5 Challenges in the Implementation of Generative AI


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5 Challenges

Developing and applying generative AI models is not an easy task. It involves many challenges, such as data quality and availability, ethical and social implications

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1. Quality of Generated Outputs

Generative AI systems may not always produce high-quality outputs, and the generated content may not be suitable for the intended use case.

2. Control Over the Generated Outputs

Generative AI systems are typically trained on a dataset and can generate new content based on that dataset. However, it can be challenging to control the output of these systems.

3. Computational Resources

Generative AI models can be computationally expensive to train and run, requiring significant computational resources.

4. Data Privacy and Security

Generative AI models require large amounts of data to train effectively, which can raise concerns about data privacy and security.

5. Bias and  Fairness

Generative AI models can perpetuate biases present in the training data, leading to unfair or discriminatory outcomes.

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