Data analytics is becoming a cornerstone of the financial industry, with startups and established financial service firms looking to give investors clearer guidance with information collected and captured from multiple sources. Advances in machine learning and artificial intelligence (AI) in particular are providing greater insights and better customer experiences. AI-powered data analytics not only captures vast amounts of data in real-time, but also helps users understand how different data points relate to each other, providing insights that might otherwise be lost. Faced with a breakdown in brand loyalty as younger customers prioritize user experience, financial services are now racing to leverage data-driven cognitive technologies. Cambridge, MA-based Kensho, which recently received 58 million in funding from Goldman Sachs, San Francisco-based Alphasense, backed by Tribeca Venture Partners, and Toronto-based Bigterminal are some of the fintech players leveraging AI.
IBM is teaming up with eight North American universities to further tune its cognitive system to tackle cybersecurity problems. Watson for Cyber Security, a platform already in pre-beta, will be further trained in "learning the nuances of security research findings and discovering patterns and evidence of hidden cyber attacks and threats that could otherwise be missed". IBM will work with eight US universities from autumn onwards for a year in order to push forward the project. The universities selected are California State Polytechnic University, Pomona; Pennsylvania State University; Massachusetts Institute of Technology; New York University; the University of Maryland, Baltimore County (UMBC); the University of New Brunswick; the University of Ottawa; and the University of Waterloo. The project is ultimately designed to bridge the cyber-security skills gap, a perennial issue in the industry.
David Feinberg, Google's Vice President of Healthcare, recently described "a search bar on top of ... [ ] your [electronic health records] that needs no training," on stage at a conference in Las Vegas. Google is testing a service that would use its search and artificial intelligence technology to analyze patient records for Ascension, the largest nonprofit health system in the U.S., according to documents about the efforts reviewed by Forbes. Called "'Nightingale," the Google-Ascension project indicates that Google's push into health analysis is farther along than previously believed, even as the company has faced a growing backlash over health-related privacy concerns. Ascension said in a statement that all its work with Google complies with privacy law and is "underpinned by a robust data security and protection effort, which Google echoed in its own blog post later Monday, including that "patient data cannot and will not be combined with any Google consumer data. " The Wall Street Journal first published details of the Ascension partnership earlier on Monday.
The National Airspace System (NAS) is a large and complex system with thousands of interrelated components: administration, control centers, airports, airlines, aircraft, passengers, etc. The complexity of the NAS creates many difficulties in management and control. One of the most pressing problems is flight delay. Delay creates high cost to airlines, complaints from passengers, and difficulties for airport operations. As demand on the system increases, the delay problem becomes more and more prominent. For this reason, it is essential for the Federal Aviation Administration to understand the causes of delay and to find ways to reduce delay. Major contributing factors to delay are congestion at the origin airport, weather, increasing demand, and air traffic management (ATM) decisions such as the Ground Delay Programs (GDP). Delay is an inherently stochastic phenomenon. Even if all known causal factors could be accounted for, macro-level national airspace system (NAS) delays could not be predicted with certainty from micro-level aircraft information. This paper presents a stochastic model that uses Bayesian Networks (BNs) to model the relationships among different components of aircraft delay and the causal factors that affect delays. A case study on delays of departure flights from Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta International Airport (ATL) reveals how local and system level environmental and human-caused factors combine to affect components of delay, and how these components contribute to the final arrival delay at the destination airport.
Google, Facebook and other internet giants would disclose the algorithms they use to return search results under new legislation proposed by US law makers. The bipartisan Filter Bubble Transparency Act also would require the online companies to offer users an unfiltered search option that delivers results without any algorithmic tinkering. Senator John Thune, a Republican from North Dakota, filed the bill on Friday. The legislation was co-sponsored by Republican senators Jerry Moran of Kansas and Marsha blackburn of Tennessee, as well as Democrats Richard Blumenthal of Connecticut and Mark Warner of Virginia. Senator John Thune, a Republican from North Dakota, filed the bipartisan'Filter Bubble Transparency Act,' which would require internet companies to reveal algorithms used to determine online searches The online firm, owned by Alphabet, like other internet companies relies on algorithms - a highly-specific set of instructions to computers - that track users' behavior and location Thune says the legislation is needed because'people are increasingly impatient with the lack of transparency,' on the internet, reports the Wall Street Journal.