Data Structure Innovations for Machine Learning and AI Algorithms

With the increasing complexity and size of data in machine learning (ML) and artificial intelligence (AI) applications, efficient data structures have become critical for enhancing performance, scalability, and memory management. Traditional data structures often fail to meet the specific requirements of modern ML and AI algorithms, particularly in terms of speed, flexibility, and storage efficiency. This paper explores recent innovations in data structures tailored for ML and AI tasks, including dynamic data structures, compressed storage techniques, and specialized graph- based structures. We present a detailed review of advanced data structures such as KD-trees, hash maps, Bloom filters, sparse matrices, and priority queues, and how they contribute to the performance improvements in common AI applications like deep learning, reinforcement learning, and large-scale data analysis. Furthermore, we propose a new hybrid data structure that combines the strengths of multiple existing structures to address challenges related to real-time processing, memory constraints, and high-dimensional data.