In July this year, Holly Ransom (Fulbright Anne Wexler Scholar, CEO Emergent) interviewed former US Ambassador to Australia Jeffrey Bleich (Professorial Fellow at Flinders University) for the launch of JBC and the first instalment in the Jeff Bleich Series – a multimedia platform for the Centre's research, engagement, and education. In a wide-ranging interview, Ambassador Bleich outlines the JBC vision, its values, goals, and aspirations, and addresses the spectrum of challenges and opportunities Australia and the United States confront in the digital age. From the impacts of automation, Artificial Intelligence, 5G & Blockchain, to the need to reinvigorate good governance, democratic participation, civil society and industry collaboration, and community and individual level empowerment, this timely and important discussion is not to be missed.

Costa Group, one of Australia's largest horticulturist companies, has begun rolling out an artificial intelligence (AI) system to help the company better understand and manage the quantity and quality of its berry crops. The Sensing system, developed by Sydney-based company, The Yield, has been designed to measure 14 variables of a typical agriculture model such as rain, light, wind, temperature, and soil moisture in real time. The information is then ingested into an Internet of Things (IoT) platform and combined with existing data sets shared by Costa before AI is applied to create a localised prediction of each berry crop. "We literally describe the system like a maths robot because it's effectively crunching through data and selecting the most important feature sets, creating models, putting them into production, measuring the accuracy, feeding that back in, and continually adjusting," The Yield founder and managing director Ros Harvey told ZDNet. The system was recently installed within the polytunnels of Costa's eight berry farms in New South Wales, Queensland, and Tasmania.

The API provides pre-trained object detection models that have been trained on the COCO dataset. COCO dataset is a set of 90 commonly found objects. See image below of objects that are part of COCO dataset. In this case we care about classes -- persons and soccer ball which are both part of COCO dataset. The API also has a big set of models it supports. See table below for reference. The models have a trade off between speed and accuracy. Since I was interested in real time analysis, I chose SSDLite mobilenet v2. Once we identify the players using the object detection API, to predict which team they are in we can use OpenCV which is powerful library for image processing.

Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depressive patients (MDD) and 29 normal controls using 128 channel HydroCel Geodesic Sensor Net (HCGSN). To better identify depression, we extracted different types of EEG features including linear features, nonlinear features and functional connectivity features phase lagging index (PLI) to comprehensively analyze the EEG signals in patients with MDD. And using different feature selection methods and classifiers to evaluate the optimal feature sets. Results: Functional connectivity feature PLI is superior to the linear features and nonlinear features. And when combining all the types of features to classify MDD patients, we can obtain the highest classification accuracy 82.31% using ReliefF feature selection method and logistic regression (LR) classifier. Analyzing the distribution of optimal feature set, it was found that intrahemispheric connection edges of PLI were much more than the interhemispheric connection edges, and the intrahemispheric connection edges had a significant differences between two groups. Conclusion: Functional connectivity feature PLI plays an important role in depression recognition. Especially, intrahemispheric connection edges of PLI might be an effective biomarker to identify depression. And statistic results suggested that MDD patients might exist functional dysfunction in left hemisphere.

Deep neural networks often consist of a great number of trainable parameters for extracting powerful features from given datasets. On one hand, massive trainable parameters significantly enhance the performance of these deep networks. On the other hand, they bring the problem of over-fitting. To this end, dropout based methods disable some elements in the output feature maps during the training phase for reducing the co-adaptation of neurons. Although the generalization ability of the resulting models can be enhanced by these approaches, the conventional binary dropout is not the optimal solution. Therefore, we investigate the empirical Rademacher complexity related to intermediate layers of deep neural networks and propose a feature distortion method (Disout) for addressing the aforementioned problem. In the training period, randomly selected elements in the feature maps will be replaced with specific values by exploiting the generalization error bound. The superiority of the proposed feature map distortion for producing deep neural network with higher testing performance is analyzed and demonstrated on several benchmark image datasets.

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging for allowing investigation of association between many imaging and non-imaging variables. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by canonical variables of lower rank. Here we propose solutions for both problems: in the case of nuisance variables, we show that projecting the residuals to a lower dimensional space where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained. We also discuss how to address the multiplicity of tests via closure, which leads to an admissible test that is not conservative. We also provide a complete algorithm for permutation inference for CCA.

In the context of learning to map an input $I$ to a function $h_I:\mathcal{X}\to \mathbb{R}$, we compare two alternative methods: (i) an embedding-based method, which learns a fixed function in which $I$ is encoded as a conditioning signal $e(I)$ and the learned function takes the form $h_I(x) = q(x,e(I))$, and (ii) hypernetworks, in which the weights $\theta_I$ of the function $h_I(x) = g(x;\theta_I)$ are given by a hypernetwork $f$ as $\theta_I=f(I)$. We extend the theory of~\cite{devore} and provide a lower bound on the complexity of neural networks as function approximators, i.e., the number of trainable parameters. This extension, eliminates the requirements for the approximation method to be robust. Our results are then used to compare the complexities of $q$ and $g$, showing that under certain conditions and when letting the functions $e$ and $f$ be as large as we wish, $g$ can be smaller than $q$ by orders of magnitude. In addition, we show that for typical assumptions on the function to be approximated, the overall number of trainable parameters in a hypernetwork is smaller by orders of magnitude than the number of trainable parameters of a standard neural network and an embedding method.

OR-constrained (ORC) graphical user interface layouts unify conventional constraint-based layouts with flow layouts, which enables the definition of flexible layouts that adapt to screens with different sizes, orientations, or aspect ratios with only a single layout specification. Unfortunately, solving ORC layouts with current solvers is time-consuming and the needed time increases exponentially with the number of widgets and constraints. To address this challenge, we propose ORCSolver, a novel solving technique for adaptive ORC layouts, based on a branch-and-bound approach with heuristic preprocessing. We demonstrate that ORCSolver simplifies ORC specifications at runtime and our approach can solve ORC layout specifications efficiently at near-interactive rates.

The pace of adoption for AI and cognitive technologies continues unabated with widespread, worldwide, rapid adoption. Adoption of AI by enterprises and organizations continues to grow, as evidenced by a recent survey showing growth across each of the seven patterns of AI. However, with this growth of adoption comes strain as existing regulation and laws struggle to deal with emerging challenges. As a result, governments around the world are moving quickly to ensure that existing laws, regulations, and legal constructs remain relevant in the face of technology change and can deal with new, emerging challenges posed by AI. Research firm Cognilytica recently published a report on Worldwide AI Laws and Regulations that explores the latest legal and regulatory actions taken by countries around the world across nine different AI-relevant areas. Specifically, the report analyzed emerging laws and regulations pertaining to the use of facial recognition and computer vision, operation and development of autonomous vehicles, issues of AI-relevant data privacy, challenges arising from conversational systems and chatbots, the emergence of the possibility of lethal autonomous weapons systems (LAWS), concerns around AI ethics and bias, aspects of AI-supported decision making, the potential for malicious use of AI, and other regulations and laws pertaining to the use, creation, or interaction with AI systems.

In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model-based solutions provide useful insights, but cannot be directly implemented in practice. In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and real-world data in analyzing the latency and reliability and training deep neural networks (DNNs). Deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks. Further considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Finally, we provide some experimental and simulation results and discuss some future directions.