Despite its potential, there are several challenges: 1. Data Quality: High-quality training data is essential for effective AI models. Inconsistent or poor-quality data can lead to inaccurate results. 2. Computational Resources: AI algorithms require significant computational power and storage, which can be a constraint. 3. Interdisciplinary Knowledge: Combining AI with nanotechnology requires expertise in both fields, which can be difficult to find. 4. Interpretability: The 'black box' nature of some AI algorithms can make it challenging to understand how decisions are made, which is critical for scientific validation.
