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.
By Li Yuan Some of the most critical work in advancing China's technology goals takes place in a former cement factory in the middle of the country's heartland, far from the aspiring Silicon Valleys of Beijing and Shenzhen. An idled concrete mixer still stands in the middle of the courtyard. Boxes of melamine dinnerware are stacked in a warehouse next door. Inside, Hou Xiameng runs a company that helps artificial intelligence make sense of the world. Two dozen young people go through photos and videos, labeling just about everything they see.
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.
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.
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)."
After the acquisition of Podium Data by Qlik last July, we knew a couple of things: consolidation in the analytics market was continuing, and interest in data governance/data management was increasing, even for self-service BI vendors like Qlik. What we didn't know, though, was what would happen to the Podium team and product post-acquisition. The Podium team is apparently intact, now constituting a distinct Enterprise Data Management team at Qlik, with its own P&L. Paul Barth, Podium's erstwhile CEO, is now Managing Director of that group, and carries that P&L responsibility. And in a discussion with both Barth and Joe DosSantos, Qlik's new Global Head of Enterprise Data Strategy (and formerly a Podium Data customer at TD Bank), I found out how the product has evolved as well.
This article was originally published by Ozy. In 2016, Houston's petrochemical industry had countless job positions that were unfilled. And at the same time, a number of the city's residents were looking for work. So, how was Houston going to fix this? In an effort to help match eligible candidates with open positions, private companies began to step in.
God might just as well have begun with a toaster oven. A few years ago at a yard sale, Nicholas Hud spotted a good candidate: A vintage General Electric model, chrome-plated with wood-grain panels, nestled in an old yellowed box, practically unused. The perfect appliance for cooking up the chemical precursors of life, he thought. He bought it for $5. At home in his basement, with the help of his college-age son, he cut a rectangular hole in the oven's backside, through which an automated sliding table (recycled from an old document scanner) could move a tray of experiments in and out. He then attached a syringe pump to some inkjet printer parts, and rigged the system to periodically drip water onto the tray.