It admittedly sounds a little like Big Brother, that a robot can tell significant things about your personality, merely by looking into your eyes. Yet, that is the hiring territory that we are fast approaching – although we may not be sitting across from androids in interviews anytime soon. The use of artificial intelligence in making HR decisions is, while fraught with peril, not without its promising aspects. In an era when it is increasingly difficult for businesses to unearth the best job candidates, we may yet see the day when technology makes it possible to separate good from bad in the blink of an eye. Despite caveats about security and privacy, relying on AI would appear to be a method far superior to digging through a pile of resumes or asking ice-breaking questions like, "What's the last book you read?" Hiring good people – people who are talented, agreeable and work well with their coworkers – goes a long way toward nipping workplace conflicts in the bud.

As a crucial component in intelligent transportation systems, crowd flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of large-scale traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal laws and spatial dependencies) to infer the evolution trend of crowd flow. To address this problem, we propose a unified neural network called Attentive Crowd Flow Machine (ACFM), which can effectively learn the spatial-temporal feature representations of crowd flow with an attention mechanism. In particular, our ACFM is composed of two progressive ConvLSTM units connected with a convolutional layer. Specifically, the first LSTM unit takes normal crowd flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second LSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted crowd flow features. Further, we develop two deep frameworks based on ACFM to predict citywide short-term/long-term crowd flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for crowd flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands and show its superior performance in this traffic prediction task.

In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel task of producing a critical response to a long argumentative text, and suggest a method based on general rebuttal arguments to address it. We do this in the context of the recently-suggested task of listening comprehension over argumentative content: given a speech on some specified topic, and a list of relevant arguments, the goal is to determine which of the arguments appear in the speech. The general rebuttals we describe here (written in English) overcome the need for topic-specific arguments to be provided, by proving to be applicable for a large set of topics. This allows creating responses beyond the scope of topics for which specific arguments are available. All data collected during this work is freely available for research.

Domino's Pizza Enterprises built a proof-of-concept using machine learning to personalise vouchers and deals for customers in Australia. The master franchise, which operates in nine countries, is an early tester of an AWS service called Personalize, which was only made publicly available last month. Speaking at the recent AWS Summit in Sydney, Domino's lead data scientist Thomas Atkins said the pizza maker wanted to find a way to scale its ability to personalise deals using a wider array of "variants", such as time of day, pickup or delivery, price, and type of pizza. "Making communication via SMS and all our marketing channels personalised is a real challenge because of scale," Atkins said. "To get from segmented marketing to personalised marketing requires a really deep level of automation. "With the typical level of automation that we have, we can do campaigns with four to six variants relatively easily, but there are a number of manual steps that make this a little bit cumbersome.

The idea of robots taking our jobs is not radically new. But artificial intelligence (AI) is now completely reorganising the global economy. Some estimates of productivity-driven economic growth conclude that AI will contribute approximately $US16 trillion ($23 trillion) to the global economy by 2030. Unfortunately -- compared to the European Union, Japan, United States and United Kingdom -- Australia has been relatively late in turning to address the challenges of AI, and creating the right policies to deal with its many implications (good and bad). For our economy to thrive, what we need now is the right mix of governance, regulation, civil society participation, industry support and business compliance -- as well as the development and deepening of digital literacy throughout Australian communities.

The idea of robots taking our jobs is not radically new. But artificial intelligence (AI) is now completely reorganising the global economy. Some estimates of productivity-driven economic growth conclude that AI will contribute approximately $US16 trillion ($23 trillion) to the global economy by 2030. Unfortunately -- compared to the European Union, Japan, United States and United Kingdom -- Australia has been relatively late in turning to address the challenges of AI, and creating the right policies to deal with its many implications (good and bad). For our economy to thrive, what we need now is the right mix of governance, regulation, civil society participation, industry support and business compliance -- as well as the development and deepening of digital literacy throughout Australian communities.

TECHNOLOGIES such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT), 5G, and robotics have plenty of potential in the healthcare space. However, most healthcare organizations are unable to leverage those technologies because they neither have the digital infrastructure nor the technology talent to turn their vision into real projects. "Health services have a lot to do before they can make the most of these opportunities," Waitematā District Health Board (DHB) Clinical Director Innovation Robyn Whittaker told Tech Wire Asia underscoring the fact that industry professionals aren't oblivious to the power that technology holds. Whittaker and her team at Waitematā DHB have just made it to the list of finalists in the talent accelerator category at the IDC Digital Transformation Awards. To prepare for the digital age, Whittaker's team is working on the foundational layers, digitizing data, and making it available for clinicians and researchers looking to make sense of that data.

The rise in online hate speech and the way it is reflected in the offline world is a hot topic in politics right now. The internet has given everyone a voice, which clearly has positive implications for the way citizens can publicly challenge authority and debate issues. It's fairly commonly assumed that this form of hate speech, particularly when encountered alongside other factors such as social deprivation or mental illness, has the potential to radicalize individuals in dangerous ways, and inspire them to commit illegal and violent acts. Just as terrorist organizations like ISIS can be seen using hate speech in videos and propaganda material intended to incite violence, racist and anti-Islamic material is thought to have inspired killers like Anders Breivik, who killed 69 youths in a 2011 shooting spree, and the 2019 Christchurch mosque shooting in which 51 died. So far these links between online and real-world actions, though common sense tells us they are likely to exist, have been difficult to prove scientifically.

Adcore Inc. (ADCO:TSX.V), the developer of machine-learning powered advertising technologies, reported record second quarter financials, including an increase in revenue growth of 53% year over year. Adcore uses artificial intelligence (AI) algorithms to automate online search and shopping advertising and is used by digital agencies and advertisers to enhance and maximize search engine marketing. The company also announced two major contracts for online advertising, one with the Israeli Government Advertising Agency for CA$125 million over five years, and the second with Experience Oz, Australia and New Zealand's number one site for travel experiences, with an online advertising budget of up to CA$5 million per year. "Our growth continues to outpace the market and our competitors, with yet another record quarter of revenue and earnings,-- said Omri Brill, Adcore's CEO. "Our outlook for the second half of the year, historically the stronger half of the year, remains extremely bullish and we expect to make several exciting announcements over the coming months to drive shareholder value." The firm has developed three sets of tools to assist advertising agencies and advertisers with digital media marketing. Ben Mann, founder and director of Experience Oz, stated, "Adcore is the perfect partner to take our search and shopping to the next level.

UNSW Sydney has transformed the learning experience for engineering students with an AI-infused and Teams-enriched solution while instituting reforms designed to reduce the risk of student drop outs. UNSW Sydney is one of Australia's leading universities with around 17,000 students enrolled in the engineering faculty. The large student cohort means that there can be 500 students enrolled in a class at any one time. To optimise their learning opportunity and reduce the risk of drop out, Dr David Kellermann, a senior lecturer in the school of mechanical and manufacturing engineering, has developed and deployed Microsoft Teams to promote collaboration and communication. This has been augmented by artificial intelligence and rich data analytics.