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• Personalisation: Generative AI takes personalisation to a whole new level. It can tailor content specifically for
each person, taking into account their likes, dislikes, and behaviours. This means that instead of getting generic
recommendations or articles, users get content that's made just for them. Whether it's suggesting products
they might love or delivering news articles on topics they're interested in, Generative AI ensures that each user's
experience is unique and relevant to their tastes and preferences. It's like having a personal assistant that knows
exactly what you want, making the online experience more enjoyable and engaging.
• Exploration: Generative AI helps us explore new things and make existing stuff better. For example, it can help
scientists design new drugs or make industrial processes work smoother. It's like having a super-smart assistant
that helps us discover new ideas and improve how things work in different fields.
• Accessibility: Generative AI makes it easier for everyone to create top-notch content, even if they don't have
fancy tools or tonnes of know-how. It's like leveling the playing field, giving everyone a chance to make something
awesome without needing special skills or expensive equipment. Whether it's designing graphics, writing stories,
or making music, Generative AI opens up a world of creative possibilities for people from all walks of life.
• 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!
206 Artificial Intelligence Play (Ver 1.0)-IX

