As COVID-19 continues to affect millions of lives and livelihoods, it is delivering perhaps the most significant shock to industries--from education to healthcare to food supply--in almost a century. Mineral processing companies also have to grapple with profound uncertainty and volatility. Before COVID-19, some were already taking steps to build their capabilities to cope with fluctuations inherent in commodities markets. But recent events triggering challenges in workforce availability, supply chains, and demand created a need for higher levels of operational resilience in a short period of time. Here is where recent advances in artificial intelligence (AI) helped.
The construction industry contributes to 39% of global carbon emissions while aviation contributes to only 2% which means we need to look for alternative building materials if we are to make a big impact on the climate crisis soon. We've seen buildings being made using mushrooms, bricks made from recycled plastic and sand waste, organic concrete, and now are seeing another innovative solution – a floating 3D printed house! Prvok is the name of this project and it will be the first 3D printed house in the Czech Republic built by Michal Trpak, a sculptor, and Stavebni Sporitelna Ceske Sporitelny who is a notable member of the Erste building society. The house is designed to float and only takes 48 hours to build! Not only is that seven times faster than traditional houses, but it also reduces construction costs by 50%.
Mining is no stranger to digitalisation. The widely held perception of the resources industry is one of workers in mines and not one of machines running almost everything. But technological advances have already resulted in adoption of mechanisation, automation and data-driven production optimisation. Companies such as BHP, Anglo American and Rio Tinto have embraced digitalisation to gain a competitive advantage, mitigate risk and improve performance. They use advanced data analytics, virtual reality and artificial intelligence to reduce costs and increase efficiency in their processes, leading to enhanced ore recovery and less waste, to name a couple of benefits.
IBM and Shell have joined forces to launch a mining services marketplace, aimed at helping companies in the industry with safety, sustainability, mine planning, and operational efficiency. Oren aims to accelerate the digitisation of the mining industry. It matches ideas or solutions from companies, including startups, with global mining firms that are looking for digital assistance. IBM said this would allow mining companies to speed up digital transformation initiatives. "Oren helps to solve the pain-points for mining customers," Shell VP of global marketing lubricants Carol Chen said during a press briefing.
What do Ted Cruz, Neil deGrasse Tyson and Goldman Sachs all have in common? They predict that the world's first trillionaire will make their innumerable fortune in space. While Cruz is not precisely sure how this will come to be, Tyson and Goldman Sachs believe that the gateway to this immense wealth is through mining asteroids. The reason why space mining is so sought after is due to what is happening here on Earth. Based on known terrestrial reserves and estimates of the growing consumption in countries, essential elements needed for modern industry and food production (such as lead, phosphorus and gold) could be exhausted within the next 60 years.
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 firstname.lastname@example.org.
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.
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.
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.