ANALYZING BY MEANS OF NEURAL NETWORKS: THE BLEEDING OF GROWTH IN STREAMLINED AND ATTAINABLE AUTOMATED REASONING FRAMEWORKS

Machine learning has advanced considerably in recent years, with models matching human capabilities in various tasks. However, the main hurdle lies not just in developing these models, but in utilizing them effectively in practical scenarios. This is where inference in AI comes into play, arising as a primary concern for researchers and innovators