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.

SINGAPORE - Singapore is the most prepared for the next wave of technology disruption that will be brought about by artificial intelligence (AI), according to a new index on AI readiness that ranks 105 global cities. The technique that allows machines to learn from enormous sets of data is expected to bring new conveniences in modern living and economic benefits. But jobs risk being displaced, people's privacy risks being exposed and inequality may be perpetuated by AI being fed biased data, with cities at various stages of readiness for the new future. In determining how prepared global cities are for this disrupted future, New York-based research outfit Oliver Wyman Forum's inaugural Global Cities AI Disruption Index, released on Thursday (Sept 26), scored cities on 31 metrics across four broad categories: vision, activation ability, asset base and growth trajectory. Singapore received the best overall score of 75.8, bolstered by its strong performance in the vision category, which measures the presence of plans to respond to technology changes and plans to upgrade labour skills and infrastructure such as mobile networks.

It was the beginning of September, spring was coming in Prague, when I attended Tech Conference Europe organized by PICANTE. The publishing website picks the latest news about technology, finance, and many other topics that resonate today. If you asked me why to attend any tech conference, I would say – to meet interesting people and get inspired. Events like this one are an amazing opportunity to broaden your horizons and discover what's going on in the industry. Picante conference focused on different fields such as blockchain, Artificial Intelligence (AI) or Virtual Reality (VR).

THE ECONOMIST'S Open Future essay competition winner was announced in September, beating nearly 2,400 entries from over 110 countries. But how might artificial intelligence tackle the question? Specifically, we fed the essay question and the 58-word description through a natural-language processing algorithm called GPT-2, released publicly in February by OpenAI, a group working on AI research and ethics, based in San Francisco. The result was six roughly 400-word texts. We took the larger parts of three of them and placed them one after another with no other editing.

Prolific science and science fiction writer Isaac Asimov (1920–1992) developed the Three Laws of Robotics in the hope of guarding against potentially dangerous artificial intelligence. They first appeared in his 1942 short story Runaround. "Many computer engineers use the three laws as a tool for how they think about programming," says Chris Stokes, a philosopher at Wuhan University in China. But the trouble is, they don't work. In "Why the Three Laws of Robotics Do Not Work," published in the International Journal of Research in Engineering and Innovation, Stokes writes that "the Three Laws are not sufficient when it comes to controlling an artificial intelligence."

Tractica predicts that the global artificial intelligence (AI) software revenue will grow from the already quite impressive US $9.5 billion back in 2018 to an even more astounding US $ 118.6 by 2025. What exactly is driving this growth? While massive companies such as Amazon contribute in terms of resources in order to develop AI tech, much of the growth actually comes from startups. So who are the leading investors in AI as of the moment? This ranking is based on Index's listing of companies based on the number of investments they have as of May 2019.

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.

We develop an adversarial-reinforcement learning scheme for microswimmers in statistically homogeneous and isotropic turbulent fluid flows, in both two (2D) and three dimensions (3D). We show that this scheme allows microswimmers to find non-trivial paths, which enable them to reach a target on average in less time than a na\"ive microswimmer, which tries, at any instant of time and at a given position in space, to swim in the direction of the target. We use pseudospectral direct numerical simulations (DNSs) of the 2D and 3D (incompressible) Navier-Stokes equations to obtain the turbulent flows. We then introduce passive microswimmers that try to swim along a given direction in these flows; the microswimmwers do not affect the flow, but they are advected by it. Two, non-dimensional, control parameters play important roles in our learning scheme: (a) the ratio $\tilde{V}_s$ of the microswimmer's bare velocity $V_s$ and the root-mean-square (rms) velocity $u_{rms}$ of the turbulent fluid; and (b) the product $\tilde{B}$ of the microswimmer-response time $B$ and the rms vorticity $\omega_{rms}$ of the fluid. We show that, in a substantial part of the $\tilde{V}_s-\tilde{B}$ plane, the average time required for the microswimmers to reach the target, by using our adversarial-learning scheme, eventually reduces below the average time taken by microswimmers that follow the na\"ive strategy.

Recent theoretical work has established connections between over-parametrized neural networks and linearized models governed by he Neural Tangent Kernels (NTKs). NTK theory leads to concrete convergence and generalization results, yet the empirical performance of neural networks are observed to exceed their linearized models, suggesting insufficiency of this theory. Towards closing this gap, we investigate the training of over-parametrized neural networks that are beyond the NTK regime yet still governed by the Taylor expansion of the network. We bring forward the idea of \emph{randomizing} the neural networks, which allows them to escape their NTK and couple with quadratic models. We show that the optimization landscape of randomized two-layer networks are nice and amenable to escaping-saddle algorithms. We prove concrete generalization and expressivity results on these randomized networks, which leads to sample complexity bounds (of learning certain simple functions) that match the NTK and can in addition be better by a dimension factor when mild distributional assumptions are present. We demonstrate that our randomization technique can be generalized systematically beyond the quadratic case, by using it to find networks that are coupled with higher-order terms in their Taylor series.