INTERPRETING BY MEANS OF NEURAL NETWORKS: THE FOREFRONT OF IMPROVEMENT IN STREAMLINED AND ATTAINABLE INTELLIGENT ALGORITHM FRAMEWORKS

Artificial Intelligence has made remarkable strides in recent years, with systems achieving human-level performance in diverse tasks. However, the true difficulty lies not just in training these models, but in utilizing them effectively in everyday use cases. This is where AI inference takes center stage, arising as a primary concern for scientists