The proportion of packaging companies hiring for machine learning related positions rose significantly in April 2022 compared with the equivalent month last year, with 25% of the companies included in our analysis recruiting for at least one such position. This latest figure was higher than the 10.7% of companies who were hiring for machine learning related jobs a year ago and an increase compared to the figure of 17.9% in March 2022. When it came to the rate of all job openings that were linked to machine learning, related job postings rose in April 2022, with 0.5% of newly posted job advertisements being linked to the topic. This latest figure was an increase compared to the 0.2% of newly advertised jobs that were linked to machine learning in the equivalent month a year ago. Machine learning is one of the topics that GlobalData, from whom our data for this article is taken, have identified as being a key disruptive force facing companies in the coming years.

Litmus, the Edge Data Platform for Industry 4.0, today announced CHIMEI, a leading Taiwan-based performance materials manufacturer, has deployed Litmus Edge to power artificial intelligence at the edge. CHIMEI will deploy Litmus Edge to many of their plants to expand factory data collection and run AI models at the edge to improve quality in production processes. Previously CHIMEI was collecting data and running AI models for each of their production processes separately using different edge devices, which was not efficient and maintenance costs were high. Complex integration with multiple AI model servers prompted them to look for a new solution that would allow them to collect data from more machines and provide AI model runtime functionality. CHIMEI chose Litmus Edge to consolidate production processes and run most AI models from the same edge device.

The University of Western Australia (UWA) and Rio Tinto Iron Ore (RTIO) have entered into a four-year, $6.1 million research partnership to develop innovative data science solutions (artificial intelligence) for automated geological logging to improve mining practice. The partnership, which follows more than 10 years of collaboration between UWA's data science team and RTIO, will employ five full-time researchers and provide training opportunities for a number of industry-driven PhD programmes. Dr Daniel Wedge, from (CDG) in UWA's School of Geosciences, said UWA's expertise will be resorted to help RTIO's mine geology team tackle the challenge of objective well geological materials. "Until recently, geologist's specialists had to manually interpret and document material found in core samples, a process that was time-consuming and challenging," Dr Wedge said. "Our project can use artificial intelligence: machine learning, pc vision, spacial modelling and improvement techniques to integrate disparate borehole information, together with analysis, imagery, geochemical and natural science informationalong side chemical analysis, imagery, geochemical and earth science info, to."RTIO head Dr. Angus McFarlane said the past partnership between UWA and RTIO has led to the commercialisation of UWA's automated downhole image analysis software and three joint patent applications for RTIO-driven machine learning-based geological modelling.

A few years ago, many people imagined a world run by robots. The promises and challenges associated with artificial intelligence (AI) were widely discussed as this technology moved out of the labs and into the mainstream. Many of these predictions seemed contradictory. Robots were mooted to steal our jobs, but also create millions of new ones. As more applications were rolled out, AI hit the headlines for all the right (and wrong) reasons, promising everything from revolutionizing the healthcare sector to making light of the weight of data now created in our digitized world.

Across midwestern farms, if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms. Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it -- like almost every other segment of the agricultural economy -- faces daunting problems, including changing weather patterns, environmental degradation, severe labor shortages, and the rising cost of key supplies, or inputs: herbicides, pesticides, and seed. Agribusiness as a whole is betting that the world has reached the tipping point where desperate need caused by a growing population, the economic realities of conventional farming, and advancing technology converge to require something called precision agriculture, which aims to minimize inputs and the costs and environmental problems that go with them. No segment of agriculture is without its passionate advocates of robotics and artificial intelligence as solutions to, basically, all the problems facing farmers today.

Project proposal, submitted under the MINE.THE.GAP 2nd open call, passed the evaluation phase and was selected for funding. The digital solution proposed will provide a 3D interactive imaging for smart exploration and exploitation of an industrial minerals deposit according to its final uses (e.g., insulation, construction, agriculture, filtration etc.). The main objective of this PoC is a digital Demo interface that will provide a view of a future Prototype and allow use to engage Bêta-Testers of the future Prototype. This innovation will allow users to define their own scenarios regarding final applications for the deposit and retrieve a 3D bloc model of the corresponding market value. The solution will advocate a better use of mineral resources and will democratize best practices.

This story originally appeared on Undark and is part of the Climate Desk collaboration. Across Midwestern farms, if Girish Chowdhary has his way, farmers will someday release beagle-sized robots into their fields like a pack of hounds flushing pheasant. The robots, he says, will scurry in the cool shade beneath a wide diversity of plants, pulling weeds, planting cover crops, diagnosing plant infections, and gathering data to help farmers optimize their farms. Chowdhary, a researcher at the University of Illinois, works surrounded by corn, one of the most productive monocultures in the world. In the United States, the corn industry was valued at $82.6 billion in 2021, but it--like almost every other segment of the agricultural economy--faces daunting problems, including changing weather patterns, environmental degradation, severe labor shortages, and the rising cost of key inputs: herbicides, pesticides, and seed.

This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the interactions of different geometric features of a design on mechanical response, including energy absorption. We proposed a combinatorial, key-based design system to generate different lattice designs and used the finite element method to simulate their response with the Johnson-Cook material model. Using an autoencoder, we encoded the cross-sectional images of the lattices into latent design feature vectors, which were supplied to the neural network model to generate predictions. The trained models can accurately predict lattice energy absorption curves in the key-based design system and can be extended to new designs outside of the system via transfer learning.

Blue-green algae sealed within a small container have powered a computer for six months. Similar photosynthetic power generators could run a range of small devices cheaply in the coming years, without the need for the rare and unsustainable materials used in batteries. Christopher Howe at the University of Cambridge and his colleagues built a small enclosure about the size of an AA battery out of aluminium and clear plastic. Inside, they placed a colony of a type of cyanobacteria called Synechocystis sp. PCC 6803 – commonly known as "blue-green algae" – which produce oxygen through photosynthesis when exposed to sunlight.

We present a multi-camera visual-inertial odometry system based on factor graph optimization which estimates motion by using all cameras simultaneously while retaining a fixed overall feature budget. We focus on motion tracking in challenging environments, such as narrow corridors, dark spaces with aggressive motions, and abrupt lighting changes. These scenarios cause traditional monocular or stereo odometry to fail. While tracking motion with extra cameras should theoretically prevent failures, it leads to additional complexity and computational burden. To overcome these challenges, we introduce two novel methods to improve multi-camera feature tracking. First, instead of tracking features separately in each camera, we track features continuously as they move from one camera to another. This increases accuracy and achieves a more compact factor graph representation. Second, we select a fixed budget of tracked features across the cameras to reduce back-end optimization time. We have found that using a smaller set of informative features can maintain the same tracking accuracy. Our proposed method was extensively tested using a hardware-synchronized device consisting of an IMU and four cameras (a front stereo pair and two lateral) in scenarios including: an underground mine, large open spaces, and building interiors with narrow stairs and corridors. Compared to stereo-only state-of-the-art visual-inertial odometry methods, our approach reduces the drift rate, relative pose error, by up to 80% in translation and 39% in rotation.