Scientists have developed an automated system that uses artificial intelligence (AI) to detect and capture fast radio bursts (FRBs) in real-time. FRBs are mysterious and powerful flashes of radio waves from space, thought to originate billions of light years from the Earth, said researchers from Swinburne University of Technology in Australia. They last for only a few milliseconds or a thousandth of a second and their cause is one of astronomy's biggest puzzles. Astronomical Society, has already identified five bursts -- including one of the most energetic ever detected, as well as the broadest. Wael Farah from Swinburne University of Technology trained the on-site computer at the Molonglo Radio Observatory in Australia to recognise the signs and signatures of FRBs, and trigger an immediate capture of the finest details seen to date.

We introduce the HSIC (Hilbert-Schmidt independence criterion) bottleneck for training deep neural networks. The HSIC bottleneck is an alternative to conventional backpropagation, that has a number of distinct advantages. The method facilitates parallel processing and requires significantly less operations. It does not suffer from exploding or vanishing gradients. It is biologically more plausible than backpropagation as there is no requirement for symmetric feedback. We find that the HSIC bottleneck provides a performance on the MNIST/FashionMNIST/CIFAR10 classification comparable to backpropagation with a cross-entropy target, even when the system is not encouraged to make the output resemble the classification labels. Appending a single layer trained with SGD (without backpropagation) results in state-of-the-art performance.

We present the first unsupervised deep learning method for pollen analysis using bright-field microscopy. Using a modest dataset of 650 images of pollen grains collected from honey, we achieve family level identification of pollen. We embed images of pollen grains into a low-dimensional latent space and compare Euclidean and Riemannian metrics on these spaces for clustering. We propose this system for automated analysis of pollen and other microscopic biological structures which have only small or unlabelled datasets available.

Artificial Intelligence (AI) has incredible potential to improve lives and create a better world, but the stakes are high and the consequences will be disastrous if the technology is misused. Those are the findings of a new "horizon scanning" report by the Australian Council of Learned Academies (ACOLA), titled The Effective and Ethical Development of Artificial Intelligence – An Opportunity to Improve our Wellbeing. "Horizon scanning" is a way for governments and decision-makers to "look at the future challenges and opportunities that the technologies pose", UNSW Professor of Artificial Intelligence Toby Walsh, co-chair of the report's expert working group, said. The report draws on research and expertise from a wide range of disciplines including science, medicine, economics, philosophy and law. At its best, AI has the power to enhance Australia's wellbeing, lift the economy, improve environmental sustainability and create a more equitable, inclusive and fair society, the report said.

It is a challenging and complex task to acquire information from different regions of a disaster-affected area in a timely fashion. The extensive spread and reach of social media and networks allow people to share information in real-time. However, the processing of social media data and gathering of valuable information require a series of operations such as (1) processing each specific tweet for a text classification, (2) possible location determination of people needing help based on tweets, and (3) priority calculations of rescue tasks based on the classification of tweets. These are three primary challenges in developing an effective rescue scheduling operation using social media data. In this paper, first, we propose a deep learning model combining attention based Bi-directional Long Short-Term Memory (BLSTM) and Convolutional Neural Network (CNN) to classify the tweets under different categories. We use pre-trained crisis word vectors and global vectors for word representation (GLoVe) for capturing semantic meaning from tweets. Next, we perform feature engineering to create an auxiliary feature map which dramatically increases the model accuracy. In our experiments using real data sets from Hurricanes Harvey and Irma, it is observed that our proposed approach performs better compared to other classification methods based on Precision, Recall, F1-score, and Accuracy, and is highly effective to determine the correct priority of a tweet. Furthermore, to evaluate the effectiveness and robustness of the proposed classification model a merged dataset comprises of 4 different datasets from CrisisNLP and another 15 different disasters data from CrisisLex are used. Finally, we develop an adaptive multitask hybrid scheduling algorithm considering resource constraints to perform an effective rescue scheduling operation considering different rescue priorities.

--Recreating cyber-attack alert data with a high level of fidelity is challenging due to the intricate interaction between features, non-homogeneity of alerts, and potential for rare yet critical samples. Generative Adversarial Networks (GANs) have been shown to effectively learn complex data distributions with the intent of creating increasingly realistic data. This paper presents the application of GANs to cyber-attack alert data and shows that GANs not only successfully learn to generate realistic alerts, but also reveal feature dependencies within alerts. This is accomplished by reviewing the intersection of histograms for varying alert-feature combinations between the ground truth and generated datsets. Traditional statistical metrics, such as conditional and joint entropy, are also employed to verify the accuracy of these dependencies. Finally, it is shown that a Mutual Information constraint on the network can be used to increase the generation of low probability, critical, alert values. By mapping alerts to a set of attack stages it is shown that the output of these low probability alerts has a direct contextual meaning for Cyber Security analysts. Overall, this work provides the basis for generating new cyber intrusion alerts and provides evidence that synthesized alerts emulate critical dependencies from the source dataset. I NTRODUCTION Classifying, predicting, and generating cyber-attack alert data provides a unique set of challenges due to imbalance and a lack of homogeneity in alert datasets. Furthering these challenges critical exploits in a network are often rare and difficult to identify. Despite this is has been shown that alert data can be used to identify anomalous traffic [1] [2] [3], network vulnerabilities [4], and bad actor behavior profiling [5]. However, to fully realize the potential of cyber-attack alert data, a means to acquire more data and analyze critical dependencies within alerts is needed. This work seeks to provide solutions to these challenges by showing that deep learning models are able to recreate cyber-attack alert data when given representative real world data. This includes a means for driving better coverage of the feature domain in model outputs, allowing more rare but critical events to be synthesized.

A new study suggest that the same racial stereotypes applied to people are also applied to their mechanical kin. Researchers from the Human Interface Technology Laboratory in New Zealand say humans perceive robots that resemble humans to have a certain race and may apply stereotypes on the bot depending on the shade of its'skin'. The findings come from what's known as a shooter bias test. In the experiment, participants were shown various images of armed and unarmed subjects and asked to make a split-second reaction test based on the level of'threat.' Robots are more likely to be deemed a threat if their'skin' is darker An affirmative reaction came in the form of participants pressing a button, or in other words, choosing to pull the trigger. What they found was that people were more apt to'shoot' robots with darker tones than lighter ones even when they were posing no threat.

My family and I continue to have more and more conversations with Alexa, Siri and Google Assistant lately. Having three AI based sources within speaking range of each other, we have a tendency to fact check them against one another - especially when someone doesn't quite trust or agree with the answer they get. For example, is Australia considered a continent or is it Oceania? Is a hot dog a sandwich? Who is the best NBA player of all time ever?

--Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry. However, this model suffers from the lack of interpretability and the item cold-start problem, which limit its reliability and practicability. In this paper, we propose an interpretable recommendation model called Multi-Matrix F actorization (MMF), which addresses these two limitations and achieves the state-of-the-art prediction accuracy by exploiting common attributes that are present in different items. In the model, predicted item ratings are regarded as weighted aggregations of attribute ratings generated by the inner product of the user latent vectors and the attribute latent vectors. MMF provides more fine grained analyses than matrix factorization in the following ways: attribute ratings with weights allow the understanding of how much each attribute contributes to the recommendation and hence provide interpretability; the common attributes can act as a link between existing and new items, which solves the item cold-start problem when no rating exists on an item. We evaluate the interpretability of MMF comprehensively, and conduct extensive experiments on real datasets to show that MMF outperforms state-of-the-art baselines in terms of accuracy. I NTRODUCTION In recent years, recommendation systems gain increasing interest by both researchers and the industry [1], [2]. The most popular recommendation systems are based on collaborative filtering (CF) technique, which provides recommendations based on other similar users' choice [3]. Matrix factorization (MF) is one of the most common collaborative filtering models, whose main idea is to learn user latent vectors and item latent vectors, so that the inner product of the two vectors can approximate the original matrix with the minimal approximation error. MF has advantages of simplicity and performing well in many domains, such as recommendation systems, computer vision and document clustering [4]-[7]. However, it suffers from two limitations.

Nearly all established sports are going through some degree of hand-wringing over attracting younger fans as their older core ages out. The death of monoculture and explosion of entertainment options, many accessible without leaving one's bedroom, have seen attendance drops across the board. MLB and NFL teams have fallen over themselves installing on-site daily fantasy lounges to lure second-screeners. Even the hidebound International Olympic Committee has made transparent plays for youth, most recently with the addition of skateboarding, surfing and three-on-three basketball to next year's Summer Olympics in Tokyo. The demographic they're so thirsty for could be found in droves over the weekend at New York's Billie Jean King National Tennis Center, where three days of sold-out crowds turned out for the biggest video game competition of all time – the Fortnite World Cup – where a 16-year-old from Pennsylvania named Kyle Giersdorf (aka Bugha) brought home the winner's share of $3m with a dominant performance in Sunday's solos competition.