The rapid advancement of AI is currently hindered by several technical and practical limitations. One major limitation is the lack of robustness and reliability in AI systems. AI models often struggle with handling unexpected or adversarial inputs, leading to errors and inaccuracies in their predictions. Additionally, the need for large amounts of high-quality labeled data for training AI models poses a significant challenge, especially in domains where data collection is expensive or time-consuming. Another limitation is the computational resources required for training complex AI models, which can be prohibitively expensive and time-consuming. Furthermore, the interpretability and explainability of AI models remain a challenge, as black-box algorithms can make it difficult to understand how decisions are being made.