Ending hunger is one of the top priorities of the United Nations this decade. Yet the world appears to be backsliding, with an uptick of 60 million people experiencing hunger in the last five years to an estimated 690 million worldwide. To help turn this trend around, a team of 70 researchers published a landmark series of eight studies in Nature Food, Nature Plants, and Nature Sustainability on Monday. The scientists turned to machine learning to comb 500,000 studies and white papers chronicling the world's food system. The results show that there are routes to address world hunger this decade, but also that there are also huge gaps in knowledge we need to fill to ensure those routes are equitable and don't destroy the biosphere.

Our traditional solution to the unpredictable nature of renewable energy sources like solar and wind power has generally been to simply dump the excess wattage back into the local grid or sequester it away in utility-scale batteries. But as more and more of our power generation is created by renewables, their production capacities can potentially outstrip that of the local grid while battery technology can quickly become prohibitively expensive at scale. One alternative is putting that excess power to work driving catalytic reactions. "There are a lot of different ways that we can store the energy," Zack Ulissi, CMU Assistant Professor of Chemical Engineering and Materials Science and Engineering, told Engadget. "The most well known is you take water and you electrolyze it to split it into hydrogen and oxygen. And then you can take that hydrogen and run it into a hydrogen fuel cell."

The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions--such as adopting climate-resilient crops through improving extension services--all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers--authored by 77 scientists, researchers and librarians in 23 countries--as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell University, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals--Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food--and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.

Caterpillar's autonomous driving technology, which can be bolted on to existing machines, is helping the U.S. heavy equipment maker mitigate the heavy impact of the coronavirus crisis on sales of its traditional workhorses. With both small and large customers looking to protect their operations from future disruptions, demand has surged for machines that don't require human operators on board. Sales of Caterpillar's autonomous technology for mining operations have been growing at a double-digit percentage clip this year compared with 2019, according to previously unreported internal company data shared with Reuters. Fred Rio, worldwide product manager at Caterpillar's construction digital & technology division, told Reuters that a remote-control technology, which allows users to operate machines from several miles away, would be available for construction sites in January. The company is also working with space agencies to use satellite technology to allow an operator sitting in the United States to remotely communicate with machines on job sites in, say, Africa or elsewhere in the world, he said.

CHICAGO (Reuters) - Question: How can a company like Caterpillar CAT.N try to counter a slump in sales of bulldozers and trucks during a pandemic that has made every human a potential disease vector? Caterpillar's autonomous driving technology, which can be bolted on to existing machines, is helping the U.S. heavy equipment maker mitigate the heavy impact of the coronavirus crisis on sales of its traditional workhorses. With both small and large customers looking to protect their operations from future disruptions, demand has surged for machines that don't require human operators on board. Sales of Caterpillar's autonomous technology for mining operations have been growing at a double-digit percentage clip this year compared with 2019, according to previously unreported internal company data shared with Reuters. By contrast, sales of its yellow bulldozers, mining trucks and other equipment have been falling for the past nine months, a trend that's also hit its main rivals including Japan's Komatsu Ltd 6301.T and American player Deere & Co DE.N .

The world's small-scale farmers now can see a path to solving global hunger over the next decade, with solutions – such as adopting climate-resilient crops through improving extension services – all culled rapidly via artificial intelligence from more than 500,000 scientific research articles. The results are synthesized in 10 new research papers – authored by 77 scientists, researchers and librarians in 23 countries – as part of Ceres2030: Sustainable Solutions to End Hunger. The project is headquartered at Cornell, with partners from the International Food Policy Research Institute (IFPRI) and the International Institute for Sustainable Development (IISD). The papers were published concurrently on Oct. 12 in four journals – Nature Plants, Nature Sustainability, Nature Machine Intelligence and Nature Food – and assembled in a comprehensive package online: Sustainable Solutions to End Hunger. Ceres2030 employed machine learning, librarian savvy and research synthesis methods to quickly scan a trove of thousands of scientific journals for ideas and websites from more than 60 agencies that can help eradicate world hunger.

A popular YouTuber has created the first functioning lightsaber, using propane gas burning at around 4,000 C to create a retractable plasma beam. Canadian James Hobson, known as'the Hacksmith', has a following of ten million subscribers and works on turning popular science fiction items into reality. Inspired by a love of Star Wars, he has previously made various lightsabers, but wanted to produce'the world's first, retractable, plasma-based' version. Canadian James Hobson, known as'the Hacksmith', has a following of ten million subscribers and works on turning popular science fiction items into a reality. He claims to have built'the world's first, retractable, plasma-based lightsaber' For this, the internet-famous engineers used liquid petroleum gas, a fuel tucked away in many sheds and often used to power barbecues.

Hitachi Construction Machinery (HCM) and its consolidated subsidiary, Wenco International Mining Systems, have jointly developed "ConSite Mine", a new technology platform that helps resolve problems at mine sites by remotely monitoring mining machines on a 24/7 basis through the use of IoT and AI based analysis of equipment operations data. According to Hitachi, it has developed this technology to help customers and HCM dealers predict costly maintenance issues before they occur, such as the occurrence of cracks in excavator booms or arms, by utilising machine learning and applied analysis technologies. Detailed information from these predictive alerts are provided on the web-based ConSite Mine dashboard and other items. Currently, Hitachi is piloting the technology in Australia, Zambia and Indonesia. "ConSite Mine" will be further modified based on customer feedback before wider commercial release in 2021.

We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions of each relationship between object combinations. This framework is advantageous in both diversity and amount of information, leading to a comprehensive image understanding based on relationships, e.g., relational proposal generation. For relational understanding between objects, the part-of-speech (POS, i.e., subject-object-predicate categories) can be a valuable prior information to guide the causal sequence of words in a caption. We enforce our framework to not only learn to generate captions but also predict the POS of each word. To this end, we propose the multi-task triple-stream network (MTTSNet) which consists of three recurrent units responsible for each POS which is trained by jointly predicting the correct captions and POS for each word. In addition, we found that the performance of MTTSNet can be improved by modulating the object embeddings with an explicit relational module. We demonstrate that our proposed model can generate more diverse and richer captions, via extensive experimental analysis on large scale datasets and several metrics. We additionally extend analysis to an ablation study, applications on holistic image captioning, scene graph generation, and retrieval tasks.