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Answer :
The main difference between generative AI and discriminative AI lies in their approach to handling and utilizing data.
Generative AI:
- What: Generative AI models are designed to create new data samples. They learn the underlying distribution of a class or a dataset and can generate new instances that are similar to the original data. Examples include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
- Why: The purpose of generative models is to understand how data is generated, which allows them to create new, artificially-generated content such as images, text, or even audio.
- How: These models often work by estimating the joint probability distribution [tex]P(X, Y)[/tex] and use this to sample new data points.
Discriminative AI:
- What: Discriminative AI models, on the other hand, focus on distinguishing between different types of data. They learn the boundary between various classes and are primarily used for classification tasks. Examples include Logistic Regression, Support Vector Machines, and many neural network models used for classification.
- Why: The goal of discriminative models is to accurately classify or label existing data by finding the decision boundary between different classes.
- How: Discriminative models estimate the conditional probability [tex]P(Y|X)[/tex], focusing on the likelihood of the output (class) given the input data.
In summary, generative AI creates new instances of data similar to what's been provided, while discriminative AI is concerned with identifying patterns or making predictions based on existing data.
The correct answer is: Generative AI creates new data, while discriminative AI identifies patterns in existing data.
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