Fourier transformations have been used to uncover the inner workings of artificial intelligence (AI). By applying a 200-year-old mathematical technique known as Fourier analysis, scientists can gain insight into how AI learns complex physics. This method has proven to be more effective than trying to understand the “black box” that is deep neural networks.
The Fourier transformation reveals how AI processes information and breaks it down into its parts. It also helps researchers identify patterns in data sets that would otherwise remain hidden from view. These insights allow scientists to better understand and improve upon existing models of machine learning algorithms, such as those used for autonomous vehicles or natural language processing applications like Google Translate.
In addition, this technique could potentially help us build smarter robots by providing a better understanding of their decision-making process when faced with complex tasks in real-world scenarios. Furthermore, it could enable us to create machines that are capable of tackling problems beyond current capabilities — something essential if we want our technology advancements to continue at an ever-increasing pace over time!
Read more at SciTechDaily
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