Ocean County, New Jersey Sheriff Michael Mastronardy shares how he launched his own drones to learn more information about the mysterious drones hovering over his state on'Your World.' Government officials have responded to the recent drone sightings near an Air Force base in Ohio on Monday, noting that the incidents appear unrelated to the unusual sightings in the Northeast. The drones were seen near Wright-Patterson Air Force Base in Greene County over the weekend. Following the sightings, the base closed its airspace for four hours on Saturday. According to its website, Wright-Patterson is "headquarters for a vast, worldwide logistics system, a world-class laboratory research function, and is the foremost acquisition and development center in the U.S. Air Force." In a statement to Fox News, Robert Purtiman, Chief of Public Affairs of the 88th Air Base Wing, confirmed that officials were aware of the drones.

In this setting our objective is to provide low regret with respect to the sequence of losses as well as with respect to the constraints. This setting is useful when one would like to avoid projections which might be costly. Previous works focused on the regret with the respect to the cumulative constraints, where constraint violation may be compensated by constrained satisfaction. In this work the authors consider a more appropriate measure of regret which accounts only for constraints violation. In this case, the authors come up with a new algorithm (similar in spirit to previous approaches), which provides regret guarantees with respect to the sum of squared constrained violations. They also extend their approach to the strongly convex setting.

The organizational use of artificial intelligence (AI) has rapidly spread across various sectors. Alongside the awareness of the benefits brought by AI, there is a growing consensus on the necessity of tackling the risks and potential harms, such as bias and discrimination, brought about by advanced AI technologies. A multitude of AI ethics principles have been proposed to tackle these risks, but the outlines of organizational processes and practices for ensuring socially responsible AI development are in a nascent state. To address the paucity of comprehensive governance models, we present an AI governance framework, the hourglass model of organizational AI governance, which targets organizations that develop and use AI systems. The framework is designed to help organizations deploying AI systems translate ethical AI principles into practice and align their AI systems and processes with the forthcoming European AI Act. The hourglass framework includes governance requirements at the environmental, organizational, and AI system levels. At the AI system level, we connect governance requirements to AI system life cycles to ensure governance throughout the system's life span. The governance model highlights the systemic nature of AI governance and opens new research avenues into its practical implementation, the mechanisms that connect different AI governance layers, and the dynamics between the AI governance actors. The model also offers a starting point for organizational decision-makers to consider the governance components needed to ensure social acceptability, mitigate risks, and realize the potential of AI.