Brain.fm Uses An Artificial Intelligence to Improve Your Brain Function
Music has been around in one form or another throughout the world for as long as anyone can remember. Some people listen to music to relax, others to party, others just because.
Music has been around in one form or another throughout the world for as long as anyone can remember. Some people listen to music to relax, others to party, others just because. Scientists have now discovered that you can improve brain function by listening to certain types of music, and now a new artificial intelligence has been created specifically for developing that magical blend of tones.
In using the AI, brain.fm can make available to users music that could help them decrease anxiety, help with sleep disorders and improve mental performance. Scientists have discovered that brain waves are altered when it hears sound and different sounds provoke different responses within the brain.
Brain.fm was founded by Adam Hewett and Junaid Kalmadi and is currently online inviting users to sign up and try it out for free. By teaming up with neuroscience experts, the team is ensuring that the responses they are hoping to achieve from the music are the ones they are getting. The team were originally creating songs to add to their database but proved to be quite a mission in itself as trying to create new, original tunes that emit the correct sounds is not an easy task – for a human.
So, in comes the artificial intelligence. Kalmadi and team have created a machine that can take all the rules from making neuroscience music and create unique music that sounds like it’s been produced by a human. They are still working continuously to try and improve on this marvelous creation, and could soon be used to help patients all around the world. But if you are intrigued, want to know more and want to try it now, just go to brain.fm and prepare to be dazzled.
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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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