Washington State University researchers are creating the first-ever "IQ test" for artificial intelligence (AI) systems that would score systems on how well they learn and adapt to new, unknown environments. Diane Cook, Regents Professor and Huie-Rogers Chair Professor, and Larry Holder, professor in the School of Electrical Engineering and Computer Science, received a grant of just over $1 million from the Defense Advanced Research Projects Agency (DARPA) to create a framework to test the "intelligence" of AI systems. "Previously, research on measuring intelligence in AI systems has been mostly theoretical," Holder said. Holder and Cook will design a test that will grade AI systems based on the difficulty of problems that they can solve. Creating methods to rank problems on their difficulty will be one of the major parts of the research.

However, the existing DL models are highly vulnerable to adversarial examples [55, 20], which are maliciously generated by an adversary to make a model produce erroneous predictions. As DL models have been integrated into various security-sensitive applications ( e.g., autonomous driving, healthcare, and finance), the study of the adversarial robustness issue has attracted increasing attention with an enormous number of adversarial attack and defense methods proposed. Therefore, it is crucial to conduct correct and rigorous evaluations of these methods for understanding their pros and cons, comparing their performance, and providing insights for building new methods [6]. The research on adversarial robustness is faced with an "arms race " between attacks and defenses, i.e ., a defense method proposed to prevent the existing attacks was soon evaded by new attacks, and vice versa [7, 8, 23, 1, 57, 67]. For instance, defensive distillation [43] was proposed to improve adversarial robustness, but was later shown to be ineffective against a strong attack [8].

In this work, learning schemes for measure-valued data are proposed, i.e. data that their structure can be more efficiently represented as probability measures instead of points on $\R^d$, employing the concept of probability barycenters as defined with respect to the Wasserstein metric. Such type of learning approaches are highly appreciated in many fields where the observational/experimental error is significant (e.g. astronomy, biology, remote sensing, etc.) or the data nature is more complex and the traditional learning algorithms are not applicable or effective to treat them (e.g. network data, interval data, high frequency records, matrix data, etc.). Under this perspective, each observation is identified by an appropriate probability measure and the proposed statistical learning schemes rely on discrimination criteria that utilize the geometric structure of the space of probability measures through core techniques from the optimal transport theory. The discussed approaches are implemented in two real world applications: (a) clustering eurozone countries according to their observed government bond yield curves and (b) classifying the areas of a satellite image to certain land uses categories which is a standard task in remote sensing. In both case studies the results are particularly interesting and meaningful while the accuracy obtained is high.

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm.

Much of the existing research on the social and ethical impact of Artificial Intelligence has been focused on defining ethical principles and guidelines surrounding Machine Learning (ML) and other Artificial Intelligence (AI) algorithms [IEEE, 2017, Jobin et al., 2019]. While this is extremely useful for helping define the appropriate social norms of AI, we believe that it is equally important to discuss both the potential and risks of ML and to inspire the community to use ML for beneficial objectives. In the present article, which is specifically aimed at ML practitioners, we thus focus more on the latter, carrying out an overview of existing high-level ethical frameworks and guidelines, but above all proposing both conceptual and practical principles and guidelines for ML research and deployment, insisting on concrete actions that can be taken by practitioners to pursue a more ethical and moral practice of ML aimed at using AI for social good.

With the presence of artificial intelligence (AI) everywhere and the increased use of deep learning (DL), many security practitioners are being hooked into believing that these approaches are the solution for the security challenges. Nevertheless, like any tool, AI is a double-edged sword that can be used as a security solution or as a weapon by hackers. In fact, many security researchers and industry have told AI will be the biggest ally of security. Moreover, we can see that by the increased number of companies that merging AI and Cybersecurity to keep us safe. But has anyone ever thought that these same techniques can be applied to improve the tools and methods used by hackers?

Financial markets were cheered recently by the news that the US and China have reached a "phase one" deal to prevent further escalation of their bilateral trade war. But there is actually very little to cheer about. In exchange for China's tentative commitment to buy more US agricultural (and some other) goods, and modest concessions on intellectual property rights and the yuan, the US agreed to withhold tariffs on another $160bn (£124bn) worth of Chinese exports, and to roll back some of the tariffs introduced on 1 September. The good news for investors is that the deal averted a new round of tariffs that could have tipped the US and the global economy into recession and crashed global stock markets. The bad news is that it represents just another temporary truce amid a much larger strategic rivalry encompassing trade, technology, investment, currency and geopolitical issues.

You go around a curve, and suddenly see something in the middle of the road ahead. Of course, the answer depends on what that'something' is. A torn paper bag, a lost shoe, or a tumbleweed? You can drive right over it without a second thought, but you'll definitely swerve around a pile of broken glass. You'll probably stop for a dog standing in the road but move straight into a flock of pigeons, knowing that the birds will fly out of the way.

The'biometric verification of identities' of all travelers crossing US borders is set for a 2021 start date, with Homeland Security scrambling to get the system in place after Trump issued an executive order in March 2017 expediting the process

Courts in China are increasingly relying on blockchain technology and artificial intelligence to resolve legal issues through "smart internet courts," where witnesses communicate with virtual, AI-enabled judges. According to a Cointelegraph report, Xinhua state news agency revealed that over 3.1 million cases in the country were settled using the technologies in smart internet courts from March to October 2019. Xinhua reported that the smart courts are made up of virtual judges enabled by AI technology that can communicate with citizens through multiple screens. The first-ever smart court in China was introduced in 2017 in Hangzhou city, before the Chinese government expanded these courts to Beijing and Guangzhou. Zhang Wen, President of the Beijing Internet Court, has stated that these new courts have adopted both AI and blockchain technology to resolve legal issues.