Dependency on Data:
- Example: In fraud detection using machine learning, if the training data doesn't include enough fraudulent cases, the model may struggle to accurately detect fraud in real-time.
- Application: Fraud detection systems in banking and finance rely heavily on data quality and diversity to be effective.
Computational Resources:
- Example: Training deep neural networks with millions of parameters requires significant computational power, making it challenging for small companies or individuals with limited resources.
- Application: Training complex image recognition models in computer vision applications often requires high-performance GPUs or TPUs.
Overfitting:
- Example: A spam email classifier trained on a small dataset may perform exceptionally well on that data but generalize poorly to new, unseen emails.
- Application: Email filtering systems need to balance accuracy in identifying spam with avoiding overfitting to ensure robust performance.
Ethical Concerns:
- Example: Facial recognition systems have raised concerns about privacy violations and potential biases, especially when used by law enforcement without proper safeguards.
- Application: Ethical considerations are crucial in deploying AI systems in sensitive areas like surveillance, healthcare, and criminal justice.
Interpretability Challenges:
- Example: Deep learning models like neural networks are often termed "black boxes" due to their complex internal workings, making it difficult to understand why a specific prediction was made.
- Application: In healthcare, where decisions impact patient lives, interpretability challenges must be addressed to ensure trust and accountability in AI-powered diagnostic systems.
These limitations highlight the need for ongoing research and development in machine learning to overcome these challenges and build more robust, ethical, and interpretable AI systems.
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