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New machine learning method accurately predicts battery state of health Researchers from the Smart Systems Group at Heriot-Watt University in Edinburgh, UK, working together with researchers from the CALCE group at the University of Maryland in the US, have developed a new method to estimate battery health irrespective of operating conditions and battery design or chemistry, by feeding artificial intelligence (AI) algorithms with the raw battery voltage and current operational data. A paper describing the method is published in the journal Nature Machine Intelligence. In the reported study, ...

Strolling and learning: multi-terrain robots Artificial intelligence helps inform robot’s balance. A team of researchers, including Australian scientists, have developed a robot that learns to walk across different terrains as it moves. Autonomous robots have trouble walking, because they typically have a fixed shape and so can’t handle uneven ground or changes in density. This robot can change the shape of its inform legs as needed, and is controlled by an AI that can detect and adapt to different environments. The robot is described in a paper, published today in  Nature Machine Inte...

16 March 2021, 11:10 am EDT By The transition from one terrain to another is not easy for a machine. The change in the environment could affect its functions, so its parts should perfectly fit in different situations.  Shape-Shifting Robot Can Adjust at Different Environments (Photo : Screenshot from YouTube/NBC) A robot, for example, will have a hard time operating in places that it is not suitable for it. But, a new robot could make a big difference through its shape-shifting mechanism. Shape Shifting Robot As humans, our adjustments are easier than robots. We could walk and then shift to...

Tønnes Nygaard, Author provided Imagine running on a cement footpath, and then suddenly through dry sand. Just to keep upright, you would have to slow down and change the way you run.

Systematic review finds that machine learning models for detecting and diagnosing COVID-19 from medical images have major flaws and biases, making them unsuitable for use in patients. However, researchers have suggested ways to remedy the problem.