Difference between revisions of "Being-Creative-with-AI/C2/Introduction to Generative AI/English"
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| − | || Visual Cue | + | || '''Visual Cue''' |
| − | | Narration | + | | '''Narration''' |
|- | |- | ||
| − | || Title Slide: | + | || Title Slide: '''Introduction to Generative AI''' |
| Welcome to this Spoken Tutorial on '''Introduction to Generative AI'''. | | Welcome to this Spoken Tutorial on '''Introduction to Generative AI'''. | ||
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Bulleted list | Bulleted list | ||
| − | | In this tutorial, you will learn about | + | | In this tutorial, you will learn about - |
| − | + | *'''Generative AI''' and '''Predictive AI''' | |
| − | Some important '''Generative AI''' terms | + | *Differences between '''Predictive AI''' and '''Generative AI''' |
| + | |||
| + | *Some important '''Generative AI''' terms | ||
|- | |- | ||
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http://EduPyramids.org | http://EduPyramids.org | ||
| − | | For the Pre-requisites of this tutorial, visit the website shown on your screen. | + | | For the '''Pre-requisites''' of this '''tutorial''', visit the '''website''' shown on your screen. |
|- | |- | ||
| Line 90: | Line 92: | ||
|| Split screen: Spam filter vs AI painting | || Split screen: Spam filter vs AI painting | ||
| − | | Most '''AI''' we use daily is '''Predictive AI'''. '''Predictive AI''' classifies '''data''' and makes predictions. | + | | Most '''AI''' we use daily is '''Predictive AI'''. |
| + | |||
| + | '''Predictive AI''' classifies '''data''' and makes predictions. | ||
|- | |- | ||
| Line 98: | Line 102: | ||
| '''Predictive AI''' helps in understanding two things. | | '''Predictive AI''' helps in understanding two things. | ||
| − | + | First, it can identify "What is this?". | |
| + | |||
| + | Second, it can guess "What will 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. |
|- | |- | ||
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| We use '''Predictive AI''' in many everyday '''apps'''. | | We use '''Predictive AI''' in many everyday '''apps'''. | ||
| − | Some of them are '''weather apps''', '''movie apps''', '''map apps''', and '''photo''' | + | Some of them are '''weather apps''', '''movie apps''', '''map apps''', and '''photo apps'''. |
|- | |- | ||
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|| Text highlight: "Predictive → classify / Generative → create" | || Text highlight: "Predictive → classify / Generative → create" | ||
| − | | So remember this difference. '''Predictive AI''' classifies and predicts '''data''', whereas '''GenAI''' creates examples. | + | | So remember this difference. |
| + | |||
| + | '''Predictive AI''' classifies and predicts '''data''', whereas '''GenAI''' creates examples. | ||
|- | |- | ||
| Line 152: | Line 160: | ||
| '''GenAI''' uses a '''model''' called '''Transformer'''. | | '''GenAI''' uses a '''model''' called '''Transformer'''. | ||
| − | '''Transformers''' process language in parallel, rather than word by word. This makes them faster, smarter, and more efficient. | + | '''Transformers''' process language in parallel, rather than word by word. |
| + | |||
| + | This makes them faster, smarter, and more efficient. | ||
|- | |- | ||
| Line 164: | Line 174: | ||
|| Side-by-side: before vs after Transformer | || 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. | + | | This lets them generate well-structured, meaningful, human-like text. |
| + | |||
| + | '''GenAI''' grew rapidly after '''Transformer architecture''' made a breakthrough in 2017. | ||
|- | |- | ||
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| First, let's understand the term '''Model'''. | | 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. | + | 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. | For example, '''ChatGPT''' is a '''model''' trained on large amounts of text. | ||
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|| Training animation | || Training animation | ||
| − | | Next term is '''Training'''. It means teaching the '''model''' to use a large amount of data. | + | | 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''' is given numerous examples, including entire books and sample texts. | ||
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|| Prompt box + user typing | || Prompt box + user typing | ||
| − | | Next term is '''Prompt'''. A '''prompt''' is the instruction you give to | + | | Next term is '''Prompt'''. |
| + | |||
| + | A '''prompt''' is the instruction you give to a '''Gen AI model'''. | ||
It tells the '''model''' what task it has to perform. | It tells the '''model''' what task it has to perform. | ||
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|| Text cum Visual tokens like puzzle pieces: | || Text cum Visual 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. | + | 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'''. | | Next term is '''Tokens'''. '''AI models''' break the input into small pieces called '''tokens'''. | ||
| − | A '''token''' is a unit of '''data'''. It can be in any form, such as text, image, or audio. | + | A '''token''' is a unit of '''data'''. |
| + | |||
| + | It can be in any form, such as text, image, or audio. | ||
The '''model''' reads, understands, and generates information '''token''' by '''token'''. | The '''model''' reads, understands, and generates information '''token''' by '''token'''. | ||
| − | Think of '''tokens''' like puzzle | + | 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. | ||
|- | |- | ||
| Line 224: | Line 250: | ||
|| LLM logo examples (GPT, Gemini, LLaMA) | || LLM logo examples (GPT, Gemini, LLaMA) | ||
| − | | Next term is '''LLM'''. It stands for '''Large Language Model'''. | + | | Next term is '''LLM'''. |
| + | |||
| + | 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'''. | ||
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|| Model responding to prompt | || Model responding to prompt | ||
| − | | Next term is '''Inference'''. '''Inference''' is when the '''model''' takes a '''prompt''' and generates an '''output'''. | + | | 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. | 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 245: | Line 275: | ||
Bulleted list | Bulleted list | ||
| − | | Let's summarize what we learnt: | + | | Let's quickly summarize what we learnt: |
'''Predictive AI''' is used to '''classify or predict'''. | '''Predictive AI''' is used to '''classify or predict'''. | ||
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Also, reflect on any risks or biases that may exist in your examples. | Also, reflect on any risks or biases that may exist in your examples. | ||
| − | | | + | | As an assignment do the following. |
| + | |||
| + | |||
| + | |||
| + | |- | ||
| + | |||
| + | ||Acknowledgement slides | ||
| + | |||
| + | | This brings us to the end of the tutorial. | ||
| + | |||
| + | Thank you for watching! | ||
| + | |||
|- | |- | ||
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This '''Spoken Tutorial''' is brought to you by | This '''Spoken Tutorial''' is brought to you by | ||
| − | + | '''EduPyramids Educational Services Private Limited''', '''SINE, IIT Bombay'''. | |
| − | + | ||
| | | | ||
Latest revision as of 14:44, 22 May 2026
| 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 -
|
| Disclaimer Content Slide | As AI tools constantly evolve, if you are unable to locate any icon or encounter difficulty at any step, you may use any conversational AI Chatbot for guidance. |
| 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. |
| Slide 4
Pre-requisite http://EduPyramids.org |
For the Pre-requisites of this tutorial, visit the website shown on your screen. |
| 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 5
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.
First, it can identify "What is this?". Second, it can guess "What will happen next?". So, Predictive AI recognizes things and makes smart guesses about the future. |
| Spam filter animation | For example, use of a spam filter in an email inbox.
Here, Predictive AI checks each email and decides whether it is spam or not. |
| Recommender system visual | We use Predictive AI in many everyday 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 to a Gen AI model. It tells the model what task it has to perform. For example, "Write a story about a robot." |
| Text cum Visual 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 data. It can be in any form, such as text, image, or audio. The model reads, understands, and generates information 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: ChatGPT, Gemini, and 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 learnt:
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
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. |
As an assignment do the following.
|
| Acknowledgement slides | This brings us to the end of the tutorial.
Thank you for watching!
|
| EduPyramids logo
This Spoken Tutorial is brought to you by EduPyramids Educational Services Private Limited, SINE, IIT Bombay. |
