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•    Scalability: Generative AI is a powerhouse when it comes to creating
                                                         lots of content in a short time. It's like having a super-speedy content
                                                         creator that can churn out stuff on a large scale without breaking a
                                                         sweat. This makes it perfect for businesses and organisations that need
                                                         to produce heaps of content, whether it's articles, images, videos, or
                                                         anything  else. With Generative AI, scaling up  content  production  is
                                                         a breeze, helping businesses keep up with demand and reach more
                                                         people without sacrificing quality.



                 Limitations of Using Generative AI
                 Some disadvantages of Generative AI are as follows:
                    • Data bias: When Generative AI learns from biased or incomplete data, it tends to reflect those biases in its
                   output. This means the results may be skewed or flawed, especially in critical areas like facial recognition or
                   natural language processing, where accuracy is paramount.


                    • Uncertainty: Generative AI has a knack for surprising us with unexpected results. While this can sometimes
                   lead to exciting discoveries, it can also be a bit of a mixed bag, as the outcomes can be unpredictable.

                    • Computational demands: Generative AI isn't shy about its appetite for computational power. It demands
                   hefty resources for training and generating output, which can be both expensive and time-consuming. So,
                   while it's undeniably powerful, it can also drain the resources.



                               Task                                                 #Experiential Learning




                   GAN Paint                                                                           CBSE Handbook
                   Link: https://ganpaint-v2.vizhub.ai/

                   ●   GAN Paint directly activates and deactivates neurons in a deep network
                      trained to create pictures.

                   ●  Each left button ("door", "brick", etc.) represents 20 neurons.
                   ●   The software shows that the network learns
                      about trees, doorways, and roofs by drawing.
                   ●   Switching  neurons  directly  shows  the
                      network's visual world model.

                   ●   To use GAN Paint, you will first need to select
                      a base image from the website's library. You
                      can then use the brush tool to add objects
                      and textures to the image. As you paint, the
                      GAN network will learn to generate more
                      realistic images.
                   ●   You are encouraged to experiment with GAN
                      Paint and see what you can create. Have fun!



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