Moving to a new country can be hard. You don't know the language. Cultural differences create conversational landmines. And you just can't be sure that everyone will like you. As it turns out, that as true for people as it is for Amazon's Alexa voice assistant, which officially sets up residence in France today.

QBE Insurance Group's streak of investing in technology start-ups continues. Through its investment arm QBE Ventures, the company recently partnered with HyperScience, a machine-learning company putting artificial intelligence to work. The start-up automates office work and will allow QBE to glean useful data from thousands of documents that often just get filed away in a box or the cloud, never to see the light of day again. "HyperScience, to us, was a really obvious choice and an obvious partner because of the problem they were solving and the way that they were doing it," said Ted Stuckey, head of QBE's Global Innovation Lab. "If you look across the normal operations of your standard property and casualty insurer, so much of what we do is back-office processing of information. Just the volume of the documents that we deal with, the volume of human-readable content that is touched by so many different people across the organization, automating that [by] using artificial intelligence to influence and add efficiency to that process was not only a huge operational efficiency game, but an obvious partnership."

AI is an area of computer science dedicated to creating software that can be taught to perform complex procedures. What makes AI "intelligent" is that it can learn new behaviours, improve performance as more experience is gained, and make decisions and predictions based on available data. The algorithms at the core of some AI systems are trained using the large datasets that are now available thanks to the "big data" revolution. It is the intelligent capabilities of AI systems that allow for the automation of tasks that until now required human judgement to deliver. There is enormous potential for how AI can benefit the developing world and what it can contribute towards achieving the UN's Sustainable Development Goals: AI can play a crucial role in augmenting healthcare capacity by filling gaps in human expertise, increasing productivity, and enhancing disease surveillance.

Who says you can't get smart playing video games? Although the idea of spending hours playing video games isn't usually recommended for humans to increase their intelligence, the realistic 3-D graphics and environments of many video games just might make video games the perfect learning tool for artificial intelligence. AI algorithms get smarter and learn to perform tasks by being fed enormous amounts of data. When you're Facebook, this doesn't present a huge obstacle. Facebook creates huge data sets daily and also has the financial capability to close any gaps.

Dixons Carphone has been hit by a major cyber attack that has left millions of customers' details exposed. The company has lost nearly 6 million customer bank card details and more than a million personal data records, it said. The group said that, while 5.8 million of the payment cards targeted were protected by chip and pin, around 105,000 non-EU cards without chip and pin protection were compromised. Dixons Carphone said relevant card companies had been notified, but added that there was no evidence of fraud on the cards as a result of the incident. The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Two weeks after meeting US President Donald Trump to talk about prison reform, reality TV star Kim Kardashian West revealed on Twitter that she has been lobbying once again – this time with one of Silicon Valley's most influential tech figures. Kardashian West claimed she might have persuaded Twitter CEO Jack Dorsey to introduce an edit feature for tweets, sparking anger among Twitter users. "I had a very good convo with @jack this weekend at Kanye's bday and I think he really heard me out on the edit button," Kardashian West tweeted on Wednesday. Mr Dorsey replied:"Now I see why I was invited!" The I.F.O. is fuelled by eight electric engines, which is able to push the flying object to an estimated top speed of about 120mph.

Motivated by safety-critical applications, test-time attacks on classifiers via adversarial examples has recently received a great deal of attention. However, there is a general lack of understanding on why adversarial examples arise; whether they originate due to inherent properties of data or due to lack of training samples remains ill-understood. In this work, we introduce a theoretical framework analogous to bias-variance theory for understanding these effects. We use our framework to analyze the robustness of a canonical non-parametric classifier - the k-nearest neighbors. Our analysis shows that its robustness properties depend critically on the value of k - the classifier may be inherently non-robust for small k, but its robustness approaches that of the Bayes Optimal classifier for fast-growing k. We propose a novel modified 1-nearest neighbor classifier, and guarantee its robustness in the large sample limit. Our experiments suggest that this classifier may have good robustness properties even for reasonable data set sizes.

The Steiner Tree Problem (STP) is an important problem in combinatorial optimization which has numerous applications, ranging from the design of (very large) integrated circuits to computer networking, evolution theory in biology and more [8]. There are plenty variants of the STP which can be found in [7]. The common part between different variants is the requirement to connect a set of objects with the shortest interconnect possible. In this paper, we investigate the general STP in graphs. As the STP is N P-hard [10], most of the work in the literature focuses on non-exact approaches.

Parallel sentence extraction is a task addressing the data sparsity problem found in multilingual natural language processing applications. We propose a bidirectional recurrent neural network based approach to extract parallel sentences from collections of multilingual texts. Our experiments with noisy parallel corpora show that we can achieve promising results against a competitive baseline by removing the need of specific feature engineering or additional external resources. To justify the utility of our approach, we extract sentence pairs from Wikipedia articles to train machine translation systems and show significant improvements in translation performance.

We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a minimax problem in a Lagrangian form. However, due to the lack of convexity, their landscape is not well understood and how to find the stable equilibria of the Lagrangian function is still unknown. To bridge the gap, we study the landscape of the Lagrangian function. Further, we define a special class of Lagrangian functions. They enjoy two properties: 1.Equilibria are either stable or unstable (Formal definition in Section 2); 2.Stable equilibria correspond to the global optima of the original problem. We show that a generalized eigenvalue (GEV) problem, including canonical correlation analysis and other problems, belongs to the class. Specifically, we characterize its stable and unstable equilibria by leveraging an invariant group and symmetric property (more details in Section 3). Motivated by these neat geometric structures, we propose a simple, efficient, and stochastic primal-dual algorithm solving the online GEV problem. Theoretically, we provide sufficient conditions, based on which we establish an asymptotic convergence rate and obtain the first sample complexity result for the online GEV problem by diffusion approximations, which are widely used in applied probability and stochastic control. Numerical results are provided to support our theory.