This Real Life Wall-E Employed By Chinese Power Companies Help Keep Things In Order
China has been striving for a place high in the technological world for a while now, and they are regularly using robots to help them do that. One of the countries latest developments is in using Disney’s Wall-E type robots to take some of the load off the human workers.
China has been striving for a place high in the technological world for a while now, and they are regularly using robots to help them do that. One of the countries latest developments is in using Disney’s Wall-E type robots to take some of the load off the human workers.
The patrol robots that have been introduced at the power station in Chuzhou, China are the first of its kind in the Anhui Province of China and are capable of inspecting the work environment, equipment, workers and roads of the power station. The amazing robot charges and moves around all on its own and uses it eyes as it tools. While one eye takes pictures using a light camera, the other uses infrared sensors and a thermal imaging camera to produce clear pictures of power lines and transistors which can then be transferred to a computer for further analysis.
VCG via Getty Images
VCG via Getty Images
This is a significant breakthrough for China and one that seems to be paying off. Hopefully, once other companies around the world take note, they will soon follow in their footsteps. Using robots to perform human tasks not only allows the humans to do other important work, but robots can also work anytime night or day, without any rest, so are always going to be more efficient than us too, no matter how hard we try. The use of robots will only increase as technology advances at an alarming rate, so jump on board and make the most of them!
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Daniel Okafor
Daniel Okafor is the Senior AI Reporter at TrendinTech, where he covers large language models, machine learning research and the practical use of artificial intelligence across business and government. He previously reported on artificial intelligence for MIT Technology Review, covering the labs behind the current generation of frontier models and the policy debates in Washington and Brussels. Daniel holds a Master of Science in Machine Learning from Carnegie Mellon University and follows the research community closely, attending NeurIPS and ICML each year to speak with the people behind the papers. He has a particular interest in evaluation: how models are benchmarked, where those benchmarks fail and what that means for the companies betting on them.
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