Bank of New Zealand is ramping up cross-channel fraud prevention for its customers through a multi-million dollar deal with IBM. The banking giant said its aim was to provide its customers with the ability to bank securely while delivering a positive customer experience. Furthermore, BNZ said legacy systems were designed to see and stop easily recognisable fraud patterns, however, modern "anytime, anywhere" banking on mobile devices has made fraud detection much more challenging. "Banks' time to respond is also shrinking as real-time payments mean there are just milliseconds to detect and prevent theft before it's too late," the bank said. Global card fraud losses are also increasing.

Dark pools are electronic trading platforms that have emerged in the past decade in advanced markets. They allow traders to buy or sell large blocks of shares without having to disclose their identities, the volumes or prices, unlike traditional exchanges. They are popular with asset-management companies, pension funds and insurance firms which need to conduct a lot of large transactions, because they are cheaper and easier to carry out via electronic trading platforms. Merrin founded Liquidnet in 2001 in the US and later expanded into Europe and Asia-Pacific. The platform has seen trading volume in Asia-Pacific of US$42 billion so far this year, up 57 per cent from a year earlier.

Professor Martin Carroll is an Executive General Manager, Academic & Provost Manukau Institute of Technology, New Zealand. In 2017, Martin co-founded with Blackboard, a global forum for the Ethical Use of AI in higher education. Drawing upon the projections of realists and the visions of sci-fi, Martin's entertaining and thought provoking talk will leave you viewing education in an entirely new light! One clear conclusion is the need for ethical guidance for AI and AI-type technologies. Drawing upon the projections of realists and the visions of sci-fi, Martin's entertaining and thought-provoking talk will leave you viewing education in an entirely new light.

Artificial intelligence, extended reality and blockchain are some of the most talked about emerging digital technologies. While not typically associated with the healthcare industry, these technologies are having a big impact on the supply chain of pharmaceutical products. To meet the expectations of today's patients, supply chains need to be smart, connected and agile so they provide an efficient and personalised patient experience. Companies that don't implement digital technologies put themselves at risk of being unable to do the one thing that matters most: better serve their patients. The Therapeutic Goods Administration recently said logistical difficulties, recalls, manufacturing issues and unexpected increases in demand were the main causes of shortages between January and August this year, highlighting the need for global companies operating in Australia to rethink how they can make their supply chains more efficient.

Earlier this year (May 2018) Microsoft announced ML.NET, an open source and cross-platform machine learning framework built for .NET developers. It is exciting news to be able to integrate custom machine learning with .NET/C# applications. Although ML.NET is still in preview release version 0.5.0 at the time of writing, you can test drive it to explore the potential power of the framework. There are already a number of tutorials for ML.NET available from Microsoft and third parties. However, the example data sources are mostly flat files in the format of TSV (Tab Separated Values).

One of the key targets Enlitic is focused on is early detection of lung cancer by combining biopsies along with existing medical data to be able to diagnose lung cancer earlier than with the traditional medical methods. While based in San Francisco, Enlitic is present in Japan, Canada, Australia, and China. AI is the buzzword startups are very keen on using when describing their products. We've been seeing ideas on what it could do in movies for decades. So what qualifies as AI?

Reinforcement learning has shown great potential in generalizing over raw sensory data using only a single neural network for value optimization. There are several challenges in the current state-of-the-art reinforcement learning algorithms that prevent them from converging towards the global optima. It is likely that the solution to these problems lies in short- and long-term planning, exploration and memory management for reinforcement learning algorithms. Games are often used to benchmark reinforcement learning algorithms as they provide a flexible, reproducible, and easy to control environment. Regardless, few games feature a state-space where results in exploration, memory, and planning are easily perceived. This paper presents The Dreaming Variational Autoencoder (DVAE), a neural network based generative modeling architecture for exploration in environments with sparse feedback. We further present Deep Maze, a novel and flexible maze engine that challenges DVAE in partial and fully-observable state-spaces, long-horizon tasks, and deterministic and stochastic problems. We show initial findings and encourage further work in reinforcement learning driven by generative exploration.

Software defined networking (SDN) represents a promising networking architecture that combines central management and network programmability. SDN separates the control plane from the data plane and moves the network management to a central point, called the controller, that can be programmed and used as the brain of the network. Recently, the research community has showed an increased tendency to benefit from the recent advancements in the artificial intelligence (AI) field to provide learning abilities and better decision making in SDN. In this study, we provide a detailed overview of the recent efforts to include AI in SDN. Our study showed that the research efforts focused on three main sub-fields of AI namely: machine learning, meta-heuristics and fuzzy inference systems. Accordingly, in this work we investigate their different application areas and potential use, as well as the improvements achieved by including AI-based techniques in the SDN paradigm.

It is challenging for stochastic optimizations to handle large-scale sensitive data safely. Recently, Duchi et al. proposed private sampling strategy to solve privacy leakage in stochastic optimizations. However, this strategy leads to robustness degeneration, since this strategy is equal to the noise injection on each gradient, which adversely affects updates of the primal variable. To address this challenge, we introduce a robust stochastic optimization under the framework of local privacy, which is called Privacy-pREserving StochasTIc Gradual lEarning (PRESTIGE). PRESTIGE bridges private updates of the primal variable (by private sampling) with the gradual curriculum learning (CL). Specifically, the noise injection leads to the issue of label noise, but the robust learning process of CL can combat with label noise. Thus, PRESTIGE yields "private but robust" updates of the primal variable on the private curriculum, namely an reordered label sequence provided by CL. In theory, we reveal the convergence rate and maximum complexity of PRESTIGE. Empirical results on six datasets show that, PRESTIGE achieves a good tradeoff between privacy preservation and robustness over baselines.

Now do the same with tanks, helicopters and biped/quadruped robots. Welcome to the not-so-distant future of LAWs, or lethal autonomous weapon systems. A conclusion reached at the UN conference on regulating LAWs in warfare that took place this August in Geneva was that, instead of outright banning them, the topic should be revisited in November. The stall was initiated by the U.S., Russia, Israel, South Korea and Australia. Until the revision meeting one thing is sure -- AI-controlled robotic warfare isn't too far off.