Anomaly detection is an important problem that has been well-studied within diverse research areas and application domains. The aim of this survey is twofold, firstly we present a structured and comprehensive overviewof research methods in deep learning-based anomaly detection. Furthermore, we review the adoption of these methods for anomaly across various application domains and assess their effectiveness. We have grouped state-of-the-art deep anomaly detection research techniques into different categories based on the underlying assumptions and approach adopted. Within each category, we outline the basic anomaly detection technique, along with its variants and present key assumptions, to differentiate between normal and anomalous behavior. Besides, for each category, we also present the advantages and limitations and discuss the computational complexity of the techniques inreal application domains. Finally, we outline open issues in research and challenges faced while adopting deep anomaly detection techniques for real-world problems.

Vision-based deep reinforcement learning (RL), similar to deep learning, typically obtains a performance benefit by using high capacity and relatively large convolutional neural networks (CNN). However, a large network leads to higher inference costs (power, latency, silicon area, MAC count). Many inference optimization have been developed for CNNs. Some optimization techniques offer theoretical efficiency, but designing actual hardware to support them is difficult. On the other hand, "distillation" is a simple general-purpose optimization technique which is broadly applicable for transferring knowledge from a trained, high capacity, teacher network to an untrained, low capacity, student network. "DQN distillation" extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm. Our work adapts the DQN distillation work to the actor-critic Proximal Policy Optimization algorithm. PPO is simple to implement and has much higher performance than the seminal DQN algorithm. We show that a distilled PPO student can attain far higher performance compared to a DQN teacher. We also show that a low capacity distilled student is generally able to outperform a low capacity agent that directly trains in the environment. Finally, we show that distillation, followed by "fine-tuning" in the environment, enables the distilled PPO student to achieve parity with teacher performance. In general, the lessons learned in this work should transfer to other actor-critic RL algorithms.

There is much debate in machine ethics about the most appropriate way to introduce ethical reasoning capabilities into intelligent autonomous machines. Recent incidents involving autonomous vehicles in which humans have been killed or injured have raised questions about how we ensure that such vehicles have an ethical dimension to their behaviour and are therefore trustworthy. The main problem is that hardwiring such machines with rules not to cause harm or damage is not consistent with the notion of autonomy and intelligence. Also, such ethical hardwiring does not leave intelligent autonomous machines with any course of action if they encounter situations or dilemmas for which they are not programmed or where some harm is caused no matter what course of action is taken. Teaching machines so that they learn ethics may also be problematic given recent findings in machine learning that machines pick up the prejudices and biases embedded in their learning algorithms or data. This paper describes a fuzzy reasoning approach to machine ethics. The paper shows how it is possible for an ethics architecture to reason when taking over from a human driver is morally justified. The design behind such an ethical reasoner is also applied to an ethical dilemma resolution case. One major advantage of the approach is that the ethical reasoner can generate its own data for learning moral rules (hence, autometric) and thereby reduce the possibility of picking up human biases and prejudices. The results show that a new type of metric-based ethics appropriate for autonomous intelligent machines is feasible and that our current concept of ethical reasoning being largely qualitative in nature may need revising if want to construct future autonomous machines that have an ethical dimension to their reasoning so that they become moral machines.

One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is to map new questions to formerly answered questions that are `similar'. In this paper, we propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare machine learning and deep learning methods for RQE using different kinds of datasets, including textual inference, question similarity and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources, that we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score. The evaluation results also support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.

These transformations will open up new opportunities for individuals, the economy, and society, but they have the potential to disrupt the current livelihoods of millions of Americans. Whether AI leads to unemployment and increases in inequality over the long-run depends not only on the technology itself but also on the institutions and policies that are in place. This report examines the expected impact of AI-driven automation on the economy, and describes broad strategies that could increase the benefits of AI and mitigate its costs. Economics of AI-Driven Automation Technological progress is the main driver of growth of GDP per capita, allowing output to increase faster than labor and capital. One of the main ways that technology increases productivity is by decreasing the number of labor hours needed to create a unit of output.

AI is being employed by attackers, such as within Deeplocker malware, which avoided a tight cyber security mechanism by utilising AI models to attack target hosgts using face recognition, geolocation and speech recognition. This attack speaks volumes about the huge role of AI in cyber security domains. To counter attack and defend, AI for cybersecurity has become necessary. Large and small organizations and even startups invest heavily in building AI systems to analyze large data and in turn, help their cybersecurity professionals to identify possible threats and take precautions or immediate actions to resolve them. Phishing, one of the simplest and most common social engineering cyber attacks is now easy to master by attackers.

How does IDC define responsible or ethical AI, and what should government agencies know about this? Adelaide O'Brien: As AI proliferates in government agencies and is being deployed for mission-critical operations, it will make or assist in decisions having significant impact on almost every aspect of individuals' lives. As the role of data-enabled automated decision making becomes more pervasive, transformative applications are demonstrating the potential that human and machine pairing can bring. At the same time, ethical challenges are being raised regarding the potential for errors, due to inadvertent algorithmic or data bias in sensitive areas, particularly regarding gender, race, class or age. Responsible or ethical AI includes the practices that government agencies can and should take to manage, monitor and mitigate these risks. Responsible and ethical use of AI includes protecting individuals from harm based on either algorithmic or data bias or unintended correlation of personally identifiable information (PII) even when using anonymous data.

President Trump's plans to trade record-high budget cuts at the Environmental Protection Agency (EPA) for increases in defense spending made headlines after the release of his proposed budget in early March 2017. At the Environmental Defense Fund (EDF), our marketing and communications teams wanted to see how the EPA budget cuts were being covered in the media to study which messages were most engaging to constituents. Preliminary survey results that we collected suggested that the general public was more receptive to health-related messaging; however, we wanted to better understand the entire media landscape around the topic. To explore the health angle and other prominent themes around cuts to the EPA's budget, EDF used Quid to analyze relevant articles within a four-month period following Trump's 2017 announcement. Quid leverages natural language processing and machine learning algorithms to read thousands of news articles and blog posts and group them based on common language and keywords.

The Government of Canada has announced an approved supplier list of companies able to provide the state with artificial intelligence (AI) services and products. Chief information officer Alex Benay said it was a "big day for automation of Government of Canada services and overall modernisation of our institutions." The list of AI vendors, published on January 15, includes large tech companies such as Amazon, McKinsey & Company and Palantir, alongside smaller businesses such as Dessa, which has only been in operation since 2016. The web page announcing the pre-qualified suppliers list said each of the companies selected "met all of the mandatory criteria to provide Canada with responsible and effective AI services, solutions and products." The successful companies were banded into three groups, depending on the size of the contracts they could work on.

Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. Hal Abelson, an MIT computer scientist, talks to senior policymakers from countries in the Organization for Economic Cooperation and Development. The subject was artificial intelligence, and his students last week were mainly senior policymakers from countries in the 36-nation Organization for Economic Cooperation and Development.