What is a limitation of generative AI?

What is a limitation of generative AI?

• A. It always generates accurate content.
• B. It can hallucinate, make false claims, and repeat bias.
• C. It requires no data to function.
• D. It cannot create images or code.

Answer: (B)

Generative AI has the capacity to create content such as text, images, and code based on the data it has been trained on. However, one significant limitation is that it can hallucinate, meaning it might produce information that appears plausible but is actually incorrect or fabricated. This issue arises from the model's inability to verify facts or understand context deeply, leading to false claims in its output. Additionally, generative AI can perpetuate biases present in the training data. If the data reflects societal biases, the AI may generate content that reinforces those biases, resulting in outputs that can be misleading or harmful. This nuanced understanding of AI imperfections highlights the importance of critical evaluation of its outputs, especially in applications where accuracy is vital. The other options present characteristics that do not accurately describe the limitations of generative AI. Generative AI does not always generate accurate content (thus making option A incorrect). It indeed requires training data to function effectively (making option C incorrect). Lastly, generative AI can create images and code, which makes option D incorrect.

Generative AI has the capacity to create content such as text, images, and code based on the data it has been trained on. However, one significant limitation is that it can hallucinate, meaning it might produce information that appears plausible but is actually incorrect or fabricated. This issue arises from the model's inability to verify facts or understand context deeply, leading to false claims in its output.

Additionally, generative AI can perpetuate biases present in the training data. If the data reflects societal biases, the AI may generate content that reinforces those biases, resulting in outputs that can be misleading or harmful. This nuanced understanding of AI imperfections highlights the importance of critical evaluation of its outputs, especially in applications where accuracy is vital.

The other options present characteristics that do not accurately describe the limitations of generative AI. Generative AI does not always generate accurate content (thus making option A incorrect). It indeed requires training data to function effectively (making option C incorrect). Lastly, generative AI can create images and code, which makes option D incorrect.