Erik Learned-Miller is one reason we talk about facial recognition at all. In 2007, years before the current A.I. boom made "deep learning" and "neural networks" common phrases in Silicon Valley, Learned-Miller and three colleagues at the University of Massachusetts Amherst released a dataset of faces titled Labelled Faces in the Wild. To you or me, Labelled Faces in the Wild just looks like folders of unremarkable images. You can download them and look for yourself. There's boxer Joe Gatti, gloves raised mid-fight.
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.
The Federal Aviation Administration (FAA) is moving forward with its plans to accelerate drone testing in the US -- with help from technology companies including Alphabet, FedEx and Intel. The agency announced 10 states that will participate in the the Unmanned Aircraft Systems (UAS) Integration Pilot Program, an effort that aims to study the potential uses of drones in agriculture, commerce, emergency management, and human transportation. The 10 pilot winners include the Choctaw Nation of Oklahoma, in Durant, Oklahoma; the City of San Diego, California; the Innovation and Entrepreneurship Investment Authority, in Herndon, Virginia; the Kansas Department of Transportation; the Lee County Mosquito Control District in Fort Meyers, Florida; the Memphis-Shelby County Airport Authority; the North Carolina Department of Transportation; the North Dakota Department of Transportation; the City of Reno, Nevada; and the University of Alaska-Fairbanks. First announced last October, the UAS program aims to partner the FAA with local, state and tribal governments, along with private sector technology companies, to explore the integration of drone operations across industries. The program will also address public safety and security risks that go along with bringing drones into the national airspace.
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.
The first FDA-approved AI system for diagnosing eye diseases caused by diabetes is completely autonomous, and doesn't require a doctor to interpret the results. Several corporations including Google and DeepMind have been working on building algorithms for diabetic retinography, a leading cause of blindness amongst adults. The first biz to release a device approved by the US Food and Drug Administration (FDA) earlier this year in April, however, is less well-known. IDx LLC, an AI diagnostics company based in Iowa, developed the tool known as IDx-DR. The details about the system were published in a paper in Nature Digital Medicine on Tuesday.