Unit 4: Introduction to Generative AI (AI Course; Class – IX)-Part 2

In this tutorial, we will cover subunit 4.2 Generative AI Elaboration of Unit 4: Introduction to Generative AI of PART B – Subject Specific Skills.

For 4.1 Introduction to Generative AI, click on the link below:

Unit 4: Introduction to Generative AI (AI Course; Class – IX)-Part 1

4.2 Generative AI Elaboration

Let’s discuss various topics of Generative AI one by one.

Types of Generative AI

There are various types of Generative AI:

1. GANs (Generative Adversarial Networks)

• GANs were the first model for Generative AI introduced in 2014 by Ian Goodfellow.
• GANs are composed of two neural networks: a generator network and a discriminator network.
• These two neural networks compete with each other to generate new data.
• The generator network produces new data, and the discriminator network analyzes the data and gives feedback.
• This generation and feedback loop continue till it reaches the point where newly generated data looks like real data.
• Examples of GANs are: creating portraits, creating images from textual description, creating realistic videos, creating art, etc.

2. VAEs (Variational Auto Encoders)

• Variational Autoencoder is based on an encoder-decoder architecture, which implements a probabilistic model that compresses input data and generates new, similar data points.
• Examples of VAEs are: image reconstruction, music composition, generating textual data, etc.

3. RNNs (Recurrent Neural Networks)

• RNNs are special types of neural networks that are specially designed for coherent sequences of data, like text or music.
• Examples of RNNs are: predicting the next character or word in a sequence, the next note of music, etc.

4. Transformer Models

• Transformer models excel in natural language processing and complex sequence generation.
• Transformers are based on self-attention techniques that work in language understanding tasks.
• Examples of transformer models: language translation, text generation, and question answering.

Examples of Generative AI

Generative AI has many applications, from art, music, video, to voice and natural language processing.
Below are some examples of how generative AI is being used in various fields.

1. Art

Generative AI is being used to create a unique piece of art. Watch the video by The Next Rembrandt, which uses data analysis and 3D printing to create a new painting in the style of Rembrandt.

Try and have fun!!

• Try your hand with DALL·E 2 to generate new and unique art by clicking on the link below.

             DALL·E 2 | OpenAI

• Check out the fun video created with Meta Llama 3.1 by clicking the link shared below:

Meta’s Llama 3.1 Unique Feature

2. Music

Generative AI is being used to create new music, either by composing original pieces or by
remixing existing ones.

Try and have fun!!

Try your hand with AIVA, the AI Music Generation Assistant, to compose new and unique music by clicking on the link below.

AIVA, the AI Music Generation Assistant

3. Language

Generative AI is being used to generate new language, such as chatbots that can hold conversations with users or natural language generation systems that can produce written content.

Try and have fun!!

Try your hand with ChatGPT for generating new textual content by clicking on the link below.

ChatGPT | OpenAI

Benefits of using Generative AI

Generative AI offers significant advantages, including enhanced efficiency through automation, boosted creativity by generating novel ideas, and increased productivity by automating repetitive tasks. Here’s a more detailed look at the benefits of Generative AI:

Limitations of using Generative AI

With so many benefits of Generative AI, it also comes with some limitations:

1. Creative Block

Generative AI gives answers to almost anything and even generates art, music, code, etc. If we humans start depending on Generative AI, we will forget to think, imagine, and create something new.

Where is the imagination? Where is the creativity?

2. Huge data to train

Generative AI applications require huge data to train to give realistic results.

3. Computation resources

To train and run a Generative AI application, we need huge and expensive computational resources, and it is very time-consuming as well.

4. Data Bias

As Generative AI models are highly dependent on training data, the model’s outputs can amplify biases in the data.

5. Uncertain Results

Generative AI can produce unexpected and undesirable results, which come with their benefits and drawbacks.

Generative AI Tools

Various Generative AI tools are available that can be used in daily life for repetitive tasks, creativity, or data generation.

• AIVA (Artificial Intelligence Virtual Artist): Popular Generative AI tool for Music Generation.
• Notion AI: Popular Generative AI tool for generating textual content.
• Midjourney: Popular Generative AI tool for generating images.
• Replit: Popular Generative AI tool for coding that turns your ideas into an app.
• Syntheisa: Popular Generative AI tool for video generation.
• Copilot: Popular AI companion launched by Microsoft for writing, designing, generating images, etc.

Ethical considerations of using Generative AI

Generative AI offers many benefits, but some ethical considerations are necessary for safe and secure use of Generative AI.

Negative Impact of Generative AI on Society

Every technology offers benefits, but also comes with some negative impacts. Let’s discuss the negative impacts of Generative AI on society.

1. The first negative impact will be on human jobs. Generative AI will take over some jobs that were previously done by humans.
2. Data security is another major issue. Generative AI may lead to the leakage of sensitive information.
3. With Generative AI, it is difficult to distinguish between the real world and the fake world. Deepfakes are a real-world example.
4. It also led to an increase in cybercrime, like faking biometric data, phishing emails, and fake websites. writing malware, etc.

Stay Tuned!!

Stay tuned with us at www.datasciencehorizon.com for upcoming content.

For any queries and clarification, kindly email us at datasciencehorizon@gmail.com

For the first part of the Generative AI chapter, click on the link below:

Unit 4: Introduction to Generative AI (AI Course; Class – IX)-Part 1

Keep Learning and Keep Implementing!!