WhatsApp is the most popular messaging app in the world. Due to its popularity, WhatsApp has become a powerful and cheap tool for political campaigning being widely used during the 2019 Indian general election, where it was used to connect to the voters on a large scale. Along with the campaigning, there have been reports that WhatsApp has also become a breeding ground for harmful speech against various protected groups and religious minorities. Many such messages attempt to instil fear among the population about a specific (minority) community. According to research on inter-group conflict, such `fear speech' messages could have a lasting impact and might lead to real offline violence. In this paper, we perform the first large scale study on fear speech across thousands of public WhatsApp groups discussing politics in India. We curate a new dataset and try to characterize fear speech from this dataset. We observe that users writing fear speech messages use various events and symbols to create the illusion of fear among the reader about a target community. We build models to classify fear speech and observe that current state-of-the-art NLP models do not perform well at this task. Fear speech messages tend to spread faster and could potentially go undetected by classifiers built to detect traditional toxic speech due to their low toxic nature. Finally, using a novel methodology to target users with Facebook ads, we conduct a survey among the users of these WhatsApp groups to understand the types of users who consume and share fear speech. We believe that this work opens up new research questions that are very different from tackling hate speech which the research community has been traditionally involved in.

In 2019, as the Department of Defense considered adopting AI ethics principles, the Defense Innovation Unit held a series of meetings across the U.S. to gather opinions from experts and the public. At one such meeting in Silicon Valley, Stanford University professor Herb Lin argued that he was concerned about people trusting AI too easily and said any application of AI should include a confidence score indicating the algorithm's degree of certainty. "AI systems should not only be the best possible. Sometimes they should say'I have no idea what I'm doing here, don't trust me.' That's going to be really important," he said. The concern Lin raised is an important one: People can be manipulated by artificial intelligence, with cute robots a classic example of the human tendency to trust machines.

Needless to say, budget presented every year draws attention from all corners of the country. This surely remains a hot topic for quite some time and the progress with respect to what has been promised is kept an eye on, undoubtedly. This time too, the budget garnered attention of many. Be it the agricultural sector, education, technology or healthcare, a lot has been said and promised. But what seems to have stood out from the rest is the discussion pertaining to Artificial Intelligence and machine learning.

As artificial intelligence (AI) becomes a hot topic, there is also an increasing amount of misinformation and confusion about what it can do and the potential risks it presents. The cultural legacy of decades of literature and film has depicted dystopian visions of human downfall at the feet of omniscient machines. On the other hand, many people understand the beneficial potential of AI to speed up and help the evolution of our society. Although computer systems can learn, reason, and act, these behaviors are still in their early stages. Machine Learning (often abbreviated as ML) needs huge amounts of data even for just learning, translated into training or coaching depending on the function that is assigned to Artificial Intelligence. Allowing AI access to information and giving it full autonomy therefore carries serious risks that must be considered.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Delta Air Lines is bringing facial recognition technology to domestic flights. Last week, the airline announced that it is launching its digital ID technology for domestic flights out of Detroit Metropolitan Wayne County Airport. Delta previously debuted the technology in 2018 for international flights.

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First classifiers are implemented with a deep convolutional neural network(CNN) andsecond a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentratedtumor boundary is contoured for the segmentation process by using the Chan-Vesesegmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, Chan- Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score,Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean absolute error (MAE), and Peak Signal to Noise Ratio (PSNR) werecalculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with average dice score of 0.92, (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076 and MAE of 52.946), pointing to high reliability of the proposed architecture.

In April, as COVID-19 cases exploded across the U.S. and local officials scrambled for solutions, a police department in Connecticut tried a new way to monitor the spread of the virus. One morning, as masked shoppers lined up 6 feet apart outside Trader Joe's in Westport, the police department flew a drone overhead to observe their social distancing and detect potential coronavirus symptoms, such as high temperature and increased heart rate. According to internal emails, the captain flying the mission wanted to "take advantage" of the store's line. But the store had no heads-up about the flight, and neither did the customers on their grocery runs, even though the drone technology managed to track figures both inside and outside. The drone program was unveiled a week later when the department announced its "Flatten the Curve Pilot Program" in collaboration with the Canadian drone company Draganfly, which was due to last through the summer. But less than 48 hours later after the program's public unveiling, the police department was forced to dump it amid intense backlash from Westport residents.

If you need something to watch this weekend that isn't the latest episode of WandaVision, take a virtual vacation with these tour videos of Super Nintendo World. While the park isn't officially open yet, fans are already checking out the rides and cafes and have made videos of the experience for everyone who can't get inside. A port of Goldeneye 007 for Xbox 360 never saw the light of day due to licensing issues, but a leaked ROM means you can now play it on PC or just watch a video of someone else playing. According to a source with "direct knowledge" of the device, The Information reports Apple's mixed-reality headset will contain more than a dozen cameras for tracking movement and showing real-world video to the person wearing it. It apparently also includes two 8K displays, giving it an effective resolution that would far outstrip anything currently on the market.

Deborah Raji, a fellow at nonprofit Mozilla, and Genevieve Fried, who advises members of the US Congress on algorithmic accountability, examined over 130 facial-recognition data sets compiled over 43 years. They found that researchers, driven by the exploding data requirements of deep learning, gradually abandoned asking for people's consent. This has led more and more of people's personal photos to be incorporated into systems of surveillance without their knowledge. It has also led to far messier data sets: they may unintentionally include photos of minors, use racist and sexist labels, or have inconsistent quality and lighting. The trend could help explain the growing number of cases in which facial-recognition systems have failed with troubling consequences, such as the false arrests of two Black men in the Detroit area last year.

With large-scale integration of renewable generation and ubiquitous distributed energy resources (DERs), modern power systems confront a series of new challenges in operation and control, such as growing complexity, increasing uncertainty, and aggravating volatility. While the upside is that more and more data are available owing to the widely-deployed smart meters, smart sensors, and upgraded communication networks. As a result, data-driven control techniques, especially reinforcement learning (RL), have attracted surging attention in recent years. In this paper, we focus on RL and aim to provide a tutorial on various RL techniques and how they can be applied to the decision-making and control in power systems. In particular, we select three key applications, including frequency regulation, voltage control, and energy management, for illustration, and present the typical ways to model and tackle them with RL methods. We conclude by emphasizing two critical issues in the application of RL, i.e., safety and scalability. Several potential future directions are discussed as well.