This paper introduces the Affordance Template framework used to supervise task behaviors on the NASA-JSC Valkyrie robot at the 2013 DARPA Robotics Challenge (DRC) Trials. This framework provides graphical interfaces to human supervisors that are adjustable based on the run-time environmental context (e.g., size, location, and shape of objects that the robot must interact with, etc.). Additional improvements, described below, inject degrees of autonomy into instantiations of affordance templates at run-time in order to enable efficient human supervision of the robot for accomplishing tasks.

Advances in artificial intelligence (AI) and machine learning (ML) are already demonstrating great utility in a variety of domains that point to great opportunities for adoption in a wide variety of military applications. For this reason, data will become the life-blood of many AI and ML enabled technologies. Yet, developing trusted data sets for the purposes of training and testing AI and ML applications will become a central issue to eliciting predictable behaviors of such technology in order to foster trust in autonomous systems. Therefore, the US Department of Defense (DoD) cannot sit idle while AI and ML developments remain forthcoming. Rather, the US military can begin laying the ground work for constructing training data in domain specific ways that will help convergence with AI and ML in the future.

Government regulations are critical to understanding how to do business with a government entity and receive other benefits. However, government regulations are also notoriously long and organized in ways that can be confusing for novice users. Developing cognitive assistance tools that remove some of the burden from human users is of potential benefit to a variety of users. The volume of data found in United States federal government regulation suggests a multiple-step approach to process the data into machine-readable text, create an automated legal knowledge base capturing various facts and rules, and eventually building a legal question and answer system to acquire understanding from various regulations and provisions. Our work discussed in this paper represents our initial efforts to build a framework for Federal Acquisition Regulations System (Title 48, Code of Federal Regulations) in order to create an efficient legal knowledge base representing relationships between various legal elements, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules.

Discourse on social media platforms is often plagued by acute polarization, with different camps promoting different perspectives on the issue at hand--compare, for example, the differences in the liberal and conservative discourse on the U.S. immigration debate. A large body of research has studied this phenomenon by focusing on the affiliation of groups and individuals. We propose a new finer-grained perspective: studying the impartiality of individual messages. While the notion of message impartiality is quite intuitive, the lack of an objective definition and of a way to measure it directly has largely obstructed scientific examination. In this work we operationalize message impartiality in terms of how discernible the affiliation of its author is, and introduce a methodology for quantifying it automatically. Unlike a supervised machine learning approach, our method can be used in the context of emerging events where impartiality labels are not immediately available. Our framework enables us to study the effects of (im)partiality on social media discussions at scale. We show that this phenomenon is highly consequential, with partial messages being twice more likely to spread than impartial ones, even after controlling for author and topic. By taking this fine-grained approach to polarization, we also provide new insights into the temporal evolution of online discussions centered around major political and sporting events.

While the most ambitious polls are based on standardized interviews with a few thousand people, millions are tweeting freely and publicly in their own voices about issues they care about. This data offers a vibrant 24/7 snapshot of people's response to various events and topics. The sheer scale of the data on Twitter allows us to measure in aggregate how the various issues are rising and falling in prominence over time. However, the volume of the data also means that an intelligent tool is required to allow the users to make sense of the data. To this end, we built a novel, interactive web-based tool for mapping the conversation landscapes on Twitter. Our system utilizes recent advances in natural language processing and deep neural networks that are robust with respect to the noisy and unconventional nature of tweets, in conjunction with a scalable clustering algorithm an interactive visualization engine to allow users to tap the mine of information that is Twitter. We ran a user study with 40 participants using tweets about the 2016 US presidential election and the summer 2016 Orlando shooting, demonstrating that compared to more conventional methods, our tool can increase the speed and the accuracy with which users can identify and make sense of the various conversation topics on Twitter.

There's no question that the world around us is getting smarter. In recent years, the exponential growth of artificial intelligence (AI) has created an increasing demand for AI solutions across a broad range of industries. Accelerated in part by the global pandemic, businesses and governments around the world are recognising the benefits of becoming early adopters of AI as part of ongoing digital transformation. A regional hub for AI entrepreneurs At the forefront of this movement (and as far back as 2017), the UAE government released a first-of-its-kind strategy setting a clear roadmap for developing its regional capabilities and becoming the world's premier AI destination. And it's looking to become a game-changer, with the region as a whole estimated to accrue 2 per cent of the global benefits of AI – $320bn by 2030.

With countries across the globe experiencing the effects of climate change, environmental sustainability is becoming a business imperative everywhere. The recognition that long-term profits necessitate environmental preservation has been increasing. However, the fact remains that including environmentally sustainable practices can be costly for corporations, which is discouraging in highly competitive industries. Minister of Finance Nirmala Sitharaman recognised this need for Sustainable Development in her 2022 Budget Proposal since the dangers of climate change are the most significant negative externalities that affect India and other countries today. She highlighted that Artificial Intelligence has the potential to reconcile profits and sustainability as "They provide employment opportunities for youth, and make Indian industry more efficient and competitive".

The US national tax authority announced Monday that it will stop using facial recognition software to verify taxpayers' identities when they create online accounts, following a chorus of privacy concerns. Internal Revenue Service officials had put forth the authentication system as a security measure following years of growing fears over online scams and identity theft, but the program ended up also prompting worries. The initiative involved identity verification company ID.me, which won a nearly $90 million contract to make taxpayers' accounts more secure. The IRS said "it will transition away from using a third-party service for facial recognition to help authenticate people creating new online accounts." "The IRS will quickly develop and bring online an additional authentication process that does not involve facial recognition," it said, as the agency faces staffing shortages and significant backlogs.

Generating images from textual descriptions has gained a lot of attention. Recently, DALL-E, a multimodal transformer language model, and its variants have shown high-quality text-to-image generation capabilities with a simple architecture and training objective, powered by large-scale training data and computation. However, despite the interesting image generation results, there has not been a detailed analysis on how to evaluate such models. In this work, we investigate the reasoning capabilities and social biases of such text-to-image generative transformers in detail. First, we measure four visual reasoning skills: object recognition, object counting, color recognition, and spatial relation understanding. For this, we propose PaintSkills, a diagnostic dataset and evaluation toolkit that measures these four visual reasoning skills. Second, we measure the text alignment and quality of the generated images based on pretrained image captioning, image-text retrieval, and image classification models. Third, we assess social biases in the models. For this, we suggest evaluation of gender and racial biases of text-to-image generation models based on a pretrained image-text retrieval model and human evaluation. In our experiments, we show that recent text-to-image models perform better in recognizing and counting objects than recognizing colors and understanding spatial relations, while there exists a large gap between model performances and oracle accuracy on all skills. Next, we demonstrate that recent text-to-image models learn specific gender/racial biases from web image-text pairs. We also show that our automatic evaluations of visual reasoning skills and gender bias are highly correlated with human judgments. We hope our work will help guide future progress in improving text-to-image models on visual reasoning skills and social biases. Code and data at: https://github.com/j-min/DallEval

We develop a new method to detect anomalies within time series, which is essential in many application domains, reaching from self-driving cars, finance, and marketing to medical diagnosis and epidemiology. The method is based on self-supervised deep learning that has played a key role in facilitating deep anomaly detection on images, where powerful image transformations are available. However, such transformations are widely unavailable for time series. Addressing this, we develop Local Neural Transformations(LNT), a method learning local transformations of time series from data. The method produces an anomaly score for each time step and thus can be used to detect anomalies within time series. We prove in a theoretical analysis that our novel training objective is more suitable for transformation learning than previous deep Anomaly detection(AD) methods. Our experiments demonstrate that LNT can find anomalies in speech segments from the LibriSpeech data set and better detect interruptions to cyber-physical systems than previous work. Visualization of the learned transformations gives insight into the type of transformations that LNT learns.