Earthquake Alert! 6.7 temblor, epicenter 3.8 miles west of Ventura, California--impact will be in eleven minutes--evacuate, evacuate!" While you run to the hall closet to grab your earthquake kit, you shout out: "Alexa, where is my emergency evac location?" Walk north to Wilshire, then take a left on Warner," she responds. As you and your neighbors pour into the building stairwell, you hear audio from a phone: "Google Earth Q estimates substantial potential for structural damage in the West San Fernando Valley and Coastal West Los Angeles to pre-2006 code dwellings and buildings. Most of West LA will experience total loss of power for anywhere from six to twenty-four hours in duration."

Trevor Paglen explores the unseen networks of power that monitor and control us, documenting secret US government bases, offshore prisons and surveillance drones. In the run up to his show at Milan's Fondazione Prada (until 24 February 2020), Paglen collaborated with the artificial intelligence researcher Kate Crawford to launch ImageNet Roulette, an online interactive project which revealed the often racist or misogynistic ways in which ImageNet--one of the largest online databases that is widely used to train machines how to read pictures--classifies images of people. At London's Barbican, Paglen is again examining ImageNet's classifications, starting from everyday objects like apples and moving towards more abstract concepts to arrive at the category of "anomaly". We spoke to him about surveillance, AI and how we can begin to imagine a different future. The Art Newspaper: In 2015, I joined you on a scuba-diving expedition off the coast of Florida to see the fibre-optic cables that carry internet communications between continents.

Rapidly rising e-commerce activities will be the key driver for the global cybersecurity market growth during the forecast period. E-commerce giants such as Amazon are fast diversifying their businesses and product offerings and ecosystem of connected devices is getting wider and bigger. For example, in 2017, Amazon reportedly shipped more than 5 billion products globally. According to the OECD's Creditor Reporting System, funds to the tune of USD 6.6 billion were disbursed to promote cross-border electronic connectivity between 2006 and 2016. Thus, as more people shop and transact online, the global cybersecurity market revenue is set to get fueled in the forecast period.

It was a version of a talk that Kearns had given before. But he couldn't ignore the irony of discussing the dangers inherent in new technologies in this particular place. The Santa Fe Institute is just 40 miles from the town of Los Alamos, site of the Manhattan Project, where more than 6,000 scientists and support staff worked together from 1939 to 1945 to produce the world's first atomic bomb. The ultimate impact of the project was enormous: Some 200,000 lives lost at Hiroshima and Nagasaki, and the unleashing of a new technological threat that has loomed over humankind for more than seven decades since. Looking back at those physicists involved in the Manhattan Project and their response to the social and ethical challenges their work presented offers a valuable precedent.

False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics Gautam Raj Mode, Prasad Calyam, Khaza Anuarul Hoque Department of Electrical Engineering & Computer Science University of Missouri, Columbia, MO, USA gmwyc@mail.missouri.edu, Abstract --Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms (especially deep learning algorithms) and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. T o date, the majority of the published literature focuses on the accuracy of the IoT and DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks (in the form of false data injection attack) on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-T erm Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. Our obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model and apply two different types of false data injection attacks (FDIA), specifically, continuous and interim FDIAs on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. Our results demonstrate that attacks on even a small number of IoT sensors can strongly defect the RUL prediction in all cases. However, the GRU-based PdM model performs better in terms of accuracy and FDIA resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths.

This paper provides statistical theory and intuition for Personalized PageRank (PPR), a popular technique that samples a small community from a massive network. We study a setting where the entire network is expensive to thoroughly obtain or maintain, but we can start from a seed node of interest and "crawl" the network to find other nodes through their connections. By crawling the graph in a designed way, the PPR vector can be approximated without querying the entire massive graph, making it an alternative to snowball sampling. Using the Degree-Corrected Stochastic Blockmodel, we study whether the PPR vector can select nodes that belong to the same block as the seed node. We provide a simple and interpretable form for the PPR vector, highlighting its biases towards high degree nodes outside of the target block. We examine a simple adjustment based on node degrees and establish consistency results for PPR clustering that allows for directed graphs. We illustrate the method with the Twitter friendship graph and find that (i) the adjusted and unadjusted PPR techniques are complementary approaches, where the adjustment makes the results particularly localized around the seed node and (ii) the bias adjustment greatly benefits from degree regularization.

There is significant interest in using modern neural networks for scientific applications due to their effectiveness in modeling highly complex, non-linear problems in a data-driven fashion. However, a common challenge is to verify the scientific plausibility or validity of outputs predicted by a neural network. This work advocates the use of known scientific constraints as a lens into evaluating, exploring, and understanding such predictions for the problem of inertial confinement fusion.

The concept of explainability is envisioned to satisfy society's demands for transparency on machine learning decisions. The concept is simple: like humans, algorithms should explain the rationale behind their decisions so that their fairness can be assessed. While this approach is promising in a local context (e.g. to explain a model during debugging at training time), we argue that this reasoning cannot simply be transposed in a remote context, where a trained model by a service provider is only accessible through its API. This is problematic as it constitutes precisely the target use-case requiring transparency from a societal perspective. Through an analogy with a club bouncer (which may provide untruthful explanations upon customer reject), we show that providing explanations cannot prevent a remote service from lying about the true reasons leading to its decisions. More precisely, we prove the impossibility of remote explainability for single explanations, by constructing an attack on explanations that hides discriminatory features to the querying user. We provide an example implementation of this attack. We then show that the probability that an observer spots the attack, using several explanations for attempting to find incoherences, is low in practical settings. This undermines the very concept of remote ex-plainability in general. 1 Introduction Modern decision-making driven by black-box systems now impacts a significant share of our lives [9, 29]. Those systems build on user data, and range from rec-ommenders [21] ( e.g., for personalized ranking of information on websites) to predictive algorithms ( e.g., credit default) [29]. This widespread deployment, along with the opaque decision process provided by those systems raises concerns about transparency for the general public or for policy makers [12]. This translated in some jurisdictions ( e.g., United States of America and Europe) into a so called right to explanation [12, 26], that states that the output decisions of an algorithm must be motivated. Explainability of in-house models An already large body of work is interested in the explainability of implicit machine learning models (such as neural network models) [2, 13, 20]. Indeed, those models show state-of-art performances when it comes to a task accuracy, but they are not designed to provide explanations -or at least intelligible decision processes-when one wants to obtain more than the output decision of the model. In the context of recommendation, the expression "post hoc explanation" has been coined [32].

Deep neural network image classifiers are reported to be susceptible to adversarial evasion attacks, which use carefully crafted images created to mislead a classifier. Recently, various kinds of adversarial attack methods have been proposed, most of which focus on adding small perturbations to input images. Despite the success of existing approaches, the way to generate realistic adversarial images with small perturbations remains a challenging problem. In this paper, we aim to address this problem by proposing a novel adversarial method, which generates adversarial examples by imposing not only perturbations but also spatial distortions on input images, including scaling, rotation, shear, and translation. As humans are less susceptible to small spatial distortions, the proposed approach can produce visually more realistic attacks with smaller perturbations, able to deceive classifiers without affecting human predictions. We learn our method by amortized techniques with neural networks and generate adversarial examples efficiently by a forward pass of the networks. Extensive experiments on attacking different types of non-robustified classifiers and robust classifiers with defence show that our method has state-of-the-art performance in comparison with advanced attack parallels.

Synthetic media generated by AI and represents another dark side of technology and the issue. A deepfake is created by pitting two computer programs against each other -- which are called Generative Adversarial Networks, or GANs. Although this form of Artificial Intelligence has been in the spotlight for the most part of 2019, the biggest news that blew the issue wide open was a LinkedIn user by the name Katie Jones, who appeared on the platform & started connecting with the Who's Who of the political elite in Washington DC. The ease with which deep learning created a real-life image of a person & then penetrated the social media was alarming not just for lawmakers & regulators, but for the general public as well. Lawmakers are especially worried about how this can affect and/or manipulate the 2020 Presidential elections in the U.S. People falling prey to misinformation can greatly jeopardize the transparency of the democratic process.