Introduction to Generative AI – Class 9 AI (417) | Exam Ready Notes

Get Exam-Ready Notes on Introduction to Generative AI for Class 9 AI subject code 417 designed as per the CBSE latest syllabus. These Generative AI Class 9 Notes are written in a pointwise and easy-to-understand manner, covering all important topics for quick learning and revision, helping students prepare effectively and score high marks in exams.

Generative AI

• Generative Artificial Intelligence (AI) refers to algorithms that generate new data resembling human-generated content.
• It can create audio, code, images, text, simulations, and videos.
• Generative AI is trained using existing data and content.
• It has applications in natural language processing, computer vision, the metaverse, and speech synthesis.

Evolution of Generative AI – Timeline of Generative AI

• Generative AI has evolved over several years to reach its current form.
• Advancements in neural networks and deep learning techniques have enhanced its capabilities.
• Early experiments in generative models led to breakthroughs in natural language processing and image generation.
• The development of generative AI has been a continuous journey of innovation and refinement.
• Today, generative AI is used for text generation, image synthesis, and creative content creation.
• These applications reflect years of research and development efforts.

Example of Generative AI

• ChatGPT generates text that closely mimics human conversation.
• DALL·E generates pictures based on descriptive text prompts.
• DeepDream creates artistic photos using AI.
• Jukebox by OpenAI creates AI-generated music.
• AIVA composes original music in multiple genres.
• Gemini can generate text, answer questions, and assist in learning tasks.
• Canva AI assists users in designing presentations, posters, and graphics.

Types of Generative AI

GANs (Generative Adversarial Networks)

• GANs are neural networks that collaborate to produce fresh data
• Made up of two neural networks: Generator Network & Discriminator Network
• The generator network produces the data, while the discriminator network analyses the data and provides feedback.
• Until the generator can generate data that is identical to real data, the two networks collaborate in a feedback loop.
• Examples-creating portraits of non-existing people, convert images from day to night, generate images based on textual description, generate realistic video etc.

VAEs (Variational Auto encoders)

• Another class of generative models is VAEs. In order to produce fresh data, VAEs learn the distribution of the data and then sample from it.
• Examples- Generation of new images similar to given training set, image reconstruction, generating drafts for writer, generating new sounds and music composition etc.

RNNs (Recurrent Neural Networks)

• RNNs are a special class of neural networks that excel at handling sequential data, like music or text.
• They function by ingesting past inputs and applying that knowledge to anticipate future inputs.
• Example- Generating novel text in the style of a specific author or genre, predicting next character or word in a sequence etc.

Auto encoder

• These are Neural networks that have been trained to learn a compressed representation of data
• They function by compressing data first, then decompressing that compressed data to restore it to its original form.
• Auto encoders can be applied to denoising or picture compression applications.
• Examples- artistic image creation, drug discovery. They generate highly realistic samples.

Generative AI vs Conventional AI

Category	Generative AI	Conventional AI
Goal	Creates new content.	Analyzes, processes, and classifies data.
Training	Uses vast libraries of samples to train neural networks and other complicated structures to produce new content based on those patterns.	Employs fewer complex algorithms and training methods.
Output	Fresh, innovative, and often unexpected.	More predictable output based on existing data.
Applications	Benefits art, music, literature, gaming, and design.	Used in banking, healthcare, image recognition, and language processing.

Benefits of using Generative AI

• Creativity: Generative AI helps creators make creative processes more efficient and personalized in fields like art, design, and music.
• Efficiency: Generative AI automates content creation, saving time and reducing costs compared to manual methods.
• Personalization: Generative AI creates personalized content based on user preferences and behavior, such as product recommendations and personalized news articles.
• Exploration: Generative AI helps explore new design possibilities and optimize complex systems like drug design and industrial processes.
• Accessibility: Generative AI makes content creation tools accessible to people with limited resources or technical expertise.
• Scalability: Generative AI can quickly generate large amounts of content, making it useful for businesses and organizations.

Limitations of Using Generative AI

Data Bias

• If generative AI is trained on biased or incomplete data, the output may also become biased or inaccurate.
• This can create problems in applications such as facial recognition and natural language processing.

Uncertainty

• Generative AI can sometimes produce unexpected and unpredictable results.
• These results may be useful in some cases but problematic in others.

Computational Demands

• Generative AI requires large computational resources for training and generating outputs.
• This process can be expensive and time-consuming.

Lack of Originality

• Generative AI may produce content by repeating or combining existing content instead of creating truly original ideas.

Generative AI Tools

There are many Generative AI tools available that help users create and experiment with AI-generated content.

Artbreeder

• Artbreeder is a web-based tool used to generate new images by combining different GAN models.
• Users can mix and modify images to create unique artwork.

Runway ML

• Runway ML is a platform used for creating, training, and deploying generative AI models.
• It supports models such as GANs, VAEs, and image classifiers.
• It provides user-friendly AI tools for generating creative content.

ChatGPT

• ChatGPT is an AI chatbot that generates human-like text responses.
• It can answer questions, write content, summarize text, and assist in learning and coding tasks.

Gemini

• Gemini is a generative AI tool developed by Google.
• It can generate text, answer questions, and assist users with various tasks.

Some more Generative AI Tools

Ethical Concerns of Generative AI

Ownership

• There are concerns about who owns the content created by generative AI.
• In fields like music, literature, and art, AI-generated works can blur the difference between human and machine authorship.

Human Agency

• Generative AI raises questions about human control and decision-making.
• It may become difficult to distinguish between human-created and AI-generated content.
• This could reduce human autonomy and agency.

Bias

• Generative AI can reproduce and amplify biases present in training data.
• This may lead to unfair or discriminatory outcomes in areas such as hiring, loan approval, and criminal justice.

Misinformation

• Generative AI can create fake news and deepfakes.
• These can spread misinformation and manipulate public opinion.
• This may negatively affect trust in institutions and democracy.

Privacy

• Generative AI may generate sensitive personal information such as financial or medical data.
• Such information could be misused for malicious purposes.