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Hitachi Energy's new AI solution analyzes trees to prevent wildfires

#artificialintelligence

The massive, beautiful tree canopies in the Western U.S., which may grow perilously close to power lines, can quickly spark destructive wildfires. In fact, 70% of electrical outages are caused by vegetation, and this number has increased by 19% year over year from 2009-2020. The second-largest wildfire in California's history, The Dixie Fire, sparked when power lines came into contact with a fir tree. Could AI-driven solutions help prevent wildfires before they start by analyzing the tree growth that can spark them? Hitachi Energy, the Zurich, Switzerland-based global technology company, says yes. Hitachi Energy, formerly known as Hitachi ABB Power Grids (the name was changed last October) is currently focused on "powering good for a sustainable energy future."


Global Big Data Conference

#artificialintelligence

Researchers at ETH Zurich and the Frankfurt School have developed an artificial neural network that can solve challenging control problems. The self-learning system can be used for the optimization of supply chains and production processes as well as for smart grids or traffic control systems. Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes--and can also be used to develop new business models. Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis--both from the Chair of Computational Social Science--have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states.


Controlling complex systems with artificial intelligence

#artificialintelligence

Researchers at ETH Zurich and the Frankfurt School have developed an artificial neural network that can solve challenging control problems. The self-learning system can be used for the optimization of supply chains and production processes as well as for smart grids or traffic control systems. Power cuts, financial network failures and supply chain disruptions are just some of the many of problems typically encountered in complex systems that are very difficult or even impossible to control using existing methods. Control systems based on artificial intelligence (AI) can help to optimize complex processes--and can also be used to develop new business models. Together with Professor Lucas Böttcher from the Frankfurt School of Finance and Management, ETH researchers Nino Antulov-Fantulin and Thomas Asikis--both from the Chair of Computational Social Science--have developed a versatile AI-based control system called AI Pontryagin which is designed to steer complex systems and networks towards desired target states.