The field of process mining started in the late 1990s when Wil van der Aalst, who is now a professor leading the Process and Data Science group at RWTH Aachen University, began looking for ways to combine process science and data science. Much of this early work was theoretical, but the field has started accelerating over the last couple of year with advancements in data gathering and analytics technologies. "The adoption of process mining has accelerated over the last couple of years," van der Aalst said in an interview. There are now over 30 vendors of commercial process mining tools, including leaders like Celonis, Disco, UiPath (ProcessGold), myInvenio, Minit, Mehrwerk, Lana Labs, StereoLOGIC and Everflow. This has made it easier for large organizations, like Siemens and BMW, to apply process mining at scale with thousands of process mining users.

To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry.

Mercury Systems, Inc. today unveiled the EnterpriseSeries RES AI rugged rackmount server line, bringing High Performance Computing (HPC) capabilities to aerospace, defense and other mission-critical applications at the edge. "The proliferation of sensors, ever-growing data loads and the evolution of complex deep learning neural networks continues to increase computational demands, driving the need for supercomputing infrastructure closer to the edge," said Scott Orton, Vice President and General Manager of Mercury's Trusted Mission Solutions group. "Through close collaboration with technology leaders such as NVIDIA and Intel, we've developed reliable parallel computing systems that accelerate demanding artificial intelligence (AI), signal intelligence (SIGINT), and sensor fusion applications where it's needed the most." Why it Matters: Evolving compute-intensive AI, virtualization, big data analytics, SIGINT, autonomous vehicle, Electronic Warfare (EW) and sensor fusion applications require data center supercomputing capabilities closer to the source of data origin. Delivering HPC capabilities to the edge presents challenges as every application has its own security, performance, footprint, budget and reliability requirements.

What's your plan for steel?" is a question Bill Gates always uses whenever someone pitches him an idea of how to stop global warming [1]. Agriculture and the industry are responsible for almost half of the gas emissions worldwide and the steel industry is a major contributor. We encounter steel everywhere in life. I guess most of you are reading this article sitting on a steel chair – and for a good reason. The adaptability and durability of steel are unique and it is used to construct cars, buildings, gas pipelines, electrical transmission towers, and tools that we use on a daily basis.

As Japan's construction firms are squeezed by the tightest labor market since the 1970s and a rapidly aging population, they are pouring investment into technology -- and providing unexpected support to an economy reeling from the bitter U.S.-China trade war. The industry sees artificial intelligence and robots -- which can scurry around building sites day and night, preparing equipment and moving materials for the next day's construction -- as a way to future-proof and close the labor gap. But a side effect is that one of the country's least-productive sectors is bolstering capital expenditures even as the world's third-largest economy flirts with recession amid a global growth slowdown. Construction company Shimizu Corp., which spent about ¥3 billion ($27.7 million) on robots over three years, is a case in point. Equipped with state-of-the-art AI, cameras and sensors, the machines handle everything from transporting building materials and welding steel to installing ceilings.

Editor's Note: Get caught up in minutes with our speedy summary of today's must-read news stories and expert opinions that moved the precious metals and financial markets. The Australian government is setting up two mining research centres in partnership with universities and commercial supporters, according to an announcement made on Tuesday. The centers will be based in Sydney and Adelaide. The Australian government said research activity at the University of Sydney "...will focus on data analytics related to the long-term impact of resource use on Australia's economy, society and environment. It will help develop the necessary data science skills for Australia's resource industries to make the best possible evidence-based decisions when using our natural resources." The University of Adelaide will focus on advanced sensors and data analytics.

Current commercial robots generally contain hard parts that represent a risk to the security of their administrators or there is a point of confinement for their usability. Because of this, soft robots have as of late pulled in impressive consideration, in spite of the fact that their absence of structural inflexibility intensely restricts their utilization in numerous practical applications. In the course of recent years, analysts have tried to make mechanical robotic personal assistants and bionic limbs or prosthetics that consolidate the strength of regular robots with the flexibility of soft robots. More recently, combinations of cellular structures have demonstrated intriguing advancement toward the enhancement of non-trifling abilities, for example, getting a handle on exceptionally shaped articles. In any case, tuning the mechanical properties of the robotic body for custom applications is still exceptionally challenging.

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.

When we hear technology we think of electronic gadgets and a hundred types of software. But the problems of the future are going to be more basic. Food, water, and shelter are important to talk about. They're essential to sustain human life and limited in availability. Moreover, the increasing population and concentration of population in major cities will possibly lead to scarcity unless we take due action.

AI in healthcare is having a tremendous impact for the benefit of patients, providers, and payers. The opportunities to deploy AI in health care are increasing exponentially as we become better at capturing and integrating vast amounts of data from multiple sources, making sense of this data in a clinically relevant way, and understand methodologies that explain its use. In some cases, AI will replicate human intelligence, in others it will it will augment what we can do to improve health and lower cost. Pros and cons of various approaches and use cases will be discussed in this exciting session. Recon Strategy is a boutique strategy consulting firm founded in 2010 by alumni of the Boston Consulting Group.