This paper addresses the challenge of efficiently solving the optimal power flow problem in real-time electricity markets. The proposed solution, named Physics-Informed Market-Aware Active Set learning OPF (PIMA-AS-OPF), leverages physical constraints and market properties to ensure physical and economic feasibility of market-clearing outcomes. Specifically, PIMA-AS-OPF employs the active set learning technique and expands its capabilities to account for curtailment in load or renewable power generation, which is a common challenge in real-world power systems. The core of PIMA-AS-OPF is a fully-connected neural network that takes the net load and the system topology as input. The outputs of this neural network include active constraints such as saturated generators and transmission lines, as well as non-zero load shedding and wind curtailments. These outputs allow for reducing the original market-clearing optimization to a system of linear equations, which can be solved efficiently and yield both the dispatch decisions and the locational marginal prices (LMPs). The dispatch decisions and LMPs are then tested for their feasibility with respect to the requirements for efficient market-clearing results. The accuracy and scalability of the proposed method is tested on a realistic 1814-bus NYISO system with current and future renewable energy penetration levels.

We've found that a whopping four out of five invoices don't align with their agreed-upon contract. Usually, what's incorrect is the payment terms. The contract may list payment terms as net 60, while the invoice specifies net 30 or even net 15. While this may sound like a relatively small difference, longer payment terms actually make a big difference for your company's cash flow. An additional 30 or 45 days of having money on your ledger allows your company to maximize profits via interest, external investments, and/or internal re-investments.

Morgan Lewis intellectual property partners Brett Lovejoy and Jeffry Mann will present at the 2019 NIHA AI in Healthcare Forum: Policies and Regulations in Asia-Pacific. The National University of Singapore (NUS) Initiative to Improve Health in Asia (NIHA) created this one-day forum to bring together key thought leaders from healthcare professions, regulators, industry, and government agencies on a neutral platform to discuss the adoption of Al in Healthcare in the Asia-Pacific region. Artificial Intelligence Innovation in Healthcare: Who are the Players and How Do They Protect Innovation? Are We Ready for an AI Future in Healthcare?

In July 2017, China's State Council released the country's strategy for developing artificial intelligence (AI), entitled'New Generation Artificial Intelligence Development Plan' (新一代人工智能发展规划). This strategy outlined China's aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan ($150 billion) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China's AI policies or have assessed the country's technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China's AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use.

With appropriate policies in place, robots should become our "best friends," not our "worst nightmare," experts said at the 41st Annual AAAS Forum on Science & Technology Policy on 14 April. During a panel, entitled "Best Friend or Worst Nightmare? Autonomy and AI in the Lab and in Society," experts on artificial intelligence (AI) spoke about the role of policy in integrating new technologies into people's lives. They both praised current AI advancements, and urged more policymaking in the arena of autonomous systems, particularly related to disaster relief, sustainability, and the military, among other applications. The panel, co-organized by AAAS staff member Jonathan Drake and retired Vice President of Sandia's California Laboratory Miriam John, urged a stronger focus on the promise of AI, rather than its perils.