The UK has been knocked from the top spot of a global ranking of countries whose governments are ready to capitalise on artificial intelligence technologies in public services. The UK was narrowly beaten to the number one position by Singapore in this year's Government AI Readiness Index, which the ranking's authors described as a "timely reminder of the ongoing inequality of access to AI". This is the second time the ranking has been produced, with the UK having topped the leaderboard in the first iteration in 2017. Technology consultancy Oxford Insights and the Canadian government-sponsored International Development Research Centre said the 2019 Government AI Readiness Index should prompt governments to "act to ensure that global inequalities are not further entrenched or exacerbated by AI". Unsurprisingly, the upper echelons of the ranking were dominated by higher-income countries with strong economies.

According to OECD's Director of the Science, Technology and Innovation Directorate Andrew Wyckoff, the released document, titled "Recommendations of the Council on Artificial Intelligence," will hopefully establish a regulatory environment to promote AI technology in an ethical manner. "AI is what we would call a'general purpose technology.' It's going to change the way we do things in nearly every single sector of the economy -- that's part of the reason we give so much importance to its development," he told reporters Wednesday, according to Defense One. "Some have termed it as'the invention of a method of inventions,' and in fact we can see it already affecting the process of scientific discovery and science itself." The principles outlined in the document have been signed by the OECD's 36 member countries, as well as by the US, Argentina, Brazil, Colombia, Costa Rica, Peru and Romania.

Unmanned drones, powered by artificial intelligence, may soon accompany US Air Force Pilots on missions as autonomous wingmen. Both Boeing's F-15 and Lockheed Martin's F-35 fighter jets are being considered for the'Skyborg' drone support program. The scheme would cut down on the amount of people in the jets and could both reduce the risk to pilots and be more economical. Drones can be manufactured for a fortieth of the cost of a new fighter jet and may be guided by the sole pilot inside the nearby fighter plane. To safely manage any such drones, however, AI will need to be sufficiently developed to make it immune to attacks that could exploit its operating features.

We also demonstrate the the detection of inflammatory, inauthentic, or otherwise ability to use this architecture to transfer knowledge nefarious communication. Character-level convolutional from labeled data in one domain to related neural networks (CNNs) are particularly well-suited for (supervised and unsupervised) tasks. Characterlevel this task--as opposed to a word-level model--because they neural networks and transfer learning are allow for non-vernacular discourse, misspelling, and other particularly valuable tools in the disinformation social media features (e.g., emoticons) to be learned without space because of the messy nature of social media, the constraint of fixed vocabularies (Zhang et al., 2015). We lack of labeled data, and the multi-channel tactics implement an adaptation of a neural network architecture of influence campaigns. We demonstrate their effectiveness recently demonstrated to be effective for text classification in several tasks relevant for detecting (Zhang et al., 2015; Józefowicz et al., 2016). The method disinformation: spam emails, review bombing, is purely content-based and does not require any additional political sentiment, and conversation clustering.

In the past years, industrial networks have become increasingly interconnected and opened to private or public networks. This leads to an increase in efficiency and manageability, but also increases the attack surface. Industrial networks often consist of legacy systems that have not been designed with security in mind. In the last decade, an increase in attacks on cyber-physical systems was observed, with drastic consequences on the physical work. In this work, attack vectors on industrial networks are categorised. A real-world process is simulated, attacks are then introduced. Finally, two machine learning-based methods for time series anomaly detection are employed to detect the attacks. Matrix Profiles are employed more successfully than a predictor Long Short-Term Memory network, a class of neural networks.

In this effort we propose a new deep architecture utilizing residual blocks inspired by implicit discretization schemes. As opposed to the standard feed-forward networks, the outputs of the proposed implicit residual blocks are defined as the fixed points of the appropriately chosen nonlinear transformations. We show that this choice leads to improved stability of both forward and backward propagations, has a favorable impact on the generalization power of the network and allows for higher learning rates. In addition, we consider a reformulation of ResNet which does not introduce new parameters and can potentially lead to a reduction in the number of required layers due to improved forward stability and robustness. Finally, we derive the memory efficient reversible training algorithm and provide numerical results in support of our findings.

The paper studies how to release data about a critical infrastructure network (e.g., the power network or a transportation network) without disclosing sensitive information that can be exploited by malevolent agents, while preserving the realism of the network. It proposes a novel obfuscation mechanism that combines several privacy-preserving building blocks with a bi-level optimization model to significantly improve accuracy. The obfuscation is evaluated for both realism and privacy properties on real energy and transportation networks. Experimental results show the obfuscation mechanism substantially reduces the potential damage of an attack exploiting the released data to harm the real network.

Joint replacement is the most common inpatient surgical treatment in the US. We investigate the clinical pathway optimization for knee replacement, which is a sequential decision process from onset to recovery. Based on episodic claims from previous cases, we view the pathway optimization as an intelligence crowdsourcing problem and learn the optimal decision policy from data by imitating the best expert at every intermediate state. We develop a reinforcement learning-based pipeline that uses value iteration, state compression and aggregation learning, kernel representation and cross validation to predict the best treatment policy. It also provides forecast of the clinical pathway under the optimized policy. Empirical validation shows that the optimized policy reduces the overall cost by 7 percent and reduces the excessive cost premium by 33 percent.

We present a novel greedy approach to obtain a single layer neural network approximation to a target function with the use of a ReLU activation function. In our approach we construct a shallow network by utilizing a greedy algorithm where the set of possible inner weights acts as a parametrization of the prescribed dictionary. To facilitate the greedy selection we employ an integral representation of the network, based on the ridgelet transform, that significantly reduces the cardinality of the dictionary and hence promotes feasibility of the proposed method. Our approach allows for the construction of efficient architectures which can be treated either as improved initializations to be used in place of random-based alternatives, or as fully-trained networks, thus potentially nullifying the need for training and/or calibrating based on backpropagation. Numerical experiments demonstrate the tenability of the proposed concept and its advantages compared to the classical techniques for training and constructing neural networks.

Nowadays, considering the speed of the processes and the amount of data used in cyber defense, it cannot be expected to have an effective defense by using only human power without the help of automation systems. However, for the effective defense against dynamically evolving attacks on networks, it is difficult to develop software with conventional fixed algorithms. This can be achieved by using artificial intelligence methods that provide flexibility and learning capability. The likelihood of developing cyber defense capabilities through increased intelligence of defense systems is quite high. Given the problems associated with cyber defense in real life, it is clear that many cyber defense problems can be successfully solved only when artificial intelligence methods are used. In this article, the current artificial intelligence practices and techniques are reviewed and the use and importance of artificial intelligence in cyber defense systems is mentioned. The aim of this article is to be able to explain the use of these methods in the field of cyber defense with current examples by considering and analyzing the artificial intelligence technologies and methodologies that are currently being developed and integrating them with the role and adaptation of the technology and methodology in the defense of cyberspace.