how to get abstraction and recreate to other problem

by adijaya — on  ,  , 

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The statement contains some truth, but it also oversimplifies the capabilities of generative AI.

Generative AI, which includes models such as Generative Adversarial Networks (GANs) and Transformers, can indeed create new content by learning patterns from existing data. These models can generate images, text, and even music that are remarkably similar to the training data.

However, the statement that generative AI can only do repetitive work and cannot solve problems without existing data is not entirely accurate. While it is true that generative AI models rely on data to learn patterns and generate new content, they can also be used to generate data when none exists. For example, GANs can be used to generate synthetic medical images for training other machine learning models when real data is scarce.

Furthermore, generative AI models can be used to explore hypothetical scenarios and generate new ideas. For instance, in the field of drug discovery, generative models can be used to generate new molecular structures that have not been seen before, which can then be tested for their potential therapeutic effects.

That being said, it is important to acknowledge the limitations of generative AI. These models can sometimes produce outputs that are unrealistic or nonsensical, and they can also perpetuate and amplify biases present in the training data. Therefore, it is crucial to carefully evaluate and validate the outputs of generative AI models before using them in practical applications.