A new type of plastic that can heal itself when damaged could mean satellites will be able to stay in orbit for longer, scientists have revealed. The polymer heals cracks when exposed to certain light by converting from a rigid structure to a much softer, malleable substance. Under certain conditions, the plastic used by researchers could become up to ten times softer and more dynamic. Such plastics could also be used to coat vehicles on Earth, including cars, giving them the ability to heal after being involved in crashes. A new type of plastic that can heal itself when damaged could mean satellites may stay in orbit for even longer, scientists have revealed.

Maura Kolb is more familiar with analyzing rock samples than computer code, but as exploration manager at Goldcorp's Red Lake gold mine, she now finds herself in charge of an innovative approach to exploration that involves both. The project, in partnership with IBM, uses IBM's Watson artificial intelligence platform to identify exploration targets at the Ontario mine. Since the project was launched in March 2017 at the Disrupt Mining event at PDAC, Goldcorp says the time it takes to process survey data has fallen by 97 per cent. "Being able to go through [our data] quickly, that's something no one else can do right now," said Kolb. "We can ask questions of our data that we haven't been able to in the past." The main goal of the project is to use Watson to help identify high-grade areas at Red Lake that were previously overlooked by human eyes.

Machine learning is beginning to make a large impact in catalysis research, according to Bryan Goldsmith, Jacques Esterhuizen, and Jin-Xun Liu of the Univ. of Michigan, Christopher Bartel of the Univ. of Colorado Boulder, and Christopher Sutton of the Fritz Haber Institute of the Max Planck Society in their July AIChE Journal Perspective article, "Machine Learning for Heterogeneous Catalyst Design and Discovery." Novel catalysts are crucial for several applications, such as energy generation and storage, sustainable chemical production, and pollution mitigation. The current trial-and-error approaches to new catalyst discovery and synthesis are expensive and time-consuming. As an alternative, machine learning can be used to identify the top catalyst candidates before experimental testing, thereby accelerating catalyst discovery and design. Goldsmith and colleagues highlight several examples where machine learning is making an impact on heterogeneous catalysis research, such as: accelerating the determination of catalyst active sites and catalyst screening; finding descriptors and patterns in catalysis data; determining interatomic potentials for catalyst simulation; and discovering and analyzing catalytic mechanisms.

A report from McKinsey & Company asserts that the construction and engineering industry is behind the curve in implementing artificial intelligence solutions, and that AI proliferation will be modest in the immediate future because relatively few firms have the personnel, processes and tools to manage it. The report compared the buildings materials and construction industry to 12 other industries, 10 of which are further along in AI adoption and all of which are projected to increase spending on AI at faster pace during the next three years. It also details how engineering and construction firms potentially can apply AI solutions from the retail, transportation, health care and pharmaceutical sectors. For example, AI could be used to recommend various designs and finishes, compare drone-collected images of defects to existing drawings, gauge the lump-sum price discounts clients may be willing to pay for a project and perform a sentiment analysis on a firm's market perception. On the workforce front, AI can segment employees based on the likelihood of attrition and develop targeted plans to retain them.

Computer programs can, in some cases, predict chemical toxicity as well as tests done on rats and other animals.Credit: Coneyl Jay/SPL Machine-learning software trained on masses of chemical-safety data is so good at predicting some kinds of toxicity that it now rivals -- and sometimes outperforms -- expensive animal studies, researchers report. Computer models could replace some standard safety studies conducted on millions of animals each year, such as dropping compounds into rabbits' eyes to check if they are irritants, or feeding chemicals to rats to work out lethal doses, says Thomas Hartung, a toxicologist at Johns Hopkins University in Baltimore, Maryland. "The power of big data means we can produce a tool more predictive than many animal tests." In a paper published in Toxicological Sciences1 on 11 July, Hartung's team reports that its algorithm can accurately predict toxicity for tens of thousands of chemicals -- a range much broader than other published models achieve -- across nine kinds of test, from inhalation damage to harm to aquatic ecosystems. The paper "draws attention to the new possibilities of big data", says Bennard van Ravenzwaay, a toxicologist at the chemicals firm BASF in Ludwigshafen, Germany.

You are free to share this article under the Attribution 4.0 International license. Scientists may be able to better predict the toxicity of new chemicals through data analysis than with standard tests on animals, according to a new study. The researchers say they developed a large database of known chemicals and then used it to map the toxic properties of different chemical structures. They then showed they could predict the toxic properties of a new chemical compound with structures similar to a known chemical, and do it more accurately than with an animal test. "A new pesticide, for example, might require 30 separate animal tests, costing the sponsoring company about $20 million…" The most advanced toxicity-prediction tool the team developed was on average about 87 percent accurate in reproducing consensus animal-test-based results across nine common tests, which account for 57 percent of the world's animal toxicology testing.

IBM Almaden Research Center, 650 Harry Road, San Jose, California 95136 Abstract We report development of a data infrastructure for drug repurposing that takes advantage of two currently available chemical ontologies. The data infrastructure includes a database of compoundtarget associations augmented with molecular ontological labels. It also contains two computational tools for prediction of new associations. We describe two drug-repurposing systems: one, Nascent Ontological Information Retrieval for Drug Repurposing (NOIR-DR), based on an information retrieval strategy, and another, based on nonnegative matrix factorization together with compound similarity, that was inspired by recommender systems. We report the performance of both tools on a drug-repurposing task. 1 Introduction Drug repurposing is an efficient strategy for drug discovery, where new targets or activities are found for known drugs [1-5]. Drug repurposing requires the efficient representation of existing information about the activity of chemical compounds as drugs, and the development of algorithms that leverage such information and propose new indications.

Artificial intelligence (AI) today is the new frontier in the digital transformation journey enterprises have already embarked on. But adoption to solve real problems and drive business outcomes has been slow. Driving up adoption is critical to unlock the real promise of AI and is going to depend on how we approach AI. And that opportunity is in front of us thanks to industry-optimized augmented intelligence. Most realistic and successful AI initiatives have been focused on augmenting human abilities with powerful machine intelligence.

It's hard to know, at first, what problem the Brava smart oven is supposed to solve. Its value proposition--to use the Silicon Valley parlance--is a bit diluted. Is it supposed to heat up more quickly than your current oven? Is it designed to distribute heat in an innovative way? Is it supposed to be more energy efficient?

The most obvious use currently of AI in mining operations is providing a'better experience' for automated pit vehicles. Autonomous mining vehicles, from the haul trucks to the graders, loaders and excavators as well as drilling rigs, are hooked into the IoT. These machines produce copious amounts of data about their routines and routes.AI applied to this data allows operators based in operation centres hundreds of kilometres away to improve these routes and routines. They can do things like tweak the way the dump trucks take corners, or excavators load trucks, to make the entire process more efficient.This has potential cost and time saving implications. The vehicles can also safely work around the clock and don't need to stop for shift changes.