Scientists have found that robotic vacuum cleaners could allow snoopers to remotely listen in to household conversations, despite not being fitted with microphones. US experts found they can perform a remote eavesdropping attack on a Xiaomi Roborock robot cleaner by remotely accessing its Lidar readings – which helps these cleaners to avoid bumping into furniture. Lidar is a method for measuring distances by illuminating the target with laser beams and measuring their reflection with a sensor. But Lidar can also capture sound signals by obtaining reflections off of objects in the home, like a rubbish bin, that vibrate due to nearby sound sources, such as a person talking. A hacker could repurpose a vacuum's Lidar sensor to sense acoustic signals in the environment, remotely harvest the Lidar data from the cloud and process the raw signal with deep learning techniques to extract audio information.

Devastated by industrial crisis, America's former "steel city" has reinvented itself as an innovation hub. But today its main challenge is to keep its "One Pittsburgh" promise by ensuring that everybody in its diverse population shares the benefits of new growth. Pittsburgh is back from the brink. A flagship of triumphant industrialisation in the early 20th century, the city has since seen its steel mills decline and then shut down. As the economy lurched from one crisis to another, Pennsylvania's rusting "steel city" became an emblem of decline, like other urban "dead stars" in the rustbelt of America's Middle West. But Pittsburgh never gave up.

Understanding the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. With physical and mechanistic complexity of industrial catalysts, it is critical to obtain kinetic information through experimental methods. As such, this work details a methodology based on the combination of transient rate/concentration dependencies and machine learning to measure the number of active sites, the individual rate constants, and gain insight into the mechanism under a complex set of elementary steps. This new methodology was applied to simulated transient responses to verify its ability to obtain correct estimates of the micro-kinetic coefficients. Furthermore, experimental CO oxidation data was analyzed to reveal the Langmuir-Hinshelwood mechanism driving the reaction. As oxygen accumulated on the catalyst, a transition in the mechanism was clearly defined in the machine learning analysis due to the large amount of kinetic information available from transient reaction techniques. This methodology is proposed as a new data driven approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data.

Machine learning and other artificial intelligence (AI) algorithms are becoming more commonplace in modern-day society. They are starting to become a very valuable tool for chemical research--at both the fundamental research and industrial-scale optimisation levels. This is primarily due to the rise in computational chemistry methods which use simulations and advanced numerical algorithms to predict best how molecules will behave (and how they will look structurally in the case of complex systems). While research is going into a lot of different chemicals, the various allotropes of elemental phosphorus (i.e. Still, it is relatively hard to simulate using conventional computational methods compared to other elements and molecules.

Time series modelling is essential for solving tasks such as predictive maintenance, quality control and optimisation. Deep learning is widely used for solving such problems. When managing complex manufacturing process with neural networks, engineers need to know why machine learning model made specific decision and what are possible outcomes of following model recommendation. In this paper we develop framework for capturing and explaining temporal dependencies in time series data using deep neural networks and test it on various synthetic and real world datasets.

While Artificial Intelligence (AI) is a much touted technology in mining, it would seem that the sector is yet to fully embrace this advance technology. Why is this and how can we insure that AI can be beneficial to mining in Africa. According to Prof. Frederick Cawood, Director of Wits Mining Institute at the University of the Witwatersrand, it will take a policy change to ensure that it can benefit mining in Africa. Cawood was a panellist on a recent Mining Review Africa webinar titled Mining 2025: A 5-year vision for AI in mining. Cawood was joined on the panel by Eric Croeser, MD for Africa at Accenture Industry X and Jean-Jacques Verhaeghe, programme manager for real-time information management systems at Mandela Mining Precinct.

I love talking about business models because in the end, it's usually the best business model, not the best technology, that wins the day. And digital transformation has the potential to reinvent business models by leveraging superior customer, product and operational insights to disrupt industry value chains and disintermediate customer relationships (see Figure 1). As the title of the book "Moneyball" states ("Moneyball: The Art of Winning an Unfair Game"), some of these reinvented business models will be based on "winning an unfair game". We have historical lessons about how Robber Barons[1] of the late 1800's created and won "an unfair game" that gave them monopoly power over suppliers, customers and competitors. To create this unfair game, Robber Barons leveraged a concept called "vertical integration" to dominate industry value chains and construct indissoluble customer and supplier dependencies. Let's review the lessons of these Robber Barons to understand how digital transformation might enable modern companies to win the digital unfair game.

At least a quarter of the warming that the Earth is experiencing today is due to anthropogenic methane emissions. There are multiple satellites in orbit and planned for launch in the next few years which can detect and quantify these emissions; however, to attribute methane emissions to their sources on the ground, a comprehensive database of the locations and characteristics of emission sources worldwide is essential. In this work, we develop deep learning algorithms that leverage freely available high-resolution aerial imagery to automatically detect oil and gas infrastructure, one of the largest contributors to global methane emissions. We use the best algorithm, which we call OGNet, together with expert review to identify the locations of oil refineries and petroleum terminals in the U.S. We show that OGNet detects many facilities which are not present in four standard public datasets of oil and gas infrastructure. All detected facilities are associated with characteristics known to contribute to methane emissions, including the infrastructure type and the number of storage tanks.

Progressive digitalization is changing the game of many industrial sectors. Focus-ing on product quality the main profitability driver of this so-called Industry 4.0 will be the horizontal integration of information over the complete supply chain. Therefore, the European RFCS project 'Quality4.0' aims in developing an adap-tive platform, which releases decisions on product quality and provides tailored information of high reliability that can be individually exchanged with customers. In this context Machine Learning will be used to detect outliers in the quality data. This paper discusses the intermediate project results and the concepts developed so far for this horizontal integration of quality information.

Roof falls due to geological conditions are major safety hazards in mining and tunneling industries, causing lost work times, injuries, and fatalities. Several large-opening limestone mines in the Eastern and Midwestern United States have roof fall problems caused by high horizontal stresses. The typical hazard management approach for this type of roof fall hazard relies heavily on visual inspections and expert knowledge. In this study, we propose an artificial intelligence (AI) based system for the detection roof fall hazards caused by high horizontal stresses. We use images depicting hazardous and non-hazardous roof conditions to develop a convolutional neural network for autonomous detection of hazardous roof conditions. To compensate for limited input data, we utilize a transfer learning approach. In transfer learning, an already-trained network is used as a starting point for classification in a similar domain. Results confirm that this approach works well for classifying roof conditions as hazardous or safe, achieving a statistical accuracy of 86%. However, accuracy alone is not enough to ensure a reliable hazard management system. System constraints and reliability are improved when the features being used by the network are understood. Therefore, we used a deep learning interpretation technique called integrated gradients to identify the important geologic features in each image for prediction. The analysis of integrated gradients shows that the system mimics expert judgment on roof fall hazard detection. The system developed in this paper demonstrates the potential of deep learning in geological hazard management to complement human experts, and likely to become an essential part of autonomous tunneling operations in those cases where hazard identification heavily depends on expert knowledge.