Derrick Monet and his wife, Jenna, were driving on an Indiana interstate in 2019 when their Tesla Model 3 sedan operating on Autopilot crashed into a parked fire truck. Derrick, then 25, sustained spine, neck, shoulder, rib and leg fractures. Jenna, 23, died at the hospital. The incident was one of a dozen in the last four years in which Teslas using this driver-assistance system collided with first-responder vehicles, raising questions about the safety of technology the world's most valuable car company considers one of its crown jewels. Now, U.S. regulators are applying greater scrutiny to Autopilot than ever before.
For this smaller group of patients, physicians may have limited clinical decision-making experience or evidence-based guidance for choosing drug combinations. The solution is to expand the number of patients to support development of general principles to guide decision-making. Combining patient data from multiple healthcare institutions, however, requires deep expertise in artificial intelligence (AI) and wide-ranging experience in developing machine learning models using sensitive and complex healthcare data. Hitachi, U of U Health, and Regenstrief researchers partnered to develop and test a new AI method that analyzed electronic health record data across Utah and Indiana and learned generalizable treatment patterns of type 2 diabetes patients with similar characteristics. Those patterns can now be used to help determine an optimal drug regimen for a specific patient.
The movement toward AI allows predictive models to weigh in on hiring decisions, but the proprietary nature of such algorithms shrouds them in mystery, making it exceedingly difficult to determine if and when bias and discrimination unintentionally skew the results, warned Alex Engler, Brookings Rubenstein Fellow, adjunct professor and affiliated scholar at Georgetown University and senior consulting data scientist at social policy research nonprofit MDRC. Such tools are actively being deployed, with a 2020 global study from HR consulting services provider Mercer finding that 41 percent of HR professionals already use AI algorithms to analyze public data to identify promising job candidates and 30 percent use algorithms during recruitment to screen and evaluate the prospective new hires. Efforts to reduce AI bias include adjusting data sets to provide AI systems with a more balanced look. The tools can be designed to obscure applicant characteristics seen as irrelevant to the actual skills demanded by the roles and avoid considering certain protected demographic details when evaluating potential candidates, for example – something that Indiana's AI solution provider Eightfold AI says it does. But the proprietary nature of many hiring algorithms can mean that employers lack full knowledge of which factors are being considered and how the automated systems are weighing them to arrive at interviewing and hiring recommendations.
Given the strain on hospital resources caused by the pandemic, many informaticists have focused on the ability to predict patient populations. In January, researchers at the Regenstrief Institute and Indiana University found that machine learning models trained using statewide health information exchange data can actually predict a patient's likelihood of being hospitalized with COVID-19. Joining Healthcare IT News Senior Editor Kat Jercich to discuss the study's implications are two of its lead authors, Dr. Shaun Grannis and Suranga Kasturi.
Comparisons between Kogonada's new film, "After Yang," and his earlier one, "Columbus," are inevitable, and their differences obscure the big idea that unites them. "After Yang" is a science-fiction film, set in a vague future time at an unspecified place, seemingly in the United States; its title character is an android, or "technosapien." "Columbus," his first feature, from 2017, is set in its own present day, in the real-life city of Columbus, Indiana, and centered on a young woman played by Haley Lu Richardson. "After Yang" is a synthetic work of dystopian imagination, and "Columbus" is a carefully realistic view of its place and time. Nonetheless, the two films are propelled by the same impulse: the artistic basis of mental life, the politics of aesthetics.
There are worse movies than Uncharted, especially when it comes to the seemingly cursed genre of video game adaptations. But as I struggled to stay awake through the finale -- yet another weightless action sequence where our heroes quip, defy physics and never feel like they're in any genuine danger -- I couldn't help but wonder why the film was so aggressively average. The PlayStation franchise started out as a Tomb Raider clone starring a dude who wasn't Indiana Jones. But, starting with Uncharted 2: Among Thieves, the games tapped into the language of action movies to put you in the center of innovative set pieces. They were cinematic in ways that few titles were in the early 2010s.
It's easy to be dubious of a video game movie adaptation, considering their shaky history. Directed by Ruben Fleischer (of Zombieland fame), this adaptation stands on its own as an origin story for our pal Nate. Rather than pulling from any one game, Uncharted draws inspiration from a series that was itself inspired by the likes of Tomb Raider and Indiana Jones. Holland's Nate is a bartending, smooth-talking history nerd, who supplements his income by conning customers out of their valuables. We learn early on that the source of Nate's penchant for criminal mischief is his long-absent brother Sam, who -- after being expelled from the boarding school the Drake both boys attended -- disappeared into the wider world.
Wischnewski, Alexander, Geisslinger, Maximilian, Betz, Johannes, Betz, Tobias, Fent, Felix, Heilmeier, Alexander, Hermansdorfer, Leonhard, Herrmann, Thomas, Huch, Sebastian, Karle, Phillip, Nobis, Felix, Ögretmen, Levent, Rowold, Matthias, Sauerbeck, Florian, Stahl, Tim, Trauth, Rainer, Lienkamp, Markus, Lohmann, Boris
Motorsport has always been an enabler for technological advancement, and the same applies to the autonomous driving industry. The team TUM Auton-omous Motorsports will participate in the Indy Autonomous Challenge in Octo-ber 2021 to benchmark its self-driving software-stack by racing one out of ten autonomous Dallara AV-21 racecars at the Indianapolis Motor Speedway. The first part of this paper explains the reasons for entering an autonomous vehicle race from an academic perspective: It allows focusing on several edge cases en-countered by autonomous vehicles, such as challenging evasion maneuvers and unstructured scenarios. At the same time, it is inherently safe due to the motor-sport related track safety precautions. It is therefore an ideal testing ground for the development of autonomous driving algorithms capable of mastering the most challenging and rare situations. In addition, we provide insight into our soft-ware development workflow and present our Hardware-in-the-Loop simulation setup. It is capable of running simulations of up to eight autonomous vehicles in real time. The second part of the paper gives a high-level overview of the soft-ware architecture and covers our development priorities in building a high-per-formance autonomous racing software: maximum sensor detection range, relia-ble handling of multi-vehicle situations, as well as reliable motion control under uncertainty.
This story was originally published by the Guardian and is reproduced here as part of the Climate Desk collaboration. There are an estimated 73,300 species of tree on Earth, 9,000 of which have yet to be discovered, according to a global count of tree species by thousands of researchers who used second world war codebreaking techniques created at Bletchley Park to evaluate the number of unknown species. Researchers working on the ground in 90 countries collected information on 38 million trees, sometimes walking for days and camping in remote places to reach them. The study found there are about 14 percent more tree species than previously reported and that a third of undiscovered tree species are rare, meaning they could be vulnerable to extinction by human-driven changes in land use and the climate crisis. "It is a massive effort for the whole world to document our forests," said Jingjing Liang, a lead author of the paper and professor of quantitative forest ecology at Purdue University in Indiana, US. "Counting the number of tree species worldwide is like a puzzle with pieces spreading all over the world. We solved it together as a team, each sharing our own piece."
Experts say the technology isn't replacing human workers anytime soon. But the latest steps show warehouse robots are evolving as the computer vision and software that guide them grow more sophisticated, allowing them to take on more tasks that have been largely done by people. Puma North America Inc., a division of Puma SE, is using several robotic arms to assemble orders of clothing and shoes at a distribution center in Torrance, Calif.; the company plans to install more robots at another site outside Indianapolis. The technology from Nimble Robotics Inc., whose customers include Best Buy Co. and Victoria's Secret & Co., uses a combination of cameras, grippers and artificial intelligence to pluck items from bins that another automated system delivers to workstations usually staffed by people. Remote operators are on hand to assist if the robot has trouble picking up an object.