A daredevil retired pilot has been captured on camera performing loops, rolls and a dramatic dive while flying the'world's smallest' twin-jet aircraft. Bob Grimstead, 70, flew at an altitude of 5,000ft (1,524m) in the diminutive plane which has been described as a'bubble car with wings'. At just 13ft (4m) long, 4ft (1.2m) wide and weighing a mere 180lbs, Mr Grimstead, from West Sussex, was able to reach speeds of 140mph (225kmh). The former British Airways airline pilot used to fly 400 tonne jumbo jets and said he had no fear taking to the skies in the micro plane and said it was'superb fun'. Bob Grimstead, 70, (pictured) flew the diminutive jet at 5,000ft (1,524m).

Drone delivery service Wing is launching its own air-traffic control app to keep its craft safe in the skies. The company, owned by Google-parent Alphabet, recently started making deliveries in parts of Australia and Finland. Wing's new iOS and Android app aims to'help users comply with rules and plan flights more safely and effectively,' providing a rundown of airspace restrictions and hazards as well as events nearby that could interfere. The new app, Open Sky, is being released to drone flyers in Australia this month according to Wing. 'The design of our software has required a detailed understanding of flight rules -- along with buildings, roads, trees, and other terrain -- that allow aircraft to navigate safely at low altitudes, and we've used it to complete tens of thousands of flights on three continents,' Wing said in a blog post.

Digital Rock Imaging is constrained by detector hardware, and a trade-off between the image field of view (FOV) and the image resolution must be made. This can be compensated for with super resolution (SR) techniques that take a wide FOV, low resolution (LR) image, and super resolve a high resolution (HR), high FOV image. The Enhanced Deep Super Resolution Generative Adversarial Network (EDSRGAN) is trained on the Deep Learning Digital Rock Super Resolution Dataset, a diverse compilation 12000 of raw and processed uCT images. The network shows comparable performance of 50% to 70% reduction in relative error over bicubic interpolation. GAN performance in recovering texture shows superior visual similarity compared to SRCNN and other methods. Difference maps indicate that the SRCNN section of the SRGAN network recovers large scale edge (grain boundaries) features while the GAN network regenerates perceptually indistinguishable high frequency texture. Network performance is generalised with augmentation, showing high adaptability to noise and blur. HR images are fed into the network, generating HR-SR images to extrapolate network performance to sub-resolution features present in the HR images themselves. Results show that under-resolution features such as dissolved minerals and thin fractures are regenerated despite the network operating outside of trained specifications. Comparison with Scanning Electron Microscope images shows details are consistent with the underlying geometry of the sample. Recovery of textures benefits the characterisation of digital rocks with a high proportion of under-resolution micro-porous features, such as carbonate and coal samples. Images that are normally constrained by the mineralogy of the rock (coal), by fast transient imaging (waterflooding), or by the energy of the source (microporosity), can be super resolved accurately for further analysis downstream.

Contact Info: Name: Khushal Email: Send Email Organization: Meticulous Market Research Pvt. Ltd.

A team of researchers at Flinders University in South Australia has created a vaccine that is considered to be the first human drug to be fully designed by artificial intelligence. Drugs have been previously designed with the help of computers. However, this vaccine was independently designed by an AI software known as SAM or Search Algorithm for Ligands. Nikolai Petrovsky, professor at Flinders University who also led the development said that its name has been derived from the task it was assigned to perform which was searching the universe for all possible compounds for a good human drug also known as a ligand. Petrovsky, also a Research Director for an Australian company, Vaxine added that the AI software was first taught about the set of compounds which activate the immune system in human beings and a set of compounds which do not.

The presumed data owners' right to explanations brought about by the General Data Protection Regulation in Europe has shed light on the social challenges of explainable artificial intelligence (XAI). In this paper, we present a case study with Deep Learning (DL) experts from a research and development laboratory focused on the delivery of industrial-strength AI technologies. Our aim was to investigate the social meaning (i.e. meaning to others) that DL experts assign to what they do, given a richly contextualized and familiar domain of application. Using qualitative research techniques to collect and analyze empirical data, our study has shown that participating DL experts did not spontaneously engage into considerations about the social meaning of machine learning models that they build. Moreover, when explicitly stimulated to do so, these experts expressed expectations that, with real-world DL application, there will be available mediators to bridge the gap between technical meanings that drive DL work, and social meanings that AI technology users assign to it. We concluded that current research incentives and values guiding the participants' scientific interests and conduct are at odds with those required to face some of the scientific challenges involved in advancing XAI, and thus responding to the alleged data owners' right to explanations or similar societal demands emerging from current debates. As a concrete contribution to mitigate what seems to be a more general problem, we propose three preliminary XAI Mediation Challenges with the potential to bring together technical and social meanings of DL applications, as well as to foster much needed interdisciplinary collaboration among AI and the Social Sciences researchers.

Congestion prediction represents a major priority for traffic management centres around the world to ensure timely incident response handling. The increasing amounts of generated traffic data have been used to train machine learning predictors for traffic, however this is a challenging task due to inter-dependencies of traffic flow both in time and space. Recently, deep learning techniques have shown significant prediction improvements over traditional models, however open questions remain around their applicability, accuracy and parameter tuning. This paper proposes an advanced deep learning framework for simultaneously predicting the traffic flow on a large number of monitoring stations along a highly circulated motorway in Sydney, Australia, including exit and entry loop count stations, and over varying training and prediction time horizons. The spatial and temporal features extracted from the 36.34 million data points are used in various deep learning architectures that exploit their spatial structure (convolutional neuronal networks), their temporal dynamics (recurrent neuronal networks), or both through a hybrid spatio-temporal modelling (CNN-LSTM). We show that our deep learning models consistently outperform traditional methods, and we conduct a comparative analysis of the optimal time horizon of historical data required to predict traffic flow at different time points in the future.

These and many other insights are from Capgemini's Reinventing Cybersecurity with Artificial Intelligence Report published this week. Capgemini Research Institute surveyed 850 senior executives from seven industries, including consumer products, retail, banking, insurance, automotive, utilities, and telecom. Enterprises headquartered in France, Germany, the UK, the US, Australia, the Netherlands, India, Italy, Spain, and Sweden are included in the report. Please see page 21 of the report for a description of the methodology. Capgemini found that as digital businesses grow, their risk of cyberattacks exponentially increases.

A team at Flinders University in South Australia has developed a new vaccine believed to be the first human drug in the world to be completely designed by artificial intelligence (AI). While drugs have been designed using computers before, this vaccine went one step further being independently created by an AI program called SAM (Search Algorithm for Ligands). Flinders University Professor Nikolai Petrovsky who led the development told Business Insider Australia its name is derived from what it was tasked to do: search the universe for all conceivable compounds to find a good human drug (also called a ligand). "We had to teach the AI program on a set of compounds that are known to activate the human immune system, and a set of compounds that don't work. The job of the AI was then to work out for itself what distinguished a drug that worked from one that doesn't," Petrovsky said, who is also the Research Director of Australian biotechnology company Vaxine.

The Myers-Briggs Type Indicator (MBTI) is a popular personality metric that uses four dichotomies as indicators of personality traits. This paper examines the use of pre-trained language models to predict MBTI personality types based on scraped labeled texts. The proposed model reaches an accuracy of $0.47$ for correctly predicting all 4 types and $0.86$ for correctly predicting at least 2 types. Furthermore, we investigate the possible uses of a fine-tuned BERT model for personality-specific language generation. This is a task essential for both modern psychology and for intelligent empathetic systems.