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Answer :
The correct option is c. both a and b.
Generative AI faces significant challenges with respect to data, which can be summarized as follows:
a. Access to High-Quality Data:
- One of the major issues is obtaining large volumes of high-quality data that are necessary for training generative AI models.
- High-quality data is crucial for AI to learn effectively and produce accurate and reliable outputs.
- Without access to good data, the performance of generative AI can be severely hindered.
b. Overfitting on Low-Quality Data:
- Another challenge is the risk of overfitting when the AI is trained on low-quality or limited datasets.
- Overfitting occurs when the AI learns the training data too well, including noise and inaccuracies, making it less effective at generalizing from new, unseen data.
- This issue is particularly concerning when the goal is to deploy generative AI in real-world applications where diverse and reliable data inputs are necessary.
Generative AI must navigate both the challenge of accessing high-quality data and avoiding overfitting low-quality data to be effective and reliable.
Complete Question:
What challenge does generative AI face with respect to data?
a. access to high-quality data
b. overfitting on low-quality data
c. both a and b
d. neither a nor b
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Rewritten by : Jeany
Generative AI faces challenges such as algorithmic bias, ensuring data quality, distinguishing between accurate information and AI-generated false statements, and maintaining data privacy and security.
Generative AI faces several key challenges with respect to data. Algorithmic bias is a significant issue, where biases present in the training data lead to biased outcomes. This can result in unethical or unfair predictions and decisions made by AI systems. Additionally, ensuring the quality of training data is critical, as poor-quality or incomplete data can impair the performance of generative AI models. Another challenge is the veracity of data, as AI-generated content often produces convincing yet false statements, complicating the identification of reliable information. Lastly, there are serious concerns around data privacy and security, where sensitive information must be handled with care to prevent breaches and misuse.
Key Challenges
- Algorithmic bias: Biases in training data leading to unfair outcomes.
- Data quality: Poor-quality data can impair AI performance.
- False statements: Difficulty in distinguishing between accurate and AI-generated misinformation.
- Data privacy and security: Risks of data breaches and misuse.
