Computational Limit

Computational limits explore the boundaries of what's efficiently computable, focusing on the inherent difficulty of certain problems and the limitations of algorithms and hardware in solving them. Current research investigates these limits across diverse areas, including deep learning (analyzing training efficiency and the impact of quantization), generative AI (assessing ethical and algorithmic challenges in healthcare applications), and optimization problems (exploring the effectiveness of techniques like low-rank approximations and annealing machines). Understanding these limitations is crucial for developing more efficient algorithms, designing more powerful hardware, and setting realistic expectations for the capabilities of artificial intelligence in various applications.