Automation in public administration is inevitable and can bring great benefits. The broadly accepted law of robotics is that a robot may not injure a human being. In an attempt to reduce welfare costs in 2016, the commonwealth government engaged in an unlawful debt recovery process. The bureaucratic process was malign and was meant either directly or collaterally to harm and stigmatise welfare recipients. The Online Compliance Intervention – or OCI, but more commonly known as robodebt – used algorithms to average out incomes of welfare recipients by matching ATO income data with social welfare recipients' income as self-reported to Centrelink with Centrelink.

Several methods have been proposed to solve such derivative-free stochastic optimization problems, and we refer the reader to [3, 38] for surveys of these methods. A popular class of these methods estimate the gradients using function values and employ standard gradient-based optimization methods using these estimators. Quasi-Newton methods are recognized as one of the most powerful methods for solving deterministic optimization problems. These methods build quadratic models of the objective information using only gradient information. Recently, researchers have been adapting these methods for stochastic settings when the gradient information is available. The empirical results in [15] indicate that a careful implementation of these methods can be efficient compared with the popular stochastic gradient methods. We adapt these methods to make them suitable for situations where the gradients are estimated using function values. We propose finite-difference derivative-free stochastic quasi-Newton methods for solving (1) by exploiting common random number (CRN) evaluations of f.

Integer programs provide a powerful abstraction for representing a wide range of real-world scheduling problems. Despite their ability to model general scheduling problems, solving large-scale integer programs (IP) remains a computational challenge in practice. The incorporation of more complex objectives such as robustness to disruptions further exacerbates the computational challenge. We present NICE (Neural network IP Coefficient Extraction), a novel technique that combines reinforcement learning and integer programming to tackle the problem of robust scheduling. More specifically, NICE uses reinforcement learning to approximately represent complex objectives in an integer programming formulation. We use NICE to determine assignments of pilots to a flight crew schedule so as to reduce the impact of disruptions. We compare NICE with (1) a baseline integer programming formulation that produces a feasible crew schedule, and (2) a robust integer programming formulation that explicitly tries to minimize the impact of disruptions. Our experiments show that, across a variety of scenarios, NICE produces schedules resulting in 33\% to 48\% fewer disruptions than the baseline formulation. Moreover, in more severely constrained scheduling scenarios in which the robust integer program fails to produce a schedule within 90 minutes, NICE is able to build robust schedules in less than 2 seconds on average.

In an emergency room (ER) setting, the diagnosis of stroke is a common challenge. Due to excessive execution time and cost, an MRI scan is usually not available in the ER. Clinical tests are commonly referred to in stroke screening, but neurologists may not be immediately available. We propose a novel multimodal deep learning framework, DeepStroke, to achieve computer-aided stroke presence assessment by recognizing the patterns of facial motion incoordination and speech inability for patients with suspicion of stroke in an acute setting. Our proposed DeepStroke takes video data for local facial paralysis detection and audio data for global speech disorder analysis. It further leverages a multi-modal lateral fusion to combine the low- and high-level features and provides mutual regularization for joint training. A novel adversarial training loss is also introduced to obtain identity-independent and stroke-discriminative features. Experiments on our video-audio dataset with actual ER patients show that the proposed approach outperforms state-of-the-art models and achieves better performance than ER doctors, attaining a 6.60% higher sensitivity and maintaining 4.62% higher accuracy when specificity is aligned. Meanwhile, each assessment can be completed in less than 6 minutes, demonstrating the framework's great potential for clinical implementation.

State-of-the-art approaches for visually-guided audio source separation typically assume sources that have characteristic sounds, such as musical instruments. These approaches often ignore the visual context of these sound sources or avoid modeling object interactions that may be useful to better characterize the sources, especially when the same object class may produce varied sounds from distinct interactions. To address this challenging problem, we propose Audio Visual Scene Graph Segmenter (AVSGS), a novel deep learning model that embeds the visual structure of the scene as a graph and segments this graph into subgraphs, each subgraph being associated with a unique sound obtained by co-segmenting the audio spectrogram. At its core, AVSGS uses a recursive neural network that emits mutually-orthogonal sub-graph embeddings of the visual graph using multi-head attention. These embeddings are used for conditioning an audio encoder-decoder towards source separation. Our pipeline is trained end-to-end via a self-supervised task consisting of separating audio sources using the visual graph from artificially mixed sounds. In this paper, we also introduce an "in the wild'' video dataset for sound source separation that contains multiple non-musical sources, which we call Audio Separation in the Wild (ASIW). This dataset is adapted from the AudioCaps dataset, and provides a challenging, natural, and daily-life setting for source separation. Thorough experiments on the proposed ASIW and the standard MUSIC datasets demonstrate state-of-the-art sound separation performance of our method against recent prior approaches.

AI can improve populations' lives by providing better services in the following aspects: The COVID-19 crisis has sped up the adoption of artificial intelligence in the sector. With the on-going pandemic, governments are rethinking and reconfiguring their business models to navigate the uncertainties of the post COVID-19 world, they have started realising the potential of artificial intelligence to increase resilience, spot growth opportunities and drive innovation. Taken together, these benefits would equip public sector organizations to move beyond process optimization to deliver world class services and tackle long-term global challenges. Governments face particular barriers to deploying AI on a bigger scale. Not surprisingly, the historically low levels of IT investment in the public sector have slowed the introduction of AI in the public sector.

Regulatory bodies around the world increasingly recognize that they need to regulate how governments use machine learning algorithms when making high-stakes decisions. This is a welcome development, but current approaches fall short. As regulators develop policies, they must consider how human decisionmakers interact with algorithms. If they do not, regulations will provide a false sense of security in governments adopting algorithms. In recent years, researchers and journalists have exposed how algorithmic systems used by courts, police, education departments, welfare agencies and other government bodies are rife with errors and biases.

The United Arab Emirates and the U.K. recently signed a memorandum of understanding on artificial intelligence that would see the transfer of related knowledge, investment and standards. And the next day saw the UAE's Tawazun Economic Council sign a memo with the U.K. Ministry of Defence to strengthen cooperation in defense-related research and development. On Sept. 16, Mohamed bin Zayed, the crown prince of Abu Dhabi and deputy supreme commander of the armed forces, met British Prime Minister Boris Johnson in the U.K., when the two parties launched a "Partnership for the Future" between the two nations, which involved the AI effort. "The UK looks forward to further collaboration with the UAE Presidential Guard; between our two air forces through UK participation in the Advanced Tactical Leadership Course, with UK jets from the Carrier Strike Group, and increased land exercises in the UAE," read a joint communique released after the meeting. "Both countries have developed stronger industrial ties through collaboration in defence and security. This includes blossoming relationships, including Tawazun Economic Council and EDGE Group. The Leaders agreed on working together to support these emerging and future partnerships in order to promote prosperity whilst strengthening business opportunities for both."

A technology-led revolution, dubbed Industry 4.0, is gathering pace in the industrial world where traditional processes and legacy technologies are being replaced by smart devices, automated machines and advanced forms of computing. The rise of Cyber Physical Systems (CPS), owing to exponential growth in technologies like the Internet of Things (IoT), artificial intelligence (AI), cloud, robots, drones, sensors, etc., is helping manufacturers improve efficiencies, productivity and the autonomous operation of production lines. Businesses are pouring billions of dollars in AI and automation, and the Industrial IoT (IIoT) alone is set to become a $500 billion market by 2025. IT/OT convergence could spell disaster for industries. As smart factories and supply chains connect the production line to the outside world via IIoT, digitally connected industries are becoming increasingly appealing to cybercriminals, who now have the opportunity to hijack high-value targets.

A number of experts believe the Earth is rapidly approaching its'tipping point' for reversing climate change, but researchers at Canada's University of Waterloo are creating artificial intelligence that could act as an'early warning system' against a runway threat to the planet. The deep learning algorithm was created to better predict the tipping points, while also understanding what happens after they have been reached, the study's co-author, Chris Bauch, a professor of applied mathematics at the University of Waterloo, said. 'Many of these tipping points are undesirable, and we'd like to prevent them if we can,' Bauch said in a statement. Canadian researchers are creating AI that could act as an'early warning system' against runway climate change. In May, scientists said there was a 40 percent chance that annual temperature rises would go beyond the the 1.5C (2.7F) set by the 2015 Paris Agreement.