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How Documentary Theater Goes From Interviews to Final Production

Slate

This week, host Isaac Butler talks to documentary theater makers Jessica Blank and Erik Jensen, whose plays include The Exonerated, about the criminal justice system, and Coal Country, about the Upper Big Branch mine disaster in West Virginia. Blank and Jensen explain how documentary theater works, from interviews with subjects to the final product, where actors perform interview excerpts verbatim. After the interview, Isaac and co-host June Thomas discuss why documentary theater is such a great way to communicate important information to an audience. Send your questions about creativity and any other feedback to working@slate.com.


Artificial Intelligence Helps Researchers Up-Cycle Waste Carbon With Record Efficiency – IAM Network

#artificialintelligence

Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Credit: Daria Perevezentsev / University of Toronto Engineering Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.


AI And The Digital Mine

#artificialintelligence

When you think of the words "data" and "mine", no doubt the idea of data mining comes first. However, just as much as we find value in mining the rich resources of data, so too can we apply the advanced techniques for dealing with data to real-world mining -- that is, extracting natural resources from the earth. The world is just as dependent on natural resources as it is data resources, so it makes sense to see how the evolving areas of artificial intelligence and machine learning have an impact on the world of mining and natural resource extraction. Mining has always been a dangerous profession, since extracting minerals, natural gas, petroleum, and other resources requires working in conditions that can be dangerous for human life. Increasingly, we are needing to go to harsher climates such as deep under the ocean or deep inside the earth to extract the resources we still need.


CIOs and the circular economy: 'Ultimately, businesses will need to go this way'

#artificialintelligence

In 10 years, the circular economy will be the only economy, replacing wasteful linear economies, predicts Gartner. According to Gartner, circular economic business models encourage continuous reuse of materials to minimise waste and the demand for additional natural resource consumption. "The circular economy creates an ecosystem of materials," notes Sarah Watt, senior director analyst at Gartner. "What was previously viewed as waste now has value. However those ecosystems are complex, and include many interdependencies and feedback loops."


Artificial Intelligence Helps Researchers Up-Cycle Waste Carbon With Record Efficiency

#artificialintelligence

Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.


Spotting Controversy with NLP - KDnuggets

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Before committing to a company, investors want to know if there are any potential controversies brewing, or if the company shows particular leadership in an area of ESG, such as diversity in the workforce. Refinitiv is a global provider of financial market data and infrastructure, and this article describes how their Labs team is exploring the use of NLP to give their clients a competitive edge in global financial markets. Currently, Refinitiv analysts search for news stories about a specific company using a set of ESG-related keywords, and if there's a positive match, the story is subject to further scrutiny. CHICAGO (Reuters) -- The agricultural unit of German chemicals company Bayer AG will halt future U.S. sales of an insecticide that can be used on more than 200 crops after losing a fight with the U.S. Environment Protection Agency, the company said on Friday. This can take the analyst some considerable time.


Here's how we could mine the moon for rocket fuel

MIT Technology Review

The moon is a treasure trove of valuable resources. Gold, platinum, and many rare Earth metals await extraction to be used in next-generation electronics. But there's one resource in particular that has excited scientists, rocket engineers, space agency officials, industry entrepreneurs--virtually anyone with a vested interest in making spaceflight to distant worlds more affordable. If you split water into hydrogen and oxygen, and then liquefy those constituents, you have rocket fuel. If you can stop at the moon's orbit or a lunar base to refuel, you no longer need to bring all your propellant with you as you take off, making your spacecraft significantly lighter and cheaper to launch.


Your Ultimate Data Mining & Machine Learning Cheat Sheet

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Dimensionality reduction is the process of expressing high-dimensional data in a reduced number of dimensions such that each one contains the most amount of information. Dimensionality reduction may be used for visualization of high-dimensional data or to speed up machine learning models by removing low-information or correlated features. Principal Component Analysis, or PCA, is a popular method of reducing the dimensionality of data by drawing several orthogonal (perpendicular) vectors in the feature space to represent the reduced number of dimensions. The variable number represents the number of dimensions the reduced data will have. In the case of visualization, for example, it would be two dimensions.


Using big data to design gas separation membranes, reduce CO2

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Their study, published today in Science Advances, is the first to apply an experimentally validated machine learning method to rapidly design and develop advanced gas separation membranes. "Our work points to a new way of materials design and we expect it to revolutionize the field," says the study's PI Sanat Kumar, Bykhovsky Professor of Chemical Engineering and a pioneer in developing polymer nanocomposites with improved properties. Plastic films or membranes are often used to separate mixtures of simple gases, like carbon dioxide (CO2), nitrogen (N2), and methane (CH4). Scientists have proposed using membrane technology to separate CO2 from other gases for natural gas purification and carbon capture, but there are potentially hundreds of thousands of plastics that can be produced with our current synthetic toolbox, all of which vary in their chemical structure. Manufacturing and testing all of these materials is an expensive and time-consuming process, and to date, only about 1,000 have been evaluated as gas separation membranes.


South Africa's Impala Closes Platinum Mine Due to Coronavirus Cases

U.S. News

"Implats has identified 19 positive cases during the week, all of them asymptomatic. Of these cases, 14 were identified as the result of proactive testing of employees returning to work. None of these employees had started work at the mine," the company said in a statement.