We present a framework for automatically structuring and training fast, approximate, deep neural surrogates of existing stochastic simulators. Unlike traditional approaches to surrogate modeling, our surrogates retain the interpretable structure of the reference simulators. The particular way we achieve this allows us to replace the reference simulator with the surrogate when undertaking amortized inference in the probabilistic programming sense. The fidelity and speed of our surrogates allow for not only faster "forward" stochastic simulation but also for accurate and substantially faster inference. We support these claims via experiments that involve a commercial composite-materials curing simulator. Employing our surrogate modeling technique makes inference an order of magnitude faster, opening up the possibility of doing simulator-based, non-invasive, just-in-time parts quality testing; in this case inferring safety-critical latent internal temperature profiles of composite materials undergoing curing from surface temperature profile measurements.

Technology skills such as Artificial Intelligence/Machine Learning (AI/ML), digital marketing and design thinking will be important to drive the future growth, finds a survey by ed-tech firm Great Learning. The analysis of 307 corporates (ranging from small and medium enterprises (SMEs) to large corporate) focussed on finding out top skills that organisations will need to drive future performance and how they may plan to bridge the impending skill deficit among their ranks. As per the survey, 25 per cent of all companies believe AI/ML are the most crucial skills needed to ensure an organisation's future growth. Digital marketing emerged second with 19 per cent finding it most crucial. Prime Minister Narendra Modi on Tuesday met with the members of JP Morgan's International Council in New Delhi and discussed his vision for making India a USD 5 trillion economy by 2024.

In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook's Anatoly Frenkel have described a way to use artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts. "Improving our ability to convert CO2 to methane would'kill two birds with one stone' by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions," said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences. Frenkel's group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions.

Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise, pattern recognition algorithms have the potential to interpret sensor recordings as soon as this information is available. This paper proposes a robust framework to build a damage prediction model for building structures. Support vector machines are used to predict the existence as well as the probable location of the damage. The model is designed to consider probabilistic approaches in determining hazard intensity given the existing attenuation models in performance-based earthquake engineering. Performance of the model regarding accurate and safe predictions is enhanced using Bayesian optimization. The proposed framework is evaluated on a reinforced concrete moment frame. Targeting a selected large earthquake scenario, 6,240 nonlinear time history analyses are performed using OpenSees. Simulation results are engineered to extract low-dimensional intensity-based features that can be used as damage indicators. For the given case study, the proposed model achieves a promising accuracy of 83.1% to identify damage location, demonstrating the great potential of model capabilities.

Artificial intelligence (AI) isn't magic, it's just really complicated math, said Greg Mulholland, CEO and founder of Citrine Informatics (Redwood City, CA), at a press roundtable hosted by Lanxess (Cologne, Germany) at K 2019. But Mulholland's hosts seemed quite bedazzled by his AI-enabled platform, nonetheless. Lanxess is the first company to adopt Citrine's technology at scale, and Dr. Markus Eckert, Senior Vice President, Head of Business Unit Urethane Systems at Lanxess was eager to explain what it means for customers. Citrine is a Silicon Valley startup that couldn't be more niche: It has developed a platform that leverages data and AI specifically to accelerate the development of materials and chemicals. Citrine has been recognized for technology innovation by the World Economic Forum as a Tech Pioneer, and collaborates with world-class academic institutions such as Carnegie Mellon University in Pittsburgh and the University of California, Berkeley.

Digitalization is occurring across all manufacturing industries, and the coatings sector is no exception. The quantity of data that can be leveraged to improve all business activities--from new product development to production to customer service--is increasing dramatically. The challenge is to determine where and how to apply technologies such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP) and how to make the data on hand relevant to the problem or question of interest. These questions and others were considered by members of the coatings value chain and their insights are presented below. What types of Big Data can be leveraged by the coatings industry to facilitate research, development, and innovation in general? Sapper, Cal Poly: We need to be asking three questions when it comes to data needs in our industry. What data do we have? What data do we need? And what questions are we trying to answer? A lot of valuable data already exists, but it is tied up in reports, published literature, or subject matter expertise. The data is there, but not collected in a way that allows helpful artificial intelligence and machine learning projects to be performed. Understanding what type of data is needed for a particular project is the first step in identifying where that data might already exist.

Our Random Forest model predicts a 73% probability of the Oakland Raiders keeping it within the 4.5 points being offered at Westgate.

People living in neighborhoods with poor building standards are more likely to be killed by a disaster. Their homes, too often built in a cheap or makeshift manner, are susceptible to dangerous events like earthquakes, hurricanes, and landslides. These (typically poor) inhabitants make up a disproportionate number of the 1,300,000 lives taken by disasters in the last 25 years. Families move into poor urban communities seeking better jobs and opportunities but don't possess the money or technical knowledge to access safe, resilient housing. Governments and communities strive to retrofit these structures for safety -- usually a simple, cheap, and effective process -- but dangerous housing remains a dilemma.

A birds-eye view of three of SpaceX's glimmering Starship spacecraft shows that the vessels are slowly but surely coming together. In aerial footage taken by videographer John Winkopp, the Starship craft's shimmering stainless steel body can be seen taking ship at the company's facility in Cocoa, Florida. As reported by CNBC, the video also shows the first stainless steel bands of another Starship prototype, the Mark 4, being assembled. The progress gives credence to a claim from SpaceX CEO and Tesla founder, Elon Musk, who claimed that the next-generation craft will be ready for test flights between October and November. An FCC filing surfaced in September that revealed SpaceX requested permission to fly Starship more than 12 miles into orbit and then land the craft back down in the same spot.

In areas like the Caribbean that face considerable risk from natural hazards like earthquakes, hurricanes, and floods, these forces of nature can have a devastating effect. This is especially true where houses and buildings are not up to modern construction standards, often in poor and informal settlements. While buildings can be retrofit to better prepare them for disaster, the traditional method for identifying high-risk buildings involves going door to door by foot, taking weeks if not months and costing millions of dollars. This is where AI can help. WeRobotics and the World Bank Global Program for Resilient Housing have teamed up to prepare aerial drone imagery of buildings across the Caribbean annotated with characteristics that matter to building inspectors.