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.
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.
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.
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.
Investment in artificial intelligence is growing in Canada. In 2017, venture capital investment in AI nearly doubled - to $12 billion. And looking at the agriculture sector, AI is helping farmers to increase crop yields, save costs and reduce environmental damages. For generations, farmers have relied on their own knowledge of the land and past experience to get the most profit from their farms, regardless of if they had a dairy or raised food crops. With the new technologies available today, farmers can now target their use of fertilizers or herbicides, saving money and minimizing environmental damage.
Digital transformation is no longer an "if" but a "when" for enterprises across both public and private sector. The promise of greater efficiency, customer-centric products and services, rapid response to changing regulatory or economic requirements – and the chance to compete with disruptive start-ups percolating every sector of the economy can no longer be overlooked. The challenge is how to get started and get the runs on the board that build innovation momentum. Google Cloud's Nigel Watson believes machine learning and artificial intelligence (AI) offer the most straightforward way to demonstrate what digital transformation can deliver. Watson is Head of Cloud Technology Partners, Japan and Asia Pacific for Google Cloud.
If you were inventing the farm today, why would you put it outside, on a giant plot of land? OK, there's the sunlight thing, but then you get droughts and frosts and plant-munching insects that have to be battled with harmful pesticides. And because outdoor farms need so much acreage, they're usually far from most of their customers -- which means that by the time a tomato gets to you in a city, it tastes like a baseball. But now, upstarts such as Bowery farming, AeroFarms, and Lettuce Networks are doing something different. They're using data and artificial intelligence to operate more efficiently than traditional farms.
This is just a small slice of how technology automation has changed over the past 20 years, and I assume we can all acknowledge that AI is gaining momentum, albeit regulatory authorities, legislators and lawyers not being fully sure how to adapt or embrace the change that's currently happening. Artificial Intelligence is here, it's the hot topic or the popular kid everyone wants to play in the park with. AI and automation are bringing us daily benefits; Internet and Big Data are becoming an essential part of both our work and private lives and we now have the capacity to collect huge sums of information too cumbersome for a person to process. But what will this future bring in terms of issues, policies and regulations? Will programmers and researchers be obliged to study ethics and morals as compulsory modules throughout their learning paths?
India's Mahindra & Mahindra, one of the biggest suppliers of smaller tractors to the U.S., and other manufacturers are racing to develop what they see as the future of farming: robo-tractors and other farming equipment to help produce more food, more sustainably at a lower cost. John Deere has tractors and combines on the market that free the driver in the cabin from the actual driving so he or she can monitor the crops and adjust pesticide, water and soil levels. Technology from Agco Corp.'s Fendt lets several driverless tractors follow a lead tractor driven by a human. Japanese firms Kubota and Yanmar are planning to launch driverless tractors that they expect to be popular with elderly farmers. The next generation is tractors that can drive entirely by themselves.