Generative AI For Developers | Generative AI Series

Summary

This video script introduces the viewer to the transformative world of generative AI, tracing its evolution from traditional machine learning to deep learning and neural networks. It highlights the role of open-source models and tools in shaping digital experiences and emphasizes the importance of understanding AI's history and milestones. The script promises a series of tutorials on deploying and scaling generative AI models like LLaMA 2, aiming to equip developers with the knowledge to integrate generative intelligence into applications.

Takeaways

• 🌟 Generative AI is reshaping digital experiences and is the focus of a new video series exploring its capabilities and applications.
• 📈 The evolution of AI has progressed from traditional machine learning to deep learning and now to generative AI, which has gained mainstream popularity in 2023.
• 🤖 Traditional machine learning involves algorithms that learn patterns from data for predictions, heavily relying on feature engineering.
• 🧠 Deep learning and neural networks, inspired by the human brain, are subsets of machine learning that have enabled capabilities like voice recognition and autonomous vehicles.
• 🛠️ Generative AI models, such as GANs and VAEs, can create new data that mimics the input, unlike traditional neural networks that classify or predict based on input.
• 🎨 Applications of generative AI are vast, including text generation, art and design, music composition, AI-assisted coding, drug discovery, video and image enhancement, and fashion design.
• 🔍 Discriminative AI models, in contrast to generative AI, focus on classifying data into specific categories and are trained using supervised or unsupervised learning techniques.
• 🚀 Advancements in deep learning algorithms, large-scale data availability, computational power, open-source software, and diverse use cases are key factors contributing to the rise of generative AI.
• 🏆 Open-source technologies and models have democratized AI, fostering a culture of shared knowledge and accelerating the adoption of generative AI in various fields.
• 🔬 Generative AI's versatility in applications from genome analysis to molecular biology and healthcare has increased interest and driven further research and development in the field.
• 📚 The video series will delve into Foundation models, exploring their deployment and scaling, and provide a developer's perspective on integrating generative intelligence into applications.

Q & A

What is the main focus of the video series presented by Janaki Ram?

The video series focuses on the exploration of generative AI, its evolution, and its applications, powered by open source Foundation models and tools.

What is the significance of the partnership with Welchire mentioned in the script?

Welchire is a cloud provider offering affordable GPU infrastructure, which is essential for the video series to demonstrate the potential of generative AI through tutorials and deployments of Foundation models.

What is the difference between traditional machine learning and generative AI?

Traditional machine learning involves algorithms that learn patterns from data to make predictions, whereas generative AI models generate new data that mimics the given data distribution, rather than just predicting labels or values.

What are the key components of neural networks as discussed in the script?

The key components of neural networks include neurons, layers (input, hidden, and output), weights, biases, and activation functions.

How do generative adversarial networks (GANs) work in the context of generative AI?

GANs consist of two networks: a generator that produces fake data and a discriminator that distinguishes between real and fake data. Over time, the generator improves to the point where the discriminator can't reliably tell real from fake.

What is the role of variational autoencoders (VAEs) in generative AI?

VAEs work by encoding data into a lower-dimensional space and then decoding it back. They ensure the encoded data is close to the original and can generate new, similar data during this process.

What is the difference between discriminative AI and generative AI?

Discriminative AI models, such as traditional machine learning and deep learning models, focus on classifying or predicting based on input data. Generative AI models, on the other hand, learn the underlying probability distribution of data and can generate new samples that resemble the original data.

What are some of the key factors that have led to the rise of generative AI?

Key factors include advancements in deep learning algorithms and architectures, availability of large-scale datasets, increased computational power, the rise of open-source software and libraries, and the wide range of applications and use cases for generative AI.

How does generative AI apply to text generation?

Generative AI can be used for text generation by creating human-like text based on a given prompt, with models like OpenAI's GPT capable of writing essays, answering questions, and creating written content for various purposes.

What is the role of generative AI in the field of drug discovery?

Generative AI is used in drug discovery to generate novel molecular structures for potential new drugs, with in silico methods and generative models creating new molecules for further research and development.

How does generative AI enhance video and image quality?

Generative AI can be used to enhance the quality of images and videos through tools that apply generative models to transform faces, change age, gender, or hairstyles, or to improve the resolution and clarity of visual media.

Outlines

00:00

🌟 Introduction to Generative AI

Janaki Ram introduces the series on generative AI, outlining its evolution from traditional machine learning to deep learning and neural networks. The series will explore the capabilities of generative AI, powered by open-source Foundation models, and will be supported by Welchire's affordable GPU infrastructure. The content will range from the basics of generative AI to deploying and scaling Foundation models. The first video will focus on an introduction to generative AI for developers, comparing traditional machine learning with deep learning and generative AI, and touching upon the key factors and applications of generative AI.

05:04

📚 Traditional Machine Learning Basics

This paragraph delves into the fundamentals of traditional machine learning (ML), emphasizing its role in enabling computers to learn from data and make predictions. It explains the importance of feature engineering in selecting relevant input variables and discusses various ML algorithms like linear regression, logistic regression, decision trees, and clustering. The paragraph highlights that traditional ML is less computationally intensive and can be trained on standard PCs, contrasting it with deep learning approaches that require more resources.

10:07

🧠 Deep Learning and Neural Networks

The third paragraph introduces deep learning as a transformative subset of AI, utilizing neural networks with many layers to analyze data and make decisions. It describes the structure of neural networks, including neurons, layers, weights, biases, and activation functions, and explains how these networks learn by adjusting parameters through forward and backward propagation. The paragraph also mentions popular deep learning architectures like CNNs, RNNs, LSTM, and GRU, and notes the computational demands of deep learning models, which often act as black boxes due to their complexity.

15:09

🎨 Generative AI and Its Models

The focus shifts to generative AI, which builds on deep learning and neural networks to create models that generate new data similar to the input. The paragraph explains generative AI's distinction from traditional neural networks and introduces popular generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). It discusses the training dynamics of generative models, such as the competitive game in GANs between the generator and discriminator, and the encoding-decoding process in VAEs.

20:10

🔍 Discriminative vs. Generative AI

This section contrasts discriminative AI, which classifies or predicts based on input data, with generative AI, which learns the underlying data distribution to create new samples. Discriminative models, such as CNNs for image classification, are trained using supervised learning, while generative models, capable of tasks like text or image generation, are often trained using self-supervised learning techniques. The primary difference lies in their objectives: discriminative models classify or predict, while generative models replicate and create.

25:10

🚀 Applications and Impact of Generative AI

The final paragraph explores the wide range of applications for generative AI across various fields. It mentions text generation, art and design, music composition, AI-assisted coding, drug discovery, video and image enhancement, and fashion and retail as key areas where generative AI is making an impact. The paragraph also discusses factors contributing to the rise of generative AI, including advancements in algorithms and architectures, the availability of large-scale datasets, increased computational power, the role of open-source software, and the versatility of generative AI in numerous applications.

Keywords

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as text, images, or music, that is similar to the input data they were trained on. It is a central theme of the video, which aims to explore the capabilities and applications of this technology. The script mentions Generative AI's ability to create humanlike text, digital art, and even new molecular structures for drug discovery.

💡Deep Learning

Deep Learning is a subset of machine learning that utilizes neural networks with many layers, also known as deep neural networks. It is foundational to Generative AI, as it provides the complex structures necessary for creating new data samples. The video script discusses deep learning in the context of its evolution from traditional machine learning and its role in powering advanced AI capabilities.

💡Neural Networks

Neural Networks are computing systems inspired by the human brain that are composed of interconnected neurons, or nodes, which process information. They are the building blocks of deep learning and are essential for the functioning of Generative AI. The script explains that neural networks consist of layers, weights, biases, and activation functions, which allow them to learn from data.

💡Feature Engineering

Feature Engineering is the process of selecting and creating input variables that influence a model's learning ability. It is a key requirement in traditional machine learning, as mentioned in the script. The concept is contrasted with the self-supervised learning approach used in Generative AI, where the model learns from the data itself rather than relying on engineered features.

💡Discriminative AI

Discriminative AI, as opposed to Generative AI, focuses on classifying or distinguishing between different classes of data. The script explains that discriminative models, such as those used in image classification, learn the boundaries between classes rather than generating new data samples. This concept is important for understanding the different approaches in AI.

💡Generative Adversarial Networks (GANs)

Generative Adversarial Networks, or GANs, are a type of generative model consisting of two networks: a generator that creates fake data and a discriminator that evaluates the authenticity of the data. The script highlights GANs as a popular model for tasks like image generation, where they compete with each other, leading to highly realistic outputs.

💡Variational Autoencoders (VAEs)

Variational Autoencoders, or VAEs, are another type of generative model that work by encoding data into a lower-dimensional space and then decoding it back to generate new, similar data. The script mentions VAEs in the context of their ability to recreate content, such as generating new images that resemble the original input.

💡Self-Supervised Learning

Self-Supervised Learning is a machine learning technique where the model learns to predict part of the input data from other parts of the input data. It is a key method used in training generative models, as explained in the script, allowing them to understand and replicate the data structure without relying on external labels or guidance.

💡Foundation Models

Foundation Models refer to the underlying models or architectures that form the basis for various AI applications. The script mentions that the series will delve into these models, indicating their importance in the development and deployment of Generative AI systems.

💡Open Source

Open Source in the context of the video refers to the practice of making software, libraries, and frameworks freely available for use and modification. The script credits open source as a key factor in the rise of Generative AI, as it has enabled wider access to the technology and fostered a culture of shared knowledge within the AI community.

💡GPU Infrastructure

GPU Infrastructure refers to the hardware and software systems that support the use of graphics processing units (GPUs) for computation, particularly for training deep learning models. The script mentions a partnership with a cloud provider that offers affordable GPU infrastructure, emphasizing the importance of such resources in the development of Generative AI.

Highlights

Introduction to the world of generative AI, its evolution from traditional machine learning to deep learning and neural networks.

Generative AI's impact on reshaping digital experiences through open-source Foundation models and tools.

Partnership with Welchire, a cloud provider offering affordable GPU infrastructure for AI tutorials.

Series overview: From generative AI basics to deploying and scaling Foundation models.

The significance of subscribing for instant notifications on new video uploads.

Generative AI's rise to prominence with the launch of Chat GPT in 2022.

The importance of understanding the history of AI research for developers leveraging generative AI.

Traditional machine learning defined and its foundational role in AI.

Feature engineering as a key requirement in traditional machine learning.

Deep learning's transformative influence in AI, employing neural networks with many layers.

Neural networks' structure inspired by the human brain, comprising neurons, layers, weights, biases, and activation functions.

Deep learning models' reliance on large datasets and significant computational resources.

Generative AI's ability to generate new data mimicking given data, extending neural networks with advanced architectures.

Discriminative AI versus generative AI: The former classifies data, while the latter generates new, similar data.

Generative models' training dynamics, such as GANs' competitive game between generator and discriminator.

The versatility of generative AI applications across fields like text generation, art, music, coding, drug discovery, and fashion.

Key factors contributing to the rise of generative AI, including new algorithms, large-scale data sets, computational power, open-source contributions, and diverse use cases.

Upcoming deep dive into Foundation models in the next video of the series.

Transcripts

hello I am janaki RAM and I'm excited to

welcome you to the fascinating world of

generative AI from the dawn of

traditional machine learning to the

intricacies of deep learning and neural

networks we have reached the era of

generative AI That's reshaping our

digital

experiences in this brand new series we

delve deep into the magic of generative

AI powered by open source Foundation

model and tools I'm very excited to

partner with welchire a specialist cloud

provider that offers the most affordable

GPU infrastructure to bring you a series

of video tutorials unleashing the

potential of generative AI through open

source Foundation

models from the basics of generative AI

to deploying and scaling Foundation

models this Series has it all in the

upcoming videos I will walk you through

the detailed steps of deploying some of

the most capable llms such as Lama 2 on

the vure GPU stack to build modern

applications join the journey as we

uncover the models of generative AI now

don't forget to subscribe to my channel

and click on the Bell icon to get

instant notifications each time I upload

a new video let's get

[Music]

[Applause]

started

all right the very first Topic in this

series is Introduction to generative AI

for

developers so I'm going to cover the

overview of generative Ai and then we'll

take a closer look at the evolution of

generative AI where I'm going to compare

and contrast the traditional machine

learning versus deep learning versus

generative Ai and then we'll take a

closer look at discriminative AI versus

generative Ai and then I'll also touch

upon the key factors that led to the

raise of generative AI followed by the

applications and the use cases of

generative AI so this is a

foundational aspect of this series where

we will understand generative AI from a

developers perspective so let's get to

the

[Music]

overview welcome to the fascinating

world of generative AI

this concise series dels into the

mechanisms that Empower machines to

create innovate and even REM make

humanlike

creativity from the foundational

principles of neural networks to the

intricacies of models such as llama and

stable diffusion this course will equip

you with the knowledge and tools to

integrate generative intelligence into

your

applications so let's embark on this

transformative journey

[Music]

together

generative AI hit the headlines with the

launch of Chad GPT in November

2022 it grew in popularity and became

mainstream in 2023 however generative AI

did not appear out of nowhere it's more

of an evolution than a revolution as a

developer leveraging the power of

generative AI to build nextg

applications it's important to

understand understand the history of AI

research and the key Milestones that led

to generative AI so let's take a look at

traditional machine learning and then

understand more about deep learning and

neural networks before delving into the

details of generative

AI so machine learning or ml is a subset

of artificial intelligence that focuses

on developing algorithms that enable

computers to learn from and make

datadriven decisions

while deep learning and neural networks

have taken center stage in recent years

they are just a fraction of the ml

Universe before neural networks became

mainstream there was traditional or

classical machine learning which

continues to be widely applicable and

foundational to the field of artificial

intelligence so at its core traditional

machine learning involves algorithms

that learn patterns from data

and then use these patterns to predict

future data or make other kinds of

decisions a machine learning model is an

entity that learns patterns from

existing data to perform predictions on

unseen data the fundamental difference

between conventional programming and

machine learning is the way you write

the program in conventional Pro in

conventional programming you create

business logic that's going to take the

data as input and then gives the result

whereas in machine learning we take

historical data and a mathematical

algorithm to train the algorithm with

the data to evolve a pattern which is

called the machine learning

model one of the key requirements of

machine learning the traditional machine

learning is feature engineering which

involves selecting and creating the most

relevant input variables that influence

the learning ability of a model and does

the accuracy of

predictions for example in a

typical model that's going to predict

the price of a house the features would

involve selecting variables like the

location the size of the house

the the age of the house and some of

those parameters now these parameters

that influence the learnability of the

algorithm or the model is actually

called as a feature so traditional

machine learning is heavily dependent on

what is called as feature

engineering now traditional machine

learning deals with algorithms such as

linear regression logistic regression

decision trees Nave base theorem and K

means clustering algorithm now these are

some of the mathematical models that are

applied in the world of traditional

machine learning and along with data

they are used to train the model

models traditional ml remains an

indispensable tool in data scientist

Arsenal it needs less computing power

and the training process is not resource

intensive which means you can actually

train traditional ml models on your

desktop PC or a Mac without the

requirement of having a GPU or uh High

highly powerful compute resource at your

disposal now let's take a look at Deep

learning and neural

networks so deep learning is one of the

most transformative and influential

subsets in

AI powering applications from voice

recognition to autonomous vehicles it

offers capabilities once thought to be

exclusive to human cognition deep

learning is a subset of machine learning

that employs neural networks with many

layers hence the term deep neural

networks now these networks are used to

analyze various factors of data these

networks can learn and make independent

Decisions by analyzing vast amounts of

data and identifying

patterns so Central to deep learning is

the concept of neural networks that's

highly inspired by the structure of a

human

brain neural networks are composed of

neurons layers weights and biases and

also activation functions

though this is debatable their

architecture is similar to how the human

brain works so let's take a look at some

of these building blocks of neural

networks so neurons are the basic units

of a neural network they inspired by the

neurons in the human brain each neuron

receives input processes it and then

passes that to uh another neuron in the

next

layer so that is the basic unit of any

neural

network layers are the different levels

of a neural network there are three main

types of layers an input layer a hidden

layer and an output layer the input

layer receives the input data the hidden

layer performs the processing and the

output layer generates the final

output weights and biases are parameters

within the network that transform input

data within the network SL layers a

neural network learns by adjusting these

weights and biases to minimize the

difference between the predicted output

and the actual Target

value you you heard of what is called as

hyperparameter tuning now in deep

learning this is a very important

process where you adjust certain

parameters of the neural network like

the number of neurons the number of

layers the number of activation

functions

to adjust how accurate the neural

network is and that is what is called as

the hyperparameter

tuning activation functions are used to

determine the output of a neuron uh for

example an activation function includes

the relu sigmoid tan H functions now

deep learning models learn by

iteratively processing a data

set uh adjusting the internal parameters

to minimize the the prediction error

they Ray on techniques called forward

propagation and backward propagation to

learn from the input data so let's

understand more about the uh backward

propagation and forward

propagation forward propagation involves

calculating the predicted output using

the current model parameters once the

output is determined the loss

calculation measures the difference

between the predicted output and the

actual

Target to refine the model further back

propagation is employed which adjust the

model weights and biases to minimize the

loss additionally optimization

algorithms such as gradient descent and

its variants like stochastic mini batch

Adam are used to update the model

weights ensuring better and more

accurate predictions over a over a

period of

time some of the the popular deep

learning neural network architectures

include convolutional neural networks or

CNN recurrent neural networks are rnns

long short-term memory lstm and gated

recurrent units Gru unlike traditional

machine learning deep learning depends

on vast amounts of data for training it

demands significant computational

resources especially for training larger

models deep neural netor Works can often

act as blackboxes making it challenging

to understand their decision- making

process so now let's take a look at

generative

AI so deep learning and neural network

serve as the foundations for generative

AI some recent advances and in in

research in deep learning have resulted

in the raise of generative AI generative

AI is about building models that can

generate new data that mimic some uh

given data rather than simply predicting

a label or a value generative models

output a sample that is drawn from the

distribution as the uh training data so

Char GPT is one such example where you

input certain prompt and you get back a

very different output so you are

essentially dealing with a generative AI

model behind the

scenes so deep learning and networks as

we have seen serves as the foundations

of generative AI now imagine having a

set of photos of cats a typical neural

network would classify whether a given

image is a cat or

not a generative model on the other hand

would try to create a new image that

looks like a cat but it is a different

version of the input image so as

discussed earlier generative AI extends

neural networks with Advanced and

complex architectures capable of

producing and recreating content the

most popular generative models are Gan

which is generative advi networks or

variational Auto encoders or VES that

leverage deep neural network

structures so Gans comprise of two

networks a generator and a discriminator

the generator tries to produce fake data

while the discriminator tries to

distinguish between the data and The

Fakes over time the generator gets so

good that the discriminator can't tell

real from

fake now vaes or the on the other hand

the variational autoencoders work by

encoding data into a lower dimensional

space and then decoding it back they

ensure that the encoded data is close to

the original and during this process

they can generate new similar

data

so unlike typical neural networks where

you adjust weights based on predictions

generative models often have different

training Dynamics for instance Gans

involve a game where the generator and

discriminator compete leading the model

as a whole to a point where it generates

data almost indistinguishable from the

real

samples so that was a quick walkth

through of various Milestones that we

have seen in the AI

research traditional or classical

machine learning followed by Deep neural

networks and now we are experiencing

generative

AI now let's take a closer look at what

is called as the generative Ai and the

discriminative

AI

[Music]

traditional machine learning and deep

learning models are categorized as

discriminative AI they typically deal

with models that discriminate the input

data as opposed to generative AI that

generates new data which is similar to

the

input so discriminative Ai and

generative AI are two sides of the

machine learning coin each with a very

distinct approach and set of

applications so let's let's take a

closer look at discriminative

AI so discriminative models learn to

distinguish between different classes or

labels of data they map input data to a

specific output these models capture the

boundaries between various classes

instead of modeling how each class of

data is generated they focus on modeling

the decision boundary between

classes let's consider a data set of

images containing cats and dogs a

discriminative model like a

convolutional neural network or a CNN is

trained to label an input image as

either a cat or a dog it learns the

features and patterns that distinguish

cats from dogs and vice versa

discriminative models are trained to

classify data into a specific class or

predict a discrete value models that can

perform face recognition or models that

are trained to predict the price of a

house are examp of discriminative

models when it comes to learning

techniques used by discriminative models

they are mostly trained through

supervisor learning it's a common

approach used in deep learning where the

models are trained to make predictions

based on an input output pair it's

called supervised because much like a

student learning under the supervision

of a teacher the model learns from label

data when a neural network is based

based on unsupervised learning the

models can be adopted for tasks such as

clustering where the objective is to

separate data into distant groups

without pre-existing

labels now if that was discriminative AI

let's take a look at generative AI

unlike supervised or the discriminative

AI models generative models learn the

underlying probability distribution of

the data they can generate new data

samples that are similar to the input

data these models try to understand how

the data in each class is generated by

learning the distribution they can

produce new samples from the same

distribution these models try to

understand how the data in each class is

generated by learning the distribution

they can produce new samples from the

same distribution of the sample data

some of the examples of generative AI

include text generation where a given

data set let's say of Shakespeare's

writings a generative model like an RNN

or more recently a Transformer can

produce new sentences or even entire

passages that mimic Shakespeare's style

the output isn't any existing sentence

from the original works but rather a new

creation inspired by the patterns and

structures from from the model

observed image creation is based on

generative adverse advi networks uh or

Gans that we have seen in the previous

uh discussion they are very popular for

image generation tasks trained on a data

set of human faces a gan can generate

images of entirely new faces that it has

never seen before but which look

convincingly real the generative AI

models are often trained based on

self-supervised uh learning which is a

type of machine learning where the data

provides the supervision itself in other

words it's a method the model learns to

predict a part of input data from other

parts of part of the input data

so for example in a

self-supervised machine learning task

utilizing images the model might be

tasked with predicting a part of image

given the rest or predicting the color

version of a black and white image the

primary difference between the two

approaches lies in the objective

discriminative models trained using

supervised or unsupervised techniques

aim to classify or distinguish between

classes focusing on their

differences in contrast generative

models aim to understand and replicate

the data structure focusing on

generating new samples that resemble the

original data these models are are

trained using the self-supervised

learning

technique the primary difference between

the two approaches lies in their

underlying objective discriminative

models trained using supervised or

unsupervised technique aim to classify

or distinguish between classes while

generative models focus on generating

new samples that resemble the original

data these models are trained using the

self supervised learning

technique so that was a differentiation

between what is called as discriminative

Ai and generative AI I hope you found

this section useful now we're going to

look at some of the other use cases and

scenarios where generative AI is going

to be

[Music]

applied so let's take a look at the

applications and use cases of generative

AI so geni can be used in a wide range

of applications across numerous Fields

uh some of the areas include uh text

generation so AI models can generate

humanlike text given some prompt for

example openis GPT powered by a large

language model called GPT is a

well-known example of this it can write

essays answer questions create written

content for websites and even write

poetry now in uh some of the videos in

this series we are going to take a look

at Foundation models that are quite

capable almost as capable as gp4 to do

some of these tasks now coming back to

the use cases when it comes to Art and

Design generative AI can be used to

create new pieces of digital art or to

assist in design for instance mid

Journey a very popular tool to generate

AI based images uses generative AI

models to create and transform images in

unique and artistic ways uh stable

diffusion a well-known text to image

model released in 2022 is used by many

image generation tools such as uh of

course mid

journey and uh when it comes to music

composition generative models based on

open AI museet or metas audiocraft can

create original pieces of music by

learning from a wide range of musical

styles and

compositions now continuing on the

scenarios we have quite a few uh one of

the most popular use case is AI assisted

code large language models are llms that

are trained on code available in the

public domain or used to build AI

assistance for Developers for example

GitHub copilot is a popular tool that's

integrated with idees like visual stud

Studio code to automatically generate

code in mainstream programming languages

like python go and so on similarly gen

is also used in drug Discovery it is

used to generate Noble molecular

structures for potential new drugs an

example of this is in silico medicines

generative models which are used to

create new molecules for drug

Discovery and of course going Beyond

these are the video and image

enhancement tools gen can be used to

enhance the quality of images and videos

for instance face app uses generative

models to transform faces in photos such

as changing the person's age gender or

even hairstyle looking at uh the last

use case which is becoming very popular

is fashion and retail generative AI

models are used to create new fashion

designs or to visualize clothes on

different body types Stitch fix one of

the tools uses generative models to

create new fashion designs based on user

preferences and Trends so going forward

we'll see how generative AI has been

influenced by some key factors making it

mainstream So Many Factors have

contributed to the raise of generative

AI let's take a look at some of the top

factors the new algorithms and

architectures that have come as a result

of advancements in deep learning

algorithms have led to significant

improvements in capabilities of

generative

models for example the Gans or the other

neural network architectur such as

Transformers have been game changes in

this field enabling the generation of

Highly realistic images audio and even

video

the second important factor is the

availability of large scale data sets

the raise of big data has provided the

fuel for training more sophisticated

generative models these models often

require large amounts of data to capture

the underlying distribution effectively

and the availability of large scale data

sets have made it

possible the third important element is

of course the computational power the

advancement in Hardware technology

especially gpus tpus and some of the

cloud-based distributed computing

architectures have made it possible to

train complex multi-layered deep neural

networks the these advancements uh have

also made it possible to work with

larger data sets and more complicated

models the other important factor is

definitely giving credit to open source

um and the raise of Open Source software

and

libraries libraries and Frameworks such

as tensorflow pie torch and carers have

made it possible to build train and

deploy generative

models they provide highlevel flexible

apis and have been instrumental in

democratizing Ai and for fostering a

culture of shared knowledge within the

AI Community today the open-source

Technologies and the open models are

accelerating the generative AI

significantly by making the more

accessible and making them possible to

adopt in a wide range of use cases which

brings us to the last key factor the use

cases generative AI has potential

applications in many fields that we have

seen earlier and this versatility uh

when it comes to infusing gen into a

variety of applications and use cases

has created a lot of momentum and uh

both the research Community the academic

Community uh as well as the technology

Enterprise technology Community have

been embracing generative Ai and uh uh

bringing some of these models to very

Advanced use cases like the one that we

have seen including the genome analysis

and um areas like molecular biology and

Healthcare

so the versatility of geni increases

interest in the field and drives further

research and development which is a

great

sign

so that brings us to the end of the

first one the first video in this series

where we have seen the evolution of

generative Ai and discuss some of the

concepts like discriminative versus

generative Ai and some of the use cases

applications of generative Ai and of

course the factors that have led to the

raise of generative AI so in the next

video I'm going to do a deep live on

Foundation models stay tuned and don't

forget to subscribe to my channel and

click on the notification button I'll

see you in the next

video