Generative AI

Generative AI is a form of Artificial Intelligence (AI) that focuses on creating new content. Unlike conventional AI, which analyzes data or makes predictions (like estimating travel time in Google Maps or recommending ads), Generative AI produces entirely new outputs.

Examples include:

• ChatGPT generating human-like text (e.g., writing an email).

• DALL·E generating images from text prompts.

• GitHub Copilot generating source code.

Key Idea:

Generative AI doesn't retrieve existing content; it creates new text, images, code, audio, video, and more — freshly generated using AI models.

How is it different from Conventional AI?

• Conventional AI is used for:

○ Predictions (e.g., Google Maps estimating time)

○ Classification (e.g., showing relevant ads)

○ Analysis (e.g., sentiment detection)

○ Actions (e.g., self-driving cars)

• Generative AI goes a step further — it creates new things like:

○ Text (e.g., ChatGPT writing emails)

○ Images (e.g., DALL·E generating images)

○ Code (e.g., GitHub Copilot writing code)

○ Audio, Video, and more 

Summary: What is Generative AI?

1. Generative AI is a subset of Deep Learning:

• It uses neural networks (like those in Deep Learning) to understand patterns in data and then generate new content (e.g., text, images, audio, video).

• Unlike traditional AI, which focuses on prediction or classification, Generative AI focuses on creation.

2. Conventional AI vs Generative AI:

Conventional AI                                                              Generative AI

Learns from training data to classify or predict         Learns from data to create new content

Example: Identifies whether an image is of an apple Example: Generates a new image of an apple

Works on extractive or analytical tasks                         Works on generative/creative tasks

3. Example Explained:

• If trained on apple images:

○ A traditional AI model would tell whether a new image is an apple.

○ A generative AI model would create a new image of an apple that didn't exist in the training set.

4. Three Key Takeaways for Understanding Generative AI:

1. Data Quality & Quantity Matter:

○ Models need to be trained on huge volumes of clean, diverse data.

○ Term to remember: “Garbage in = Garbage out”.

2. High Computational Power is Essential:

○ Neural networks and large datasets require powerful hardware (e.g., GPUs).

○ Fast performance (like ChatGPT responding instantly) needs significant back-end compute power.

3. Natural & Contextual Interaction is the New Standard:

○ Users prefer conversational interfaces over keyword-based searches.

Generative AI can maintain context across multiple queries (e.g., remembering that "there" refers to New Delhi).