The General Services Administration is modernizing how agencies review regulations using machine learning (ML), in a procurement through its Centers of Excellence (CoE) initiative. GSA awarded a $9.9 million contract to Deloitte and to Esper, Inc. for ML support for agencies. ML can review rules and regulations to identify trends in the data, which can help eliminate redundancies and streamline the process of writing new ones. Both the CoEs within GSA's Technology Transformation Services and the Federal Systems Integration and Management Center (FEDSIM) have used ML, a subset of artificial intelligence, to conduct regulatory reviews. The contract extends their work to CoE partner agencies.

When QAnon emerged in 2017, the game designer Adrian Hon felt a shock of recognition. QAnon, as you very likely know, is the right-wing conspiracy theory that revolves around a figure named Q. This supposedly high-ranking insider claims that the deep state--an alleged cabal led by Barack Obama, Hillary Clinton, and George Soros and abetted by decadent celebrities--is running a global child-sex-trafficking ring and plotting a left-wing coup. Only Donald Trump heroically stands in the way. But what intrigued Hon was the style of nonsense.

In Gujarat, a tiny independent studio is drawing on India's rich literary history to create surreal games that flow like visual poems, evoking decades of colonial literature and folk theatre to draw attention to the politics of today. Through fantastical environments where buildings and oversized monuments are made of rubber sandals and toothpaste tubes, Studio Oleomingus – made up of writer/artist Dhruv Jani and programmer Sushant Chakraborty, with help from another programmer, Vivek Savsaiya – crafts interactive stories that cast a playful light on India's complicated past and present. "We find video games to be excellent spaces for political discourse," Jani tells me over Skype. "The government is hardly bothered about something as'trivial' as video games, and they also give you a lot of room to think and ponder complex ideas." The studio's short, experimental games, drenched in vibrant colours and otherworldly imagery, pay homage to the magical realist, nonsense literature that defined many Indian childhoods.

Entitled "Trusted Sensor Integration", the Phase I STTR focuses on building both analytical/statistical and Machine Learning based models of the static and dynamic behavior of individual sensors and systems. The proposed solution uses the imperfections of the sensor in translating the physical input to a numeric output to derive a fingerprint. It will stimulate a simple cyber-physical system, e.g. an engine, to measure the sensor output, and to feed both stimulation signals and sensor outputs into an RNN. A realistic training here depends on the realistic stimulation. As stated in the original solicitation titled "Cyber Resilience of Condition Based Monitoring Capabilities", The project, carried out in collaboration between ObjectSecurity LLC and subcontractor Mississippi State University, aims for successful technology development and transition that will result in a secure CBM sensor node that can minimize human intervention and reduce the number of machinery overhauls, shorten time spent in depot for repairs, and optimize maintenance logistics by at least 50%.

Do you want to create a legacy of meaningful research for the greater good? Do you want to lead and contribute to work in support of an organization that addresses some of today's most challenging problems that face our Nation? Then join us in the Data Sciences and Analytics Group at the Pacific Northwest National Laboratory (PNNL)! For more than 50 years, PNNL has advanced the frontiers of science and engineering in the service of our nation and the world in the areas of energy, the environment and national security. PNNL is committed to advancing the state-of-the-art in artificial intelligence through applied machine learning and deep learning to support scientific discovery and our sponsors' missions.

Artificial intelligence (AI) is quickly making inroads into medical practice, especially in forms that rely on machine learning, with a mix of hope and hype.1 Multiple AI-based products have now been approved or cleared by the US Food and Drug Administration (FDA), and health systems and hospitals are increasingly deploying AI-based systems.2 For example, medical AI can support clinical decisions, such as recommending drugs or dosages or interpreting radiological images.2 One key difference from most traditional clinical decision support software is that some medical AI may communicate results or recommendations to the care team without being able to communicate the underlying reasons for those results.3

Software flaw detection using multimodal deep learning models has been demonstrated as a very competitive approach on benchmark problems. In this work, we demonstrate that even better performance can be achieved using neural architecture search (NAS) combined with multimodal learning models. We adapt a NAS framework aimed at investigating image classification to the problem of software flaw detection and demonstrate improved results on the Juliet Test Suite, a popular benchmarking data set for measuring performance of machine learning models in this problem domain.

Botnet is one of the major threats to computer security. In previous botnet command and control (C&C) scenarios using online social networks (OSNs), methods for finding botmasters (e.g. ids, links, DGAs, etc.) are hardcoded into bots. Once a bot is reverse engineered, botmaster is exposed. Meanwhile, abnormal contents from explicit commands may expose botmaster and raise anomalies on OSNs. To overcome these deficiencies, we propose an AI-powered covert C&C channel. On leverage of neural networks, bots can find botmasters by avatars, which are converted into feature vectors. Commands are embedded into normal contents (e.g. tweets, comments, etc.) using text data augmentation and hash collision. Experiment on Twitter shows that the command-embedded contents can be generated efficiently, and bots can find botmaster and obtain commands accurately. By demonstrating how AI may help promote a covert communication on OSNs, this work provides a new perspective on botnet detection and confrontation.

Each year there are nearly 57 million deaths around the world, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, as institutions and government agencies rely on death reports to analyze vital statistics and to formulate responses to communicable diseases. Inaccurate death reporting may result in potential misdirection of public health policies. Determining the causes of death is, nevertheless, challenging even for experienced physicians. To facilitate physicians in accurately reporting causes of death, we present an advanced AI approach to determine a chronically ordered sequence of clinical conditions that lead to death, based on decedent's last hospital admission discharge record. The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); the priority-ordered clinical conditions on the discharge record are coded in ICD-9. We identify three challenges in proposing the causal chain of death: two versions of coding system in clinical codes, medical domain knowledge conflict, and data interoperability. To overcome the first challenge in this sequence-to-sequence problem, we apply neural machine translation models to generate target sequence. We evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.44 out of 100. To address the second challenge, we incorporate expert-verified medical domain knowledge as constraint in generating output sequence to exclude infeasible causal chains. Lastly, we demonstrate the usability of our work in a Fast Healthcare Interoperability Resources (FHIR) interface to address the third challenge.

Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed. The Robust RL formulation tackles this by adding worst-case adversarial noise to the dynamics and constructing the noise distribution as the solution to a zero-sum minimax game. However, existing work on learning solutions to the Robust RL formulation has primarily focused on training a single RL agent against a single adversary. In this work, we demonstrate that using a single adversary does not consistently yield robustness to dynamics variations under standard parametrizations of the adversary; the resulting policy is highly exploitable by new adversaries. We propose a population-based augmentation to the Robust RL formulation in which we randomly initialize a population of adversaries and sample from the population uniformly during training. We empirically validate across robotics benchmarks that the use of an adversarial population results in a more robust policy that also improves out-of-distribution generalization. Finally, we demonstrate that this approach provides comparable robustness and generalization as domain randomization on these benchmarks while avoiding a ubiquitous domain randomization failure mode.