Understanding the Speed of Light, How Much do We Know?
You might not think there’s much to know about the speed of light, but you’d be wrong. The actual speed of light in a vacuum is 299,792 kilometers per second (186,000 miles per second), which may sound ridiculously fast to us, but in the grand scheme of the Universe, it’s quite slow.
You might not think there’s much to know about the speed of light, but you’d be wrong. The actual speed of light in a vacuum is 299,792 kilometers per second (186,000 miles per second), which may sound ridiculously fast to us, but in the grand scheme of the Universe, it’s quite slow. To give you an idea, if you were traveling at the speed of light, it would take you just 45 minutes to get to Jupiter from the Sun. According to Einstein and his special theory of relativity, times gets slower the closer you get to the speed of light, stops when you reach the speed of light and would go backward if you were to exceed the speed of light.
Some physicists argue that there may be another factor to consider that may influence the speed of light, and that’s quantum vacuum fluctuation. This theory proposes that what we think are empty pockets of the Universe, aren’t actually empty and that they’re packed with particles instead, and these particles are constantly alternating from existent to non-existent states. This quantum fluctuation could quite easily slow down the speed of light.
But, this is still just a theory remember and still a very controversial one at that. And still, we have no answer to the most simple of questions: what of the speed of light is something different than we proposed? Could it be infinite? Although that would solve many of our problems, if that were true, then nothing would exist, so I guess it’s back to the drawing board with that one.
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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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