That's the question faced by Prakash Shenoy, the Ronald G. Harper Distinguished Professor of Artificial Intelligence at the University of Kansas School of Business. His answer can be found in the article "An Interval-Valued Utility Theory for Decision Making with Dempster-Shafer Belief Functions," which appears in the September issue of the International Journal of Approximate Reasoning. "People assume that you can always attach probabilities to uncertain events," Shenoy said. "But in real life, you never know what the probabilities are. You don't know if it's 50 percent or 60 percent. This is the essence of the theory of belief functions that Arthur Dempster and Glenn Shafer formulated in the 1970s."

WASHINGTON: In an extraordinary meeting that highlights how crucial artificial intelligence is becoming to the US and its allies, some 100 officials from 13 democratic countries met online Tuesday and Wednesday to discuss how their militaries could ethically use AI, the first summit of its kind. Hosted by the Pentagon's Joint AI Center, the virtual conference kicked off what JAIC's calling the AI Partnership for Defense, an international forum it hopes will evolve from broad principles and policy to technical cooperation on data and algorithms. "This is historic," said Mark Beall, who's been working on international cooperation since he became the JAIC's chief strategy and policy 18 months ago. "This group of my countries, to my knowledge, has never been brought together under one banner before." Defense Secretary Mark Esper officially adopted a set of AI ethics principles in February, although implementation is still nascent.

Textual adversarial attacking has received wide and increasing attention in recent years. Various attack models have been proposed, which are enormously distinct and implemented with different programming frameworks and settings. These facts hinder quick utilization and apt comparison of attack models. In this paper, we present an open-source textual adversarial attack toolkit named OpenAttack. It currently builds in 12 typical attack models that cover all the attack types. Its highly inclusive modular design not only supports quick utilization of existing attack models, but also enables great flexibility and extensibility. OpenAttack has broad uses including comparing and evaluating attack models, measuring robustness of a victim model, assisting in developing new attack models, and adversarial training. Source code, built-in models and documentation can be obtained at https://github.com/thunlp/OpenAttack.

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.

Time series anomalies can offer information relevant to critical situations facing various fields, from finance and aerospace to the IT, security, and medical domains. However, detecting anomalies in time series data is particularly challenging due to the vague definition of anomalies and said data's frequent lack of labels and highly complex temporal correlations. Current state-of-the-art unsupervised machine learning methods for anomaly detection suffer from scalability and portability issues, and may have high false positive rates. In this paper, we propose TadGAN, an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs). To capture the temporal correlations of time series distributions, we use LSTM Recurrent Neural Networks as base models for Generators and Critics. TadGAN is trained with cycle consistency loss to allow for effective time-series data reconstruction. We further propose several novel methods to compute reconstruction errors, as well as different approaches to combine reconstruction errors and Critic outputs to compute anomaly scores. To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one. We compare our approach to 8 baseline anomaly detection methods on 11 datasets from multiple reputable sources such as NASA, Yahoo, Numenta, Amazon, and Twitter. The results show that our approach can effectively detect anomalies and outperform baseline methods in most cases (6 out of 11). Notably, our method has the highest averaged F1 score across all the datasets. Our code is open source and is available as a benchmarking tool.

The localization of self-driving cars is needed for several tasks such as keeping maps updated, tracking objects, and planning. Localization algorithms often take advantage of maps for estimating the car pose. Since maintaining and using several maps is computationally expensive, it is important to analyze which type of map is more adequate for each application. In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps. To the best of our knowledge, such evaluation is missing in the literature. For building semantic and colour grid maps, point clouds from a Light Detection and Ranging (LiDAR) sensor are fused with images captured by a front-facing camera. Semantic information is extracted from images with a deep neural network. Experiments are performed in varied environments, under diverse conditions of illumination and traffic. Results show that occupancy grid maps lead to more accurate localization, followed by reflectivity grid maps. In most scenarios, the localization with semantic grid maps kept the position tracking without catastrophic losses, but with errors from 2 to 3 times bigger than the previous. Colour grid maps led to inaccurate and unstable localization even using a robust metric, the entropy correlation coefficient, for comparing online data and the map.

The Alberta government is giving $9 million in funding to the Alberta Machine Intelligence Institute (Amii) in an effort to promote the province's tech sector. The funding is made up of $4 million from Alberta Innovates and $5 million through the Technology Innovation and Emissions Reduction system. The government says it's Investment and Growth Strategy has identified developing Alberta's technology sector as a top priority. They hope it will make way for investment and innovation in other Alberta industries including agriculture, aviation and energy. "Our investment demonstrates that Alberta's government recognizes the important role that Amii and the University of Alberta plays in creating a stronger economy," said Minister of Advanced Education Demetrios Nicolaides.

AppZen, the world's leading AI solution for modern finance teams, is launching Mastermind Analytics, a first of its kind analytics AI software that identifies spend risks and provides on-demand benchmarks. This gives finance teams visibility of behavior patterns and a valuable assessment of success or areas of improvement. AppZen is experiencing increased demand as CFOs lead digital transformation initiatives within their finance teams and throughout companies. Mastermind Analytics gives finance teams on-demand insights into spend, risk, and operational performance so they can focus on what matters most. It helps eliminate wasteful spend, and provides metrics on how to make processes and the teams more efficient.

Police in Canada say they recently charged a Tesla Model S owner with driving dangerously for sleeping at his car's wheel. In July, the Royal Canadian Mounted Police (RCMP) say they responded to a speeding complaint on Highway 2 near Ponoka -- a town in Alberta, south of the province's capital of Edmonton. Those who saw the car report it was traveling faster than 140 kilometers per hour (86MPH), with the front seats "completely reclined," and both the driver and passenger seemingly asleep. When a police officer found the 2019 Model S and turned on their emergency lights, the vehicle accelerated to 150 kilometers per hour (about 93MPH) before it eventually stopped. Police initially charged the driver, a 20-year-old man from the province of British Columbia, with speeding and handed him a 24-hour license suspension for driving while fatigued. He was also later charged with dangerous driving and has a court date in December.

Facebook, Twitter and YouTube have your data. The Trump administration said Friday that it would bar two popular Chinese-owned mobile apps WeChat and TikTok from U.S. app stores as of midnight Sunday, escalating the U.S. standoff with China. "Today's actions prove once again that President Trump will do everything in his power to guarantee our national security and protect Americans from the threats of the Chinese Communist Party," Commerce Secretary Wilbur Ross said in a statement. The Trump administration contends the data collected from American users by TikTok and WeChat could be accessed by the Chinese government. "The Trump administration is looking to make sure U.S. TikTok consumer data stays out of Beijing," said Wedbush Securities analyst Daniel Ives.