Researchers from the University of Copenhagen have mathematically proven that creating algorithms for Machine Learning that are constantly stable beyond simple problems is impossible. This discovery could lead to better algorithm testing protocols and highlight the differences between machine processing and human intelligence. The research team aims to reveal the weaknesses of even the most advanced algorithms, which may misinterpret medical scanning images, translations, and road signs with stickers. The article contributes to the theory, suggesting a firm definition of how much noise an algorithm should withstand to be considered stable. It may lead to developing guidelines for testing algorithms and, in the long run, better and more stable algorithms. It’s important to remember that machines do not possess human intelligence, and even the best algorithms have limitations.
Source: SciTechDaily
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