IBM (NYSE: IBM) has announced that in less than 12 months since the release of IBM Cloud Private – an open source technology that brings cloud capabilities to organizations running on-premises IT systems – hundreds of leading enterprises worldwide have turned to the platform to help modernize their operations. They include New Zealand Police, China's Fuyao Group, Japan's Aflac Insurance, Turkey's credit bureau Kredi Kayıt Bürosu and Brazil's Fidelity National Information Services. Building on this momentum, IBM is announcing a slate of new advanced features for the on-premises private cloud platform, including the integration of powerful AI capabilities such as IBM Watson Assistant and IBM Watson Speech-to-Text, as well as support for additional public clouds, including the IBM Cloud. The updates provide clients with even more choice and flexibility for their IT journeys, and, for the first time, bring the power of IBM's Watson AI behind the company firewall. "The cloud has evolved in a very short time from being a way to cut costs to a platform for business transformation and innovation," said Robin Hernandez, Director, IBM Private Cloud Offering Management.

Technology and innovation company Lockheed Martin Australia has become the first Foundation Partner with the University of Adelaide's new Australian Institute for Machine Learning. The strategic partnership will deliver world-leading machine learning research for national security, the space industry, business, and the broader community. Machine learning is a form of artificial intelligence that enables computers and machines to learn how to do complex tasks without being programmed by humans. This technology is driving what is known as the "fourth industrial revolution". The University's new Australian Institute for Machine Learning (AIML) – which builds on decades of expertise in artificial intelligence and computer vision – will be based in the South Australian Government's new innovation precinct at Lot Fourteen (the old Royal Adelaide Hospital site).

Unmanned drones equipped with thermal imaging cameras could save dolphins, according to a new scientific study out of NZ and the UK. According to the study, the endangered Maui and Hector's dolphins in New Zealand and other marine mammal species around the world could be saved from caught up accidentally when fishing for other species. The successful study proved aerial thermal detection and identification of Maui and Hector's dolphins, and other marine mammals would be possible from both manned and unmanned aircraft and could be used on drones. Martin Stanley from Ocean Life Survey, who led the study, has designed and developed an unmanned remotely operated thermal imaging drone system that can be used for marine mammal study and protection. The thermal drone system can be operated from vessels such as fishing boats to provide real-time detection of and protection to marine mammals.

AT the heart of technological changes over time are core things like processing power, developments in network infrastructure, falls in price and re-imaginings of the user interface. But the next 5 years will deliver on some building trends that are going to need to be carefully managed. Artificial intelligence is one of these trends. It's been coming for decades but with many countries, including Australia, taking different layers of citizen ID biometric, and most manufacturers starting to deliver on past ROI promises, it's clear that AI is going to be central to the future of our systems. It will make them more efficient, more powerful and more frightening. The paradox of AI is that it will need to be kept on a short leash to achieve its full potential – whether we're already past that point of control remains to be seen.

Robotic weed control has seen increased research in the past decade with its potential for boosting productivity in agriculture. Majority of works focus on developing robotics for arable croplands, ignoring the significant weed management problems facing rangeland stock farmers. Perhaps the greatest obstacle to widespread uptake of robotic weed control is the robust detection of weed species in their natural environment. The unparalleled successes of deep learning make it an ideal candidate for recognising various weed species in the highly complex Australian rangeland environment. This work contributes the first large, public, multiclass image dataset of weed species from the Australian rangelands; allowing for the development of robust detection methods to make robotic weed control viable. The DeepWeeds dataset consists of 17,509 labelled images of eight nationally significant weed species native to eight locations across northern Australia. This paper also presents a baseline for classification performance on the dataset using the benchmark deep learning models, Inception-v3 and ResNet-50. These models achieved an average classification performance of 87.9% and 90.5%, respectively. This strong result bodes well for future field implementation of robotic weed control methods in the Australian rangelands.

Australia's current government has a protectionist policy on fossil fuels that caused Australia to miss a big opportunity in renewables, but fossil fuels only contribute about 3% of GDP. Action on climate change is important but the impact of AI could be comparable in scale and impact. The coming AI revolution, that most of the population is not yet awake to, is going to turn the global economy upside down. Yet the Australian Government's announced just $29m over 4 yrs in The Budget. "Paltry" is the only word I can use to adequately describe that.

Divergence functions play a key role as to measure the discrepancy between two points in the field of machine learning, statistics and signal processing. Well-known divergences are the Bregman divergences, the Jensen divergences and the f-divergences. In this paper, we show that the symmetric Bregman divergence can be decomposed into the sum of two types of Jensen divergences and the Bregman divergence. Furthermore, applying this result, we show another sum decomposition of divergence is possible which includes f-divergences explicitly.

Text analytics based on supervised machine learning classifiers has shown great promise in a multitude of domains, but has yet to be applied to Seismology. We test various standard models (Naive Bayes, k-Nearest Neighbors, Support Vector Machines, and Random Forests) on a seismological corpus of 100 articles related to the topic of precursory accelerating seismicity, spanning from 1988 to 2010. This corpus was labelled in Mignan (2011) with the precursor whether explained by critical processes (i.e., cascade triggering) or by other processes (such as signature of main fault loading). We investigate rather the classification process can be automatized to help analyze larger corpora in order to better understand trends in earthquake predictability research. We find that the Naive Bayes model performs best, in agreement with the machine learning literature for the case of small datasets, with cross-validation accuracies of 86% for binary classification. For a refined multiclass classification ('non-critical process' < 'agnostic' < 'critical process assumed' < 'critical process demonstrated'), we obtain up to 78% accuracy. Prediction on a dozen of articles published since 2011 shows however a weak generalization with a F1-score of 60%, only slightly better than a random classifier, which can be explained by a change of authorship and use of different terminologies. Yet, the model shows F1-scores greater than 80% for the two multiclass extremes ('non-critical process' versus 'critical process demonstrated') while it falls to random classifier results (around 25%) for papers labelled 'agnostic' or 'critical process assumed'. Those results are encouraging in view of the small size of the corpus and of the high degree of abstraction of the labelling. Domain knowledge engineering remains essential but can be made transparent by an investigation of Naive Bayes keyword posterior probabilities.

The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches such as the score function method. Although there is overwhelming empirical evidence in the literature showing its success, there is relatively little research exploring why the reparameterization trick is so effective. We explore this under the idealized assumptions that the variational approximation is a mean-field Gaussian density and that the log of the joint density of the model parameters and the data is a quadratic function that depends on the variational mean. From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator. We apply the result of our idealized analysis to real-world examples.

Abstract--The extraction of geological lineaments from digital satellite data is a fundamental application in remote sensing. The location of geological lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to mineral exploration is lacking. We present a framework for extracting geological lineaments using computer vision techniques which is a combination of edge detection and line extraction algorithms for extracting geological lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia using different dimension reduction techniques and convolutional filters. To validate the results, the extracted lineaments are compared to our manual photointerpretation and geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted geological lineaments and the GSWA geological lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter instead shows a stronger correlation with the output of our manual photointerpretation and known sites of hydrothermal mineralization. Hence, our framework using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. IGITAL satellite data with different spatial and spectral resolution are available for almost every locality on the Earth's land surface [1]-[5]. This enables the procurement of detailed information from surficial features and processes at different scales. Linear features are considered as one of the most important surficial features in different fields of study [6]-[8]. R. Scalzo is with the Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia (email: richard.scalzo@sydney.edu.au). Linear features represent the expression of some degree of linearity of a single or diverse grouping of both natural and cultural features [9], [10].