GRC traditionally used to be siloed, and the technology underpinning it, monolithic. How is the nature of varied risks encountered by organizations and financial institutions shifting in the changed circumstances? Post the global financial crisis of 2008, risk management as a function has evolved in shape and form, becoming a business imperative. Fast forward to 2021, and our world is going through a series of dramatic changes, as the ripple effect of unprecedented and potentially catastrophic events, like the COVID-19 pandemic. As a consequence, the global landscape of Governance, Risk, and Compliance (GRC) is becoming increasingly complex.

While significant differences exist, there is more overlap than one might think. As the U.S. considers its own approach, we may see more agreement and less technological balkanization. In 2018, when the European Union's General Data Protection Regulation (GDPR) came into force, it was a relatively unique piece of legislation. The two major centers of data-driven innovation and disruption -- the United States and China -- did not have anything comparable. Fast forward to 2021, stakeholders are paying similar attention to the regulation of AI.

The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generated. One strategy to recover this information is to decode both the content and ordering of tokens. Existing approaches supervise content and ordering by designing problem-specific loss functions and pre-training with an ordering pre-selected. Other recent works use iterative search to discover problem-specific orderings for training, but suffer from high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised parallelizable learner that discovers high-quality generation orders purely from training data -- no domain knowledge required. The learner contains an encoder network and decoder language model that perform variational inference with autoregressive orders (represented as permutation matrices) as latent variables. The corresponding ELBO is not differentiable, so we develop a practical algorithm for end-to-end optimization using policy gradients. We implement the encoder as a Transformer with non-causal attention that outputs permutations in one forward pass. Permutations then serve as target generation orders for training an insertion-based Transformer language model. Empirical results in language modeling tasks demonstrate that our method is context-aware and discovers orderings that are competitive with or even better than fixed orders.

Behavior prediction remains one of the most challenging tasks in the autonomous vehicle (AV) software stack. Forecasting the future trajectories of nearby agents plays a critical role in ensuring road safety, as it equips AVs with the necessary information to plan safe routes of travel. However, these prediction models are data-driven and trained on data collected in real life that may not represent the full range of scenarios an AV can encounter. Hence, it is important that these prediction models are extensively tested in various test scenarios involving interactive behaviors prior to deployment. To support this need, we present a simulation-based testing platform which supports (1) intuitive scenario modeling with a probabilistic programming language called Scenic, (2) specifying a multi-objective evaluation metric with a partial priority ordering, (3) falsification of the provided metric, and (4) parallelization of simulations for scalable testing. As a part of the platform, we provide a library of 25 Scenic programs that model challenging test scenarios involving interactive traffic participant behaviors. We demonstrate the effectiveness and the scalability of our platform by testing a trained behavior prediction model and searching for failure scenarios.

Most state-of-the-art Graph Neural Networks (GNNs) can be defined as a form of graph convolution which can be realized by message passing between direct neighbors or beyond. To scale such GNNs to large graphs, various neighbor-, layer-, or subgraph-sampling techniques are proposed to alleviate the "neighbor explosion" problem by considering only a small subset of messages passed to the nodes in a mini-batch. However, sampling-based methods are difficult to apply to GNNs that utilize many-hops-away or global context each layer, show unstable performance for different tasks and datasets, and do not speed up model inference. We propose a principled and fundamentally different approach, VQ-GNN, a universal framework to scale up any convolution-based GNNs using Vector Quantization (VQ) without compromising the performance. In contrast to sampling-based techniques, our approach can effectively preserve all the messages passed to a mini-batch of nodes by learning and updating a small number of quantized reference vectors of global node representations, using VQ within each GNN layer. Our framework avoids the "neighbor explosion" problem of GNNs using quantized representations combined with a low-rank version of the graph convolution matrix. We show that such a compact low-rank version of the gigantic convolution matrix is sufficient both theoretically and experimentally. In company with VQ, we design a novel approximated message passing algorithm and a nontrivial back-propagation rule for our framework. Experiments on various types of GNN backbones demonstrate the scalability and competitive performance of our framework on large-graph node classification and link prediction benchmarks.

Previous research has shown mixed evidence on the associations between mobility data and COVID-19 case rates, analysis of which is complicated by differences between places on factors influencing both behavior and health outcomes. We aimed to evaluate the county-level impact of shifting the distribution of mobility on the growth in COVID-19 case rates from June 1 - November 14, 2020. We utilized a modified treatment policy (MTP) approach, which considers the impact of shifting an exposure away from its observed value. The MTP approach facilitates studying the effects of continuous exposures while minimizing parametric modeling assumptions. Ten mobility indices were selected to capture several aspects of behavior expected to influence and be influenced by COVID-19 case rates. The outcome was defined as the number of new cases per 100,000 residents two weeks ahead of each mobility measure. Primary analyses used targeted minimum loss-based estimation (TMLE) with a Super Learner ensemble of machine learning algorithms, considering over 20 potential confounders capturing counties' recent case rates as well as social, economic, health, and demographic variables. For comparison, we also implemented unadjusted analyses. For most weeks considered, unadjusted analyses suggested strong associations between mobility indices and subsequent growth in case rates. However, after confounder adjustment, none of the indices showed consistent associations after hypothetical shifts to reduce mobility. While identifiability concerns limit our ability to make causal claims in this analysis, MTPs are a powerful and underutilized tool for studying the effects of continuous exposures.

Artificial intelligence (AI) is the ability of computer systems to simulate human intelligence. It has not taken long for AI to become indispensable in most facets of human life, with the realm of cybersecurity being one of the beneficiaries. AI can predict cyberattacks, help create improved security processes to reduce the likelihood of cyberattacks, and mitigate their impact on IT infrastructure. AI can also free up cybersecurity professionals to focus on more critical tasks in the organization. One such concern is AI bias.

Officials at the U.S. National Counterintelligence and Security Center (NCSC) warned on Friday that China's pursuit of artificial intelligence (AI) technology could have major implications for the future of the military and economic competition between the two nations. Among other topics, the warnings restated the U.S. warning against private companies in key areas allowing Chinese investment or expertise, urging them to take significant precautions in protecting their intellectual property. Under the Trump and Biden administrations, relations between Washington and Beijing have steadily become more acrimonious, with increasing consensus from America's national security agencies that China represents a strategic threat to the United States. Although Biden has made statements advising against the creation of a "new Cold War" with China, and advocated in favor of working together on mutual concerns such as climate change, relations have still remained tense--particularly since the onset of the coronavirus pandemic, when the United States reproached China over for its failure to share certain information about the virus's origins. For its part, Beijing has accused Washington of acting in bad faith.

China's recent satellite launch is sparking fears from some experts who say it can be used as a weapon capable of grabbing hold of and crushing American satellites. The satellite named Shijian-21, after the Chinese word "practice," was propelled into space Saturday atop of Long March 3B rocket launched from the Xichang Satellite Launch Center, a military facility in the mountains of the mountains in Sichuan province of southwestern China, Space Flight Now reported. Its exact mission is classified, though the state-run China Aerospace Science and Technology Corp., said the satellite is "tasked with demonstrating technologies to alleviate and neutralize space debris," The Washington Times reported. During a Senate committee hearing in April, U.S. Air Force Gen. James Dickinson, commander of the U.S. Space Command, said spacecraft like Shijian-21 are being utilized as part of the Chinese effort to seek "space superiority through space and space-attack systems." An earlier model, the Shijian-17 satellite, which was launched in 2016, also was equipped with a robotic arm that could be used to grapple other spacecraft, Dickinson testified.

A. Lawfulness: AI applications will be developed and used in accordance with national and international law, including international humanitarian law and human rights law, as applicable. B. Responsibility and Accountability: AI applications will be developed and used with appropriate levels of judgment and care; clear human responsibility shall apply in order to ensure accountability. C. Explainability and Traceability: AI applications will be appropriately understandable and transparent, including through the use of review methodologies, sources, and procedures. This includes verification, assessment and validation mechanisms at either a NATO and/or national level. D. Reliability: AI applications will have explicit, well-defined use cases.