Artificial intelligence can be used to better monitor Maine’s forests, UMaine study finds

Read the full story from the University of Maine.

Monitoring and measuring forest ecosystems is a complex challenge because of an existing combination of softwares, collection systems and computing environments that require increasing amounts of energy to power. The University of Maine’s Wireless Sensor Networks (WiSe-Net) laboratory has developed a novel method of using artificial intelligence and machine learning to make monitoring soil moisture more energy and cost efficient — one that could be used to make measuring more efficient across the broad forest ecosystems of Maine and beyond.

Soil moisture is an important variable in forested and agricultural ecosystems alike, particularly under the recent drought conditions of past Maine summers. Despite the robust soil moisture monitoring networks and large, freely available databases, the cost of commercial soil moisture sensors and the power that they use to run can be prohibitive for researchers, foresters, farmers and others tracking the health of the land.

Along with researchers at the University of New Hampshire and University of Vermont, UMaine’s WiSe-Net designed a wireless sensor network that uses artificial intelligence to learn how to be more power efficient in monitoring soil moisture and processing the data. The research was funded by a grant from the National Science Foundation

Artificial intelligence paves the way to discovering new rare-earth compounds

Read the full story from Ames Laboratory.

Artificial intelligence advances how scientists explore materials. Researchers from Ames Laboratory and Texas A&M University trained a machine-learning (ML) model to assess the stability of rare-earth compounds. This work was supported by Laboratory Directed Research and Development Program (LDRD) program at Ames Laboratory. The framework they developed builds on current state-of-the-art methods for experimenting with compounds and understanding chemical instabilities.

Google turns its AI on traffic lights to reduce pollution

Read the full story at Engadget.

Poorly timed traffic lights don’t just waste precious minutes. Like Google’s chief sustainability officer Kate Brandt pointed out at a media event yesterday, they’re also bad for the environment and public health. The company unveiled a slew of sustainability-centric products and updates today that aim to help users make more informed, environmentally friendly decisions. But it’s also been working on a project that could use AI to make traffic lights more efficient and, as a result, decrease pollution in general. 

When your vehicle stops at an intersection, that idling time leads to wasted fuel and “more street-level air pollution,” Brandt said. Google’s new project would use AI to measure and calculate traffic conditions and timing at a city’s intersections, then time them more efficiently. Brandt said one of the company’s AI research groups has been able to accurately calculate and gather this data and train a model to optimize inefficient intersections.