What are the limitations of Generative AI?
Several challenges and limitations are represented by Generative AI.
- Dependency of Data: Generative AI models are dependent on the quantity of data available on datasets it can only provide responses based on the data present in a dataset. If the given dataset is based then the given data will also be biased and transferred to generated content. The dependency of data doesn’t identify the source.
- Controlling is difficult: It generates different content so sometimes it is difficult to control the data it is creating. Sometimes we will not be getting the same data as required by the user.
- Computational Requirement: Training generative AI models can be difficult, It requires a high quality of resources that must be a limited resource for once.
- Ethical and Legal Concerns: Generative AI can serve several issues. such as Deepfakes created by generative AI can be used to spread misinformation or violate privacy.
What is Generative AI?
Nowadays as we all know the power of Artificial Intelligence is developing day by day, and after the introduction of Generative AI is taking creativity to the next level Generative AI is a subset of Deep learning that is again a part of Artificial Intelligence.
In this article, we will explore,
What is Generative AI? Examples, Definition, Models and limitations.
Generative AI helps to create new artificial content or data that includes Images, Videos, Music, or even 3D models without any effort required by humans. The advancements in LLM have led to the development of Generative AI.
Generative AI models are trained and learn the datasets and design within the data based on large datasets and Patterns. They can generate new examples that are similar to the training data. These models are capable of generating new content without any human instructions.
In simple words, It generally involves training AI models to understand different patterns and structures within existing data and using that to generate new original data.