Technology spending in the banking and securities sector in Australia is expected to reach A$18.5 billion in 2020, an increase of 5.2% from 2019, according to Gartner, Inc. Behind this growth are new investments in modern business intelligence (BI), augmented analytics and robotic process automation (RPA) software. Globally, the banking and securities industry spends the most on information technology products and services. In Australia, it is the second-largest-spending industry after communications, media and services, representing 19.2% of total enterprise IT spending. "The banking and securities industry continues to spend in pursuit of digitalization, whether through digital business optimization or transformation," said Neha Gupta, research director at Gartner. "The introduction of open banking in Australia is also driving new technology investments."

Gartner, Inc. today revealed its top strategic predictions for 2020 and beyond. Gartner's top predictions examine how the human condition is being challenged as technology creates varied and ever-changing expectations of humans. "Technology is changing the notion of what it means to be human," said Daryl Plummer, distinguished vice president and Gartner Fellow. "As workers and citizens see technology as an enhancement of their abilities, the human condition changes as well. CIOs in end-user organizations must understand the effects of the change and reset expectations for what technology means."

This holiday season we ask: How can artificial intelligence and other new technologies help make our roads safer? This article also appears on LinkedIn. Stop me if you've heard this one before: Why did the chicken cross the road? While you ponder that question, let me ask you another: Have you ever wondered if the chicken manages to cross the road safely? Every year, especially during festive seasons, hundreds of thousands of people across the world pack their bags and families, and journey back to their home towns where joyous celebrations and loved ones await.

A recent study conducted by Oracle and research firm Future Workplace found that 64% of people would trust a robot more than their manager. The study included 8,370 employees, managers and HR leaders across 10 countries. Its aim was to see how AI has changed relationships between people and technology at work. It did have some surprising results when comparing human supervisors to potential robot overlords. According to the study, 64 % of people would trust a robot over their manager.

Dr Sue Keay may love robotics, but there's nothing mechanical about her trailblazing vision for Australia's technology future. Not only did she set up the world's first robotic vision research centre, she is a graduate of the Australian Institute of Company Directors, and judges the James Dyson Awards and the Australian Museum Prizes. At present, she leads the Commonwealth Scientific and Industry Research Organization's (CSIRO) Data61 Cyber-Physical Systems program. We recently spoke with Dr Keay about CSIRO, blending science and business, and where the future may lead. "CSIRO, Australia's national science research agency has been in existence for over 100 years," she told VENTURE.

The UK's Department for International Trade (DIT) are proud to be the Lead Partner Country at CEBIT Australia 2019 in Sydney from 29 - 31 October 2019. Visit the UK Pavilion at stand F45 to find out more about UK's capability in technology or to meet with innovative British technology companies. DIT are running a series of activities throughout the week. More details to be announced shortly. DIT will be bringing 10 innovative UK tech companies to showcase on the UK pavilion.

The New Zealand Government has awarded NZ$13 million ($12.1 million) in funding to the University of Waikato for a new research project aimed at helping New Zealanders solve the nation's critical environmental problems. The funding from the Ministry of Business, Innovation and Employment's Strategic Science Investment Fund will be spread over seven years. It will be used to support the university's Time-Evolving Data Science/Artificial Intelligence for Advanced Open Environmental Science (TAIAO) project. The TAIAO project will involve the development of new machine learning methods for time series and data streams tailored to processing large quantities of data collected on the New Zealand environment. It is being conducted as part of a collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService. It has the participation of highly qualified data scientists, data engineers and environmental scientists.

Noname manuscript No. (will be inserted by the editor)ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels Angus Dempster · Fran cois Petitjean· Geoffrey I. Webb Received: date / Accepted: date Abstract Most methods for time series classification that attain state-of-the-art accuracy have high computational complexity, requiring significant training time even for smaller datasets, and are intractable for larger datasets. Additionally, many existing methods focus on a single type of feature such as shape or frequency. Building on the recent success of convolutional neural networks for time series classification, we show that simple linear classifiers using random convolutional kernels achieve state-of-the-art accuracy with a fraction of the computational expense of existing methods. Keywords scalable · time series classification · random · convolution 1 Introduction Most methods for time series classification that attain state-of-the-art ...

Rainfall prediction is one of the challenging and uncertain tasks which has a significant impact on human society. Timely and accurate predictions can help to proactively reduce human and financial loss. This study presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict whether it is going to rain tomorrow or not based on weather data for that particular day in major cities of Australia. This comparative study is conducted concentrating on three aspects: modeling inputs, modeling methods, and pre-processing techniques. The results provide a comparison of various evaluation metrics of these machine learning techniques and their reliability to predict the rainfall by analyzing the weather data.

Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was already shown to cause an autonomous vehicle to swerve into oncoming traffic. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certified defense for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose the optimal action under a worst-case deviation in input space due to possible adversaries or noise. The approach is demonstrated on a Deep Q-Network policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios and a classic control task.