Hyperloop Makes More Sense Than Ever
California is struggling to set up the high-speed rail that was hoped for but thanks to the Hyperloop – a concept by Elon Musk – it could happen. The LA Times reported that funding issues mean there is going to be a three years delay plus issues faced when it comes to the mountains of Southern California.
California is struggling to set up the high-speed rail that was hoped for but thanks to the Hyperloop – a concept by Elon Musk – it could happen. The LA Times reported that funding issues mean there is going to be a three years delay plus issues faced when it comes to the mountains of Southern California. Despite this, the central valley leg could be finished by 2025.
Hyperloop will be working somewhere around the world as soon as 2020 according to Chairman of Hyperloop Technologies Shervin Pishevar. He told CNBC that speeds of 700 miles an hour are expected, and this will have a knock-on effect on the global economy.
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It is only three years since Musk’s white paper moving pods along closed pneumatic tubes at very high speeds. The test track being built in Nevada means there are hopes of speeds of 0-335 miles in a couple of seconds.
Hyperloop should be available first, but the less speedy bullet train has an advantage – the right to land that allows them to join in large areas. The project seems more suitable for public funding especially against high-speed rail, due to the amount of rules and regulations involved.
Musk has crowd funders interested in the competition to develop the pod, and as tests have taken place, some have been chosen for testing. Reddit are involved, and the SpaceX HQ has a one-mile testing track. Hyperloop has a place in the future of rail travel, especially about cargo. A bullet train may travel in California by 2025.
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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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