Emerging technologies--and partnerships promoting their use--have proven instrumental for the State Department's Global Engagement Center, a hub that steers federal efforts to counter state-sponsored propaganda and disinformation campaigns aimed at undermining the U.S. "Artificial intelligence and the tools that it offers are really helping us to understand what's happening in the environment, and to identify coordinated activity," the GEC's Acting Coordinator Daniel Kimmage said Thursday. "There's obviously a much broader range of activity across the State Department, but for us it's a powerful way to better understand what's happening in the environment, and identify coordinated activity." The center was mandated by Congress several years ago to help tackle challenges around diplomacy in the digital age. At an event hosted by Foreign Policy, Kimmage offered a glimpse into how technology is impacting and enabling GEC's work, particularly as online disinformation campaigns led by U.S. adversaries grow in sophistication. "We've got what you might describe as our traditional sources of information--we have the cables, our diplomatic colleagues out in the field. We have an analysis from our colleagues in the intelligence community, and we have a huge and growing ocean of open-source information," he explained.

At twilight on New Year's Eve, 2020, Placido Montoya, 35, a plumber from Fort Morgan, Colorado, was driving to work. Ahead of him he noticed blinking lights in the sky. He'd heard rumours of mysterious drones, whispers in his local community, but now he was seeing them with his own eyes. In the early morning gloom, it was hard to make out how big the lights were and how many were hovering above him. But one thing was clear to Montoya: he needed to give chase.

Dimitar Kostadinov applied for a 6-year Master's program in Bulgarian and European Law at the University of Ruse, and was enrolled in 2002 following high school. He obtained a Master degree in 2009. From 2008-2012, Dimitar held a job as data entry & research for the American company Law Seminars International and its Bulgarian-Slovenian business partner DATA LAB. In 2011, he was admitted Law and Politics of International Security to Vrije Universiteit Amsterdam, the Netherlands, graduating in August of 2012. Dimitar also holds an LL.M. diploma in Intellectual Property Rights & ICT Law from KU Leuven (Brussels, Belgium).

According to IDG's 2020 Cloud Computing Study, 92% of organizations have at least some sort of cloud footprint in regard to their IT environment. Therefore, traditional cloud security approaches must evolve to keep up with the dynamic infrastructure and challenges that cloud environments present – most notably, the inundation of data insights generated within the cloud. More than one-third of IT security managers and security analysts ignore threat alerts when the queue is full. This is a common issue that is driving the high demand for machine learning-based analytics, as it helps security teams sift through massive amounts of data to prioritize risks and vulnerabilities and make more informed decisions. However, a word of caution when using machine learning-based technology: the age-old garbage-in, garbage-out applies to security-focused machine learning engines.

Network Intrusion Detection Systems (NIDSs) are an increasingly important tool for the prevention and mitigation of cyber attacks. A number of labelled synthetic datasets generated have been generated and made publicly available by researchers, and they have become the benchmarks via which new ML-based NIDS classifiers are being evaluated. Recently published results show excellent classification performance with these datasets, increasingly approaching 100 percent performance across key evaluation metrics such as accuracy, F1 score, etc. Unfortunately, we have not yet seen these excellent academic research results translated into practical NIDS systems with such near-perfect performance. This motivated our research presented in this paper, where we analyse the statistical properties of the benign traffic in three of the more recent and relevant NIDS datasets, (CIC, UNSW, ...). As a comparison, we consider two datasets obtained from real-world production networks, one from a university network and one from a medium size Internet Service Provider (ISP). Our results show that the two real-world datasets are quite similar among themselves in regards to most of the considered statistical features. Equally, the three synthetic datasets are also relatively similar within their group. However, and most importantly, our results show a distinct difference of most of the considered statistical features between the three synthetic datasets and the two real-world datasets. Since ML relies on the basic assumption of training and test datasets being sampled from the same distribution, this raises the question of how well the performance results of ML-classifiers trained on the considered synthetic datasets can translate and generalise to real-world networks. We believe this is an interesting and relevant question which provides motivation for further research in this space.

We present a unifying framework encompassing a plethora of social choice settings. Viewing each social choice setting as voting in a suitable metric space, we offer a general model of social choice over metric spaces, in which--similarly to the spatial model of elections--each voter specifies an ideal element of the metric space. The ideal element acts as a vote, where each voter prefers elements that are closer to her ideal element. But it also acts as a proposal, thus making all participants equal not only as voters but also as proposers. We consider Condorcet aggregation and a continuum of solution concepts, ranging from minimizing the sum of distances to minimizing the maximum distance. We study applications of our abstract model to various social choice settings, including single-winner elections, committee elections, participatory budgeting, and participatory legislation. For each setting, we compare each solution concept to known voting rules and study various properties of the resulting voting rules. Our framework provides expressive aggregation for a broad range of social choice settings while remaining simple for voters; and may enable a unified and integrated implementation for all these settings, as well as unified extensions such as sybil-resiliency, proxy voting, and deliberative decision making.

As language models are trained on ever more text, researchers are turning to some of the largest corpora available. Unlike most other types of datasets in NLP, large unlabeled text corpora are often presented with minimal documentation, and best practices for documenting them have not been established. In this work we provide the first documentation for the Colossal Clean Crawled Corpus (C4; Raffel et al., 2020), a dataset created by applying a set of filters to a single snapshot of Common Crawl. We begin with a high-level summary of the data, including distributions of where the text came from and when it was written. We then give more detailed analysis on salient parts of this data, including the most frequent sources of text (e.g., patents.google.com, which contains a significant percentage of machine translated and/or OCR'd text), the effect that the filters had on the data (they disproportionately remove text in AAE), and evidence that some other benchmark NLP dataset examples are contained in the text. We release a web interface to an interactive, indexed copy of this dataset, encouraging the community to continuously explore and report additional findings.

To build robust question answering systems, we need the ability to verify whether answers to questions are truly correct, not just "good enough" in the context of imperfect QA datasets. We explore the use of natural language inference (NLI) as a way to achieve this goal, as NLI inherently requires the premise (document context) to contain all necessary information to support the hypothesis (proposed answer to the question). We leverage large pre-trained models and recent prior datasets to construct powerful question converter and decontextualization modules, which can reformulate QA instances as premise-hypothesis pairs with very high reliability. Then, by combining standard NLI datasets with NLI examples automatically derived from QA training data, we can train NLI models to judge the correctness of QA models' proposed answers. We show that our NLI approach can generally improve the confidence estimation of a QA model across different domains, evaluated in a selective QA setting. Careful manual analysis over the predictions of our NLI model shows that it can further identify cases where the QA model produces the right answer for the wrong reason, or where the answer cannot be verified as addressing all aspects of the question.

On April 21, the EU Commission is expected to adopt a proposal for a regulation (the AI Regulation) of "artificial intelligence systems" (AI systems), imposing new obligations that will impact businesses across many, if not all, sectors of the economy. The AI Regulation is ambitious and will prove controversial, touching off a legislative battle lasting at least until 2022. The proposed AI Regulation will join other ambitious EU initiatives in the digital sector, such as the Data Governance Act, Digital Services Act and Digital Markets Act, all currently working their way through the EU legislative process, as well as ongoing reform of EU antitrust policy. Other jurisdictions will likely examine all of these measures closely as potential models for similar legislation. A leaked draft of the AI Regulation (the Draft) illustrates the potential scope and impact of the new law.

Despite a summer of controversy surrounding the use of artificial intelligence for military purposes, the Defense Advanced Research Projects Agency says it has no problem garnering interest in its AI research projects. "We don't see that we are having problems engaging with industry," Valerie Browning, director of the Defense Science office at DARPA, said on a Washington Post event panel last week. DARPA recently announced a $2 billion campaign called "AI Next" aimed at "third wave" AI research. The goal is to get the technology to a place where machines adapt to changing situations the way human intelligence does. Responding to a question about whether and how Google's decision to end its work with Pentagon AI initiative Project Maven has impacted DARPA, Browning downplayed any effect.