Toyota Finance New Zealand Limited (TFNZ), a regional subsidiary of Japan-based automotive finance company Toyota Financial Services Corporation, has selected Ephesoft, a content capture and data discovery solutions provider, to accelerate its automotive loan application and settlement processing. Stephen Blay, General Manager Operations, Toyota Finance New Zealand said, "For a financial organization like TFNZ, the digital transformation of loan processes is most importantly a customer service initiative." He further added, "Ephesoft will enable us to integrate an innovative, cloud-enabled solution that uses machine learning for enhanced document and data management, ultimately making it simpler for our staff to help customers secure financing for a new vehicle. This partnership will also enable our loan operations team to shift their focus on manual data handling to strategic customer support." The approach is expected to serve as a best practice for financial service providers globally.

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The capability and spread of such systems have reached the point where they are beginning to touch much of everyday life. However, regulators grapple with how to deal with autonomous systems, for example how could we certify an Unmanned Aerial System for autonomous use in civilian airspace? We here analyse what is needed in order to provide verified reliable behaviour of an autonomous system, analyse what can be done as the state-of-the-art in automated verification, and propose a roadmap towards developing regulatory guidelines, including articulating challenges to researchers, to engineers, and to regulators. Case studies in seven distinct domains illustrate the article. Keywords: autonomous systems; certification; verification; Artificial Intelligence 1 Introduction Since the dawn of human history, humans have designed, implemented and adopted tools to make it easier to perform tasks, often improving efficiency, safety, or security.

Temenos AG, a Geneva-based banking software company, announced the launch of its banking-as-a-service platform in the U.S., which it says can help launch a digital banking platform go live in 90 days. The Tenemos SaaS platform is designed to offer a range services like digital onboarding, know-your-customer verification, personal financial management and support for artificial intelligence, chatbots, wearables and other technology, according to a press release. "With our new U.S. front-to-back SaaS product for digital banks, we will revolutionize the software banking landscape in the U.S., which is a highly strategic market for us," Max Chuard, CEO of Temenos, said in the release. Temenos has worked with some of the fastest growing challenger banks in the U.S, including Grasshopper and Varo Money, Volt Bank and Judo Bank in Australia and Leumi's Pepper in Israel, according to the release. Temenos also works with incumbent financial institutions, including Commerce Bank and Partners Federal Credit Union, in Burbank, California.

ICHEC, the national high-performance computing authority of Ireland, recently participated in the xView2 disaster recovery challenge run by the US Defense Innovation Unit and other Humanitarian Assistance and Disaster Recovery (HADR) organisations. Models developed during the challenge including those developed at ICHEC are currently being tested by agencies responding to the ongoing bushfires in Australia. XView2 Challenge is based on using high resolution imagery to see the details of specific damage conditions in overhead imagery of a disaster area. The challenge involved building AI models to locate and classify the severity of damage to buildings using pairs of pre and post disaster satellite images. Models like these allow those responding to disasters to rapidly assess the damage left in their wake, enabling more effective response efforts and potentially saving lives.

The value of building data-driven businesses with AI at their core is well known today, and business executives are rushing to implement the technology into their operations and gain a competitive advantage, but it's not as simple as creating a data lake and crafting AI models. A large number of AI companies attempting to implement more AI models or build AI-first businesses have experienced challenges. A December 2018 PwC survey found that only 4% of businesses have successfully implemented AI. That's why today the World Economic Forum released the AI toolkit for Boards of Directors. The AI toolkit for Boards of Directors is being released ahead of the annual WEF meeting in Davos, Switzerland where the toolkit will be formally debuted next week.

Will the proliferation of AI and machine learning reinforce the worldwide digital divide? It's one of the questions the Global Talent Competitiveness Index (GTCI) and Global Cities Talent Competitiveness Index (GCTCI) seek to answer by benchmarking the ability of countries and cities to compete for talent. An answer has historically proven elusive, but the 7th annual reports published by Insead, Adecco Group, and Google suggest it might instead provide "significant" opportunities despite the fact that AI skills are "scarce" and "unequally distributed" across nations. "AI is changing many facets of business and society and, if properly used and governed, has potential to foster sustainable development," said Katell Le Goulven, executive director of the Insead Hoffmann Global Institute for Business and Society. "The GTCI report argues that with multi-stakeholder cooperation the technology could help achieve some of the SDGs [the United Nations' Sustainable Development Goals] such as those related to health (via personalized remote diagnosis and big data analysis to track and reduce endemic disease). But it also points to the imperative of closing the global digital skills gap to harness the potential of AI for good."

Value-at-Risk (VaR) and Expected Shortfall (ES) are widely used in the financial sector to measure the market risk and manage the extreme market movement. The recent link between the quantile score function and the Asymmetric Laplace density has led to a flexible likelihood-based framework for joint modelling of VaR and ES. It is of high interest in financial applications to be able to capture the underlying joint dynamics of these two quantities. We address this problem by developing a hybrid model that is based on the Asymmetric Laplace quasi-likelihood and employs the Long Short-Term Memory (LSTM) time series modelling technique from Machine Learning to capture efficiently the underlying dynamics of VaR and ES. We refer to this model as LSTM-AL. We adopt the adaptive Markov chain Monte Carlo (MCMC) algorithm for Bayesian inference in the LSTM-AL model. Empirical results show that the proposed LSTM-AL model can improve the VaR and ES forecasting accuracy over a range of well-established competing models.

A common problem machine learning developers are faced with is overfitting, that is, fitting a pipeline too closely to the training data that the performance degrades for unseen data. Automated machine learning aims to free (or at least ease) the developer from the burden of pipeline creation, but this overfitting problem can persist. In fact, this can become more of a problem as we look to iteratively optimise the performance of an internal cross-validation (most often \textit{k}-fold). While this internal cross-validation hopes to reduce this overfitting, we show we can still risk overfitting to the particular folds used. In this work, we aim to remedy this problem by introducing dynamic fitness evaluations which approximate repeated \textit{k}-fold cross-validation, at little extra cost over single \textit{k}-fold, and far lower cost than typical repeated \textit{k}-fold. The results show that when time equated, the proposed fitness function results in significant improvement over the current state-of-the-art baseline method which uses an internal single \textit{k}-fold. Furthermore, the proposed extension is very simple to implement on top of existing evolutionary computation methods, and can provide essentially a free boost in generalisation/testing performance.

In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal effects of nodes. Furthermore, the relational time-series causal inference models are able to estimate local effects for individual nodes by exploiting local node-centric temporal dependencies and topological/structural dependencies. We show that simpler causal models that do not consider the graph topology are recovered as special cases of the proposed relational time-series causal inference model. We describe the conditions under which the resulting estimate can be used to estimate a causal effect, and describe how the Durbin-Wu-Hausman test of specification can be used to test for the consistency of the proposed estimator from data. Empirically, we demonstrate the effectiveness of the causal inference models on both synthetic data with known ground-truth and a large-scale observational relational time-series data set collected from Wikipedia.