Munich Re has announced a new partnership with re/insurance industry consortium the Geospatial Intelligence Center (GIC) to provide its members with access to automated damage classification analytics following major catastrophe events. The GIC provides its members with access to imagery and data that enhance underwriting assessments, expedite claims, and improve fraud detection following hurricanes and other disaster events. By collaborating with Munich Re, the consortium will now be able to provide its members with a damage assessment heat map layer to complement its imagery and improve situational awareness post-disaster. Munich Re's analytics solutions can process the GIC's aerial imagery with machine learning models to detect building shapes and damage to individual properties, as well as to impacted geographic areas at large. In the lead up to a catastrophe event, the reinsurer's model can also predict estimated losses using data on property characteristics, weather forecasts, and weather stations.
We expect the landscape to be an integrated edge-to-core-to-cloud solution enabling what today is called IoT, Big Data, Fast Data and AI. Each time a promising new technology emerges, we seem to go through a period where it is proposed to be the solution to everything--until we reconcile how that technology fits into the bigger picture. Such is the case with artificial intelligence (AI). Clearly the advancements in deep learning will create new classes of solutions but rather than being a standalone solution, we are just now beginning to see how it fits into our IT landscape. AI emerges at a time when several other shifts in analytics technology are occurring.
Within two years there will be more voice assistants on the internet than there are people on the planet. Another, possibly more helpful, way of looking at these statistics is to say that there will still be only half a dozen assistants that matter: Apple's Siri, Google's Assistant, and Amazon's Alexa in the west, along with their Chinese equivalents, but these will have billions of microphones at their disposal, listening patiently for sounds they can use. Voice is going to become the chief way that we make our wants known to computers – and when they respond, they will do so with female voices. This detail may seem trivial, but it goes to the heart of the way in which the spread of digital technologies can amplify and extend social prejudice. The companies that program these assistants want them to be used, of course, and this requires making them appear helpful.
As AI algorithms--and the computing power that drives them--improve year-on-year, their ability to positively transform the world in which we live is unquestionable. In fact, PwC predicts that AI could contribute up to $15.7 trillion to the global economy by 2030. Indeed, as many as one-in-five (20 percent) of the 1,000 US organisations recently surveyed by PwC had plans to implement AI enterprise-wide in 2019. The PwC research also reveals how companies are increasingly initiating AI models at the very core of their production processes, in a bid to enhance operational decision-making and provide forward-looking intelligence to people in every function throughout the business. To many, this move to AI is no surprise.
It has the potential to ruin relationships, reputations and our online reality. "Deepfake" artificial intelligence technology promises to create doctored videos so realistic that they're almost impossible to tell from the real thing. So far it has mostly been used to create altered pornographic clips featuring celebrity women's faces but once the techniques are perfected, deepfake revenge porn purporting to show people cheating on their partners won't be far behind. But more than becoming a nasty tool for stalkers and harassers, deepfakes threaten to undermine trust in political institutions and society as a whole. The White House recently justified temporarily banning a reporter from its press conferences using reportedly sped up genuine footage of an incident involving the journalist.
AI offers exceptional opportunities particularly in digital marketing while irrefutably revolutionizing and propelling the industry. AI is the ability of a computer or computer-enabled robotic systems to process massive amounts of in-depth data and produce outcomes similar to the thought processes of humans in learning, analysing, decision making, and problem-solving. Hence, AI has enabled marketers to comprehend vast data to gain valuable consumer insights, and in turn, improve digital marketing strategies. The applications of AI are essentially limitless, and the field of computer science is on a stark ascendance. The global AI market was worth $7.35 billion in 2018, where the largest portion of revenue was stirred from enterprise applications.
Maybe it stems from science fiction's depiction of malevolent robots overtaking humanity. To get to the bottom of this, Pega surveyed 5,000 consumers from North America, the United Kingdom, Japan, Germany, and France about their views on empathy and AI. Get our report and discover how you can combine AI with human ethics for better engagement.
Artificial intelligence is increasingly present in our daily lives. This new technology presents us with many opportunities and much to consider. Creating trustworthy and ethical artificial intelligence requires an understanding not only of the technology itself, but also the societal and ethical conditions present, and how to appropriately account for and assess their impact on the way AI is designed, built, and tested, and the way we interact with it. This course explains the Ethics Guidelines for Trustworthy AI created by the European Union's High-Level Expert Group on Artificial Intelligence, with insights from members of the group as well as experts from SAP. Each of them will share their own insights, examples, and areas of particular interest when it comes to artificial intelligence.
May Masoud is a Solution Specialist at SAS Canada, as part of the Data Sciences team. Leveraging her analytics background, she helps businesses visualize the potential of their data, and surface insights using modern data mining and machine learning techniques. With a Master of Business Analytics following a Bachelor in Statistics & Economics, May aims to create value at every step of the analytics lifecycle: data discovery, model build, model deployment, and business strategy. She has touched the analytics landscape in a variety of industries, whether it is oil production models for the energy sector or solving churn problems in the telecom industry. May's aim is to ubiquitize self-serve analytics and enable citizen data scientists.
When we began our 14-week tech health sprint in October 2018, we did not realize the profound lessons we would learn in just a few months. Together with federal agencies and private sector organizations, we demonstrated the power of applying artificial intelligence (AI) to open federal data. Through this collaborative process, we showed that federal data can be turned into products for real-world health applications with the potential to help millions of Americans have a better life. Joshua Di Frances, the executive director of the Presidential Innovation Fellows (PIF) program, says that this collaboration across agencies and private companies represents a new way of approaching AI and federal open data. "Through incentivizing links between government and industry via a bidirectional AI ecosystem, we can help promote usable, actionable data that benefits the American people," Di Frances said.