Unit 4 – Generative AI Class 9 NOTES Important Points
Unit 4 – Generative AI Class 9 NOTES Important Points
Supervised Learning and Discriminative Modeling
In supervised learning the classification of data elements into categories or labels was initially taught by humans to the machine learning models.
Unsupervised Learning and Generative Modeling
In unsupervised or self-supervised learning, the machine learning model takes unlabeled datasets and identify patterns — without human intervention.
What is Generative AI?
Generative artificial intelligence (AI) refers to the algorithms that generate new data that resembles human-generated content, such as audio, code, images, text, simulations, and videos.
This technology is trained with existing data and content, creating the potential for applications such as natural language processing, computer vision, the metaverse, and speech synthesis.
Today, generative AI covers a wide range of applications, including text generation, image synthesis, and creative content creation, showcasing the culmination of years of research and development efforts.
Generative AI vs Conventional AI
| Generative AI | Conventional AI |
| Generative AI creates new content. | Conventional AI analyzes, processes, and classifies data. |
| It uses vast libraries of samples to train neural networks. | It employs fewer complex algorithms and training methods. |
| It’s output is fresh, innovative, and often unexpected. | It produces more predictable output based on existing data. |
| It is used in art, music, literature, gaming, and design. | It is used in banking, healthcare, image recognition, and language processing. |
Types of Generative AI
Generative AI comes in a variety of forms, each with unique advantages and uses. Some of the most typical varieties are listed below:
1. GANs(Generative Adversarial Networks)
- These are neural network that collaborate to produce fresh data.
- It is made up of two neural networks: Generator Network and Discriminator Network.
- The generator network produce the data, while the discriminator network analyse the data and provide feedback.
- Examples of what GANs can create include: Portraits of non-existing people, Converting images from day to night, Generating images based on textual description, Generating realistic video.
2. VAEs (Variational Auto encoders)
- 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.
3. 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.
4. 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.
- Examples- artistic image creation, drug discovery. They generate highly realistic samples.
Use of Generative AI in different fields
Generative AI has many applications, from art and music to language and natural language processing.
- Generative AI is being used to create unique works of art. for example, The Next Rembrandt project used data analysis and 3D printing to create a new painting in the style of Rembrandt.
- Generative AI is being used to create new music, either by composing original pieces or by remixing existing ones. for example, AIVA is an AI composer that can create original pieces of music in various genres.
- Generative AI is being used to generate new language, such as chatbots that can hold conversations with users.
Benefits of using Generative AI
Generative AI offers a range of benefits which are given below:
Creativity: Generative AI can assist in pushing the boundaries in making creative processes more efficient and personalized in the fields such as art, design, and music.
Efficiency: Generative AI can automate the content creation processes, which can save time and reduce costs.
Personalization: Generative AI can be used to create personalized content for individual users based on their preferences and behaviors.
Exploration: Generative AI can be used to explore new design spaces or optimize complex systems, such as designing new drugs or improving industrial processes.
Accessibility: Generative AI can facilitate the access to content creation tools, making it easier for people with limited resources.
Scalability: Generative AI can be used to generate large volumes of content quickly and efficiently.
Limitations of Using Generative AI
Data Bias: If generative AI is trained on biased or incomplete data, the output may be similarly biased or flawed.
Uncertainty: Generative AI can produce unexpected and often unpredictable results, which can be both a benefit and a drawback.
Computational Demands: Generative AI requires significant computational resources to train and generate its output, which can be expensive and time-consuming.
GANPaint – Painting with Generative Models
GAN stands for Generative Adversarial Network, which is a type of machine learning model that can generate new images. GANPaint was released 2019 as one of the first tools to allow image editing with generative networks
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.
GAN Paint directly activates and deactivates neurons in a deep network trained to create pictures. Each left button (“door”, “brick”, etc.) represents 20 neurons. Switching neurons directly shows the network’s visual world model.
Link to use GanPaint: https://ganpaint-v2.vizhub.ai/
Generative-AI tools
There are many generative AI tools available today that enable users to create and experiment with generative models. Here are some popular tools:
Artbreeder: Artbreeder is a web-based tool that enables users to generate new images by combining different GAN models. Users can select and combine different GAN models to create new and unique images.
Hands-on Activity – Generate Images with Text Prompt
- Go to artbreeder.com
- Select Create from menu bar and click on New Image under Prompter category.
- Give cool text prompt and see how AI generates a picture from those prompts.
Runway ML: Runway ML is a platform which provides a user-friendly interface for building and training various types of generative models, including GANs, VAEs, and image classifiers.
The table shows popular Generative AI tools that can be used in various fields.
Some more tools are given below
Ethical considerations of using Generative-AI
While Generative AI offers many benefits, there are also several ethical considerations that should be considered when using this technology.
1. Ownership: This is particularly relevant in creative fields such as music, literature, or art, where generative AI can create original works that blur the lines between human and machine authorship.
2. Human Agency: As technology becomes more sophisticated, it may become very difficult to distinguish between content generated by humans and machines, which could lead to a loss of human autonomy and agency.
3. Bias: Generative-AI can replicate and amplify existing biases present in the data used to train the model. This can lead to harmful or discriminatory outcomes.
4. Misinformation: Generative-AI can be used to create fake news which can be used to spread wrong information and manipulate public opinion.
5. Privacy: Generative-AI can be used to generate sensitive personal information which could be used for malicious purposes.
The Potential Negative Impact on Society
1. Generative-AI can be used to create fake news or deep fakes that can spread wrong information and manipulate public opinion.
2.Lead to job displacement for humans.
3. Generative-AI can be used to generate sensitive personal information which could be used for malicious purposes.
Responsible Use of Generative AI
● Ensuring that the training data used are diverse and representative.
● The outputs are scrutinized for bias and misinformation.
● Prioritizing user privacy and consent,
● Having clear guidelines around ownership and attribution of generative content.
In short, responsible use of Generative A.I. is essential for ensuring that this technology is developed and used in ways that benefit society. By emphasizing ethics, creating trust, limiting negative repercussions we may maximize Generative AI’s potential to improve society.
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