The wood species classification is an essential field of investigation that can help to combat illegal logging, then providing the timber certification and allowing the application of correct timber taxing. Today, the wood classification relies on highly qualified professionals that analyze texture patterns on timber sections. However, these professionals are scarce, costly, and subject to failure. Therefore, the automation of this task using computational methods is promising. Deep learning has proven to be the ultimate technique in computer vision tasks, but it has not been much exploited to perform timber classification due to the difficulty of building large databases to train such networks. In this study, we introduced the biggest data set of wood timber microscope images to the date, with 281 species, having three types of timber sections: transverse, radial, and tangential.

The space of synthesizable molecules is greater than $10^{60}$, meaning only a vanishingly small fraction of these molecules have ever been realized in the lab. In order to prioritize which regions of this space to explore next, synthetic chemists need access to accurate molecular property predictions. While great advances in molecular machine learning have been made, there is a dearth of benchmarks featuring properties that are useful for the synthetic chemist. Focussing directly on the needs of the synthetic chemist, we introduce the Photoswitch Dataset, a new benchmark for molecular machine learning where improvements in model performance can be immediately observed in the throughput of promising molecules synthesized in the lab. Photoswitches are a versatile class of molecule for medical and renewable energy applications where a molecule's efficacy is governed by its electronic transition wavelengths. We demonstrate superior performance in predicting these wavelengths compared to both time-dependent density functional theory (TD-DFT), the incumbent first principles quantum mechanical approach, as well as a panel of human experts. Our baseline models are currently being deployed in the lab as part of the decision process for candidate synthesis. It is our hope that this benchmark can drive real discoveries in photoswitch chemistry and that future benchmarks can be introduced to pivot learning algorithm development to benefit more expansive areas of synthetic chemistry.

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.

Footage has emerged of one of Boston Dynamics' robotic dogs patrolling a SpaceX test site in the US. The video allegedly shows SpaceX using the $75,000 (£60,000) robotic dog to inspect the aftermath of its test site in Boca Chica, Texas. SpaceX had just been conducting a cryogenic pressure test on the Starship SN7 dome tank prototype, according to Tesmanian. SN7 was filled with sub-cooled liquid nitrogen and it was intentionally pressurised to its capacity before it burst and collapsed on its side. The stainless-steel commercial spacecraft, once operational, will be capable of transporting passengers on long-duration voyages to the Moon and Mars. But until the launch vehicle is ready, Elon Musk's company appears to be employing a little help from a trusty robotic companion.

Lawrence Livermore National Laboratory (LLNL) scientists have taken a step forward in the design of future materials with improved performance by analyzing its microstructure using AI. The work recently appeared online in the journal Computational Materials Science. Technological progress in materials science applications spanning electronic, biomedical, alternate energy, electrolyte, catalyst design and beyond is often hindered by a lack of understanding of complex relationships between the underlying material microstructure and device performance. But AI-driven data analytics provide opportunities that can accelerate materials design and optimization by elucidating processing-performance correlations in a mathematically tractable way. However, to reliably train large networks one needs data from tens of thousands of samples, which, unfortunately is often prohibitive in new systems and new applications due to the cost of sample-preparation and data collection.

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.

Various structured output prediction problems (e.g., sequential tagging) involve constraints over the output space. By identifying these constraints, we can filter out infeasible solutions and build an accountable model. To this end, we present a general integer linear programming (ILP) framework for mining constraints from data. We model the inference of structured output prediction as an ILP problem. Then, given the coefficients of the objective function and the corresponding solution, we mine the underlying constraints by estimating the outer and inner polytopes of the feasible set. We verify the proposed constraint mining algorithm in various synthetic and real-world applications and demonstrate that the proposed approach successfully identifies the feasible set at scale. In particular, we show that our approach can learn to solve 9x9 Sudoku puzzles and minimal spanning tree problems from examples without providing the underlying rules. We also demonstrate results on hierarchical multi-label classification and conduct a theoretical analysis on how close the mined constraints are from the ground truth.

Yara, one of the world's leading fertilizer companies and a provider of environmental solutions, have created an industry-wide business platform to connect and empower independent farmers. It will use IoT sensors and AI and TWC to provide hyperlocal weather forecasting, crop damage prediction and real-time recommendations. Already downloaded by over 1,300,000 farmers, this platform is transforming existing supplier relationships and expanding its value. Yara built a digital farming platform that connects and empowers independent farmers, expanding its business model as a first-of-a-kind, competitive differentiator in the industry. Yara, one of the world's leading fertilizer companies and a provider of environmental solutions, have created an industry-wide business platform to connect and empower independent farmers.

Air Quality Index is based on the measurement of particulate matter, Ozone, Nitrogen Dioxide, Sulfur Dioxide, and Carbon Monoxide emissions. Most of the stations on the map are monitoring both PM2.5 and PM10 data, but there are few exceptions where only PM10 is available, Here we are using the Bangalore weather data, and some of the features might even make the predictions worse. An artificial neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one artificial neuron to the input of another, here it would help us how we can make neurons on-air live air data, try to find the best mean squared error. Input Layer - This is the first layer in the neural network.