Intro to Generative AI for Busy People

Summary

This video explores generative AI, explaining it as a subset of AI that creates new content like text and images. It highlights the role of GPUs in revolutionizing AI performance and the significance of breakthroughs like the transformer model. The script distinguishes between supervised and unsupervised machine learning, introduces deep learning and neural networks, and explains how generative AI models, like large language models, learn from data to produce new content.

Takeaways

• 🤖 Generative AI refers to creating new content such as text, images, and videos using artificial intelligence.
• 💡 AI is a branch of computer science that aims to make computers behave like humans by understanding language and recognizing objects and patterns.
• 🚀 The recent buzz around generative AI is due to advancements in hardware (GPUs), software, and the availability of large datasets.
• 💼 GPUs are preferred for AI tasks because they can handle many operations simultaneously, unlike CPUs which are better at complex, single tasks.
• 📈 The introduction of transformers in 2016 led to significant breakthroughs in AI, particularly in the development of models like GPT.
• 📚 Large language models (LLMs) are trained on vast amounts of text data from the internet, including books, articles, and Wikipedia.
• 🧠 Machine learning is a subset of AI that enables systems to learn from data without explicit programming, similar to human learning.
• 🔍 There are two main types of machine learning models: supervised (data with labels) and unsupervised (data without labels).
• 🧬 Deep learning is a subset of machine learning that uses artificial neural networks to learn complex patterns from data.
• 🌐 Generative AI is a type of deep learning that can process both labeled and unlabeled data to generate new content.
• 🏥 Discriminative models predict labels for data points, while generative models understand and reproduce data characteristics to create new instances.

Q & A

What is generative AI?

Generative AI is a type of artificial intelligence that can create new content such as text, images, and videos.

What is the difference between a CPU and a GPU?

A CPU is like a CEO that handles complex tasks one at a time, while a GPU is like a team of workers that can handle many simpler, repetitive tasks simultaneously.

Why are GPUs important for AI?

GPUs are important for AI because their ability to handle multiple operations at once makes them ideal for tasks such as artificial intelligence and machine learning.

What is a transformer in the context of AI?

A transformer is a significant breakthrough in AI research introduced in 2016, which is the foundation of GPT-Generative Pre-Trained Transformer.

How does a generative AI model like GPD-4 pass tests like the SATs and bar exams?

GPD-4 passes such tests because it was trained on a large corpus of text data from the internet, including thousands of books, millions of articles, and the entirety of Wikipedia.

What is machine learning and how is it related to AI?

Machine learning is a subset of AI that focuses on building systems that learn from data and behave like humans. It allows computers to learn without explicit programming.

What are the two most common types of machine learning models?

The two most common types of machine learning models are supervised and unsupervised models. Supervised models have labeled data, while unsupervised models have unlabeled data.

How are deep learning and machine learning related?

Deep learning is a subset of machine learning that uses artificial neural networks to learn complex patterns from both labeled and unlabeled data.

What are artificial neural networks and how do they work?

Artificial neural networks are inspired by the human brain and are made up of interconnected nodes called neurons that can learn to perform tasks by processing data and making predictions.

What is the difference between generative and discriminative machine learning models?

Generative models understand and reproduce the characteristics of data to generate new content, while discriminative models classify or predict labels for data points.

How are large language models like GPT related to generative AI?

Large language models are a specific type of generative model that focuses on language. They are trained on large amounts of text data and can generate new, coherent text.

What are the key components that have contributed to the rise of generative AI?

The key components contributing to the rise of generative AI are advancements in hardware (like GPUs), software, and the availability of large amounts of data.

Outlines

00:00

🤖 Introduction to Generative AI

The video introduces generative AI, explaining it as a subset of artificial intelligence that focuses on creating new content like text, images, and videos. It distinguishes AI as a computer science field that aims to mimic human behavior, such as language understanding and pattern recognition. The script highlights the impact of GPUs in revolutionizing AI tasks due to their parallel processing capabilities, which are superior for handling repetitive tasks compared to CPUs. The video also mentions the significance of the 2016 'Attention is All You Need' paper that laid the groundwork for the GPT model, which quickly gained popularity. The script emphasizes the training of AI models on vast datasets to achieve human-like performance in tasks such as passing exams.

05:01

📚 Understanding Machine Learning and Deep Learning

This section delves into machine learning as a subset of AI that enables systems to learn from data without explicit programming, similar to human learning. It differentiates between supervised and unsupervised learning, where supervised learning uses labeled data and unsupervised learning identifies patterns in unlabeled data. The script introduces deep learning as a subset of machine learning that uses artificial neural networks, inspired by the human brain, to process data and make predictions. It explains how deep learning models with many layers can learn complex patterns and can be trained on both labeled and unlabeled data. The video also draws a comparison between human learning processes and generative AI, highlighting the role of large language models in generating new content.

Keywords

💡Generative AI

Generative AI refers to the subset of artificial intelligence that is focused on creating new content such as text, images, or videos. It's a key theme in the video, as it ties together the various concepts discussed, like machine learning and deep learning. The script mentions that generative AI involves AI systems generating new content, which is a departure from traditional AI that simply processes or analyzes existing data.

💡Machine Learning

Machine learning is a subset of AI that enables computers to learn from and make predictions or decisions based on data. It's integral to the video's narrative as it forms the foundation for understanding more complex AI concepts like deep learning and generative AI. The script explains machine learning as a system that learns from data without explicit programming, similar to how humans learn.

💡Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks to model and understand complex patterns and data structures. It's highlighted in the script as a significant advancement in AI, allowing for more sophisticated tasks like image and speech recognition. The video connects deep learning to generative AI by explaining that generative models use deep learning techniques to create new content.

💡GPU (Graphics Processing Unit)

A GPU is a specialized electronic circuit designed to rapidly manipulate and alter memory to accelerate the creation of images in a frame buffer intended for output to a display device. The video script uses the analogy of a factory to explain how GPUs, with their multiple processing cores, are capable of handling many tasks simultaneously, making them ideal for AI and machine learning tasks that require parallel processing.

💡Transformers

Transformers is a type of deep learning model based on the concept of 'attention mechanisms', which allows the model to better understand the context in which words appear in a sentence. The script mentions the introduction of transformers as a significant breakthrough in AI research, leading to models like GPT which utilize this architecture to generate human-like text.

💡GPT (Generative Pre-trained Transformer)

GPT stands for Generative Pre-trained Transformer, a type of deep learning model that is used to generate human-like text. The video script describes GPT as one of the fastest-growing consumer apps, highlighting its ability to understand and produce text after being trained on a large corpus of internet data.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data. The video script uses the example of supervised learning to explain how a model learns from data that comes with tags or labels, allowing it to make predictions or classifications based on what it has learned.

💡Unsupervised Learning

Unsupervised learning is a type of machine learning where the model is trained on unlabeled data, allowing it to discover patterns or structures within the data. The script contrasts this with supervised learning, explaining that unsupervised models learn the underlying structures and patterns without any prior knowledge of the data.

💡Artificial Neural Networks

Artificial neural networks are a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling, or clustering raw input. The video script explains that deep learning models use these networks to learn complex patterns by processing data through many layers of neurons.

💡Large Language Models (LLMs)

Large language models are a type of deep learning model that have been trained on vast amounts of text data. The video script mentions LLMs as a key component of generative AI, explaining that they are capable of generating new text based on the patterns and structures they've learned from their training data.

💡Discriminative Models

Discriminative models are a type of machine learning model used to classify or predict labels for data points. The video script contrasts these with generative models, explaining that discriminative models are trained to predict labels (like spam or not spam), whereas generative models learn to reproduce the characteristics of the data to create new examples.

Highlights

Generative AI involves creating new content such as text, images, videos.

AI stands for artificial intelligence, a branch of computer science that aims to make computers behave like humans.

Generative AI is gaining attention due to advancements in hardware, software, and data.

GPUs have increasingly replaced CPUs in AI tasks due to their ability to handle multiple operations at once.

GPUs are ideal for AI and machine learning tasks, leading to a significant increase in performance.

The introduction of transformers in 2016 was a significant breakthrough for AI.

GPT (Generative Pre-Trained Transformer) became the fastest-growing consumer app, reaching 100 million monthly users in two months.

GPD-4, a large language model, passed tough tests like bar exams and SATs.

GPD-4 was trained on a vast corpus of text data from the internet, including books, articles, and Wikipedia.

Machine learning is a subset of AI that focuses on building systems that learn from data.

Supervised machine learning involves data with labels, while unsupervised machine learning involves unlabeled data.

Deep learning is a subset of machine learning that uses artificial neural networks.

Artificial Neural Networks are inspired by the human brain and consist of interconnected nodes called neurons.

Generative AI is a type of deep learning that uses artificial neural networks to generate new content.

Humans also learn from labeled and unlabeled data, similar to how AI models learn.

Large language models are a type of deep learning model that focuses on language.

Machine learning models can be generative, which generates new data, or discriminative, which classifies or predicts labels.

Generative models learn the features of data to generate new, realistic examples, unlike discriminative models that classify data.

Large language models, like GPT, are examples of generative models focusing on language.

Transcripts

In this video

we'll be covering what generative AI is.

What machine learning is and the

different types of machine learning,

what LLMs are what's deep learning

and all the other jargon you hear

when you think about generative AI.

This is Suraj and he's a non coder.

This is Siddhant and he's a pro coder.

Siddhant, every reel, blog post and

tweet is about generative AI today.

Even Ola's founder launched the AI platform.

EY says generative AI is going to add

1.5 trillion to the Indian economy.

Sam Altman says that this is a bigger

revolution than the internet itself.

So what is generative AI?

Let's break it down.

The term is made up of two things,

Generative and AI.

So Generative refers to creating new

content such as text, images, videos.

AI stands for artificial intelligence,

which is a branch of computer science.

That deals with making computers

and machines smart enough so

that they can behave like humans.

For example, understanding language,

recognizing objects and patterns.

And when this AI starts Generating new

content, that is called Generative AI.

Okay

but why is everybody

talking about it now?

What happened?

What changed?

It's a combination of these three things,

hardware, software, and data.

Since late two thousands, GPUs have

increasingly replaced CPUs in all the AI tasks

but what exactly is A GPU?

The GPU stands for Graphics Processing Unit.

Think of GPU as a team of

workers in a factory and A CPU.

As A CEO.

The CPU like the CEO

by nature is a generalist.

Which is really good at performing

complex tasks and decision making.

It can handle a variety of different

jobs, but it works on them one at a time.

On the other hand, GPUs as factory workers

aren't as versatile as the CEO, but they are

great at doing simpler, repetitive tasks.

And importantly, there are a lot of them

so they can work on many tasks at the same time.

This is similar to how a GPU works.

It has hundreds or even thousands of smaller,

less complex processing cores that

can handle many operations simultaneously.

This makes the GPU an obvious choice

for handling graphics and video games.

So whenever you play a video game

or watch a 4k movie.

Your GPU quickly renders images and videos by

processing lots of calculations in parallel.

It's like having an army of workers painting

a huge wall at the same time, while the

CPU would be like one person carefully

painting detailed features on a small canvas.

Watch this demo of GPU vs CPU by NVIDIA.

So basically GPUs are like graphic

cards we use for gaming, right?

But how is it being used in AI?

So GPUs aren't only just for graphics.

Their ability to handle multiple operations

at once makes them ideal for tasks such as

artificial intelligence and machine learning.

In fact, NVIDIA reports that since the

introduction of GPUs, The performance

in AI has seen an extraordinary increase

improving by as much as

1000 times over the span of a decade.

Now, along with hardware improvements, there

have been notable development in AI research.

In 2016, a significant breakthrough

happened with the introduction of

transformers in a research paper

titled attention is all you need.

This is the foundation of

GPT-Generative Pre-Trained Transformer

which became the fastest growing consumer app.

of all time

Getting over 100 million monthly

users in just two months of launch.

GPD-4 even passed all the tough tests

like bar exams and your SATs.

But how did GPD pass the SATs and bar exam?

Even a normal person can't do that.

It's really difficult, right?

So this is because GPD was trained

on a large corpus of

Text data from the internet, including

including thousands of books

millions of articles and  

the entirety of Wikipedia.

So you'll know everything about each topic.

Exactly.

Okay.

I'm starting to understand what

Gen AI is all about, right?

But what is machine learning

AI got to do with this?

So let's understand this one by one.

AI is a broad discipline.

AI to computer science is similar

to what physics is to your science.

Machine learning is a subset, or you can say

a type of AI that focus on building systems

that learns from data and behave like humans.

It is a program or system that trains

a model from input data that trained

model can make useful predictions from

new or never before seen data drawn from

the same one used to train the model.

Machine learning gives the computer

the ability to learn without explicit

programming, just like how human learns.

And two of the most common types

of machine learning models are

unsupervised and supervised models.

The key difference between the two

is that with supervised models.

We have labels.

Label data is the data that comes with

a tag, like a name, a type, or a number.

Unlabeled data is the data

that comes with no tags.

so what I'm understanding is So what I'm understanding is that

that supervised

machine learning

is when the data comes

with tags and labels

and the machine knows

what it's learning,

like it's a cat and dog and everything,

it knows the correct answers.

The unsupervised machine learning

is when the data is unlabeled,

so it's learning those structures

and the patterns behind the data

That's exactly correct.

You perfectly nailed it Suraj

I knew it.

But now I'm a little confused.

What is machine learning

and what are models?

So machine learning is a field of study

and you can think of it as a process.

And machine learning

model is a specific

output of this process.

it is what machine

learning system creates

after being trained on the data,

this model contains the knowledge

and the patterns

learned from its training.

Got it! That's machine learning.

But what about deep learning?

So deep learning

is a subset of machine learning.

You can think of it

as one more type of machine learning

that uses artificial neural networks,

What are Artificial Neural Networks?

So Artificial Neural Networks

are inspired by the human brains.

They are made up of

interconnected nodes called neurons

that can learn to perform tasks

by processing data

and making predictions.

Deep Learning models

typically have many layers of neurons,

which allows them to learn more complex

patterns than traditional Machine

Learning models.

And neural networks can be both labeled

and unlabeled data.

In semi supervised learning,

a neural network is trained

on a small amount of labeled data

and a large amount of unlabeled data.

But labeled data

helps the neural network

to learn the basic

concepts of the task.

While the unlabeled

data helps the neural networks

to generalize the new examples.

Okay.

Is this similar to generative AI?

Yes, that's correct.

I know that's a very good observation.

so generative. A.I.

is a type of deep learning

which uses

these artificial neural networks

and can also process

labeled and unlabeled data

to generate new content.

I guess this is all pretty much.

Humans also learn.

You go to school

where you learn the label data

and then you go to the real world

and you learn the unlabeled data.

And at the end of the day,

you come here

and generate content, right?

so where do alums

and all come into this?

so large language

model are also type of deep

learning models.

these models are large

both in terms of their physical size

and also the amount of data

they have been trained on.

now, to understand

how everything is connected.

Let's move one

step above deep learning and LLMs

based on the type of output

they generate.

Machine learning

models can be divided into two types

Generative and Discriminative

A discriminative model

is used to classify or predict labels

for data point.

For example,

a discriminative model

could be used to predict

whether or not an email is a spam.

Here, spam is the label

and email is the data point.

Discriminative models

are typically trained

on a dataset of these labeled

data points,

which means while training

we will show model

all the Emails which look like spams

so that it learns the relationship

between the label and data point.

Once a discriminative model is trained,

it can be used to predict the label

for new data points.

In health care, a discriminative model

could be used to predict

whether a patient

has a specific disease

or not based on their symptoms

and test results.

for example,

it might analyze blood test data

to predict the likelihood of diabetes.

on the other hand,

a generative model

is designed to understand and reproduce

the characteristics of data

rather than just

distinguishing between

different categories or labels.

Suppose

we are training a generative model

with pictures of cats.

the models

task is not just to identify

whether an image is a cat or not.

Instead,

it learns the features that make up cat

images, shapes, colors,

textures and patterns. Common to cats.

It understands these features

so well that it can generate

new images of cats that look realistic

but do not replicate

any specific cat

from the training data.

Large language

models are a specific type

of generative models

focusing on the language, and GPT

is one of the example

of generative large language model.

Okay, now I have a good idea

of what gender is all about.

Now I want to start using this.

I want to start building models.

So where do I start for that?

Well, lucky for you,

I'll be giving you the roadmap

to become a GenAI

engineer in this video.