The United States marked International Human Rights Day Friday with the announcement of sanctions on dozens of people and entities tied to rights abuses in China, Myanmar, North Korea and Bangladesh, while blacklisting a Chinese artificial intelligence company. The financial and visa sanctions came on the final day of President Joe Biden's virtual Summit for Democracy, where he unveiled policies to bolster democracy against threats around the world and appealed for solidarity among some 100 participants. "On International Human Rights Day, Treasury is using its tools to expose and hold accountable perpetrators of serious human rights abuse," Deputy Secretary of the Treasury Wally Adeyemo said in a statement. "Our actions today, particularly those in partnership with the United Kingdom and Canada, send a message that democracies around the world will act against those who abuse the power of the state to inflict suffering and repression," he added. The sanctions on China slapped a U.S. visa ban on the current and previous chairmen of the Xinjiang Uyghur Autonomous Region of China (XUAR), Erken Tuniyaz and Shohrat Zakir, and came a day after a tribunal in London found that Chinese policies in the region constituted genocide.

Trust is vital to economics, society, and sustainable development. That's equally true when it comes to artificial intelligence. To develop trusted AI, security should be an integral part of your AI development lifecycle. With every technology paradigm change, attackers are there to exploit capabilities. In response, cyber team defense patterns have also evolved.

It is hard to anticipate the future. Maybe it is not even possible unless we sit around and do nothing, or we take responsibility to influence it to our best advantage. The challenges that we face today we cannot solve alone. The solution lies in joint forces – across industries, organizations, disciplines, governments, the private and public sector, continents, and societies. And even if we join forces, it is unlikely that we will see immediate results.

Artificial intelligence offers great potential to positively affect virtually all areas of our lives. There is, however, significant potential for abuse and harm resulting from irresponsible use of AI. Perhaps you are a fan of "Black Mirror" or the "Terminator" series of movies, each of which portend a world where machine intelligence is a threat to humanity, in particular once AI becomes "smarter" than humankind. The concept of the "singularity" (the point where AI surpasses human intelligence) has inspired great science fiction, but it has also prompted warnings regarding responsible use of AI. Studies have also shown that AI systems can be adversely influenced by biased input data or express or inherent biases of programmers.

When a video purporting to show New Zealand Prime Minister Jacinda Ardern smoking drugs surfaced on social media in recent months, experts quickly dismissed it as a fake. The video, which was viewed and shared thousands of times, showed a woman smoking from what appeared to be a crack pipe. The PM's face had been superimposed using artificial intelligence. But the video, created for YouTube, was convincing enough to the many who shared it. It was the latest example of how disturbingly authentic-looking videos can blur the lines between reality and fantasy.

SAN ANTONIO, Dec. 7, 2021 /CNW/ -- The benefits of artificial intelligence (AI) technology have been validated in many domains, where AI has helped reduce costs, improve efficiency and productivity, and solve real-life problems. However, international communities are challenged by adverse factors such as US-China competition, changes in the supply chain, and the impact of COVID-19--and Taiwan's enterprises and their development are no exception. In such circumstances, the role of the government is even more important. In response, the Taiwanese government has adopted various measures to support industrial AI research and development (R&D) and encourage companies to introduce AI technology by executing subsidy and public-private partnership (PPP) programs. "The use cases for AI are growing rapidly, but enterprises may still be reluctant to risk adoption. Government programs that support AI companies and startups, as well as the adopting enterprises, are an important way to increase the development of the industry," said Georgia Edell, Consulting Analyst at Frost & Sullivan.

Population-level societal events, such as civil unrest and crime, often have a significant impact on our daily life. Forecasting such events is of great importance for decision-making and resource allocation. Event prediction has traditionally been challenging due to the lack of knowledge regarding the true causes and underlying mechanisms of event occurrence. In recent years, research on event forecasting has made significant progress due to two main reasons: (1) the development of machine learning and deep learning algorithms and (2) the accessibility of public data such as social media, news sources, blogs, economic indicators, and other meta-data sources. The explosive growth of data and the remarkable advancement in software/hardware technologies have led to applications of deep learning techniques in societal event studies. This paper is dedicated to providing a systematic and comprehensive overview of deep learning technologies for societal event predictions. We focus on two domains of societal events: \textit{civil unrest} and \textit{crime}. We first introduce how event forecasting problems are formulated as a machine learning prediction task. Then, we summarize data resources, traditional methods, and recent development of deep learning models for these problems. Finally, we discuss the challenges in societal event forecasting and put forward some promising directions for future research.

The training and test data for deep-neural-network-based classifiers are usually assumed to be sampled from the same distribution. When part of the test samples are drawn from a distribution that is sufficiently far away from that of the training samples (a.k.a. out-of-distribution (OOD) samples), the trained neural network has a tendency to make high confidence predictions for these OOD samples. Detection of the OOD samples is critical when training a neural network used for image classification, object detection, etc. It can enhance the classifier's robustness to irrelevant inputs, and improve the system resilience and security under different forms of attacks. Detection of OOD samples has three main challenges: (i) the proposed OOD detection method should be compatible with various architectures of classifiers (e.g., DenseNet, ResNet), without significantly increasing the model complexity and requirements on computational resources; (ii) the OOD samples may come from multiple distributions, whose class labels are commonly unavailable; (iii) a score function needs to be defined to effectively separate OOD samples from in-distribution (InD) samples. To overcome these challenges, we propose a Wasserstein-based out-of-distribution detection (WOOD) method. The basic idea is to define a Wasserstein-distance-based score that evaluates the dissimilarity between a test sample and the distribution of InD samples. An optimization problem is then formulated and solved based on the proposed score function. The statistical learning bound of the proposed method is investigated to guarantee that the loss value achieved by the empirical optimizer approximates the global optimum. The comparison study results demonstrate that the proposed WOOD consistently outperforms other existing OOD detection methods.

Clearview's controversial facial recognition technology is getting closer to being patented by the US Patent and Trademark Office. The USPTO has given Clearview a "notice of allowance", a sign that the startup's patent application will be approved once it pays administrative costs, Politico reported. Clearview said it has scraped ten billion photos from public social media accounts. Although companies like Instagram and Twitter disapprove, Clearview has continued to download these images without permission. Now, its methods and software are may be officially patented. Clearview's application describes a "downloading by a web crawler facial images of individuals and personal information associated therewith; and storing the downloaded facial images and associated personal information in the database."

China is winning the AI race due to the US army's sluggish digital transformation, private actors' reluctance to work with the state, and too many ethical debates stifling innovation. The Pentagon knew it had a problem when Nick Chaillan, its first-ever chief software officer quit, saying the United States has no competing fighting chance against China in 15 to 20 years when it comes to cyberwarfare and artificial intelligence. According to the experts, whoever leads in artificial intelligence in 2030, will rule the world until 2100. And it is a race that some say America is losing. China has strengthened its capabilities in computer vision, facial recognition, emotion recognition, and more.