The apartment complexes near Western Kentucky University sued the United States Postal Service and a postmaster in January after the agency began delivering mail in bulk to property management offices instead of tenants' mailboxes. The change came after the Postal Service reclassified the residences as dormitories, according to the lawsuit.

An anti-Trump political organization is using AI originally designed to tackle Islamic State propaganda to counter coronavirus disinformation spread by the president. The system has been repurposed to spot comments from Trump that are about to go viral. It will then identify the most popular counter-narratives, and invite a network of more than 3.4 million influencers "to share these highly visual and emotional narratives from real people in unison and at scale." The initiative is being led by Defeat Disinfo, a political action committee (PAC) advised by retired general Stanley McChrystal, who commanded US and NATO forces during the Afghanistan war. The PAC claims that it won't use any bots, sock puppets, or false information.

Often time, we have faced equal excitement and dilemma over introducing Artificial Intelligence in the military. Even the Department of Defense (DoD) of the USA is caught up in the same predicament. However, the recent findings by the Defense Intelligence Agency (DIA) may finally have a solution to this problem. Apart from it, this study also proves who shall be a better judge, human, or AI in case of analyzing an enemy activity. Throughout history, humans are perceived to be an expert in comprehending and deducing a situation, even in comparison to AI.

Humans may sometimes regard robots with apprehension or resentment over the increasing automation of labor, but the coronavirus pandemic is showing how the two can work together in new ways that might save lives during a crisis. Around the globe, robots and other technologies, like drones and telehealth devices, are being used in a variety of settings and capacities to assist in the COVID-19 response since there is a level of elevated risk for human workers. Automated devices have delivered meals to quarantined travelers in a Chinese hotel; enforced curfews in Tunisia; scanned visitors for fevers entering a South Korean hospital; monitored patients in a hard-hit Italian city; and tracked social distancing compliance from the skies in a number of cities around the world, including Elizabeth, New Jersey. Many of the technologies were available commercially prior to the coronavirus outbreak, said Texas A&M University professor Robin Murphy, who studies how robots can be deployed during disasters. But now, "they are being used 24/7 and adapted to fit the needs of those using them," Murphy added.

Florida Rep. Matt Gaetz, Republican member of the House Judiciary Committee, is pressuring the Justice Department to halt the use of Chinese-made drones. Get all the latest news on coronavirus and more delivered daily to your inbox. House Judiciary Committee member Matt Gaetz, R-Fla., is pressuring the Justice Department to halt the use of Chinese-made drones by state and local governments, telling "Tucker Carlson Tonight" Monday they may be giving hostile forces important law enforcement information. "China is massively expanding a Trojan horse spying operation in our country and your local police department may be unknowingly helping them," Gaetz. "The United States Army, the Department of Interior, they've banned their use of these DJI drones, citing vulnerability and potential value for other countries. "The Department of Homeland Security wrote a scathing report that said that China specifically goes after local law enforcement," Gaetz continued. "They did so even before the coronavirus so that they can get key information transmitted to the Chinese government.

The federal government has received plenty of well-deserved flack for slow-rolling the national launch of diagnostic tests for Covid-19. First came the flawed swab-based tests from the Centers for Disease Control and Prevention, followed by a chaotic, lost month of regulatory tango that prevented independent tests from getting scaled and out the door. So when interest arose in a different kind of testing--antibody blood tests, which are used to find evidence of past infection, not a current diagnosis--the US Food and Drug Administration was under pressure to hurry things along. In mid-March, the agency loosened its rules, declaring via an update to its emergency use guidance that antibody tests could be sold without seeking the agency's approval, provided that manufacturers did their own validation. Now FDA officials are walking back that decision.

The sudden suspension of a controversial multi-million dollar surveillance system used by several government agencies in Utah has opened up a debate about the lack of oversight for artificial intelligence systems in law enforcement. Last week, the Utah Attorney General's office suspended a $20.7 million contract with Banjo -- a technology firm using government surveillance data to develop crime detection software -- following revelations of the founder's past membership of a white supremacist group. Damien Patton, who serves as CEO of the SoftBank-backed company, was reportedly an active member of the Ku Klux Klan as a teenager, and participated in a 1990 drive-by shooting of a synagogue in suburban Nashville, according to the tech blog OneZero. In a statement, a spokesperson for Utah Attorney General Sean Reyes said the office would be moving forward an already planned third-party audit of the software to "address issues like data privacy and possible bias." Reyes recommended that other state agencies do the same.

Artificial intelligence has become widely deployed across several industries. Of course, bad news always gets preference and catches people's minds. Some of the popular bad news in AI have been related to fake news generation, autonomous vehicles killing pedestrians, AI systems attacking a production facility and data biases creating problems in AI applications. Nonetheless, there are still some positive signs, we have seen innovative devices being deployed in hospitals, AI tools helping disabled people, robots being used in increasing set of domains as well as AI assistants and smart devices guiding people in day to day queries and chores. The speed of evolution, adoption, and research in artificial intelligence is accelerating.

The heads of two prominent artificial intelligence firms came under public scrutiny this month for ties to far right organizations. A report by Matt Stroud at OneZero identified the founder and CEO of surveillance firm Banjo, Damien Patton, as a former member of the Dixie Knights of the Ku Klux Klan, who was charged with a hate crime for shooting at a synagogue in 1990. The report led the Utah Attorney General's office to suspend a contract worth at least $750,000 with the company, and reportedly the firm has also lost a $20.8 million contract with the state's Department of Public Safety. Only a few weeks earlier, Luke O'Brien at the Huffington Post uncovered that Clearview AI's founder, Cam-Hoan Ton-That, affiliated with far right extremists including former Breitbart writer Chuck Johnson, Pizzagate conspiracy theorist Mike Cernovich, and neo-Nazi hacker Andrew'weev' Auernheimer. Moreover, the reporters found evidence that Ton-That collaborated with Johnson and others in the development of Clearview AI's software.

Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning (ML) methods, such as deep neural networks, to build data-driven emulators. While the majority of existing efforts has focused on tailoring off-the-shelf ML solutions to better suit the scientific problem at hand, we study an often overlooked, yet important, problem of choosing loss functions to measure the discrepancy between observed data and the predictions from a model. Due to lack of better priors on the expected residual structure, in practice, simple choices such as the mean squared error and the mean absolute error are made. However, the inherent symmetric noise assumption made by these loss functions makes them inappropriate in cases where the data is heterogeneous or when the noise distribution is asymmetric. We propose Learn-by-Calibrating (LbC), a novel deep learning approach based on interval calibration for designing emulators in scientific applications, that are effective even with heterogeneous data and are robust to outliers. Using a large suite of use-cases, we show that LbC provides significant improvements in generalization error over widely-adopted loss function choices, achieves high-quality emulators even in small data regimes and more importantly, recovers the inherent noise structure without any explicit priors.