What are the limitations of AI and ML?
AI and ML technologies face several limitations, including:
1. Limited Availability of Training Samples: AI and ML models require a large number of training samples to learn effectively. The unavailability of sufficient data can hinder model performance.
2. Data Labelling Challenges: Supervised learning algorithms rely on labelled data, which can be a time-consuming and manual process. Human annotation of data can introduce biases and affect model accuracy.
3. Lack of Generalization: Models trained on specific datasets may not perform well when applied to different scenarios. Retraining or fine-tuning is often necessary to adapt models to new situations.
4. Lack of Explainability: AI and ML models often operate as black boxes, making it challenging to understand their internal workings. This lack of transparency can hinder debugging and troubleshooting efforts.
5. Dependency on Computational Resources: Training complex AI and ML models requires significant computational resources, which can be a limitation for organizations with limited infrastructure.
6. Ethical and Bias Issues: AI and ML models can inherit biases present in the training data, leading to unfair or discriminatory outcomes. Ensuring fairness and ethical use of AI technologies is an ongoing challenge.