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Drone Racing League embraces sports betting in partnership with DraftKings

Engadget

Since the dawn of the drone era, enterprising pilots and enthusiasts have found ways to make money off their passion for flying. Thanks to a new partnership between the Drone Racing League and DraftKings, though, gamblers can now make money off of other people's passion for flying. While DraftKings can legally operate its daily fantasy sports business in 43 states, the company stresses that betting on drone races is currently only legal in Colorado, New Hampshire, West Virginia, Tennessee and New Jersey. "The sky is now the limit for DRL fans to get skin in the game, and we're thrilled to partner with DraftKings to transform our high-speed race competition into the ultimate sport to bet on," said DRL President Rachel Jacobson in a press release. Today's announcement makes drone racing the first aerial sport people can legally bet on, and Jacobson noted to Forbes that embracing betting is part of the company's plan to scale into an "ultimately mainstream sport."


The Government Wants to Scan Your Face When You Enter the US. It Hasn't Gone Well So Far.

Mother Jones

Officials at Dulles International Airport in Virginia unveil new biometric facial recognition scanners in September 2018.Bill O'Leary/Getty In 2018, the federal government started scanning people's faces as they drove into and out of the country at the Anzalduas International Bridge, which connects the Rio Grande Valley of Texas to Mexico. Customs and Border Protection said collecting these biometric images would enhance security and make identifying travelers more efficient. But less than a year later, a data breach compromised 100,000 facial images and 105,000 license plate images. Nineteen facial images from the breach were posted to the dark web. Now, CBP wants to expand facial surveillance beyond Anzalduas and other sites that were part of a pilot program, even as the program saw potential security vulnerabilities in at least four airports, according to a report from the Department of Homeland Security inspector general.


Preparing for emergency response with partial network information

AIHub

Natural disasters cause considerable economic damage, loss of life, and network disruptions each year. As emergency response and infrastructure systems are interdependent and interconnected, quick assessment and repair in the event of disruption is critical. School of Computational Science and Engineering (CSE) Associate Professor B. Aditya Prakash is leading a collaborative effort with researchers from Georgia Institute of Technology, University of Oklahoma, University of Iowa, and University of Virginia to determine the state of an infrastructure network during such a disruption. Prakash's group has also been collaborating closely with the Oak Ridge National Laboratory on such problems in critical infrastructure networks. However, according to Prakash, quickly determining which infrastructure components are damaged in the event of a disaster is not easily done after a disruption.


Unlocking the secrets of chemical bonding with machine learning

#artificialintelligence

In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that "goldilocks zone" is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose. Bayeschem works using Bayesian learning, a specific machine learning algorithm for inferring models from data.


AI Needs to Face Up to its Invisible-worker Problem

#artificialintelligence

Saiph Savage, director of the human-computer interaction lab at West Virginia University, advocates for the workers who put in the time to develop training data for artificial intelligence. Many of the most successful and widely used machine-learning models are trained with the help of thousands of low-paid gig workers. Millions of people around the world earn money on platforms like Amazon Mechanical Turk, which allow companies and researchers to outsource small tasks to online crowdworkers. According to one estimate, more than a million people in the US alone earn money each month by doing work on these platforms. Around 250,000 of them earn at least three-quarters of their income this way.


Unlocking the secrets of chemical bonding with machine learning

#artificialintelligence

A new machine learning approach offers important insights into catalysis, a fundamental process that makes it possible to reduce the emission of toxic exhaust gases or produce essential materials like fabric. In a report published in Nature Communications, Hongliang Xin, associate professor of chemical engineering at Virginia Tech, and his team of researchers developed a Bayesian learning model of chemisorption, or Bayeschem for short, aiming to use artificial intelligence to unlock the nature of chemical bonding at catalyst surfaces. "It all comes down to how catalysts bind with molecules," said Xin. "The interaction has to be strong enough to break some chemical bonds at reasonably low temperatures, but not too strong that catalysts would be poisoned by reaction intermediates. This rule is known as the Sabatier principle in catalysis." Understanding how catalysts interact with different intermediates and determining how to control their bond strengths so that they are within that'goldilocks zone' is the key to designing efficient catalytic processes, Xin said. The research provides a tool for that purpose.


Ever wanted to test out a self-driving shuttle? Now is your chance.

#artificialintelligence

The State of Virginia, Fairfax County, and Dominion Energy recently launched Relay, a free self-driving, and 100% electric public transportation shuttle, that will circulate between the commercial hub of Mosaic District in Merrifield, Virginia, and the Dunn Loring Metrorail Station.


UVA artificial intelligence project among finalists for national challenge

#artificialintelligence

The proposal was chosen as part of the first Centers for Medicare and Medicaid Services Artificial Intelligence Health Outcomes Challenge. It predicts the outcomes for patients and suggests a personalized plan for their health care delivery to avoid unnecessary trips to the hospital.


Amazon: Here's what caused major AWS outage last week – apologies

ZDNet

Amazon Web Services (AWS) has explained the cause of last Wednesday's widespread outage, which impacted thousands of third-party online services for several hours. While dozens of AWS services were affected, AWS says the outage occurred in its Northern Virginia, US-East-1, region. It happened after a "small addition of capacity" to its front-end fleet of Kinesis servers. Kinesis is used by developers, as well as other AWS services like CloudWatch and Cognito authentication, to capture data and video streams and run them through AWS machine-learning platforms. The Kinesis service's front-end handles authentication, throttling, and distributes workloads to its back-end "workhorse" cluster via a database mechanism called sharding. As AWS notes in a lengthy summary of the outage, the addition of capacity triggered the outage but wasn't the root cause of it.


Facebook Proposes Free-Viewpoint Rendering on Monocular Video

#artificialintelligence

Just a few months after Facebook released an on-device model capable of turning common two-dimensional photos into 3D images comes a new and improved model, produced in cooperation with Cornell Tech and Virginia Tech, that enables free-viewpoint rendering of dynamic scenes in a single video. Typical 3D reconstruction algorithms require multiple cameras to capture different viewpoints, necessitating a complicated hardware setup. Some recent video depth estimation approaches have managed to acquire consistent per-frame depth estimates from a single video using scene depth estimates and view synthesis, but the team says even with perfect depth estimates, these early-stage approaches can lead to unnatural stretches and reveal holes in disoccluded regions. The Facebook approach enables free-viewpoint rendering by learning a spatiotemporal neural irradiance field -- a challenging task given that the input video contains only one viewpoint of the scene at any given moment. For continuous representations of a scene without resolution loss, the researchers used neural implicit representations to aggregate all dynamic scene spatiotemporal aspects into a single global representation. Rather than modelling view dependency, the researchers trained the neural irradiance fields as a function of both space and time for each video.