Computers have become adept at extracting patterns from very large collections of data. For example, shopping transactions can reveal consumers' preferences and message traffic on social networks can reveal political trends.
Recent studies from leading financial institutions show that financial fraud continues to rise at alarming rates. According to Experian, credit and debit card fraud has risen over 60% in the first half of 2019. Although Millennials are highly targeted (an 80% chance of being a target of fraud), no one is immune from criminal activity. Home grown fraud rules, based on in-house subject matter expertise and observations, are commonly used to counter fraud attacks as well as other financial crimes, such as Anti-Money Laundering. Altair offers an industry leading approach to augment/adjust these rules based on actual analytical models.
Recognizing that organizations are slow to adopt AI, due in part to rising data complexities, IBM (NYSE: IBM) today announced new innovations that further advance its Watson Anywhere approach to scaling AI across any cloud, and a host of clients who are leveraging the strategy to bring AI to their data, wherever it resides. "We collaborate with clients every day and around the world on their data and AI challenges, and this year we tackled one of the big drawbacks to scaling AI throughout the enterprise – vendor lock-in," said Rob Thomas, General Manager, IBM Data and AI. "When we introduced the ability to run Watson on any cloud, we opened up AI for clients in ways never imagined. Today, we pushed that even further adding even more capabilities to our Watson products running on Cloud Pak for Data." Increasing data complexity, as well as data preparation, skills shortages, and a lack of data culture are combining to slow AI adoption at a time when interest in AI continues to climb.
As you have probably heard or read, IBM's Marketing Cloud recently published that "90% of world's data today has been created in the last two years alone." Growing daily at 2.5 quintillion bytes of data daily, this number will only explode over the next few years. This may seem impressive, but much of it is simply raw data. Nonetheless, you may point out that with all this data we are advancing technology, improving outcomes, enriching lives and making better decisions. However, how vastly improved these outcomes could be if all this data was enriched?
The INSPIRE directive was passed in 2007 to facilitate easy exchange of environmental and spatial information. Its purpose is to make data from different sources easily accessible to stakeholders to boost data-driven decision-making. INSPIRE offers high-quality data and a solid foundation to build data-driven processes on. Now, the value offered by the INSPIRE directive is being recognized by private organizations outside of the EU. Minerva Intelligence Inc applies auditable and explainable AI to complex geoscience problems, ranging from geohazard assessment to mineral target identification to climate law impact.
Jorge Sicilia participated in a meeting organized by the Global Interdependence Center at the Rafael del Pino Foundation. His presentation covered "Financial technology in banking / artificial Intelligence"' focusing on the opportunities and challenges associated with the use of data in the banking sector. Even if banking functions themselves haven't changed, they now entail an extensive and dynamic use of data, the primary raw material of today's technological transformation. Data is causing the sector to change in response to new massive personalization opportunities and the fact that multiple suppliers can create individual markets for each user. The functions don't change, it's the people involved who change and how they carry out these services.
The World Anti-Doping Agency (WADA) is funding three projects to explore the possible uses of artificial intelligence (AI) in the global anti-doping programme. In conjunction with the Fonds de recherche du Québec (FRQ), it follows a call for applications for targeted research on the application and impact of AI in the area of anti-doping. Eight proposals were submitted and, after careful assessment by WADA and FRQ, the successful projects were selected for funding. WADA senior director, sciences and international partnerships, Dr Olivier Rabin, said: "AI is an exciting area to be explored and WADA believes there is enormous untapped potential for its use within anti-doping, particularly when it comes to the analysis of big data. "In time, we think it could have a hugely positive impact.
In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.
You've probably heard this term used over and over again as the next big thing in IT management. But what exactly is AIOps? And why should you care about it? AIOps or artificial intelligence for IT operations is a term first coined by Gartner. It is the application of advanced analytics--in the form of machine learning (ML) and artificial intelligence (AI), towards automating operations so that your IT Ops team can move at the speed that your business expects today.
At the Spark AI Summit, Europe, Enterprise Times sat with Ali Ghodsi, CEO and co-founder, Databricks to talk AI and big data. Ghodsi started as an AI researcher and took that knowledge and experience into Databricks when it was founded. It gives him an interesting perspective on the state of the, often overhyped, AI market. For example, Ghodsi says that one of the reasons for founding Databricks was to: "democratise artificial intelligence and bring it to the masses." One of the problems that AI faces is that it is not a new discipline, it's been around for literally decades.
ANALYTICS EXPERIENCE -- SAS, the leader in analytics, is enhancing its easy-to-use artificial intelligence (AI) solutions to help organizations improve efficiency and quickly realize value with automation. The updated SAS Platform delivers new functionality including automated data management, automated machine learning and cutting-edge interpretability features, underscoring SAS' commitment to making AI more transparent and accessible for all. Available in the fourth quarter of 2019, the newest release of SAS Viya on the SAS Platform offers the latest AI and advanced analytics techniques, accessible to both data scientists and business users. The enhancements provide an intelligent process to automate many of the manual and complex steps required for data transformations and to build machine learning models. SAS automates the analytics life cycle – from data wrangling to feature engineering and algorithm selection – in a single click.