Following are some of the limitations of Gen AI
Knowledge Cutoff: Generative AI models can’t access information beyond their training data, limiting their ability to provide up-to-date responses. For example, a model trained before 2023 won’t know about events or developments after that year.
Training Data Dependency: The quality and bias of outputs are heavily dependent on the training data. For instance, biased training data can lead to biased outputs, as seen in some AI-generated content that reflects societal prejudices.
Lack of Contextual Understanding: These models often struggle with nuanced or context-specific tasks. For example, they might misinterpret idiomatic expressions or fail to grasp the subtleties of human emotions in text.
Calculation Errors: Generative AI models can struggle with precise calculations and logical consistency. For example, they might generate incorrect arithmetic results or fail to follow logical sequences accurately.
Overfitting: Generative AI models can overfit to their training data, resulting in poor generalization to new, unseen data. This can limit their effectiveness in real-world applications.
Ethical and Privacy Concerns: Generative AI can inadvertently generate harmful or biased content. For instance, deepfake technology has been used to create misleading videos, raising ethical and privacy issues.
Interpretability: The decision-making process of these models is often opaque. For example, it can be challenging to understand why a model generated a particular piece of content, making it difficult to trust and validate the outputs.