Moven, which works with banks in eight regions, including Westpac New Zealand, provides behavioural models that help banks drive better engagement and retention – little "nudges" such as spending alerts and notifications, and savings prompts. Brett King says Australian banks will struggle to keep up with the likes of Chinese giant Ant Financial. King, who grew up in the Melbourne suburb of Berwick and was speaking at the FINSIA summit in Melbourne on Tuesday, says Moven's experience with TD Canada Trust – Canada's second-biggest bank – provides a good example of what the future might hold. Half of TD's customers use Moven's platform, called My Spend. This group has seen a 4 per cent to 8 per cent reduction in monthly spending thanks to the various "nudges" they receive compared with a controlled group.

Toby Walsh, a professor at the University of New South Wales in Sydney, is one of Australia's leading experts on artificial intelligence. He and other experts have released a report outlining the promises, and ethical pitfalls, of the country's embrace of A.I. Recently, Dr. Walsh, 55, has been working with the Campaign to Stop Killer Robots, a coalition of scientists and human rights leaders seeking to halt the development of autonomous robotic weapons. We spoke briefly at the annual meeting of the American Association for the Advancement of Science, where he was making a presentation, and then for two hours via telephone. Below is an edited version of those conversations. It happened incrementally, beginning around 2013.

The government's peak advisory body on tech and science has turned its attention to the development of an artificial intelligence ethics framework and lifelong learning of STEM skills. The National Science and Technology Council met for the third time in Brisbane last week, after it was launched to replace the Commonwealth Science Council in February this year. The meeting was chaired by Industry Minister Karen Andrews, with education minister Dan Tehan also in attendance. Council members include Professor Genevieve Bell, Professor Barbara Howlett, Professor Debra Henly and Professor Brian Schmidt. They were briefed on the government's progress in developing a national artificial intelligence ethics framework, and the "strong engagement" from stakeholders during consultation.

PERTH, Australia – There is pervasive use of artificial intelligence and machine learning (AI/ML) across the health care industry in Australia, and excitement is building on the opportunities it offers to technologies and ultimately to patients, Ausbiotech CEO Lorraine Chiroiu told BioWorld. "AI/ML is transforming clinical practice in terms of clinical trials, diagnosis, treatment, decision-making, early detection and preventative health," she said. AI is being used for everything from smart medical records to the systems that help set appointments, to hospital records and diagnostic and pathology tests. It's being used in diagnostics for cancer patients to redirect the best treatment regimens based on a number of patient variables, and patient records can be aggregated so that algorithms can narrow down diagnoses. AI is changing the precision around surgeries like knee replacements by using robotic surgery to diagnose the exact angles, Brandon Capital Managing Director Chris Nave told BioWorld.

AI can now flag people based on their clothing, behaviour or race, log an individual's emotions, understand their actions and predict their next move. It can detect when luggage is left unattended, or if someone is loitering; it can even recognise when an individual is acting'unusual' based on others around them. AI is everywhere and getting more advanced every day. Facial recognition technology, in particular, has made leaps and bounds, partially thanks to tagged photographs on Facebook and Instagram as well as government-collected images such as drivers licenses and ID cards. The quality of cameras has also drastically improved, so much so that they no longer just record, they can'see' in real-time.

A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of tasks crucial for research on natural language understanding. Recently, BERT realized a breakthrough in sentence representation learning (Devlin et al., 2019), which is broadly transferable to various NLP tasks. While BERT's performance improves by increasing its model size, the required computational power is an obstacle preventing practical applications from adopting the technology. Herein, we propose to inject phrasal paraphrase relations into BERT in order to generate suitable representations for semantic equivalence assessment instead of increasing the model size. Experiments on standard natural language understanding tasks confirm that our method effectively improves a smaller BERT model while maintaining the model size. The generated model exhibits superior performance compared to a larger BERT model on semantic equivalence assessment tasks. Furthermore, it achieves larger performance gains on tasks with limited training datasets for fine-tuning, which is a property desirable for transfer learning.

The article presents a solution approach for the Torpedo Scheduling Problem, an operational planning problem found in steel production. The problem consists of the integrated scheduling and routing of torpedo cars, i. e. steel transporting vehicles, from a blast furnace to steel converters. In the continuous metallurgic transformation of iron into steel, the discrete transportation step of molten iron must be planned with considerable care in order to ensure a continuous material flow. The problem is solved by a Simulated Annealing algorithm, coupled with an approach of reducing the set of feasible material assignments. The latter is based on logical reductions and lower bound calculations on the number of torpedo cars. Experimental investigations are performed on a larger number of problem instances, which stem from the 2016 implementation challenge of the Association of Constraint Programming (ACP). Our approach was ranked first (joint first place) in the 2016 ACP challenge and found optimal solutions for all used instances in this challenge.

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

While the Australian Securities Exchange (ASX) makes a global name for itself by implementing one of the only real use cases for distributed ledger technology (DLT) in its blockchain-based CHESS replacement project, its CIO Dan Chesterman has detailed a handful of other tech-related initiatives the "large regulated fintech" is also undertaking. Speaking with ZDNet at VMworld in San Francisco last week, Chesterman said his organisation is looking into the application of artificial intelligence (AI) and machine learning (ML), highlighting that in the ASX's context, there are a lot of examples where machines are making quite clever decisions. "The main exploration we've been doing of artificial intelligence in that AI/ML space has been in market announcements ... it's not in production, it's something we're doing as a proof of concept," he said. "And what we've come to the conclusion of is that we certainly see, in that sort of context, there is actually a serious consequence for any error." Market announcements, for example, is one element of the business where Chesterman said AI could both help, but also cause legal dilemmas.

In this July 30, 2019, photo, Paul Hildreth, emergency operations coordinator for the Fulton County School District, works in the emergency operations center at the Fulton County School District Administration Center in Atlanta. Artificial Intelligence is transforming surveillance cameras from passive sentries into active observers that can immediately spot a gunman, alert retailers when someone is shoplifting and help police quickly find suspects. Schools, such as the Fulton County School District, are among the most enthusiastic adopters of the technology. Paul Hildreth peered at a display of dozens of images from security cameras surveying his Atlanta school district and settled on one showing a woman in a bright yellow shirt walking a hallway. A mouse click instructed the artificial intelligence-equipped system to find other images of the woman, and it immediately stitched them into a video narrative of where she was currently, where she had been and where she was going. There was no threat, but Hildreth's demonstration showed what's possible with AI-powered cameras.