What are the Challenges of Implementing AI in Nanotechnology?
Despite its potential, the implementation of AI in nanotechnology faces several challenges. One major issue is the quality and quantity of available data. High-quality, labeled datasets are essential for training effective AI models, yet such datasets are often scarce in the field of nanotechnology. Additionally, the complex and interdisciplinary nature of nanotechnology requires AI models to integrate knowledge from various domains, which can be computationally intensive and technically challenging. Finally, there are concerns regarding the interpretability of AI models, as understanding the decision-making process of these models is crucial for their acceptance and adoption in scientific research.
