Master Generative AI: From Basics to Advanced LangChain Applications

Just when we thought the AI race couldn't get any crazier, Microsoft made a silent yet powerful move. Last week,

they hired over 20 top AI engineers from Google Deep Mind without much noise, but with huge impact. One of the most talked

about highest, Wun Moan, the brain behind the startup Windsor, which was recently acquired by Google in a $2.4

billion deal. We are not just watching a trend but we are witnessing an AI battlefield where Microsoft, Google and

startups are fighting for the minds that will shape the next era of intelligence. Why is all this happening? Because

everyone wants a piece of AI future from chatbot to enterprise AI tools. Every company's racing to build smarter,

faster, more humanlike technology. And here's the thing, you don't have to be a tech giant to be a part of it. But if

you're sitting on sidelines, you're already a step behind. That's exactly why we at Intellipath have created the

most practical and beginner friendly genai full course absolutely free. We have broken down everything you need to

know from deep learning algorithm, genai models, transformers, autoenccoders to hands-on tool like lang chain, hugging

face, MCP servers and even building your own AI agent. This video is your one-stop destination to confidently

start your journey into generative AI in 2025. So take your laptop, tune into Google Collab, and let's dive deep into

an immersive Gen AI learning experience right here on Intellipad's YouTube channel. Our tech revolution has already

begun. Just look around. Genai hiring, Gen AI is in picture. The AI age is here. We have reached a point in history

where we can build an app without writing a single line of code, create art without picking up a brush and write

a script, design a product, launch a business just by giving instructions to AI. Generative AI is growing fast. The

industry is worth over $ 38 billion in 2025 and is expected to cross $1 trillion in less than a decade and

companies are already hiring for roles like generative AI engineer and prompt engineer. But while these roles are

emerging, thousands of jobs are also disappearing. The layoffs are real and they are hitting hard to lay off over

12,000 job roles altogether. This time there is a clear culprit. It's artificial intelligence. If you're

wondering whether AI is coming for your job, well, spoiler alert, it may already have.

>> People in tech, marketing, design, and customer services are losing job. Not because they are not talented, but

because the tools and industry have evolved. The hard truth, skills that were valuable 5 years ago aren't enough

anymore. If you're not adapting, you are at risk of becoming replaceable, not by a person, but by a tool. And that's

exactly why this video matters. There's a small window of opportunity right now where anyone who decides to learn and

adapt can actually lead this stage. You don't need to be a coding expert or graduate from a top college. You just

need the right direction. And that's what I'm here to give you. Presenting the complete generative AI road map. A

simple 10-step guide for absolute beginners. Whether you are student figuring out your path, a working

professional trying to stay relevant, or someone genuinely excited about AI, this road map is your starting point. This

road map if you follow and study as discussed in the video you will be able to crack generative AI roles or rather

build your own genai product down the line. You can find the road map in the description below for absolutely free.

So let me clear the air by explaining two different path you can take to be a geni pro. Let's look at the very first

step. Understand the two genai path. Before we jump into coding or training model, it's important to understand

where you are headed in generative AI. There are two main routes. The first is the application path. This means using

genai tools smartly. You'll learn how to write effective prompt, use tools like chart GPT or DL and integrate AI into

real world app using APIs. The second path is for builders. Here you go deeper into how AI works behind the scene. You

learn machine learning, neural network and transformers. Basically, how these models are created and train. Most

people start on application path and slowly build the confidence to go deeper. So don't worry if you are a

beginner. The key is to just begin. Step two, learn a programming language. See, you can either go for JavaScript or

Python. But I would recommend you to learn Python. Python is the language that powers almost all AI development

today. If you have never coded before, don't worry. Python is bigger friendly. You can learn the basics like loop,

function, and condition within a few weeks. Once you have got the basics, move on to two essential libraries which

is numpy and panda. These are essential for working with data and nai because data is everything. Numpy helps you with

numbers and array while panda help you load and clean data from files like CSVs. See the coding you need to work

around AI is not just build app rather it's more about training models using available frameworks and libraries like

TensorFlow, PyTorch, third party APIs and more. You can learn Python from Google Python class, Python's official

documentation. We ourselves have recently rolled out machine learning course. You can check it via the link in

the description. Step three, learn machine learning. Now that you're comfortable with Python, let's answer

the big question. How does a machine actually learn and generate? This is where machine learning or ML comes in.

Imagine you give a machine thousands of example like houses with their size, location, and prices. Over time, the

machine starts to recognize patterns such as houses in location X with 2,000 square ft usually cost around this much.

It doesn't memorize, it learns from pattern in the data. That's the magic of ML. Start by learning main types of

machine learning such as supervised learning. You give the machine board the input like email and target variable.

Meaning let's say you want machine to predict price of house on basis of location, carpet area, number of rooms

etc. Then initially you also give the price as well for training. After training machine would be able to

understand the pattern and predict the house prices for new entries you make. This is basically a simple explanation

of how supervised learning works. Common model includes linear regression, logistic regression, decision tree,

random forest, SVM, KN&N. Moving on to unsupervised learning. In simple word, it's when you give your model a bunch of

data, but you don't label to figure out the pattern. The model has to figure things out on its own and come up with

pattern detection, grouping, etc. Now, let me give you a real world example to make it more clearer. Imagine you run an

online store. You have tons of customer data. how much they spend, how often they visit, what type of product they

buy, their age, where they live, and so on. But here's the thing, you don't know whether you should retarget them by

advertisement or if they're already a loyalist. This is where unsupervised learning comes in. You use a technique

like K mean clustering and algorithm start analyzing the data on its own to form customer groups. For example, it

might figure out okay, so these are the customer who spend a lot and shop often. they are your high value buyers or these

are the one who only buy when there are discount and they're budget conscious shoppers and maybe there's a third group

who order just once those are your impulse buyers maybe you can create more custom offers and target these impulse

buyer to buy your product so basically in unsupervised learning you didn't tell the model what kind of customers you

have it discovered them by drawing insight from their behavior and grouping them together and once you have these

insights you can make smarter decision you can show personalized ad recommend better product and create offers that

actually match each group's buying style. This is the simple intuition behind clustering algorithm. You need to

learn different versions of algorithms such as K mean clustering, hierarchical clustering, DB scan clustering etc.

Reinforcement learning. Now think of a machine learning through trial and error like a game. The model which is agent

takes action get rewards or penalties and learns the best strategy over the time. This is no fixed data set. It

learns from the experience. This is how AI learns to play games, drive cars or manage stock portfolios. Moving on to

federated learning. Lastly, federated learning help machine learn without sharing your data. Instead of collecting

everything on one server, the model trains directly on devices like your phone. Only the model updates are shared

keeping your data private. It's widely used in app like mobile keywords or health tac. Common tools include

tensorflow. To start practicing, try these tools. Scikitlearn one of the best libraries for beginners. simple, well

doumented and packed with all the essential ML algorithm for classification, regression, clustering

and more. KAS, a highle deep learning library that is beginnerfriendly and built on top of TensorFlow. Perfect for

building and training neural network with just a few lines of code. You can learn ML from Google's AI machine

learning crash course neural network zero to her by Kapati. You can learn from Intellipar's YouTube video. Now

coming to step four, understand artificial neural network and dive into deep learning where you will have to

learn about CNN and RNN. Now let's step into the real brain of AI which is artificial neural network. These are the

foundation behind many gen AI application like chat GPT and more. Let's break this down with a simple

example. Cat versus dog image detection. Suppose you want to build an AI that can identify whether an image is of cat or a

dog. You start by feeding the model thousands of labeled images of cat and dogs. These images enter the input layer

of the neural network where each image get converted into grid of pixel values which are numbers. As the data moves

through multiple hidden layer, each layer tries to learn something from the image. One layer might detect edges,

another might identify ears, tails or fur patterns. The deeper you go, the more complex the feature becomes.

Finally, the output layer gives the result. For example, predicting whether the image is of a dog or a cat. Now, if

the prediction is incorrect, the network doesn't stop here. It learns from its mistake using a technique called back

propagation. We have a complete video on back propagation. You can check it out if you want to learn about the same.

Where the model calculates the error and adjust internal connection which is called waves to do better next time. The

math behind this adjustment is called gradient descent. It helps the network make tiny precise improvement to reduce

the error. By now you understand what a basic neural network is. But when it comes to genai, especially for working

with images or text, you will need to dive into two powerful types of network which is CNN and RNN. They power tools

like chart GPT and live translation apps. CNN or convolutional neural networks are great for image task. CNN

are used in face recognition a RNN or recurrent neural network work best with sequence like text or speech. Say your

model is completing a sentence. It needs to remember earlier words to predict the next. RNNs have memory for that and

better versions like LSTM and GRU help them remember even longer. They are used in chatbots, translation tool and speech

recognition. You can start with a project that predict the next word in a sentence using PyTorch or Keras.

Understand autoenccoders and transformers. Now we dive into architecture that changed everything

which is transformers. This is the model used in all major geni tools like GPT, Claude and Gemini. Transformers

introduce the self attention mechanism which helps the model focus on the most important part of the input. Before this

model struggled with the long sequence transformer fix that. Now what's an LLM? It stands for large language model. It's

basically a massive transformer trained on tons of text data from the internet. LMS can write poems, answer question,

explain codes and more. Understanding how they work from tokenization to embeddings to attention layers give you

real power as GI engineer. Key concepts in transformers include tokenization breaking input text into smaller parts

like word or subword embeddings turning those tokens into vector or numbers that model can process. Then self attention

the magic behind how model focuses on important word. So transformer are trained on massive data set using huge

computational power and output is what we call as LLM. Step five dive into generative models. Generative models are

what make genai different. Instead of just classifying data these models create new content. You will learn about

G or generative adversial network where two models are generator and discriminator compete. One tries to

create fake data and other tries to catch it. This back and forth make the generator smarter. There are also other

types of VAEEs and diffusion model. These models are used in AI art, defix, fashion and more. If you want to work in

creative AI, this is where your journey begins. Step six, learn prompt engineering. Even without training

model, you can get amazing results by mastering how to write prompts. Prompt engineering is like giving precise

instruction to your AI assistant. It's not just about asking question. It's about guiding the model step by step.

You will learn to use context, example, tone, and chaining techniques. The skill is super valuable if you're building

tool that rely on LLM. It's also helpful when you're working with APIs from open AI or go ahead where the right prompt

means a difference between a good and a bad result. Step seven, learn to use APIs. So most company won't just ask you

build GPT from scratch. Instead they will ask you to use the existing APIs. That's where this steps comes in. You

will learn how to call open AAIS GPT, Gemini by Google or cloud via their APIs. You will build web apps or tools

that use these APIs in background. For example, a chatbot, a rum writer or a meme captioner. Use programming

languages like JavaScript or Python for back end and front end. So once you learn how to send a request and get a

response from the model, you can build real product. Moving on to step eight, which is fine-tune elements. Fine-tuning

means taking an existing model like GPD2 or LMA and training it on your custom data set. Let's say you want a chatbot

for legal advice. You feed it case file, legal terms and previous judgments. The model learn from the data and become

specialized. You will use tools like hugging face, transformer, lora and pft to fine-tune efficiently. This step lets

you build highly customized AI tool that work for specific industry or user. Moving to our step nine, which is

explore multimodal AI. The future is not just about text. It's about combining text, images, audio, and video. That's

what multimodal AI is. Imagine uploading a photo and having the AI write a story about it. Or you speak a command and the

AI draws for you. Model like Sora, Gemini, and Dali are already doing this. The exciting part, you don't have to

build everything from scratch. Platform like hugging face and tools like Langchin lets you existing model

fine-tune them for your own needs and build custom AI that solve real world problem like automating customer

support, content generation or even healthcare chatbot. Hugging face is like a huge library of pre-trained AI model

for text, images, speech and more. You can simply become a model, test it online and plug into your project

without heavy coding. Lang chain on the other hand help you build AI app that can reason, take action and use tool

almost like a brain for your AI system. It connects model with memory, API, search tools and lets you design full

workflow with multiple steps. This is also where agentic AI comes in. AI that doesn't just answer question but can

take action. Think of creating your own AI system that read emails, searches the web, book appointment and even talk to

other tools all by itself. You will need to learn how to combine different input types and build tools that can handle

them. This opens up creative possibilities that go way beyond traditional apps. Now coming to our

final step which is step number 10. Now that you understand the basics of Genai, it's time to build real project. project

show what you can actually do. They are the best way to prove your skills. Start simple. Build a news summarizer using

OpenAI's API that turns long articles into a threeline summaries or a rum rewriter that takes a job role and

rewrites your resume using GPT. Want to try something visual? Create an image generator using Dale or a widget

classifier using TensorFlow and Steam. You can also build fun stories like AI story generator that writes story from

topics or a chrome extension that rewrites emails using GPD. All you need is basic Python APIs like OpenAI or

Hugging Face and simple tool like Flask, Streamlit or Gradio to bring your ideas to life. Start by breaking problem into

step input, model, output and build each part one by one. Now let's talk about hot project areas. One of the trending

ideas is MCP or multimodal conversational platform. These are AI tools that understand text, voice and

images together. For example, you speak a prompt and the AI replies with a story or image. Use tools like GPT DALI and

connect them using lang chain. You can also try projects like medical Q&A, B train on health data or a voice to image

generator that turns your spoken word into picture. Once you build something, upload it on GitHub, deploy it using

gradual or hugging face spaces and make a small demo video. This helps recruiter or client see your work in action. With

this we come to the end of the video and all the steps mentioned above are explained in detail in Intellipath

generative AI video which is available for free. You can watch it using the link provided below. Plus you can get

the complete road map in the description below. Just a quick info guys, Intellipad offers generative AI

certification course in collaboration with IHub IIT RUI. This course is specially designed for AI enthusiasts

who want to prepare and excel in the field of generative AI. Through this course, you will master geni skills like

foundation model, large language models, transformers, prompt engineering, diffusion models, and much more from top

industry experts. With this course, we have already helped thousands of professional and successful career

transition. You can check out their testimonials on our achievers channel whose link is given in the description

below. Without a doubt, this course can set your careers to new height. So visit the course page link given below in the

description and take a first step to a career growth in the field of generative AI. So now that you guys know the road

map to become a geni engineer, it's time that we get started with mastering the right tools and concept. For this I will

be handling over the next section to an industry expert. He will walk you through the essential from an

introduction to generative AI and transformers to open AI's GPT lchain and craft prompt engineering. So let's get

started. >> So uh we'll be covering the following topics. Um as far as uh

um you know this you know my course is concerned what are the topics that we're going to be covering? Um we will be of

course be covering uh I I'll start with uh an introduction

to generative AI right so we'll be doing that in our today's session um we'll be

very high level broad brush strokes um we'll be discussing introduction to generative AI in our today's session um

and then what we will als also be doing is we will also be covering topics specifically around

um why do I see that folks are saying there's an eco so introduction to genai

so I'm going to be talking about all the different applications of u genai right so how the industry is perceiving

uh an industry point of view so we'll be covering these topics um broadly in our today's session um more of a business

point of view right so where is this this area where is this field sort of headed towards and stuff like that

that's what I'm going to be covering broadly in our today's session then from after the session we'll be going into a

lot of detail right so I will talk about um uh the transformer architecture right I'll be talking about transformer

architectures um I'll also be talking about how some of the most popular GPD models

are trained right um and then we will also be discussing uh we'll also architectures are um you know how they

work then we will go one level lower um we will actually start discussing about um you know uh we'll be doing a lot of

hands-on specifically on trying out some of these architectures. So I'll introduce you to firstly the the open AI

uh so we'll be I'll be focusing on the open AI models uh for for for a good chunk of this particular course. Um I'll

also see if I can show you some open-source models, right? So there are different types of u different ways you

can access some of these models. So um I'll also I'll focus primarily on the open AI model but also show you how you

can access the u other models that are available out there. So the open AAI GPT models um um is you know how to access

them. Then uh I'll introduce you to lang chain uh which is a library that is very very popular. It's an orchestration

library that helps you access some of these models uh very efficiently. Um then we will look at

um you know u some prompting techniques right so I I I'll prompt engineering to be specific right I'll I'll discuss

about uh some topics around prompt engineering all the different prompting techniques that you would typically have

so chain of thought right um I I'll talk about react um we'll also talk about tree of thought

um and so on and so forth. There's there there's a bunch of other things. So we'll talk about all of those uh few

short learning and stuff like that SSL and stuff like that. So we'll talk about all of that u under prompt engineering

and then once that is done we will then get into retrieval augmented generation

which is also popularly referred to as rag. So we will talk about rag. we will understand how rag works and then we'll

do a lot of hands-on on rag as well. Um and then after rag um I will also show you some more complex um u you know

agentic architectures or rather simply put let's say agents um using langra

um and stuff like that. So, so I'll probably be closing out the sessions at the end using agents um and and concepts

of agents in Langra. So, broadly this is how we will go about doing things. Um in the later parts of the session or maybe

actually here itself when I discuss OpenAI, I will discuss of course the GBD models, I'll also discuss some of the

image generation models here as well. Um so how you could access the dolly kind of models how can you actually um

generate content using the dolly kind of models I'll also be discussing that um in that session so so broadly these are

the topics that I'm going to be covering um again I don't want to while we are covering one of these topics of course

we'll end up covering some of the ancillary topics as well right so topics around this space as well I know this

might not be I mean all of these topics that you're currently seeing on the may or may not necessarily resonate with a

lot of you because you may not know what this space is but but trust me this pretty much covers the 80% of uh I would

say everything that's out there today right so a good 80 is you know 75 80% of all the happenings in this particular

space is fairly covered in the topics that you see on the screen over here let's start with the introduction to

Genai so so all of this by the way is LLM's only so when I say open AAI GPT GPT models. These are large language

models only. When I'm talking about transformers, those are large language models only. So

I'll talk about all of that as we speak. So here's the thing, right? So so let's let's start with the first topic today,

right? Let's talk about what is generative AI? Why all this drama about geni? Why has it suddenly become so

popular? Right? Let me agenda introduction to Genai. Perfect. So while I bring this up, I also want to bring up

some presentations. Give me a second. So one of the good advantages of being uh in the industry while you're doing this

is is that I also get a lot of content from uh a lot of these um

companies out there. Let me show you some interesting content that I had got very recently from Bane, from Microsoft,

from Accenture. Some lot of very interesting presentations out there. I'll try to

bring some of that up. Easy to understand kind of slides or easy to understand kind of content. So you all

have you know when we talk about jai or rather if you kind of talk about what has changed over the last couple of

months couple of maybe one and a half year or so one year or so I would say you've suddenly seen these tools that

you see on the screen suddenly pick up pace all of us agree on this right u yeah bar has become Gemini um chat GPT

has become so popular. Google has also launched Palm kind of models. Um, Anthropic has launched uh an interface

called Claude. Um, Perplexity is another tool. You have OpenAI has launched Dali. Um, Microsoft has launched Copilots. Um,

yeah. The point is we suddenly saw something change in the space, right? So cut to two years, 3 years ago, we are

still we were still talking about all right, how can I build a a deep learning model that can do

question answering, right? Or how can I build a recurrent neural network based model that can do classification?

How can I use some of the existing B models for doing sentence similarity or document similarity? How can I use word

toe to let's say do something very specific in this particular space like maybe

classifications or let's say document uh similarity question answering so on and so forth this is what we were talking

about two years ago but suddenly things have changed suddenly you start you know Chad GPT was launched and and we have to

admit that Chad GPT's launch is a marquee event in the history of AI so far right chat GPT launching is

something that will get etched in the books of history, the AI history forever and ever, right? So the moment Chad GPT

got launched, it just, you know, it was a jaw-dropping moment for everyone. People suddenly started seeing some and

I I I still remember a lot of these uh posts coming up on LinkedIn, on Twitter and everywhere where people started

saying this is revolutionary. this is going to change how we look at AI anymore. This is going to completely put

people out of jobs blah blah blah. That fear is still there even today. So this is what a outsider is looking at, right?

So you're you're using the chat interface through chat GPT. You're asking it questions. It's able to do an

amazing job of answering some of these questions, right? It is able to create content. It is able to surprisingly

create content with amazing levels of accuracy. um levels of accuracy that are far

beyond what let's say uh any human or uh uh decent it would very easily build a you

know beat a decently skilled human in some of these some of some very very specific tasks you had seen these genai

models beat um people in or rather students in bar exams and SAT exams right? Um stuff like

that. Question is like where did this suddenly come from? Like was this something that happened all of a sudden?

Does this have anything to do with anything that we learned so far or is this completely new? Right. Um the

answer to it is a bit of both. It it has a lot to do with what we have learned so far yet it is completely new. Um it has

it has lot of eerie similarities to stuff that we have discussed that we may have you know spoken about so far yet

this has um you know structurally fundamentally you know even conceptually this is very very novel very new in this

particular area. um and that is why you suddenly saw this spike in the number of apps that started

coming up, number of know chat interfaces that started picking up and so on and so forth. So long story short,

new new field of AI has suddenly emerged u new tools came in and suddenly it seems like AI has become a lot more

easier than what you and I would have imagined so far. it suddenly feels like man why did we learn all that we've

learned so far right this seems super easy right you could just you could just open a chbpt interface and get it to do

things which you would have otherwise struggled quite a bit uh with with traditional AI system so suddenly even I

went through this brief period saying okay am I going to go out of job because I I I spent a lot of time in AI and am I

going to go out of job and I decided otherwise to to kind ride the wave rather than try and sit

down and sob about it. That's a different story. We'll come to that later. But here is where we are. Okay.

So now the question is what is it about genai? What is it? How is generative AI similar or new in whichever form or

shape, right? So let's talk about that. Um so you if you remember we spoke about this prophecy, right? So

this AI prophecy or this AI um ambition, aspiration of being able to build something like a

Jarvis, right? Something like Terminator, artificial general intelligence. You all

must have heard about artificial general intelligence. So what is artificial general intelligence? So when you talk

about AI, when you talk about artificial intelligence, AI can be nicely split into two parts. artificial

narrow intelligence and artificial general intelligence.

The key operative difference between the two being here artificial narrow intelligence and artificial general

intelligence. What are what is the difference between the two? See artificial

the difference between artificial narrow intelligence versus artificial general intelligence

um is very simple. So, so far we all have spoken about all of these different

applications of you know of AI like whatever you see on the screen right be it let's say your portana your Apple

Siri your Google now your even your self-driving car recommendation systems on YouTube your um AI capabilities on

you know your machine learning AI capabil ities on your Uber app, any kind of AI that we may have done so far, ADAS

as well. Adas is a very very good example as well, even ADAS uh which has LAR um you know, you know, ultrasound

capabilities to identify what things are around it, stuff like that. So all of these are great AI capabilities, but

they are all artificial narrow intelligence. What do I mean by that? each of these activities. So take for

example YouTube as a good example. If you take YouTube, YouTube has a lot of intelligence in one very good example is

is recommendation engine. So the recommendation engine that you have that is recommending the next video that you

should watch on YouTube is only built to do just that piece of work. It can only do that part of it. it cannot I cannot

use that same piece of AI or that same model to do let's say to identify what maybe um I cannot use that same AI to to

get to get to to predict how long will you watch that particular video for or I cannot use the same model to identify

uh if you would purchase let's say the YouTube premium subscription or not, right? I cannot let it do multiple

things. It can only do one very very specific activity. It'll do a fantastic job of that one activity, but it cannot

do anything beyond that. It is just trained to do that one piece of work and it'll just do that one. It cannot help

you. That AI model which uh which predicts um if which video you will likely to watch cannot tell me cannot I

cannot use that to generate subtitles. I cannot use that to summarize that complete video. I cannot use that same

model to do other things. You can only do one piece of work. To do the others, you'll have to build other models.

You'll have to build separate solutions for it. And that's how we've been doing it so far. To solve a specific AI

capability, you need to build one model for it. You cannot have one model that does everything or one model that has a

wider application. It is very narrow. It's not to say that it is bad, right? I don't want you all to misunderstand that

this is bad. This is not bad, not at all bad. This is great. It's just that it is capable of doing one particular task at

a time. But what we are but the but the objective of the field of AI is to try and get to build something like a

terminator artificial general intelligence. I want to build or rather we intend to build I don't know if you

watched Dennis the Menace. I want to probably build a robot that has got that kind of intelligence in Dennis the

Menace or maybe in Jetsons for that matter. You we've all we've all grown up seeing that kind of intelligence. Right?

So in in in science fiction and movies some form or the other Skynetss, Terminators, your Jetsons being another,

you know, smaller silly example. The point is you want to kind of get there. Uh small wonder, not little wonder,

small wonder. >> Yes. Um Vicki from Small Wonder, one of my favorite shows right at that time. Um

all right. So point is how do you get there? So today we don't have any applications of artificial general

intelligence. Right. Um we are trying to get there. We are far away from it. we are a good few maybe I would I would

still assume like a good few decades away from it but that's where we want to get to that's what we would want to

really try and aim at right artificial general intelligence what you and I have is AGI right um we can do multiple

things all at the same time I can drive a car I can cook food I can teach my child I can I can learn something and I

can also attend a session in parallel I can do so many things in parallel with decent amount of accuracy across

everything right um that's what we're trying to get at artificial general intelligence it's very humanlike

intelligence now the thing about um AI my friends is that uh AI is such a new space

um you know when you talk about um you know when you talk about artificial

intelligence um we are more often than not always talking about artificial articial narrow

intelligence capability. We're not talking about artificial general intelligence capabilities at all, right?

Because we're a couple of decades away. But what generative AI has done is it has gotten us, it has helped us make a

huge leap towards AGI, but we're still far away. But it has gotten us far more closer to

you know to general intellig to general intelligence than what we would have done had we progressed in the current

phase. If we were to build the same kinds of pieces of technology like what we're currently doing it would have

taken us a very very very long time to get there. But at least we were able to make a massive stride in that direction.

Why? What is it about generative AI which makes us believe that we've gotten closer to general intelligence? Remember

everyone, I am not saying we've made progress on AGI. We haven't. We are still talking about narrow intelligence,

but we've been able to make much larger progress towards AGI using generative AI models. I'm not saying by any means that

we've gotten closer or rather we have made progress on AGI itself. The objective of a company like OpenAI

is to be able to build AGI. That's what that's what S Alman has always talked about. He always keeps saying we want to

build AGI. We want to build artificial general intelligence. But I not just I mean who am I, right? And I'm a small

fish in the pond. But if you take for example the the who's who of the industry out there, they all believe

that we're far away from it, right? We're at least a good decade decade and a half away from it. we need more such

wonders like Chad GPT to happen before we can get there. So that's essentially

um if you were to consider let's say colonizing um let's say another planet as as the

end objective our Chandrean 3 was a huge leap towards that consider Chad GPT or the generative AI as your Chundrean 3

success u have we gotten closer to colonizing the planet maybe yes but have we accomplished anything in that

objective probably not to make a huge we a huge leap towards that direction. So that's a fair analogy as far as how you

should treat generative AI. But the beauty of this space friends is that with that small leap itself, we are

seeing such huge or huge changes in how the industry has started to use AI in the regular in their day-to-day basis.

The good part is you and I we have the object, we have the um opportunity to ride the wave and to kind of stay on top

of it, right? So we you are all kind of getting into the field of AI exactly at the time where this transition is

happening. We've kind of made that leaprog. It's that it's that leaprog moment. What is it about um generative

AI that makes us believe that we've made that? Let me explain. Let me show you a couple of things um why why we say that

um there are certain very interesting capabilities of generative AI that makes us believe something like this. Let me

show you an interesting presentation from Microsoft. This is a Microsoft presentation. Uh they had actually

presented this to us uh in in it's a public presentation. So on what is it about Genai

that uh makes us believe um that it does things differently.

see generative AI right and these are examples right um it can do

far more things than what we are currently seeing on the screen of course um but for example you all have used the

chat interfaces right so this is a very very good example of it so if you take for example

the prompts um so you can actually just ask it a question uh and it can simply respond back to

You can follow up on those questions as well. You can sort of have a a a genuine humanlike

chat and it'll actually respond back very very much like a human being. Right? So its understanding of language

has significantly improved since the last time. Right? So models now understand language very very very well.

Right? than they would have done probably using any of the other regular AI models. Right? That's number one. So

understanding as language has significantly changed since the last u since the time we know what uh has

happened in this particular space. Um or even if you take for example the um your BERT models, you take any of your

other models that you've done learned that you may have learned about so far, your these models are far better at

understanding language. Not just that, these models are also very very good at understanding code, right? They're not

just good at understanding language, they also understand code very very well,

right? So they can actually write code for you. I have I cannot tell you how many times

right so I'll tell you a good example so today we built a capability in our in my own team we built a mobile we built an

app um in my in my in my team a product in my team and then we've decided that we should do a

complete code rehaul so we've written a lot of stuff using object-oriented programming and we decided you know what

oops in our setup makes no sense let's change it to functional programming and we just took that complete code uh bit

by bit we put it on chat GPT asked it to convert this into functional code 20,000 lines of code was rewritten

by shat GPT in a mere 3 days 20,000 lines of code we were able to just restructure our

complete code base in two to three days it's just amazing just the kind of capabilities that we starting to see um

that you know that that uh u something like a chat GPT could do. Those are the kinds of things what I you know I al I

can also talk about how content generation has changed right so one of the things about generative AI because

it is able to generate content. It is able to create new content. We can also get it to create images. I can tell it

what image I want and it actually create that beautiful image for me bases what I asked it to create. Um and and and and

not just this now you also have videos that you can create as well right so you can also get it to create some videos I

can also use Sora and stuff like that that I can also create too as well I I'll show examples um I'll show a lot of

examples as we go along for the next couple of weeks I'll just be showing a lot of hand hands-on examples itself so

so don't worry again I want you to understand that these are the kinds of some of the applications of of uh of

generative of AI. Uh when I say content creation by API, um API essentially is simply nothing but an endpoint. Um you

could have a model that's sitting somewhere and um you can interact with it. Don't worry about this this line

over here. I'll exactly explain what I mean by it. Now I want to show you something slightly more interesting. So

let me let me pull up um another interesting presentation. This is another presentation by by McKini. Uh

again very I want to talk about a couple of slides on this. Um lot of companies these days right um they have been using

traditional AI right so they've been doing pattern recognition um and and and stuff like that like

traditional AI capabilities. Now with GI you could do a lot of you could do code generation you could do image generation

you could do enhanced pattern recognition some of the capabilities that you we would have probably wanted

to have solved earlier we would have to solve it much le you know we can solve it much faster right now why are these

models so good right so why are these models that we speak about as far as uh geni so so damn good right again

approximately What you see here is and this is again a public presentation by me. So you can

actually download it from the website. So what they're saying is u these models are so good because they've been trained

on massive volumes of data right so for example one example here is if you take any of the uh GPT models GPT3 for

example was trained on 45 terabytes of data right uh a complete crawl of the internet complete internet was used uh

some lot of Reddit uh content, more than 250,000 books, the whole of Wikipedia, man. That's that's almost all of the

internet that you and I have access to. All of that data has been taken and they have provided that to the model. The

model has a very interesting way of learning. Um we'll talk about how these models learn. Um the way they learn is

very much to predict the next word. Right? So given a particular sentence, given a particular word, they try to

predict the next word. So when I say the models have been trained, they've been trained for a classification problem to

predict what the next word is. Given a set of set of sentence, given the first word, predict the next word. First two

words, predict the third word. First three words, predict the fourth word and so on and so forth. So you're sort of

predicting always the next word in the sentence. So point is that model is 175 billion parameters. When say 175 billion

parameters, what do I mean by parameters here? Weights and biases. These are your weights and biases. A total of 175

billion weights and biases not million billion. Okay, not features. They are not features. They are the weight and

biases. They are your parameters inside the model. Right? And had you trained this 45 billion terabytes of data with

75 175 billion parameters on one GPU, it would have taken you like 32 years to train that model. It would take you 32

years to train that model, right? So you can imagine if you were to train it for 32 years, then you would never get the

model. So what do you do? How do you accelerate that training? You either reduce the data or reduce the model or

throw more compute at it. Right? I'm I'm I'm kind of oversimplifying it here, but when I say throw more compute at it,

basically get get more GPUs. Now, it's that part which is where a lot of AI wars are happening right now, right? Of

course, to train such large volumes of data, such large models, you need more and more GPUs. But to get more GPUs, you

need um GPUs are not are are not made randomly, right? So, GPUs are very expensive. You need to make them. So,

who makes these GPUs? It's Nvidia's of the world. It's the AMDs of the world. They are the ones that are making these.

And Nvidia has is in such a beautiful spot here that they have uh really catched the cow left, right, and center,

right? So, they're doing a very good job with how they're positioning themselves. Anyway, we we'll talk about that later.

Point is again that it's because of that reason because there is a need for compute you started to see that you need

more and more models more and more compute um sorry more and more GPUs to accelerate the training but then even

after that right so let's say you do all the training what do you get you get a model which is 800 GB in size very

simple GPD3 is 800 GB can you imagine a model that's of the size of 800 GB it's that big it's not storing data data is

45 terabytes the the model is 800GB. So, so it's just these 175 billion weights and biases that are being stored um and

and and the size of that is approximately around 800 GB. Can you load an 800 GB model on your machine on

your personal machine? Of course no, you just cannot, right? Your machine has 16 GB RAM or 32 gig memory. Maybe if you're

rich enough, you'll probably buy a 128 gig memory machine. How can you load a 800 GB machine? How GB model? That is

where API is coming. before. So the model cannot sit on my machine. I cannot download the model unlike how you and I

were building models earlier where we took the data, we loaded the data in our local, we put it in a folder, we take

the we take the code and we actually build it uh in our machines. Um you install the libraries on your machine

and you build the model. There is no more model building, my friends. Model is not even going to be

built by you and me anymore. model is built by Microsoft, by Amazon's by Google's by Metas. They are the ones

that are going to be building the models. You and I are not building the models anymore. You'll be using the

models that they've built. Model building, not so much any of our job anymore. When I say our job, of course,

unless you choose to work in that space where you want to build models, that's that's a different story. My point is as

end users you and I are not building the models. You and I are using these models.

Um but where are these models? These models are going to be hosted somewhere else. You know in cloud, right? You can

I mean you can load these models but you will have to set up a data center to host a 800GB model. You need a data

center. You need a huge data center that can that has 800 GB in terms of RAM. you need a you need racks and racks of RAMs

to um to to support an 800GB model. What do you do otherwise? You put it on cloud. You let you let Microsoft manage

this and say, you know what, I don't care how you manage it. This is the model. You manage it. OpenAI has said

Microsoft, you manage this model for me. You host this model. Provide an interface where I can access this model.

Right? I want to access this model over internet. So, I just just give me a layer just like how I access a website.

I want an interface. If I want an API layer, that's where APIs come in where I can just simply query and I can get the

response. I don't want to be doing the business of actually loading this model by my my own self. So this is almost

like a service model as a service. Exactly. So what do you see here? See, geni my friend is not new. Jai is new is

not at all new. And this is the part that I was talking about, right? If you remember I spoke about, hey, is Jai

completely new? Well, maybe not. It's it's actually not completely new. Um, it is been there for a good seven, eight

years now. So that this is a good ex good view of all of that. The model the foundation of this particular model was

first published um in oops the first published in 2017 that to December 2017 a p paper called

attention is all you need is the paper that was first published. What happened after that? This paper took the world by

storm. Why? This paper is everything that kind of completely changed. Right? This is a game changer. What happened

here? So I again we'll I'll talk about this in much more detail when we discuss the transformer architectures. This

paper is the is the is the paper that introduced transformers to the world. So I'll give you one example. So far you

know um you all have learned RNN's I'm assuming right? You all have learned RNN's recurrent neural networks in an

RNN or LSTM for that matter or any of your models. So if you take for example a sentence which has W1, W2, W3, W4, W5.

So let's say five words or how many other words that you have in an RNN or an LSTM. You're essentially passing the

current word as an input, right? Um and then you could potentially predict the next word as an output, right? And then

of course you have a you have a bit of recurrence over here which is let's say an RNN or an NSTM so to say. You could

pass the current word as an input and you can predict the next word as the output. Then you can take these two

words as the input and then you can predict this one as the output. Then you can take these three words as the input

and you can predict this one as the output and so on and so forth. So the training in this particular case, right?

How did we learn the dependency between one word to another word? You of course had to go from you had to take the first

word, predict the next word, take the next two word, predict the next word, take the next three words, predict the

next word and so on and so forth. So you had to learn it like a language where even you had to move from left to right

or whichever way left to right, right to left, whatever. Point is that you had to sequentially learn the data. Language is

a sequence is a collection of words. There is an element of sequence associated with it. So you kind of learn

from left to right. Now in the process of learning from left to right especially when you have large volumes

of data there are issues of losing dependencies right when you have that's why LSTMs

came in or DRUS came in to kind of address for those long-term dependencies and so on and so forth but you still

were learning sequentially so when you learn sequentially it was very slow the learning process super slow but you

still were able to do a decent job of the whole learning process what but what these new models did firstly What

transformers did differently, right? Is they completely took away this concept of learning sequentially. They said no

more learning sequentially. You don't need to learn sequentially anymore. Right? Why do they say that? They say

look given any particular word this word has dependencies before this and after this. So that's where they introduced a

concept called as attention. Attention is a new concept. What it tried to do is it tried to say look when

you look at a sentence when you read a sentence if you take any one particular word in a sentence. So for example if I

say I had an amazing day at the park rather an amazing um right I had an amazing day at the park.

If you take a sentence like this every word in some form or the other has some kind of a dependency on the other words

that you see over here. When I say I, you know, I is of course partially dependent on some of the other words. If

you take the word amazing, amazing is is talking about the word day. Amazing is talking about the word I. Amazing is

also somehow talking about is also addressing the park, right? And if you take for example park, park is again

dependent on the word amazing. Park is dependent on somehow the word day and so on and so forth. Point is when you take

any sentence every word in a particular sentence has some form or the other some kind of a dependency with its

surrounding words. So and and those dependencies if you are able to capture it differently

rather than simply just trying to learn sequentially. If you capture those dependencies differently and you capture

all of those dependencies to some kind of a numeric representation like some kind of a more efficient embeddings.

What you could possibly do is you can just take these embeddings and then simply pass it into the model. So you're

eliminating, we'll discuss this in much more greater detail when we discuss transformers. But the point is you're

eliminating this sequential learning aspect of models. When you eliminate the whole process of sequentially learning,

you are accelerating learning. Um and and the concept of this exactly you can learn parallelly. And when you can do

parallel learning, you can do you can do a lot more epochs. Um your finetuning or rather your tuning could be much faster.

the the complete math becomes so much more simpler. One of the problems with LSTMs and RNN is because the math has

become so complex that you know you cannot learn with large volumes of data. But what transformers were able to do is

they kind of broke that complete sequential learning down into a very simple uh you know simple learning

process like how you would learn any of the other uh convolutional neural networks or even for that matter a

regular feed forward neural network. They just made the whole learning language a very simple exercise. Because

of that, these models have become super I mean a you were able to learn language very fast and secondly um you could go

into far more um you were able to also extract a lot more detail about these transformers as

well. uh we'll talk about transformers in much much greater detail but the point is the point that I want to make

here is these transformer models because of the introduction of these transformer models it completely revolutionized the

whole learning process of language learning language became so much more faster so much more simpler um and that

made it that that kind of added so much more uh ease with which how people could learn or how these models could learn.

That was one of the reasons why the 2017 paper is a massive massive leap. It's almost like somebody was saying, right?

It's it's almost like I I was reading about it somewhere and somebody said it's almost like somebody from the

future kind of came because this architecture is so different from how we have learned neural networks so

far, right? We it's it's very very radical. It's very different from how we have learned language so far because all

that we've learned is we've learned RNNs, we've learned LSTMs, um we've also learned encoder, decoder,

sequencetose sequence model. Point is they're all learning sequences and suddenly in 2017 somebody publishes this

paper and says all that you need is attention. Forget about sequences. Sequences is doesn't matter. You just

need to know how to encode those dependencies very well at the beginning itself. If you encode those

dependencies, everything is addressed. Which is why the paper says attention is all you need. All that you need to do is

some time of attention, everything else can be taken care of. So this paper was so radical it and somebody was also

saying right I was listening to a podcast. It was almost like somebody from the future came in and kind of

whispered this architecture in somebody's ears and then left. I I'm currently watching uh Lord of the Rings

uh the the the Amazon show uh that that u rings of power um it's almost like how the rings were you know formed right so

you have Sauron coming in and whispering it in the ears of calibb on how to uh you know forge the rings exactly like

that it's almost like that like somebody just came in and whispered in the ears of Google and said is the is the fallen

angel right exactly so it's almost uh somebody just came in and whispered how how this architecture sought to be

created. It's brilliant the way they've written the architecture and the point is that once that's sort of created

um once that architecture was published you could see what followed after that six years down the line my friend you

have seen one of the most revolutionary products that came out from there on right so you have um you have of course

the um open AI models coming up you had uh you know llamas coming out by meta uh um

uh Hugging Face launched their models, Anthropic launched their models and so on and so forth. So many models got

launched, so many models got launched. This was still very old. Sora came in um lot of things happened in this

particular space. So yeah, I mean so many different people kind of building their own models right now. uh even in

you know as we speak today um and this was uh you know I think one of the recent uh posts by uh by Eli on uh how

AI is evolving in India. I can actually show you that as well. Let me just Yeah, this is the India outlook for uh Genai

by Ernston Young. uh they very recently published this um fairly very cool uh on how they kind of published it um they're

talking about how um you know some specific areas where I think uh let me just go into this a little yeah so a lot

of people focusing in Indian organizations a lot of the work that's happening right now coding assistance

document intelligence and so on and so forth a lot of interesting work that's happening already there are companies

that are building these models itself. Somebody spoke about uh Bah GPT. Yeah, there's a lot of lot of companies

building their own um AI models as well, right? So um again, super super fast. By the way, these these images are all

created by OpenAI. Um all the images that you're seeing here, they're all created by them. Here are some um Indic

LMS right so Amari Canada Openhati Bhashini this is the the government of India's uh capability called Bhashini

um yeah so many so lot a lot of Bharat GPT somebody spoke about Bharaj GPT um yeah here you go these are all the

different uh capabilities in the India space as we speak today um Sam is the was actually the first company called

openhati um um they actually did a fairly good job as well and all of these companies

are u doing a very very good job right now and in my opinion they're doing a fantastic job as we speak of of uh of

AI. All right. So I I I don't want to go into too much uh of the detail around how AI is working but as you can see

there's a lot of Indian companies as well in this space right so that are doing it that that are doing a very good

job right so there's a lot of Indian ventures uh AI ventures that have been doing a fantastic job in this particular

area as well um they they've also secured a lot of funding by themselves but yeah okay uh let's let's go back to

what we were discussing so a huge advance adancements in the field of AI. That's what I wanted to talk about since

2017. Yeah, I don't want to get into this but I just want to talk about this this particular slide is again very

interesting in my opinion because see um the point that I wanted to speak about right who is building these models not

everyone right very few people are building these large language models these large gen models the only few

companies are building this and if you see those names at the top very few these are all big names right so these

are all big names that are being built. Of course, now you have also have these some of these Indian companies also

building their own models by themselves. But point is some models on text, some models on code, some on image, some on

audio, video, 3D, blah blah blah. So many different models that are now coming out. But all of these models in

some shape or form are all built on large large volumes of data. uh and these large volumes of data is exactly

what we are talking about as far as um or rather these these large models is uh um is is the size of these models is

exactly the reason why we require different kinds of uh infrastructure to handle. So the way you deal with these

models is going to be very different. The infrastructure requirements are different, the modeling requirements are

different. So if you're not going to build the model then how do you access these models? So what do you do with

these models? How can you customize these models for your own requirements? Because you're not building the model

and how do you get the model to work on your data? How do you get it to do what you specifically want it to do? Um so

those things we need to discuss and and there are some very new design patterns uh that have emerged over the years. Rag

being one of the most very very common very popular uh design pattern that has emerged in how you can how you can use

some of these models. uh but we can discuss them into much greater detail. Um so let's let's go back um to this. So

so what are we saying? We're saying look AI massive massive uh transformation in the

space. Um huge leap towards general intelligence. Lot of very interesting things that have

happened in the space since 2017. 2017 the paper art of attention is all you need was like the start of all of it and

then very interesting things followed. Now let me talk about the value chain of this. So who is making money the value

chain in this whole geni space right again a business perspective we'll get into the technical

details here but a business point of view very important for you to understand because again um this space

is super ripe. So who is who are the ones that are actually making a lot of money here.

So let's talk about different personas here right let's take couple of examples there are

uh you know sort of broadly speaking there are four to five different personas here or four to five different