Hong Kong has seen multiple data breaches and hacking incidents during the year, prompting worries that cybercrimes are posing a growing threat to businesses and organizations. The Hong Kong Economic Journal recently sat down with information security expert William Tam, Director of Sales Engineering, APAC, at US-based data protection services firm Forcepoint, to discuss the cyber-security threats and how latest technologies like artificial intelligence can help companies protect their data. HKEJ: We understand that Forcepoint recently published the "Forcepoint Cybersecurity Forecast Report for 2019," and in the report, you highlighted the latest market trend in artificial intelligence (AI). Can you elaborate on that? Tam: In the report, we mentioned that companies are now analyzing and looking at AI technology calmly, seeking to utilize the technology.

We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network. We zoom in to the network using lenses of different sizes to capture the local structure of the network. These network signatures are then weighted to provide a set of predicted labels for every node. We achieve a peak accuracy of $\sim42\%$ (random=$11\%$) on two networks with $\sim100,000$ and $\sim1,000,000$ nodes each. Further, we perform better than random even when the given node is connected to up to 5 different types of networks. Finally, we perform this analysis on homogeneous networks and show that highly structured networks have high homogeneity.

We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network. We conduct an extensive study of intrinsic scale for several networks, ranging from structured (e.g. road networks) to ad-hoc and unstructured (e.g. crowd sourced information networks), to biological. We find: (a) The intrinsic scale is surprisingly small (7-20 vertices), even though the networks are many orders of magnitude larger. (b) The intrinsic scale quantifies ``structure'' in a network -- networks which are explicitly constructed for specific tasks have smaller intrinsic scale. (c) The structure at different scales can be fragile (easy to disrupt) or robust.

The goal of this article is to inspire data scientists to participate in the debate on the impact that their professional work has on society, and to become active in public debates on the digital world as data science professionals. How do ethical principles (e.g., fairness, justice, beneficence, and non-maleficence) relate to our professional lives? What lies in our responsibility as professionals by our expertise in the field? More specifically this article makes an appeal to statisticians to join that debate, and to be part of the community that establishes data science as a proper profession in the sense of Airaksinen, a philosopher working on professional ethics. As we will argue, data science has one of its roots in statistics and extends beyond it. To shape the future of statistics, and to take responsibility for the statistical contributions to data science, statisticians should actively engage in the discussions. First the term data science is defined, and the technical changes that have led to a strong influence of data science on society are outlined. Next the systematic approach from CNIL is introduced. Prominent examples are given for ethical issues arising from the work of data scientists. Further we provide reasons why data scientists should engage in shaping morality around and to formulate codes of conduct and codes of practice for data science. Next we present established ethical guidelines for the related fields of statistics and computing machinery. Thereafter necessary steps in the community to develop professional ethics for data science are described. Finally we give our starting statement for the debate: Data science is in the focal point of current societal development. Without becoming a profession with professional ethics, data science will fail in building trust in its interaction with and its much needed contributions to society!

Machine-learning models have demonstrated great success in learning complex patterns that enable them to make predictions about unobserved data. In addition to using models for prediction, the ability to interpret what a model has learned is receiving an increasing amount of attention. However, this increased focus has led to considerable confusion about the notion of interpretability. In particular, it is unclear how the wide array of proposed interpretation methods are related, and what common concepts can be used to evaluate them. We aim to address these concerns by defining interpretability in the context of machine learning and introducing the Predictive, Descriptive, Relevant (PDR) framework for discussing interpretations. The PDR framework provides three overarching desiderata for evaluation: predictive accuracy, descriptive accuracy and relevancy, with relevancy judged relative to a human audience. Moreover, to help manage the deluge of interpretation methods, we introduce a categorization of existing techniques into model-based and post-hoc categories, with sub-groups including sparsity, modularity and simulatability. To demonstrate how practitioners can use the PDR framework to evaluate and understand interpretations, we provide numerous real-world examples. These examples highlight the often under-appreciated role played by human audiences in discussions of interpretability. Finally, based on our framework, we discuss limitations of existing methods and directions for future work. We hope that this work will provide a common vocabulary that will make it easier for both practitioners and researchers to discuss and choose from the full range of interpretation methods.

The field of probabilistic numerics (PN), loosely speaking, attempts to provide a statistical treatment of the errors and/or approximations that are made en route to the output of a deterministic numerical method, e.g. the approximation of an integral by quadrature, or the discretised solution of an ordinary or partial differential equation. This decade has seen a surge of activity in this field. In comparison with historical developments that can be traced back over more than a hundred years, the most recent developments are particularly interesting because they have been characterised by simultaneous input from multiple scientific disciplines: mathematics, statistics, machine learning, and computer science. The field has, therefore, advanced on a broad front, with contributions ranging from the building of overarching generaltheory to practical implementations in specific problems of interest. Over the same period of time, and because of increased interaction among researchers coming from different communities, the extent to which these developments were -- or were not -- presaged by twentieth-century researchers has also come to be better appreciated. Thus, the time appears to be ripe for an update of the 2014 Tübingen Manifesto on probabilistic numerics[Hennig, 2014, Osborne, 2014d,c,b,a] and the position paper[Hennig et al., 2015] to take account of the developments between 2014 and 2019, an improved awareness of the history of this field, and a clearer sense of its future directions. In this article, we aim to summarise some of the history of probabilistic perspectives on numerics (Section 2), to place more recent developments into context (Section 3), and to articulate a vision for future research in, and use of, probabilistic numerics (Section 4).

Russia Rolls Out New Drones: Modern Combat Drones Needed to'Master the Skies' ussia must acquire a fleet of combat drones that can go toe to toe with modern air forces, Russian air force Colonel General Viktor Bondarev said Tuesday. "The entire world is on the way to developing drone aircraft, including strike aircraft," he told state news agency Itar-Tass at Russia's MAKS aerial combat arms fair, outside Moscow. "We have no right to fall behind, which is why we are carrying out analogous work in this direction. In the future, the (drone) operator will be on the ground and still master the skies."

While it's likely AI will create new jobs, its more immediate (and lasting) potential is in helping advance the science that underlies new industries. If you were a brilliant artificial intelligence (AI) expert just graduating from a doctoral program at a prestigious school, would you pursue that startup you've been thinking about, join a company that wants to build cutting-edge AI applications, or use your expertise to help scientists in other fields conduct basic research? Admittedly, this is a bit of a silly question. The opportunities presented by the first two options are outrageous, and growing more outrageous by the day. With more than 2,000 startups absorbing much of the top-tier AI talent -- estimated by some to be just 10,000 individuals worldwide -- the combination of great scarcity and even greater demand for talent is driving salaries through industry roofs.

Yemen's government has announced that the chief of its military intelligence has died of wounds he sustained during a drone attack on an army parade last week.

Artificial Intelligence (AI) is arguably the most revolutionary technology in several decades that would completely turn the world upside down and then shape it along with new contours. AI will reinvent everything from the nature of work to our modes of communication and transportation. The'creative destruction' unleashed by AI would make a large number of current skills and jobs redundant while opening avenues for new skills. The preeminence of AI and its far-reaching influence can be gauged from the fact that the nascent AI rivalry between USA and China has been dubbed as'The New Space Race'. In 2018, increase in AI-based applications, anxiety about a'war of the worlds' esque robotic workforce and China outpacing USA in the number of AI startups and AI related patents were the most highlighted AI trends.