A decade in the making, Rio Tinto's driverless train system, called AutoHaul, now manages roughly 200 locomotives that move iron ore from inland mines to coastal ports in Western Australia. The trains are operated hundreds of miles away, in an office block in Perth. Rio Tinto's network, which began formally operating in driverless mode late last month, is the first fully autonomous, long-haul freight railroad. Rail-company executives from countries including the U.S. and Canada have visited to see the technology in action, said Ivan Vella, Rio Tinto's head of iron-ore rail services. American companies say automating tasks once handled by crew will create fluid networks more akin to a model train set.

Welcome to our HR Modernization Playbook: Tomorrow's people – Why HR matters more than ever in the age of artificial intelligence. Digital transformation is happening faster than ever. The adoption of artificial intelligence (AI) and automation will redefine jobs, enhance employee productivity and accelerate workforce development. In fact, skills and culture – not technology – are the biggest barriers to business growth in the AI era. This means CEOs are looking to their CHRO to lead culture change, manage talent and drive down costs.

A new machine-learning model developed by a West Virginia University student has potential applications in the energy, environmental and health-care fields. The model, which can be used to predict adsorption energies -- i.e., adhesive capabilities in gold nanoparticles -- was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high-technology applications such as electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," Panapitiya said.

A new machine-learning model developed by a West Virginia University student has the potential for energy, environmental and even healthcare applications. The model, which can be used to predict the adsorption energies, i.e. adhesive capabilities in gold nanoparticles, was developed by Gihan Panapitiya, a doctoral physics student from Sri Lanka. Gold nanoparticles have historically been used by artists to bring out vibrant colors via their interaction with light. Now they are increasingly used in high technology applications, electronic conductors and others. "Machine learning recently came into the spotlight, and we wanted to do something linking machine learning with gold nanoparticles as catalysts," he said.

The problem of retrosynthetic planning can be framed as one player game, in which the chemist (or a computer program) works backwards from a molecular target to simpler starting materials though a series of choices regarding which reactions to perform. This game is challenging as the combinatorial space of possible choices is astronomical, and the value of each choice remains uncertain until the synthesis plan is completed and its cost evaluated. Here, we address this problem using deep reinforcement learning to identify policies that make (near) optimal reaction choices during each step of retrosynthetic planning. Using simulated experience or self-play, we train neural networks to estimate the expected synthesis cost or value of any given molecule based on a representation of its molecular structure. We show that learned policies based on this value network outperform heuristic approaches in synthesizing unfamiliar molecules from available starting materials using the fewest number of reactions. We discuss how the learned policies described here can be incorporated into existing synthesis planning tools and how they can be adapted to changes in the synthesis cost objective or material availability.

To demonstrate the viability of our method, we predicted reaction outcomes with substrate combinations and catalysts different from the training data and simulated a situation in which highly selective reactions had not been achieved. In the first demonstration, a model was constructed by using support vector machines and validated with three different external test sets. The first test set evaluated the ability of the model to predict the selectivity of only reactions forming new products with catalysts from the training set. The model performed well, with a mean absolute deviation (MAD) of 0.161 kcal/mol. Next, the same model was used to predict the selectivity of an external test set of catalysts with substrate combinations from the training set.

In an attempt to skill Indian youths in Artificial Intelligence, Microsoft India has taken an initiative to train five lakh youths. The company aims to train five lakh youths in AI across the country and would set up AI labs in 10 universities. Additionally, the company plans to upskill 10,000 developers in emerging technology areas like AI, IoT, etc. Microsoft also started Intelligent Cloud Hub Program to equip research and higher education institutions with AI infrastructure, build curriculum and help both faculty and students to build their skills and expertise in cloud computing, data sciences, AI and IoT. Anant Maheshwari, President, Microsoft India shares, "We believe AI will enable Indian businesses and more for India's progress, especially in education, skilling, healthcare, and agriculture. Microsoft also believes that it is imperative to build higher awareness and capabilities on security, privacy, trust, and accountability. The power of AI is just beginning to be realized and can be a game-changer for India."

ABB's future of mining infographic shows how to drive profits World's largest flotation cells improve copper and molybdenum recovery in Mexico PRESS RELEASE: The solution will be released in early 2019 as part of MICROMINE's fleet management and mine control solution, Pitram. Using the processes of computer vision and deep machine learning, on-board cameras are placed on loaders to track variables such as loading time, hauling time, dumping time and travelling empty time. The video feed is processed on the Pitram vehicle computer edge device, the extracted information is then transferred to Pitram servers for processing and analyses. ABB's future of mining infographic shows how to drive profits World's largest flotation cells improve copper and molybdenum recovery in Mexico MICROMINE Chief Technology Officer Ivan Zelina explained the solution intelligently considered the information gathered to pinpoint areas of potential improvement that could bolster machinery efficiency and safety. "Pitram's new offering takes loading and haulage automation in underground mines to a new level," Mr Zelina said.

BENGALURU: Microsoft India on Wednesday announced to set up Artificial Intelligence (AI) labs in 10 universities and train five lakh youth across the country in disrupting technologies. The company also said it will upskill over 10,000 developers over the next three years. "We believe AI will enable Indian businesses and more for India's progress, especially in education, skilling, healthcare and agriculture," said Anant Maheshwari, President, Microsoft India. Microsoft AI today is fuelling digital transformation for over 700 customers and 60 per cent customers are large manufacturing and financial services enterprises. Over 700 partners have geared up to support the AI ecosystem, said the company.

I'll use a very interesting dataset presented in the book Machine Learning with R from Packt Publishing, written by Brett Lantz. My intention is to expand the analysis on this dataset by executing a full supervised machine learning workflow which I've been laying out for some time now in order to help me attack any similar problem with a systematic, methodical approach. If you are thinking this is nothing new, then you're absolutely right! I'm not coming up with anything new here, just making sure I have all the tools necessary to follow a full process without leaving behind any big detail. Hopefully some of you will find it useful too and be sure you are going to find some judgment errors from my part and/or things you would do differently. Feel free to leave me a comment and help me improve! Let's jump ahead and begin to understand what information we are going to work with: "In the field of engineering, it is crucial to have accurate estimates of the performance of building materials. These estimates are required in order to develop safety guidelines governing the materials used in the construction of building, bridges, and roadways. Estimating the strength of concrete is a challenge of particular interest. Although it is used in nearly every construction project, concrete performance varies greatly due to a wide variety of ingredients that interact in complex ways. As a result, it is difficult to accurately predict the strength of the final product. A model that could reliably predict concrete strength given a listing of the composition of the input materials could result in safer construction practices. For this analysis, we will utilize data on the compressive strength of concrete donated to the UCI Machine Learning Data Repository (http://archive.ics.uci.edu/ml) by I-Cheng Yeh. According to the website, the concrete dataset contains 1,030 examples of concrete with eight features describing the components used in the mixture. These features are thought to be related to the final compressive strength and they include the amount(in kilograms per cubic meter) of cement, slag, ash, water, superplasticizer, coarse aggregate, and fine aggregate used in the product in addition to the aging time (measured in days)."