Russia seems to actively explore innovative technologies. Now, the country wants to implement the means of artificial intelligence for pilotless aircraft. For this matter, Russia plans on establishing a special department for studying the technology. The move to use AI was unveiled by the Russian Ministry of Defense during the visit to Sukhoi's design bureau. The delegation included the First Deputy Chairman of the Military Industrial Commission (MIC) Andrey Yelchaninov and other members of the MIC.

Navigating through intersections is one of the main challenging tasks for an autonomous vehicle. However, for the majority of intersections regulated by traffic lights, the problem could be solved by a simple rule-based method in which the autonomous vehicle behavior is closely related to the traffic light states. In this work, we focus on the implementation of a system able to navigate through intersections where only traffic signs are provided. We propose a multi-agent system using a continuous, model-free Deep Reinforcement Learning algorithm used to train a neural network for predicting both the acceleration and the steering angle at each time step. We demonstrate that agents learn both the basic rules needed to handle intersections by understanding the priorities of other learners inside the environment, and to drive safely along their paths. Moreover, a comparison between our system and a rule-based method proves that our model achieves better results especially with dense traffic conditions. Finally, we test our system on real world scenarios using real recorded traffic data, proving that our module is able to generalize both to unseen environments and to different traffic conditions.

This survey provides an overview of the evolution of visually grounded models of spoken language over the last 20 years. Such models are inspired by the observation that when children pick up a language, they rely on a wide range of indirect and noisy clues, crucially including signals from the visual modality co-occurring with spoken utterances. Several fields have made important contributions to this approach to modeling or mimicking the process of learning language: Machine Learning, Natural Language and Speech Processing, Computer Vision and Cognitive Science. The current paper brings together these contributions in order to provide a useful introduction and overview for practitioners in all these areas. We discuss the central research questions addressed, the timeline of developments, and the datasets which enabled much of this work. We then summarize the main modeling architectures and offer an exhaustive overview of the evaluation metrics and analysis techniques.

Pocket-size ultrasound devices that cost 50 times less than the machines in hospitals (and connect to your phone). These are just some of the innovations now transforming medicine at a remarkable pace. No one can predict the future, but it can at least be glimpsed in the dozen inventions and concepts below. Like the people behind them, they stand at the vanguard of health care. Neither exhaustive nor exclusive, the list is, rather, representative of the recasting of public health and medical science likely to come in the 2020s.

This story was published in partnership with Type Investigations with support from the Puffin Foundation. In California, non-English speakers handed COVID-19 vaccination cards without information on what they mean. In Pennsylvania, people who speak Mandarin, Korean, and Japanese unable to make vaccine appointments due to a lack of interpreters at hospital call centers. These are just a few of the examples captured in a new complaint filed on Friday to the U.S. Department of Health and Human Services' Office for Civil Rights, Federal Emergency Management Agency's Office of Equal Rights, and Department of Homeland Security's Office for Civil Rights and Civil Liberties. The complaint, brought by the National Health Law Program, finds widespread problems across the country that inhibit access to COVID-19 resources for people with limited English proficiency (LEP).

My friend Robert Burton, a neurologist and author, wanted to share a song with me last year, and sent me a link to an NPR Tiny Desk Concert. "It's wonderful to see truly new and inspiring music," he wrote. I clicked open the link to a band who appeared to have journeyed from their mountain village in Russia to busk for tourists in the city square. Three women wore long white wedding dresses, thick strands of bead necklaces, and Cossack hats that towered from their heads like minarets of black wool. They played, respectively, a cello, djembe drum, and floor tom drum. They were joined by an accordion player who could pass for a bearded hipster from Brooklyn. The accordionist was the first to sing. A bray of syllables erupted from him like an exorcism. A steady drumbeat followed and then the women commanded the singing. Their vocals ranged from yodels to yips, whoops to whispers. At first turbulence reigned, as if the women were singing different songs at each other. But soon their voices blended into a melody that curled like a river.

In 1949, at the dawn of the computer age, the French philosopher Gabriel Marcel warned of the danger of naively applying technology to solve life's problems. Life, Marcel wrote in Being and Having, cannot be fixed the way you fix a flat tire. Any fix, any technique, is itself a product of that same problematic world, and is therefore problematic, and compromised. Marcel's admonition is often summarized in a single memorable phrase: "Life is not a problem to be solved, but a mystery to be lived." Despite that warning, seventy years later, artificial intelligence is the most powerful expression yet of humans' urge to solve or improve upon human life with computers. But what are these computer systems? As Marcel would have urged, one must ask where they come from, whether they embody the very problems they would purport to solve. Ethics in AI is essentially questioning, constantly investigating, and never taking for granted the technologies that are being rapidly imposed upon human life. That questioning is made all the more urgent because of scale. AI systems are reaching tremendous size in terms of the compute power they require, and the data they consume. And their prevalence in society, both in the scale of their deployment and the level of responsibility they assume, dwarfs the presence of computing in the PC and Internet eras. At the same time, increasing scale means many aspects of the technology, especially in its deep learning form, escape the comprehension of even the most experienced practitioners. Ethical concerns range from the esoteric, such as who is the author of an AI-created work of art; to the very real and very disturbing matter of surveillance in the hands of military authorities who can use the tools with impunity to capture and kill their fellow citizens. Somewhere in the questioning is a sliver of hope that with the right guidance, AI can help solve some of the world's biggest problems. The same technology that may propel bias can reveal bias in hiring decisions. The same technology that is a power hog can potentially contribute answers to slow or even reverse global warming. The risks of AI at the present moment arguably outweigh the benefits, but the potential benefits are large and worth pursuing. As Margaret Mitchell, formerly co-lead of Ethical AI at Google, has elegantly encapsulated, the key question is, "what could AI do to bring about a better society?" Mitchell's question would be interesting on any given day, but it comes within a context that has added urgency to the discussion. Mitchell's words come from a letter she wrote and posted on Google Drive following the departure of her co-lead, Timnit Gebru, in December.

As the world grapples with the devastation of the coronavirus, one thing is clear: The United States simply wasn't prepared. Despite repeated warnings from infectious disease experts over the years, we lacked essential beds, equipment, and medication; public health advice was confusing; and our leadership offered no clear direction while sidelining credible health professionals and institutions. Infectious disease experts agree that it's only a matter of time before the next pandemic hits, and that one could be even more deadly. So how do we fix what COVID-19 has shown was broken? In this Mother Jones series, we're asking experts from a wide range of disciplines one question: What are the most important steps we can take to make sure we're better prepared next time around? On a hazy day in early March, a drone packaged in protective red casing and carrying precious cargo descended upon a crowd gathered in the Ashanti region of Ghana.

Spatiotemporal traffic time series (e.g., traffic volume/speed) collected from sensing systems are often incomplete with considerable corruption and large amounts of missing values, preventing users from harnessing the full power of the data. Missing data imputation has been a long-standing research topic and critical application for real-world intelligent transportation systems. A widely applied imputation method is low-rank matrix/tensor completion; however, the low-rank assumption only preserves the global structure while ignores the strong local consistency in spatiotemporal data. In this paper, we propose a low-rank autoregressive tensor completion (LATC) framework by introducing \textit{temporal variation} as a new regularization term into the completion of a third-order (sensor $\times$ time of day $\times$ day) tensor. The third-order tensor structure allows us to better capture the global consistency of traffic data, such as the inherent seasonality and day-to-day similarity. To achieve local consistency, we design the temporal variation by imposing an AR($p$) model for each time series with coefficients as learnable parameters. Different from previous spatial and temporal regularization schemes, the minimization of temporal variation can better characterize temporal generative mechanisms beyond local smoothness, allowing us to deal with more challenging scenarios such "blackout" missing. To solve the optimization problem in LATC, we introduce an alternating minimization scheme that estimates the low-rank tensor and autoregressive coefficients iteratively. We conduct extensive numerical experiments on several real-world traffic data sets, and our results demonstrate the effectiveness of LATC in diverse missing scenarios.

This paper describes a machine learning approach for annotating and analyzing data curation work logs at ICPSR, a large social sciences data archive. The systems we studied track curation work and coordinate team decision-making at ICPSR. Repository staff use these systems to organize, prioritize, and document curation work done on datasets, making them promising resources for studying curation work and its impact on data reuse, especially in combination with data usage analytics. A key challenge, however, is classifying similar activities so that they can be measured and associated with impact metrics. This paper contributes: 1) a schema of data curation activities; 2) a computational model for identifying curation actions in work log descriptions; and 3) an analysis of frequent data curation activities at ICPSR over time. We first propose a schema of data curation actions to help us analyze the impact of curation work. We then use this schema to annotate a set of data curation logs, which contain records of data transformations and project management decisions completed by repository staff. Finally, we train a text classifier to detect the frequency of curation actions in a large set of work logs. Our approach supports the analysis of curation work documented in work log systems as an important step toward studying the relationship between research data curation and data reuse.