Japan’s Nuclear Problem Can be Solved By Toshiba’s Robots
Since the tsunami in Japan five years ago that destroyed the Fukushima nuclear plant the area has been radioactive and cleaning up the area is not an easy job. It is accepted that it could still be a couple of decades before the job can be fully finished.
Since the tsunami in Japan five years ago that destroyed the Fukushima nuclear plant the area has been radioactive and cleaning up the area is not an easy job. It is accepted that it could still be a couple of decades before the job can be fully finished. There are also concerns about the effect the work will have on the people who are doing it. So what is going to make it an easier and safer prospect?
It seems that the answer is robots, and it is one that is being taken seriously. There is a robot that has been designed by Toshiba that is capable of taking out fuel rod assemblies that are located in the Number 1 nuclear plant. It is fitting that it is Toshiba as they were also responsible for building the reactor and the work is due to begin in 2017.
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Back in December 2014, there were tons removed from reactor four by Tepco, but the radiation levels were lower then, and it was safer for there to be humans involved. At reactor three this is going to be too dangerous and as a result, the job will be much more challenging.
The reason 2017 is the start date is that it is not going to be easy to set up. The device will be set up this year, and there needs to be time taken for humans to understand fully how to work the robot. Clearly there will be no risks taken with the consequences will be too severe.
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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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