Rocky Mountain Power said yesterday that an artificial intelligence (AI) Home Energy Reports solution helped 330,000 of its customers saved over 41 gigawatt-hours (GWhs) of energy since being introduced less than a year ago. The solution produced the savings at an average of approximately four cents per kilowatt hour, a roughly 25 percent cost reduction as compared to conventional Home Energy Reports. "We were searching for the next wave of customer engagement and a way to drive customers toward a digital, two-way dialogue with us," Clay Monroe, director of customer relations for Rocky Mountain Power, said. "With AI reports we are able to quickly shift from conventional methods of reporting, using general peer comparisons, to true energy empowerment with itemized energy bills and personalized savings tips, while at the same time moving customers to digital reports." In 2018, Rocky Mountain Power replaced its existing Home Energy Reports program with AI-powered reports called iHERs. Approximately 330,000 customers in Utah, Idaho, and Wyoming received itemized energy reports for the first time, and more than 50 percent of these customers moved to digital reports with the help of the iHER solution.

I am Rocky Mountain elk. Now I am that: Industrial Smoke Stack. I press another button and move a cursor to become Giant Sequoia. I zoom out again, and I am Rock Planet, small and gray. Soon I am Sun, and then I am Lenticular Galaxy.

A routine Military drone test quickly turned into something more bizarre, after the missing aircraft mysteriously turned up ten days later over 600 miles away. After it failed to return to base, the Army presumed that it had quickly been destroyed until a hiker found it crashed into a tree in Evergreen, Colorado. While the story doesn't sound that odd on the surface, the $1.5 million unmanned drone's range is meant to be limited to within 77 miles of its C-band line-of-sight data link. With the rogue RQ-7 traveling over 8 times that distance, investigators are still struggling to explain its incredible journey. Data recovered from the free-spirited drone showed it reached an altitude of 12,000 feet, enabling it to soar over the Rocky Mountains.

When Randy Bilyeu disappeared, he was hunting for the Fenn Treasure, a chest allegedly filled with gold, precious stones, and jewelry, supposedly hidden in the Rocky Mountains north of Santa Fe, New Mexico. In 2010, millionaire art dealer (and Former Vietnam fighter pilot) 79-year-old Forrest Fenn filled a bronze chest with rare metals, jewels, and artifacts, and then hid it in the mountains. Later that year, he published his autobiography, The Thrill of the Chase, which included a 24-line poem that he says contains the clues necessary to track down the treasure chest. Since then, he's become something of a global celebrity; in 2013, he appeared on NBC's Today Show to issue some new clues about the place where the chest had been hidden. Bilyeu happened to catch the episode on TV and became obsessed with finding the Fenn treasure--against all odds and his friends and family's better judgement.

Here's How Google Will Use A.I. to Help Fight Cancer How UC Berkeley's New Center Could Prevent a Military A.I. Apocalypse Beauty.AI App the 1st international beauty contest judged by AI A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality

Here's How Google Will Use A.I. to Help Fight Cancer Could killer AI robots bring down America? How UC Berkeley's New Center Could Prevent a Military A.I. Apocalypse Beauty.AI App the 1st international beauty contest judged by AI A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality

A treasure hunter went missing in the Rocky Mountains, and a computer algorithm found him ... Drive.ai wants to give self-driving cars more brainpower, personality Drive.ai

Our work is motivated by an important network design application in computational sustainability concerning wildlife conservation. In the face of human development and climate change, it is important that conservation plans for protecting landscape connectivity exhibit certain level of robustness. While previous work has focused on conservation strategies that result in a connected network of habitat reserves, the robustness of the proposed solutions has not been taken into account. In order to address this important aspect, we formalize the problem as a node-weighted bi-criteria network design problem with connectivity requirements on the number of disjoint paths between pairs of nodes. While in most previous work on survivable network design the objective is to minimize the cost of the selected network, our goal is to optimize the quality of the selected paths within a specified budget, while meeting the connectivity requirements. We characterize the complexity of the problem under different restrictions. We provide a mixed-integer programming encoding that allows for finding solutions with optimality guarantees, as well as a hybrid local search method with better scaling behavior but no guarantees. We evaluate the typical-case performance of our approaches using a synthetic benchmark, and apply them to a large-scale real-world network design problem concerning the conservation of wolverine and lynx populations in the U.S. Rocky Mountains (Montana).