On October 22, 2019, the Centers for Medicare and Medicaid Services ("CMS") issued a Request for Information ("RFI") to obtain input on how CMS can utilize Artificial Intelligence ("AI") and other new technologies to improve its operations. CMS' objectives to leverage AI chiefly include identifying and preventing fraud, waste, and abuse. The RFI specifically states CMS' aim "to ensure proper claims payment, reduce provider burden, and overall, conduct program integrity activities in a more efficient manner." The RFI follows last month's White House Summit on Artificial Intelligence in Government, where over 175 government leaders and industry experts gathered to discuss how the Federal government can adopt AI "to achieve its mission and improve services to the American people." Advances in AI technologies have made the possibility of automated fraud detection at exponentially greater speed and scale a reality. A 2018 study by consulting firm McKinsey & Company estimated that machine learning could help US health insurance companies reduce fraud, waste, and abuse by $20-30 billion.

Understanding the workings and behaviors of a city requires knowledge of the different processes that allow people and other biological organisms to live and thrive, as well as understanding of their interrelationships--many of which are complicated and have yet to be deeply explored. "Cities are immensely complex, with many facets and interactions within them," said Pete Beckman, a computer scientist at the U.S. Department of Energy's (DOE) Argonne National Laboratory. "For instance, weather influences human movement; air quality affects long-term health; and availability to transportation helps determine opportunities ranging from employment to social interaction. What we need is a new generation of methods and tools that can help us find relationships hidden within the growing volume and diversity of data that are being collected about cities." Central to these methods is machine learning--the increasingly potent process by which computers train to make predictions or determinations from large quantities of data. Machine learning has revolutionized many parts of our lives, from the game of chess to facial recognition systems, and it is now coming to our cities.

RIYADH: The Global Artificial Intelligence Summit 2020 will be held in Saudi Arabia, according to Saudi Press Agency (SPA). The summit -- held under the patronage of Crown Prince Mohammed bin Salman -- will take place in Riyadh on 30-31 March next year. The event is the leading international forum to advance AI understanding and knowledge sharing among global AI experts. The summit is part of Saudi Arabia's plan to become a leader in AI technology and drive discussions and partnerships between local and international stakeholders in the AI field. The event will be a focus for discussion on AI, its importance, applications and impact on societies, economies and politics.

Former Democrat congressional aide Jackson Cosko is sentenced for posting the private information of five Republican senators on Wikipedia during the Kavanaugh Supreme Court confirmation hearings; Griff Jenkins reports. A second aide to Sen. Maggie Hassan, D-N.H., has been sentenced in a scheme to break into Hassan's office to obtain and publicly post the personal information of several Republican politicians amid contentious confirmation hearings for Justice Brett Kavanaugh. The 24-year-old former aide, Samantha Deforest Davis, was sentenced to two years of supervised probation with 200 hours of community service, with a suspended sentence of 180 days in prison. She was ordered to "stay away from [Hassan's] office to include current and former staff, and to not use Tor or anonymized computer applications," the Justice Department said in a statement. Davis was a staff assistant in Hassan's office from August 2017 until last December.

Nearly every enterprise is experimenting with artificial intelligence and deep learning. It seems like every week there's a new survey out detailing the ever-increasing amount of focus that IT shops of all sizes put on the technology. If it's true that data is the new currency, then it's artificial intelligence that mines that data for value. Your C-suite understands that, and its why they continually push to build AI and machine learning capabilities. Nowhere is AI/ML more impactful than in the world of government and government contractors.

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. This article is part of the special track on AI and Society.

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.

We consider stochastic zero-order optimization problems, which arise in settings from simulation optimization to reinforcement learning. We propose an adaptive sampling quasi-Newton method where we estimate the gradients of a stochastic function using finite differences within a common random number framework. We employ modified versions of a norm test and an inner product quasi-Newton test to control the sample sizes used in the stochastic approximations. We provide preliminary numerical experiments to illustrate potential performance benefits of the proposed method.

BART is trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text. It uses a standard Tranformer-based neural machine translation architecture which, despite its simplicity, can be seen as generalizing BERT (due to the bidirectional encoder), GPT (with the left-to-right decoder), and many other more recent pretraining schemes. We evaluate a number of noising approaches, finding the best performance by both randomly shuffling the order of the original sentences and using a novel in-filling scheme, where spans of text are replaced with a single mask token. BART is particularly effective when fine tuned for text generation but also works well for comprehension tasks. It matches the performance of RoBERTa with comparable training resources on GLUE and SQuAD, achieves new state-of-the-art results on a range of abstractive dialogue, question answering, and summarization tasks, with gains of up to 6 ROUGE. BART also provides a 1.1 BLEU increase over a back-translation system for machine translation, with only target language pretraining. We also report ablation experiments that replicate other pretraining schemes within the BART framework, to better measure which factors most influence end-task performance.

Given a Boolean formula $\phi(x)$ in conjunctive normal form (CNF), the density of states counts the number of variable assignments that violate exactly $e$ clauses, for all values of $e$. Thus, the density of states is a histogram of the number of unsatisfied clauses over all possible assignments. This computation generalizes both maximum-satisfiability (MAX-SAT) and model counting problems and not only provides insight into the entire solution space, but also yields a measure for the \emph{hardness} of the problem instance. Consequently, in real-world scenarios, this problem is typically infeasible even when using state-of-the-art algorithms. While finding an exact answer to this problem is a computationally intensive task, we propose a novel approach for estimating density of states based on the concentration of measure inequalities. The methodology results in a quadratic unconstrained binary optimization (QUBO), which is particularly amenable to quantum annealing-based solutions. We present the overall approach and compare results from the D-Wave quantum annealer against the best-known classical algorithms such as the Hamze-de Freitas-Selby (HFS) algorithm and satisfiability modulo theory (SMT) solvers.