A team of Army researchers uncovered how the human brain processes bright and contrasting light, which they say is a key to improving robotic sensing and enabling autonomous agents to team with humans. To enable developments in autonomy, a top Army priority, machine sensing must be resilient across changing environments, researchers said. "When we develop machine vision algorithms, real-world images are usually compressed to a narrower range, as a cellphone camera does, in a process called tone mapping," said Andre Harrison, a researcher at the U.S. Army Combat Capabilities Development Command's Army Research Laboratory. "This can contribute to the brittleness of machine vision algorithms because they are based on artificial images that don't quite match the patterns we see in the real world." By developing a new system with 100,000-to-1 display capability, the team discovered the brain's computations, under more real-world conditions, so they could build biological resilience into sensors, Harrison said.

Unauthorized surveillance: It is well known that drones can be easily utilized for mass surveillance This is to be comprehended in setting of computerized advances that mean to reform our day by day lives, by having more point by point records about those lives. In the name of national security and fear based oppression, observation systems are used to track and profile the residents by the state too and private offices. By the ideals of their plan and size, drones can work undetected, permitting the client to screen individuals without their insight. For occurrence, there are drones with too high goals gigapixel cameras that can be utilized to follow individuals and vehicles from heights as high as 20,000 feet. They can convey gear for example, counterfeit towers, which can break Wi-Fi codes and block instant messages and mobile phone discussions without the information on either the correspondence supplier or the client.

In a few months the United States will elect its 46th President. While some worry about whether campaigning and casting votes can be done safely during the COVID-19 pandemic, another question is just as critical: how many votes will result via the manipulative influence of artificial intelligence? Specifically, the emerging threat of deepfakes could have an unprecedented impact on this election cycle, raising serious questions about the integrity of elections, policy-making and our democratic society at large. AI-powered deepfakes have the potential to bring troubling consequences for the US 2020 elections. The technology that began as little more than a giggle-inducing gimmick for making homebrew mash-up videos has recently been supercharged by advances in AI.

Microsoft has teamed up with NASA to create three project-based learning modules that teach entry-level coders how to use the Python programming language and machine-learning algorithms to explore space, classify space rocks and predict weather and rocket-launch delays. Students need a Windows, Mac or Linux computer to complete the modules, which teach the basics of what a programming language is, how to use Microsoft's Visual Studio Code (VS Code) code editor, install extensions for Python, and how to run a basic Jupyter Notebook within VS Code – some of the key ingredients to get started on a machine-learning project. Microsoft's learning modules don't actually teach anything about how to code in Python but rather offer some ideas, focussing on NASA's space exploration activities, to illustrate how Python could be used in space exploration. It might suit students learning to code who need some ideas for how that knowledge could be applied to solving challenges NASA faces, or those considering programming to see how Python could be used. The Introduction to Python for Space Exploration module contains eight units and offers background on NASA's Artemis lunar exploration program, which aims to land the first woman and the next man on the moon by 2024.

Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provided by emergency radiology fellowship-trained physicians. Our dataset includes 34,642 radiology reports and 1479 patients of IPV victims and control patients. Our best model predicts IPV a median of 3.08 years before violence prevention program entry with a sensitivity of 64% and a specificity of 95%. We conduct error analysis to determine for which patients our model has especially high or low performance and discuss next steps for a deployed clinical risk model.

Evaluating the mechanical response of fiber-reinforced composites can be extremely time consuming and expensive. Machine learning (ML) techniques offer a means for faster predictions via models trained on existing input-output pairs and have exhibited success in composite research. This paper explores a fully convolutional neural network modified from StressNet, which was originally for lin-ear elastic materials and extended here for a non-linear finite element (FE) simulation to predict the stress field in 2D slices of segmented tomography images of a fiber-reinforced polymer specimen. The network was trained and evaluated on data generated from the FE simulations of the exact microstructure. The testing results show that the trained network accurately captures the characteristics of the stress distribution, especially on fibers, solely from the segmented microstructure images. The trained model can make predictions within seconds in a single forward pass on an ordinary laptop, given the input microstructure, compared to 92.5 hours to run the full FE simulation on a high-performance computing cluster. These results show promise in using ML techniques to conduct fast structural analysis for fiber-reinforced composites and suggest a corollary that the trained model can be used to identify the location of potential damage sites in fiber-reinforced polymers.

Diabetic retinopathy (DR) is a retinal microvascular condition that emerges in diabetic patients. DR will continue to be a leading cause of blindness worldwide, with a predicted 191.0 million globally diagnosed patients in 2030. Microaneurysms, hemorrhages, exudates, and cotton wool spots are common signs of DR. However, they can be small and hard for human eyes to detect. Early detection of DR is crucial for effective clinical treatment. Existing methods to classify images require much time for feature extraction and selection, and are limited in their performance. Convolutional Neural Networks (CNNs), as an emerging deep learning (DL) method, have proven their potential in image classification tasks. In this paper, comprehensive experimental studies of implementing state-of-the-art CNNs for the detection and classification of DR are conducted in order to determine the top performing classifiers for the task. Five CNN classifiers, namely Inception-V3, VGG19, VGG16, ResNet50, and InceptionResNetV2, are evaluated through experiments. They categorize medical images into five different classes based on DR severity. Data augmentation and transfer learning techniques are applied since annotated medical images are limited and imbalanced. Experimental results indicate that the ResNet50 classifier has top performance for binary classification and that the InceptionResNetV2 classifier has top performance for multi-class DR classification.

Over the next few years, society as a whole will need to address what core values it wishes to protect when dealing with technology. Anthropology, a field dedicated to the very notion of what it means to be human, can provide some interesting insights into how to cope and tackle these changes in our Western society and other areas of the world. It can be challenging for social science practitioners to grasp and keep up with the pace of technological innovation, with many being unfamiliar with the jargon of AI. This short guide serves as both an introduction to AI ethics and social science and anthropological perspectives on the development of AI. It intends to provide those unfamiliar with the field with an insight into the societal impact of AI systems and how, in turn, these systems can lead us to rethink how our world operates.

Although many fact-checking systems have been developed in academia and industry, fake news is still proliferating on social media. These systems mostly focus on fact-checking but usually neglect online users who are the main drivers of the spread of misinformation. How can we use fact-checked information to improve users' consciousness of fake news to which they are exposed? How can we stop users from spreading fake news? To tackle these questions, we propose a novel framework to search for fact-checking articles, which address the content of an original tweet (that may contain misinformation) posted by online users. The search can directly warn fake news posters and online users (e.g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media. Our framework uses both text and images to search for fact-checking articles, and achieves promising results on real-world datasets. Our code and datasets are released at https://github.com/nguyenvo09/EMNLP2020.

It is welcome that India is hosting a global summit on artificial intelligence (AI) and that the Prime Minister has addressed the gathering and expressed commitment at the highest level of government to wholesome development and regulation of AI. AI will fast become not just a major component of economic competitiveness but also a force multiplier in strategic capacity. It also poses serious challenges in itself and in the way it is put to use. Therefore, control and regulation of AI are global concerns of mounting importance, on which the G20 grouping of the world's 20 largest economies have adopted guidelines and principles. For India to offer something more than lip service to developing AI, the first thing to do is to put in place a robust data protection framework.