Generative AI cannot process text or images, while discriminative models can

Generative AI generates new data instances, while discriminative models classify or predict labels for data points

Generative AI and discriminative models are the same

Generative AI only works with labeled data, while discriminative models work with unlabeled data

To perform a single specific task

To be adapted or fine-tuned to a wide range of downstream tasks

To generate random data without any specific purpose

To only work with labeled data

Transformers revolutionized natural language processing in generative AI

Transformers are only used for image generation in generative AI

Transformers are used to create hallucinations in generative AI

Transformers are not used in generative AI

To provide additional training data to the model

To confuse the model and generate incorrect information

To limit the capabilities of the model

To control the output of the model

Text-to-text, text-to-image, text-to-video, text-to-3D, text-to-task

Image-to-text, audio-to-video, video-to-text, task-to-text

Text-to-number, image-to-audio, video-to-task, task-to-image

Text-to-speech, image-to-video, audio-to-text, task-to-number

To predict future values based on past examples

To create new content based on existing data

To only work with labeled data

To classify or predict labels for data points

Cause confusion and misinformation in industries

Only be used for academic research

Have no impact on any industry

Revolutionize industries such as healthcare, finance, and customer service

To predict future values based on past examples

To classify images based on text descriptions

To only work with labeled data

To generate novel combinations of text in the form of natural sounding language

They improve the model's accuracy

They have no impact on the model's output

They are necessary for the model to function properly

They can make the output text difficult to understand and generate incorrect information