Explore the common issues with Generative AI outputs, including hallucination, intellectual property concerns, potential for misuse, bias, and difficulty identifying inaccuracies. Prepare for the NVIDIA Generative AI Explained Certification Exam with this comprehensive explanation.
Table of Contents
Question
Generative Artificial Intelligence produces confident, coherent results but can often be wrong. What are the common issues of Generative AI produced outputs?
A. Hallucination
B. Intellectual property ownership of AI-generated content
C. Cheap and easy content creation can lead to misuse, abuse, or even harm
D. Content produced by Generative AI can be biased based on the underlying data on which it’s trained
E. Realistic-sounding content can make it difficult to identify inaccuracy
Answer
A. Hallucination
B. Intellectual property ownership of AI-generated content
C. Cheap and easy content creation can lead to misuse, abuse, or even harm
D. Content produced by Generative AI can be biased based on the underlying data on which it’s trained
E. Realistic-sounding content can make it difficult to identify inaccuracy
Explanation
All of the answer choices listed are common issues and pitfalls associated with Generative AI outputs:
A. Hallucination: Generative AI models can sometimes generate content that seems plausible but is actually incorrect or nonsensical. The models may mix up facts or invent things that sound realistic but aren’t true.
B. Intellectual property ownership of AI-generated content: There are ongoing legal and ethical debates around who owns the rights to content created by AI systems. Is it the AI company, the user who prompted the system, the creators of the training data, or the AI itself? Current laws and norms are still catching up to this new technology.
C. Cheap and easy content creation can lead to misuse, abuse, or even harm: The ability for anyone to rapidly generate realistic text, images, audio, etc. at low cost creates risks. People could use it to create misleading propaganda, deepfakes, spam, or abusive/hateful content at an unprecedented scale.
D. Content produced by Generative AI can be biased based on the underlying data on which it’s trained: If an AI system is trained on data that contains biases (e.g. stereotypes, under-representation of certain groups), those biases can be amplified and reproduced in the AI-generated content, perpetuating harm.
E. Realistic-sounding content can make it difficult to identify inaccuracy: Because the output of Generative AI can sound very confident and authoritative, it may be hard for humans to spot inaccuracies, leading to the spread of misinformation. Cautious skepticism is warranted with any AI-generated content.
In summary, while Generative AI is an exciting and powerful emerging technology, it comes with significant pitfalls and issues that need to be carefully navigated. Hallucination, unclear intellectual property rights, potential for misuse, amplified bias, and difficulty discerning inaccuracy are key challenges the field must grapple with going forward. Awareness of these limitations is critical for anyone working with or being affected by Generative AI systems.
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