The proliferation of artificial intelligence technology will have a bigger impact on the global economy and society than the internet, according to outgoing Cisco Australia chief technology officer Kevin Bloch. Australia's place at the table in the development of these new artificial intelligence technologies and systems that will underpin all sectors of the economy in decades to come is far from certain. Even Australia's largest companies had not yet come to grips with the importance of the shift toward AI tech and with few exceptions were not directing adequate resources into R&D. Mr Bloch will leave Cisco on Friday after 21 years at the company, including the last 12 years as its chief technology officer. It is only a little ironic that at the height of a global pandemic and all the economic uncertainty it has wrought, Mr Bloch says the scale of the opportunities in the tech sector are such that the time is right for a move.

In the late winter and spring of 2019, a group of Indigenous scholars met in Hawai'i to think through concepts around artificial intelligence (AI) and how they related to the Indigenous experience. Co-organized by Jason Edward Lewis, professor in the Department Design and Computation Arts, the multidisciplinary group included participants from Canada, the United States, Australia, New Zealand and the United Kingdom. Concordia graduate students Scott Benesiinaabandan (MFA Studio Arts) and Suzanne Kite (PhD INDI) as well as Concordia research associate Skawennati also participated. The first session in March largely consisted of brainstorm workshops about how and where Indigeneity intersects with AI. The second, in May, focused more on writing and laying the foundations of what would become the now-completed Indigenous Protocol and Artificial Intelligence Position Paper.

In recent years, many sectors have experienced significant progress in automation, associated with the growing advances in artificial intelligence and machine learning. There are already automated robotic weapons, which are able to evaluate and engage with targets on their own, and there are already autonomous vehicles that do not need a human driver. It is argued that the use of increasingly autonomous systems (AS) should be guided by the policy of human control, according to which humans should execute a certain significant level of judgment over AS. While in the military sector there is a fear that AS could mean that humans lose control over life and death decisions, in the transportation domain, on the contrary, there is a strongly held view that autonomy could bring significant operational benefits by removing the need for a human driver. This article explores the notion of human control in the United States in the two domains of defense and transportation. The operationalization of emerging policies of human control results in the typology of direct and indirect human controls exercised over the use of AS. The typology helps to steer the debate away from the linguistic complexities of the term "autonomy." It identifies instead where human factors are undergoing important changes and ultimately informs about more detailed rules and standards formulation, which differ across domains, applications, and sectors.

The perceived quality of a document is affected by various factors, including grammat- icality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text — such as images, font choices, and visual layout — we propose a joint model that combines the text content with a visual rendering of the document for document qual- ity assessment. Our joint model achieves state-of-the-art results over five datasets in two domains (Wikipedia and academic papers), which demonstrates the complementarity of textual and visual features, and the general applicability of our model. To examine what kinds of features our model has learned, we further train our model in a multi-task learning setting, where document quality assessment is the primary task and feature learning is an auxiliary task. Experimental results show that visual embeddings are better at learning structural features while textual embeddings are better at learning readability scores, which further verifies the complementarity of visual and textual features.

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contains valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation within the set of the relevant labels to an unseen instance is utilized to increase the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of a classifier is investigated for both metrics. Also, a computational complexity analysis is provided for the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.

Capsule networks offer interesting properties and provide an alternative to today's deep neural network architectures. However, recent approaches have failed to consistently achieve competitive results across different image datasets. We propose a new parameter efficient capsule architecture, that is able to tackle complex tasks by using neural networks trained with an approximate Wasserstein objective to dynamically select capsules throughout the entire architecture. This approach focuses on implementing a robust routing scheme, which can deliver improved results using little overhead. We perform several ablation studies verifying the proposed concepts and show that our network is able to substantially outperform other capsule approaches by over 1.2 % on CIFAR-10, using fewer parameters.

Graph Neural Networks (GNNs) have recently caught great attention and achieved significant progress in graph-level applications. In order to handle graphs with different features and sizes, we propose a novel graph neural network, which we call HaarNet, to predict graph labels with interrelated convolution and pooling strategies. Similar to some existing routines, the model assembles unified graph-level representations from samples by first adopting graph convolutional layers to extract mutual information followed by graph pooling layers to downsample graph resolution. By a sequence of clusterings, we embed the intrinsic topological information of each graph into the GNN. Through the fast Haar transformation, we made our contribution to forming a smooth workflow that learns multi-scale graph representation with redundancy removed. As a result, our proposed framework obtains notable accuracy gains without sacrificing performance stability. Extensive experiments validate the superiority on graph classification and regression tasks, where our proposed HaarNet outperforms various existing GNN models, especially on big data sets.

Video games do not lead to violence or aggression, according to a reanalysis of data gathered from more than 21,000 young people around the world. The researchers, led by Aaron Drummond from New Zealand's Massey University, re-examined 28 studies from previous years that looked at the link between aggressive behaviour and video gaming, a method known as a meta-analysis. The new report, published in the journal Royal Society Open Science on Wednesday, found that, when bundled together, the studies showed a statistically significant but minuscule positive correlation between gaming and aggression, below the threshold required to count as even a "small effect". "Thus, current research is unable to support the hypothesis that violent video games have a meaningful long-term predictive impact on youth aggression," the report said. Between them, the various studies included in the research dated back to 2008, and had reported a range of effects, including a small positive correlation between violence and video-game use in around a quarter of them and no overall conclusion in most of the rest, with one 2011 study finding a negative correlation. One common argument for a negative effect of gaming is that small harms can accumulate over time: if a player ends every game slightly more aggressive then, over the long term, that might add up to a meaningful change in temperament.

It has been widely tipped that 2020 is the year in which artificial intelligence is going to fully arrive in our workplace. But is it really the case? And how much is HR getting on board with this much touted digital revolution? According to a just-released report by Udemy for Business, 2020 workplace learning trends: The skills of the future: "AI brings with it a proliferation of data. Organisations and their employees will need to manage, store, process, analyse, and draw actionable insights from the data generated by AI. "Becoming a data-driven culture will be essential for organisations to harness the power of AI and big data." That said, it appears that most companies are not yet prepared factor in the impact of new technologies – with recent figures suggesting just 26% are ready. "With large-scale technology disruption, organisations will need to respond in a transformational way.

Galway start-up Chatspace has developed an artificial intelligence answers and insights platform that prevents projects on track and prevents costly failures. Chatspace works with the world's largest companies unleashing new insights for company strategy that traditional teams can't reach, automating repeatable tasks and scaling capabilities across the enterprise. The company believes that the future of work is engaged and connected employees taking advantage of the capabilities that technology provides. Its clients to date include ATOS, Nestle and Medtronic. "Project Management is integral to Enterprise," explains Chatspace CEO and founder John Clancy.