Artificial technology may escalate a long-running arms race between financial institutions and cybercriminals. The technology is helping banks' cybersecurity teams detect and deal with breaches. Unfortunately, AI also creates new vulnerabilities in systems, since leaving machines in charge opens up opportunities for mistakes and manipulation. Further, AI helps attackers do their jobs more efficiently. For example, in attacks carried out last year, the writers of the Petya malware used AI to identify vulnerabilities and scan millions of ports in seconds to find the holes.

With threats, bribes, diplomacy and sanctions, American presidents of both parties have sought for 25 years to try to halt, or at least slow, North Korea's quest for a nuclear arsenal -- to no avail. Though the brinksmanship of the last few weeks has subsided, President Trump still faces the prospect of a madman -- Kim Jong Un -- in control of a nuclear arsenal. What the United States and its allies must now do is find options between conventional war, or even nuclear holocaust, on the one hand, and appeasement on the other. The answer could be robotic, cyber, and space weapons -- if we have the will to deploy them. They already have been used for pinpoint strikes on terrorist leaders and insurgent forces in the Mideast.

Businesses ranging from startups to large corporations are increasingly looking to new technologies, like artificial intelligence (AI) and machine learning, to protect their consumers. AI can provide an effective way to stop advanced and sophisticated malware attacks that have never been seen before. There's also a real opportunity for advanced phishing attacks by automating the human bad guy. Prepare is about building a proper cybersecurity program taking a risk based and business approach to security.

SITTING IN AN office in San Francisco, Igor Barani calls up some medical scans on his screen. He is the chief executive of Enlitic, one of a host of startups applying deep learning to medicine, starting with the analysis of images such as X-rays and CT scans. It is an obvious use of the technology. Deep learning is renowned for its superhuman prowess at certain forms of image recognition; there are large sets of labelled training data to crunch; and there is tremendous potential to make health care more accurate and efficient. Dr Barani (who used to be an oncologist) points to some CT scans of a patient's lungs, taken from three different angles.

The twenty-first century has ushered in a new age of data science and analytics. With the advent of automation, Artificial Intelligence (AI) and Machine Learning (MI) companies are slowly adapting and changing their learning curve towards software analytics. Data-driven innovation forms a key pillar for the sources of growth in the 21st century. The confluence of several trends, including the increasing migration of socio-economic activities to the Internet and the decline in the cost of data collection, storage and processing, is leading to the generation and use of huge volumes of data -- commonly referred to as "Big Data". These large data sets are becoming a core asset in the economy, fostering new industries and ecosystems, processes and products and creating significant competitive advantages.

Undoubtedly, over the next few decades, artificial intelligence (AI) will begin to shape the world as Industry 4.0 prevails. Two of the world's most powerful businessmen Mark Zuckerberg and Elon Musk are already debating the merits of this phenomenon. Musk claims that AI is a "fundamental risk to the existence of civilisation", whilst Zuckerberg is presenting more of a positive stance. Whilst this claim may prove to be true or not, as the use of AI imprints on society further, time will reveal its benefits and shortcomings. Hopefully not before it is too late, as some report that technology is now growing faster than humans are adapting to it. This article will explore some artificial intelligence solutions to some of the most pressing challenges the world faces today.

Latent space models are effective tools for statistical modeling and exploration of network data. These models can effectively model real world network characteristics such as degree heterogeneity, transitivity, homophily, etc. Due to their close connection to generalized linear models, it is also natural to incorporate covariate information in them. The current paper presents two universal fitting algorithms for networks with edge covariates: one based on nuclear norm penalization and the other based on projected gradient descent. Both algorithms are motivated by maximizing likelihood for a special class of inner-product models while working simultaneously for a wide range of different latent space models, such as distance models, which allow latent vectors to affect edge formation in flexible ways. These fitting methods, especially the one based on projected gradient descent, are fast and scalable to large networks. We obtain their rates of convergence for both inner-product models and beyond. The effectiveness of the modeling approach and fitting algorithms is demonstrated on five real world network datasets for different statistical tasks, including community detection with and without edge covariates, and network assisted learning.

Gaussian Processes are widely used for regression tasks. A known limitation in the application of Gaussian Processes to regression tasks is that the computation of the solution requires performing a matrix inversion. The solution also requires the storage of a large matrix in memory. These factors restrict the application of Gaussian Process regression to small and moderate size data sets. We present an algorithm that combines estimates from models developed using subsets of the data obtained in a manner similar to the bootstrap. The sample size is a critical parameter for this algorithm. Guidelines for reasonable choices of algorithm parameters, based on detailed experimental study, are provided. Various techniques have been proposed to scale Gaussian Processes to large scale regression tasks. The most appropriate choice depends on the problem context. The proposed method is most appropriate for problems where an additive model works well and the response depends on a small number of features. The minimax rate of convergence for such problems is attractive and we can build effective models with a small subset of the data. The Stochastic Variational Gaussian Process and the Sparse Gaussian Process are also appropriate choices for such problems. These methods pick a subset of data based on theoretical considerations. The proposed algorithm uses bagging and random sampling. Results from experiments conducted as part of this study indicate that the algorithm presented in this work can be as effective as these methods. Keywords: Big Data, Gaussian Process, Regression 2010 MSC: 00-01, 99-00 1. Introduction Gaussian Processes (GP) are attractive tools to perform supervised learning tasks on complex datasets on which traditional parametric methods may not be effective. They are also easier to use in comparison to alternatives like neural networks ([1]).

We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data is available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze the distance in higher-dimensional eigenspace between a selected reference trajectory and other tracer trajectories in the flow. By analyzing this distance metric in a hierarchical clustering, the coherent structure of which the reference particle is a member can be identified. This algorithm is proven successful in identifying coherent structures of varying complexities in canonical unsteady flows. Additionally, the method is able to assess the relative coherence of the associated structure in comparison to the surrounding flow. Although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems such as neuronal activity, gene expression, or social networks.

IONIA, MICHIGAN – Michigan prison officials say three people have been arrested after trying to use a drone to smuggle a cellphone and drugs into a prison. Michigan Department of Corrections says two guards at the Richard A. Handlon Correctional Facility in the western Michigan city of Ionia heard the drone in the prison yard shortly before 4 a.m. Moments later, the drone dropped a package near a housing unit. The Corrections Department says that as officers responded to the scene, the drone dropped a second package. Department spokesman Chris Gautz says local law enforcement officers detained three people in a vehicle near the prison about 110 miles (175 km) northwest of Detroit.