Soaring over the world's highest buildings and racing a solar-powered car across the desert may not be on your vacation itinerary this summer, but you can still see what such experiences might be like, thanks to the spectacular new IMAX film, "Dream Big: Engineering Our World." The movie takes audiences from Dubai to Australia, while sharing stirring stories about the impact engineers have on society. Narrated by Academy Award winner Jeff Bridges, it celebrates the inspiring work and people behind these engineering feats. Featured footage includes the Great Wall of China and the Golden Gate Bridge, not to mention an underwater robot developed by a team of students from an underprivileged high school in Phoenix. Made up primarily of Mexican immigrants, the robotics team entered its creation in a competition and succeeded against all odds.

The Defense Department is likely within 18 months of introducing autonomous cybersecurity tools that will be capable of augmenting human analysts by predicting threats, including insider activity, and dynamically isolating parts of the network that may come under attack, the department's outgoing chief information officer said Thursday. Terry Halvorsen, speaking to reporters during his last media roundtable before retiring at the end of the month, said as the department continues deployment of its 12 Joint Regional Security Stacks--a $1.6 billion effort to eliminate hundreds of disparate firewalls with centrally managed commercial security appliances and network monitoring tools--the next major step will be the deployment and testing of AI-based security applications. "Given the volume [of attacks] and where I see the threat moving it will be impossible for humans by themselves to keep pace. We can and we're very close to being able to put more autonomy into the security tools, and we will get to the point within the next 18 months where AI is becoming a key factor in augmenting the human analyst in making those decisions about what to do," Halvorsen said. Halvorsen recently hosted a special meeting of the Five Eyes alliance (Australia, Canada, New Zealand, the United Kingdom, and the United States), plus Germany, Japan, and NATO, at which AI-based cybersecurity was discussed in detail. "This is very real inside the department.

While the latter happened IRL in late 2016, a lot of the film's other future predictions were a little off. Though what's not too far fetched is the idea of robots, or artificial intelligence, working its way into our very real and ordinary lives in the not too distant future. Self-driving cars are already a thing, and that's only the beginning. "Artificial Intelligence (or AI) is likely to do to white collar jobs like how machines have been doing blue collar work. In other words, just like our brawns have been digitised, so will our brains be," Anders Sorman-Nilsson, global futurist and TEDx speaker told The Huffington Post Australia. Sorman-Nilsson is the author of Seamless: The Futurephile's Guide To Leading Digital Adaptation And Human Transformation.

Currently, IP Australia's online virtual assistant powered by Nuance's Nina technology delivers a dynamic and engaging customer experience that lets customers easily understand trademark, patent, designs and plant breeder's rights processes. The human elements of dialogue and personalised interaction connect customers to the right information and tools, which translates into immediate, easy and effective self-servicing and increased customer satisfaction. At present, Alex is capable of answering IP rights questions in layman's terms, minimising customer confusion and maximising successful first-time transactions.

We provide a brief overview on some hot topics in the area of evolutionary computation. Our main focus is on recent developments in the areas of combinatorial optimization and real-world applications. Furthermore, we highlight recent progress on the theoretical understanding of evolutionary computing methods.

The introduction of smart meters has motivated the electricity industry to manage electrical demand, using dynamic pricing schemes such as real-time pricing. The overall aim of demand management is to minimize electricity generation and distribution costs while meeting the demands and preferences of consumers. However, rapidly scheduling consumption of large groups of households is a challenge. In this paper, we present a highly scalable approach to find the optimal consumption levels for households in an iterative and distributed manner. The complexity of this approach is independent of the number of households, which allows it to be applied to problems with large groups of households. Moreover, the intermediate results of this approach can be used by smart meters to schedule tasks with a simple randomized method.

Real world data is usually generated over multiple time periods associated with multiple labels, which can be represented as multiple labeled tensor sequences. These sequences are linked together, sharing some common features while exhibiting their own unique features. Conventional tensor factorization techniques are limited to extract either common or unique features, but not both simultaneously. However, both types of these features are important in many machine learning systems as they inherently affect the systems' performance. In this paper, we propose a novel supervised tensor factorization technique which simultaneously extracts ordered common and unique features. Classification results using features extracted by our method on CIFAR-10 database achieves significantly better performance over other factorization methods, illustrating the effectiveness of the proposed technique.

Metric learning has been widely employed, especially in various computer vision tasks, with the fundamental assumption that all samples (e.g., regions/superpixels in images/videos) are independent and identically distributed (IID). However, since the samples are usually spatially-connected or temporally-correlated with their physically-connected neighbours, they are not IID (non-IID for short), which cannot be directly handled by existing methods. Thus, we propose to learn and integrate non-IID metrics (NIME). To incorporate the non-IID spatial/temporal relations, instead of directly using non-IID features and metric learning as previous methods, NIME first builds several non-IID representations on original (non-IID) features by various graph kernel functions, and then automatically learns the metric under the best combination of various non-IID representations. NIME is applied to solve two typical computer vision tasks: interactive image segmentation and histology image identification. The results show that learning and integrating non-IID metrics improves the performance, compared to the IID methods. Moreover, our method achieves results comparable or better than that of the state-of-the-arts.

Training machine leaning algorithms on augmented data fromdifferent related sources is a challenging task. This problemarises in several applications, such as the Internet of Things(IoT), where data may be collected from devices with differentsettings. The learned model on such datasets can generalizepoorly due to distribution bias. In this paper we considerthe problem of classifying unseen datasets, given several labeledtraining samples drawn from similar distributions. Weexploit the intrinsic structure of samples in a latent subspaceand identify landmarks, a subset of training instances fromdifferent sources that should be similar. Incorporating subspacelearning and landmark selection enhances generalizationby alleviating the impact of noise and outliers, as well asimproving efficiency by reducing the size of the data. However,since addressing the two issues simultaneously resultsin an intractable problem, we relax the objective functionby leveraging the theory of nonlinear projection and solve atractable convex optimisation. Through comprehensive analysis,we show that our proposed approach outperforms stateof-the-art results on several benchmark datasets, while keepingthe computational complexity low.

We consider Max-min Share (MmS) fair allocations of indivisible chores (items with negative utilities). We show that allocation of chores and classical allocation of goods (items with positive utilities) have some fundamental connections but also differences which prevent a straightforward application of algorithms for goods in the chores setting and vice-versa. We prove that an MmS allocation does not need to exist for chores and computing an MmS allocation - if it exists - is strongly NP-hard. In view of these non-existence and complexity results, we present a polynomial-time 2-approximation algorithm for MmS fairness for chores. We then introduce a new fairness concept called optimal MmS that represents the best possible allocation in terms of MmS that is guaranteed to exist. We use connections to parallel machine scheduling to give (1) a polynomial-time approximation scheme for computing an optimal MmS allocation when the number of agents is fixed and (2) an effective and efficient heuristic with an ex-post worst-case analysis.