Basics-of-Artificial-Intelligence/C2/Generative-AI/English - Revision history

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Created page with "'''Spoken Tutorial-AI-3''' '''Generative AI''' '''Meta Tags: '''Generative AI, Predictive AI, Transformer, LLM, Prompt, Model, Training, Inference, Spoken Tutorial, Video Tu..."

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'''Spoken Tutorial-AI-3'''

'''Generative AI'''

'''Meta Tags: '''Generative AI, Predictive AI, Transformer, LLM, Prompt, Model, Training, Inference, Spoken Tutorial, Video Tutorial, EduPyramids.

'''Pre-requisite Tutorial''': Introduction to Machine Learning and Deep Learning.

{|border=1
|-
|| Visual Cue
|| Narration
|-
|| Title Slide: “Introduction to Generative AI”
|| Welcome to this Spoken Tutorial on '''Introduction to Generative AI.'''
|-
|| '''Slide 2 '''

'''Learning Objective '''

Bulleted list
|| In this tutorial, you will learn about,
* '''Generative AI''' and '''Predictive AI'''
* Differences between '''Predictive AI''' and '''Generative AI'''
* Some important '''Generative AI '''terms

|-
|| '''Slide 3 '''

'''System Requirements '''

'''Graphic'''

Laptop, browser, internet icons
|| To practice this tutorial, you will need:
* A computer, a laptop, or a smartphone.
* A stable internet connection.
* An updated web browser, such as Chrome, Edge, or Firefox.

No '''coding '''knowledge is needed.
|-
||
|| In a previous tutorial, we saw how AI helps computers to think and act smart.

But have you ever wondered how AI makes all this happen?
|-
|| '''Slide 4 '''

'''Generative AI'''
|| '''Generative AI '''can do creative tasks that we thought only humans could do.
|-
|| Text highlight: “Create new things”
|| '''Generative AI,''' or '''GenAI, '''doesn’t just analyze data but can also create new things.
|-
|| Montage: AI-generated poem, painting, code snippet
|| '''It''' can generate original content such as poems, images, music, code, and more.
|-
|| Split screen: Spam filter vs AI painting

|| Most '''AI''' we use daily is '''Predictive AI'''.

'''Predictive AI '''classifies data and makes predictions.
|-
|| Text on screen: “Predictive AI → What is this?”
|| '''Predictive AI''' helps in understanding two things.

It can give answers to “What is this?” and “What may happen next?”

'''Predictive AI''' recognizes things and makes smart guesses about the future.

For example, use of a '''spam filter '''in an '''email inbox.'''
|-
|| Spam filter animation
|| Here, '''Predictive AI''' checks each email and decides whether it is '''spam''' or not.
|-
|| Recommender system visual
|| We use '''Predictive AI''' in many every day apps.

Some of them are '''weather apps''',''' movie apps, map apps''', and '''photo''' apps.
|-
|| Transition: Text “Generative AI → Create something new”
|| On the other hand, '''Generative AI''' or''' GenAI, '''can create something new.

It creates them based on the knowledge it has learned earlier.
|-
|| Astronaut riding horse (AI art)
|| It can generate a unique image, like an astronaut riding a horse.
|-
|| Poem generation example

Code snippet generation
|| It can also write a short poem or generate a working code.
|-
|| Text highlight: “Predictive → classify / Generative → create”
|| So remember this difference.

'''Predictive AI''' classifies and predicts data, whereas '''GenAI''' creates examples.
|-
|| Timeline animation → 2017 marker
|| '''GenAI''' uses a model called '''Transformer.'''

'''Transformers''' process language in parallel, rather than word by word.

This makes them faster, smarter, and more efficient.
|-
|| Sentence visualization using a story analogy
|| '''Transformers''' understand how words relate to each other, like we follow a story.
|-
|| Side-by-side: before vs after Transformer
|| This lets them generate well-structured, meaningful, human-like text.

'''GenAI '''grew rapidly after '''Transformer architecture''' made a breakthrough in 2017'''.'''
|-
|| Transition slide: “Core Terminology”
|| Now, let’s learn a few important '''GenAI '''terms.
|-
|| Brain icon
|| First, let's understand the term '''Model.'''

Think of the''' Model''' as the “brain” of the''' AI '''system.”

A '''Model''' can be small and task-specific.

It can also be large and for general purposes.

For example, '''ChatGPT''' is a model trained on large amounts of text.
|-
|| Training animation
|| Next term is '''training.'''

It means teaching the '''model''' to use a large amount of data.

The '''model''' is given numerous examples, including entire books and sample texts.

The '''model''' learns patterns from this data during training.
|-
|| Prompt box + user typing
|| Next term is '''Prompt'''.

A '''Prompt''' is the instruction you give a '''GenAI model.'''

It tells the '''model''' what task it has to perform.

For example, “Write a story about a robot.”
|-
|| Text cum Visual

It can be:

a full word → “Monday”

part of a word → “inter”, “esting”

a punctuation mark → “!”

a space

sometimes even an emoji → 😀

Think of tokens like puzzle pieces.:-AI breaks your text into small pieces (tokens), understands each piece, and then puts the pieces together to form a meaningful response.
|| Next term is '''Tokens.'''

AI models break the input into small pieces called '''tokens'''.*

A '''token''' is a unit of text.

It can be a full word like “Monday” or a part of a word like “inter”, “esting”

It can be special characters such as a punctuation mark, or a space or even an '''emoji'''.

The '''model''' reads, understands, and generates text token by '''token'''.

Think of '''tokens''' like puzzle pieces.

AI breaks your text into small pieces.

Then it understands each piece and then puts the pieces together to form a meaningful response.
|-
|| LLM logo examples (GPT, Gemini, LLaMA)
|| Next term is '''LLM. '''

It stands for '''Large Language Model.'''

'''LLM''' is a powerful '''model '''trained on billions of text '''tokens'''.

For Example: '''GPT, Gemini, LLaMA.'''
|-
|| Model responding to prompt
|| Next term is '''Inference'''.

'''Inference''' is when the '''model''' takes a '''prompt '''and generates an output.

It’s the stage where the '''model''' produces a response after it has been trained.

So, when you give a '''prompt, '''the '''model '''generates text as an output.
|-
|| '''Summary Slide '''

Bulleted list
|| Let’s quickly summarize what we learned.

We learned,
* '''Predictive AI''' is used to '''classify or predict'''.
* '''Generative AI''' is used to '''create new content'''.
* How to differentiate '''Predictive''' AI from '''Generative AI'''
* Some key '''terms used in Generative AI'''

|-
|| Assignment slide

Bulleted list

|| As an assignment do the following.

Pick one '''Predictive AI''' example and one '''Generative AI '''example from your daily life.

Write down one line explaining why each fits its category.

Also, reflect on any risks or biases that may exist in your examples.
|-
|| EduPyramids logo
|| This Spoken Tutorial is brought to you by '''EduPyramids Educational Services Private Limited''', '''SINE, IIT Bombay.'''
|-
|| Closing slide
|| Thank you for watching!
|-
|}

@@ Line 4: / Line 4: @@
 Spoken Tutorial, Video Tutorial, EduPyramids
-'''Pre-requisite Tutorial''':  Introduction to Machine Learning and Deep Learning
+'''Pre-requisite Tutorial''':  Introduction to Machine Learning and Deep Learning.
 {|border=1
@@ Line 38: / Line 40: @@
 * A stable '''internet''' connection
-* An updated '''web browser''', such as '''Google Chrome''', '''Microsoft Edge''', or '''Mozilla Firefox'''
+* An updated '''web browser''', such as ''' Chrome''', '''  Edge''', or '''  Firefox'''
-You do not need '''coding''' or technical skills.
 |-
@@ Line 50: / Line 53: @@
 || For the prerequisite '''Artificial Intelligence tutorials''', please visit this '''website'''.
-In a previous '''tutorial''', we saw how '''AI''' helps '''computer''' to think and act smart.
+In a previous '''tutorial''', we saw how '''AI''' helps '''computers''' to think and act smart.
 But have you ever wondered how '''AI''' makes all this happen?
@@ Line 74: / Line 77: @@
 || '''Predictive AI''' helps in understanding two things.
-It can give answers to “What is this?” and “What may happen next?”
-'''Predictive AI''' recognizes things and makes smart guesses about the future.
+So, Predictive AI recognizes things and makes smart guesses about the future.
 |-
 || Spam filter animation
@@ Line 148: / Line 152: @@
 || Next term is '''Prompt'''.
-A '''prompt''' is the instruction you give to an '''AI model'''.
+A '''prompt''' is the instruction you give a '''AI model'''.
 It tells the '''model''' what task it has to perform.
@@ Line 182: / Line 186: @@
 '''LLM''' is a powerful '''model''' trained on billions of text '''tokens'''.
-For example: '''ChatGPT, Gemini,''' and '''LLaMA'''.
+For example: '''ChatGPT''', '''Gemini,''' and '''LLaMA'''.
 |-
@@ Line 192: / Line 196: @@
 It’s the stage where the '''model''' produces a response after it has been trained.
-So, when you give a '''prompt''',  the '''model''' generates an '''output'''.
+So, when you give a '''prompt''',  the '''model''' generates text as an '''output'''.
 |-
@@ Line 199: / Line 203: @@
 Bulleted list
 || Let’s quickly summarize what we learned.
 '''Predictive AI''' is used to classify or predict.
 '''Generative AI''' is used to create new content.
 Some key terms used in '''Generative AI'''.
@@ Line 220: / Line 228: @@
 || This '''Spoken Tutorial''' is brought to you by '''EduPyramids Educational Services Private Limited, SINE, IIT Bombay.'''
 |-
-|| '''Closing Slide'''
-|| Thank you for watching!
-|-
 |}

@@ Line 31: / Line 31: @@
 || '''Slide 3'''
 '''System Requirements'''
 * A '''computer''', a '''laptop''', or a '''smartphone'''
@@ Line 39: / Line 42: @@
 You do not need '''coding''' or technical skills.
 || '''Slide 4'''
@@ Line 46: / Line 50: @@
 || For the prerequisite '''Artificial Intelligence tutorials''', please visit this '''website'''.
-In a previous '''tutorial''', we saw how '''AI''' helps '''computers''' to think and act smart.
+In a previous '''tutorial''', we saw how '''AI''' helps '''computer''' to think and act smart.
 But have you ever wondered how '''AI''' makes all this happen?
@@ Line 105: / Line 109: @@
 || '''GenAI''' uses a '''model''' called '''Transformer'''.
-Transformers process language in parallel, rather than word by word.
+'''Transformers''' process language in parallel, rather than word by word.
 This makes them faster, smarter, and more efficient.
@@ Line 164: / Line 168: @@
 Think of '''tokens''' like puzzle pieces.
-AI first breaks the information into small pieces.
+'''AI''' first breaks the information into small pieces.
 Next it understands each piece.
@@ Line 176: / Line 180: @@
 It stands for '''Large Language Model'''.
-LLM is a powerful '''model''' trained on billions of text '''tokens'''.
+'''LLM''' is a powerful '''model''' trained on billions of text '''tokens'''.
 For example: '''ChatGPT, Gemini,''' and '''LLaMA'''.
@@ Line 187: / Line 191: @@
 It’s the stage where the '''model''' produces a response after it has been trained.
 So, when you give a '''prompt''',  the '''model''' generates an '''output'''.