combines state-of-the-art image classification and object detection techniques, and fine tunes them with internal data to develop a solution customized for a massive, diverse, and constantly evolving product catalog.