The optimal power flow (OPF) problem, as a critical component of power system operations, becomes increasingly difficult to solve due to the variability, intermittency, and unpredictability of renewable energy brought to the power system. Although traditional optimization techniques, such as stochastic and robust optimization approaches, could be used to address the OPF problem in the face of renewable energy uncertainty, their effectiveness in dealing with large-scale problems remains limited. As a result, deep learning techniques, such as neural networks, have recently been developed to improve computational efficiency in solving large-scale OPF problems. However, the feasibility and optimality of the solution may not be guaranteed. In this paper, we propose an optimization model-informed generative adversarial network (MI-GAN) framework to solve OPF under uncertainty. The main contributions are summarized into three aspects: (1) to ensure feasibility and improve optimality of generated solutions, three important layers are proposed: feasibility filter layer, comparison layer, and gradient-guided layer; (2) in the GAN-based framework, an efficient model-informed selector incorporating these three new layers is established; and (3) a new recursive iteration algorithm is also proposed to improve solution optimality. The numerical results on IEEE test systems show that the proposed method is very effective and promising.

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn.

Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Sen. Ron Wyden, D-Ore., speaks during a Senate Finance Committee hearing on Oct. 19, 2021. Wyden says he has long been concerned about the algorithms used by his state's child welfare system. Child welfare officials in Oregon will stop using an algorithm to help decide which families are investigated by social workers, opting instead for a new process that officials say will make better, more racially equitable decisions.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Biden administration is planning on selling four MQ-1C Gray Eagle drones – which are capable of carrying powerful Hellfire missiles – to Ukraine to help the country fight Russia, a report says. The move would provide a military boost to Ukraine in the war, which so far has been using drones like the Turkish Bayraktar-TB2, according to Reuters. The MQ-1C Gray Eagle drones can fly more than 30 hours per mission and carry eight Hellfire missiles, which are double the weight of the munitions that the Bayraktar-TB2 operates with, the news agency adds. "Generally the MQ-1C is a much larger aircraft with a max take-off weight around three times that of the Bayraktar-TB2, with commensurate advantages in payload capacity, range, and endurance," drone expert Dan Gettinger of the nonprofit Vertical Flight Society told Reuters.

The Pentagon's new Chief Digital and Artificial Intelligence Office (CDAO) has hired nearly a dozen senior leaders to serve in its top positions -- and met its June 1 deadline to reach full operating capability, FedScoop learned Wednesday. This news comes nearly six months after the Department of Defense launched a major organizational restructure to place a number of technology-driving components under this newly established office, with the ultimate aim to better scale digital and Al-enabled capabilities across its massive enterprise. "Following a multi-step process from [initial operating capability] to FOC the CDAO has fully merged and integrated the former component organizations of Advana, Chief Data Officer, Defense Digital Service, and Joint Artificial Intelligence Center. Legacy component names will no longer be recognized or used unless attributed to a product or capability specific to the department," according to a statement from CDAO's spokesperson. Diane Staheli was also recently tapped to lead the CDAO's Responsible AI (RAI) Division.

Ryan Fedasiuk expressed, in Foreign Policy, his strong sense that the United States and China'take steps' towards mitigating the escalatory risks posed by AI accidents. He noted that'Even with perfect info and ideal operating circumstances, AI systems break easily and perform in ways contrary to their intended function'. This is already true of'racially biased hiring decisions'; it may be disastrous in AI weapons systems. The piece also discussed the lack of trust between China and the US on the testing and evaluation of their military AI systems. To improve diplomatic negotiations around AI safety, the article recommended three steps: 1. Clarify their current AI processes and principles.

Researchers argue that the national, centralized regulation of clinical artificial intelligence (AI) is not sufficient and instead propose a hybrid model of centralized and decentralized regulation. In an opinion piece published in PLOS Digital Health, public health researchers at Harvard note that the increase in clinical AI applications, combined with the need to adapt applications to account for differences between local health systems, creates a significant challenge for regulators. Currently, the US Food and Drug Administration (FDA) regulates clinical AI under the classification of software-based medical devices. Medical device approval is typically obtained via premarket clearance, de novo classification, or premarket approval. In practice, this usually involves the approval of a "static" model, meaning that any change in data, algorithm, or intended use after initial approval requires reapplication for approval.

In 2015, there were approximately 3.5 billion internet of things (IoT) devices in use. Today, the number stands around 35 billion, and is expected to eclipse 75 billion by 2025. IoT devices range from connected blood pressure monitors to industrial temperature sensors, and they're indispensable. The challenge was the driving force behind Ordr, a startup focused on network-level device security. Pandian Gnanaprakasam and Sheausong Yang -- who between them had tenures at Cisco, Aruba Networks, and AT&T Bell Labs -- co-founded Ordr in 2015 to address what they call the "visibility gap" in enterprise networks.

The past two years have seen radical digital transformation. Companies and industries that have traditionally been hesitant to adopt new technology suddenly embraced their digital transformations--they needed to find new ways to work. Interestingly, many experts believe that these radical shifts are only the beginning. In a recent Deloitte survey, three-quarters of executives stated that they expect more changes in the next five years than there were in the past five years. The rate of change only increases as organizations are more open and willing to make the changes they need to keep up with the competition. Digital transformation (DX) encourages business organizations to adopt new technologies in order to deliver better value to their customers.

USAID Artificial Intelligence Action Plan outlines how to engage responsibly with AI in our programming and in the countries where we work.