Understanding Generative AI: Concepts, Models, and Applications

[Music] hi and welcome to introduction to generative AI don't know what that is

then you're in the perfect place I'm Roger Martinez and I am a developer relations engineer at Google cloud and

it's my job to help developers learn to use Google cloud in this course I'll teach you four things how to Define

generative AI explain how generative AI Works describe generative AI model types describe generative AI applications but

let's not get swept away with all of that yet let's start by defining what generative AI is first generative AI has

become a buzzword but what is it generative AI is a type of artificial intelligence technology that can produce

various types of content including text imagery audio and synthetic data but what is artificial

intelligence since we are going to explore generative artificial intelligence let's provide a bit of

context two very common questions asked are what is artificial intelligence and what is the difference between Ai and

machine learning let's get into it so one way to think about it is that AI is a discipline like how physics is a

discipline of science AI is a branch of computer science that deals with the creation of intelligent agents and our

system systems that can reason learn and act autonomously are you with me so far

essentially AI has to do with the theory and methods to build machines that think and act like humans pretty simple right

now let's talk about machine learning machine learning is a subfield of AI it is a program or system that trains a

model from input data the trained model can make useful predictions from new never-before seen data drawn from the

same one used to train the model this means that machine learning gives the computer the ability to learn without

explicit programming so what do these machine learning models look like two of the

most common classes of machine learning models are unsupervised and supervised ml models the key difference between the

two is that with supervised models we have labels labeled data is data that comes with a tag like a name a type or a

number unlabeled data is data that comes with no

tag so what can you do with supervised and unsupervised models this graph is an example of the

sort of problem a supervised model might try to solve for example let's say you're the owner of a restaurant what

type of food do they serve let's say pizza or dumplings no let's say pizza I like

pizza anyway you have historical data of the bill amount and how much different people tipped based on the order type

pickup or delivery in supervised learning the model learns from past examples to predict future values here

the model uses a total bill amount data to predict the future tip amount based on whether an order was picked up or

delivered also people tip your delivery drivers they work really hard this is an example of the sort of problem that an

unsupervised model might try to solve here you want to look at tenure and income and then group or cluster

employees to see whether someone is on the fast trck nice work blue shirt unsupervised problems are all about

discovery about looking at the raw data and seeing if it naturally falls into groups this is a good start but let's go

a little deeper to show this difference graphically because understanding these Concepts is the foundation for your

understanding of generative AI in supervised learning testing data values X our input into the model the

model outputs a prediction and Compares it to the training data used to train the model if the predicted test data

values and actual training data values are far apart that is called error the model tries to reduce this error until

the predicted and actual values are closer together this is a classic optimization

problem so let's check in so far we've explored differences between artificial intelligence and machine learning and

supervised and unsupervised learning that's a good start but what's next let's briefly explore where deep

learning fits as a subset of machine learning methods and then I promise we'll start talking about

gen while machine learning is a broad field that encompasses many different techniques deep learning is a type of

machine learning that uses artificial neural networks allowing them to process more complex patterns than machine

learning artificial neural networks are inspired by the human brain pretty cool huh like your brain they are made up of

many interconnected nodes or 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 neural networks can use both

labeled and unlabeled data this is called semi-supervised learning in semi supervised learning a neural network is

trained on a small amount of labeled data and a large amount of unlabeled data the labeled data helps the neural

network to learn the basic concepts of the tasks while the unlabeled data helps the neural network to generalize to new

examples now we finally get to where generative AI fits into this AI discipline gen AI is a subset of deep

learning which means it uses artificial neural networks can process both labeled and unlabeled data using supervised

unsupervised and semi-supervised methods large language models are also a subset of deep learning see I told you

I'd bring it all back to gen good job me deep learning models or machine learning models in general can be divided into

two types generative and discriminative a discriminative model is a type of model that is used to classify

or predict labels for data points discriminative models are typically trained on the data set of labeled data

points and they learn the relationship between the features of the data points and the

labels once a discriminative model is trained it can be used to predict the label for new data

points a generative model generates new data instances based on a learned probability distribution of existing

data generative models generate new contents take this example here the discriminative model learns the

conditional probability distribution or the probability of Y our output given X our input that this is a dog and

classifies it as a dog and not a cat which is great because I'm allergic to cats the generative model learns The

Joint probability distribution or the probability of X and Y P of x y and predicts the conditional probability

that this is a dog and can then generate a picture of a dog good boy I'm going to name him Fred

to summarize generative models can generate new data instances and discriminative models discriminate

between different kinds of data instances one more quick example the top image shows a traditional machine

learning model which attempts to learn the relationship between the data and the label or what you want to predict

the bottom image shows a generative AI model which attempts to learn patterns on content so that it can generate new

content so what if someone challenges you to a game of is it gen or not I've got your

back this illustration shows a good way to distinguish between what is Gen and what is

not it is not gen when the output or why or label is a number or a class for example spam or not spam or a

probability it is Gen when the output is natural language like speech or text audio or an image like Fred from before

for example let's get a little mathy to really show the difference visualizing

this mathematically would look like this if you haven't seen this for a while the yal F ofx equation calculates the

dependent output of a process given different inputs the y stands for the model output the F embodies a function

used in the calculation or model and and the X represents the input or inputs used for the

formula as a reminder inputs are the data like comma separated value files text files audio files or image files

like Fred so the model output is a function of all the inputs if the Y is a number like predicted sales it is not

generative AI if Y is a sentence like Define sales it is generative as the question would elicit a text

response the response will be based on all the massive large data the model was already trained on so the traditional ml

supervised learning process takes training code and label data to build a model depending on the use case or

problem the model can give you a prediction classify something or cluster something now let's check out how much

more robust the generative AI process is in comparison the generative AI process can take

training code labeled data and unlabeled data of all data types and build a foundation model the foundation model

can then generate new content it can generate text code images audio video and more we've come a long way from

traditional programming to neural networks to generative models in traditional programming we

used to have to hardcode the rules for distinguishing a cat type animal legs four ears two fur yes

likes yarn catnip dislikes Fred in the wave of neural networks we could give the networks pictures of cats

and dogs and ask is this a cat and it would predict a cat or not a cat what's really cool is that in the generative

wave we as users can generate our own content whether it be text images audio video or more for example models like

Gemini Google's multimodal AI model or Lambda language model for dialogue applications ingest very very large data

from multiple sources across the internet and build Foundation language models we can use simply by asking a

question whether typing it into a prompt or verbally talking into the prompt itself so when you ask it what's a cat

it can give you everything it's learned about a cat now let's make things a little more

formal with an official definition what is generative AI gen is a type of artificial

intelligence that creates new content based on what it has learned from existing content the process of learning

from existing content is called training and results in the creation of a statistical

model when given a prompt gen uses a statistical model to predict what an expected response might be and this this

generates new content it learns the underlying structure of the data and can then generate new samples that are

similar to the data it was trained on like I mentioned earlier a generative language model can take what it has

learned from the examples it's been shown and create something entirely new based on that

information that's why we use the word generative but large language models which generate novel combinations of

texts in the form of natural sounding language are only one type of generative AI a generative image model takes an

image as input and can output text another image or video for example under the output text you can get visual

question and answering while under output image an image completion is generated and under output video

animation is generated a generative language model takes text as input and can output more

text an image audio or decisions for example under the output text question answering is generated and

under output image a video is generated I mentioned that generative language models learn about patterns in

language through training data check out this example based on things learned from its training data it offers

predictions of how to complete this sentence I'm making a sandwich for peanut butter

and jelly pretty simple right so given some text it can predict what comes next thus generative language models are

pattern matching systems they learn about patterns based on the data that you provide here is the same example

using Gemini which is trained on a massive amount of Text data and it's able to communicate and generate

humanlike texts in response to a wide range of prompts and questions see how detailed the response can

be here is another example that's just a little more complicated than peanut butter and jelly sandwiches the meaning

of life is and even with a more ambiguous question Gemini gives you a contextual

answer and then shows the highest probability response the power of generative AI comes from the use of

Transformers Transformers produced the 2018 revolution in natural language processing at a high level a Transformer

model consists of an encoder and a decoder the encoder encodes the input sequence and passes it to the decoder

which learns how to decode the representations for a relevant task sometimes Transformers run into

issues though hallucinations are words or phrases that are generated by the model that are often nonsensical or

grammatically incorrect see not great hallucinations can be caused by a number of factors like when the model is not

trained on enough data is trained on noisy or dirty data is not given enough context or is not given enough

constraints hallucinations can be a problem for Transformers because they can make the output text difficult to

understand they can also make the model more likely to generate incorrect or misleading information so put simply

hallucinations are bad let's pivot slightly and talk about prompts a prompt is a short piece of text that is given

to a large language model or llm as input and it can be used to control the output of the model in a variety of ways

prompt design is the process of creating a prompt that will generate desired output from an

llm like I mentioned earlier generative AI depends a lot on the training data that you have fed into it it analyzes

the patterns and structures of the input data and thus learns both with access to a browser

based prompt you the user can generate your own content so let's talk a little bit about

the model types available to us when text is our input and how they can be helpful in solving problems like never

being able to understand my friends when they talk about soccer the first is

texttext texttext models take a natural language input and produce text output these models are trained to learn the

mapping between a pair of text for example translating from one language to others next we have text to image text

to image models are trained on a large set of images each caption with a short text

description diffusion is one method used to achieve this there's also text to video and text to

3D textto video models aim to generate a video representation from text input the input text can be anything from a single

sentence to a full script and the output is a video that corresponds to the input text similarly texted 3D models generate

three-dimensional objects that correspond to a user's text description for use in games or other 3D

worlds and finally there's text to task text to task models are trained to perform a defined task or action based

on text input this task can be a wide range of actions such as answering a question performing a search making a

prediction or taking some sort of action for example a textto tax model could be trained to navigate a web user interface

or make changes to a doc through a graphical user interface see with these models I can

actually understand what my friends are talking about when the game is on another model that's larger than

those I mentioned is a foundation model which is a large AI model pre-trained on a vast quantity of data designed to be

adapted or find tuned to a wide range of Downstream tasks such as sentiment analysis image captioning and object

recognition Foundation models have the potential to revolutionize many Industries including healthare finance

and customer service they can even be used to detect fraud and provide personalized customer

support if you're looking for foundation models vertex AI offers a model Garden that includes Foundation models the

language Foundation models include chat text and code the vision Foundation models include stable diffusion which

have been shown to be effective at generating highquality images from text descriptions let's say you have a use

case where you need to gather sentiments about how your customers feel about your product or service you can use the

classification task sentiment analysis task model same for vision tasks if you need to perform occupancy

analytics there is a task specific model for your use case so those are some examples of

foundation models we can use but can gen help with code for your apps absolutely shown here are generative AI

applications you can see there's quite a lot let's look at an example of code generation shown in the second block

under the code at the top in this example I've input a code file conversion problem converting from from

python to Json I use Gemini and insert into the prompt box I have a pandas data frame

with two columns one with a file name and one with the hour in which it is generated I'm trying to convert it into

a Json file in the format shown on screen Gemini Returns the steps I need to do this and here my output is an a

Json format pretty cool huh well get ready it gets even better I happen to be using Google's free browser based

jupyter notebook and can simply export the python code to Google's cab so to summarize Gemini code generation can

help you debug your lines of source code explain your code to you line by line craft SQL queries for your database

translate code from one language to another and generate documentation and tutorials for source code I'm going to

tell you about three other ways Google Cloud can help you get more out of generative AI the first is vertex AI

Studio vertex AI Studio lets you quickly explore and customize generative AI

models that you can leverage in your applications on Google Cloud vertex AI Studio helps developers create and

deploy generative AI models by providing a variety of tools and resources that make it easy to get

started for example there is a library of pre-trained models tool for fine-tuning models tool for deploying

models to production and Community forum for developers to share ideas and collaborate next we have vertex AI which

is particularly helpful for all of you who don't have much coding experience you can build generative AI search and

conversations for customers and employees with vertex AI agent Builder formerly vertex AI search and

conversation build with little or no coding and no prior machine learning experience vertex AI can help you create

your own chat Bots digital assistants custom search engines knowledge bases training applications and

more lastly there is Gemini a multimodal AI model unlike traditional language models it's not limited to understanding

text alone it can analyze images understand the nuances of audio and even interpret programming code this allows

Gemini to perform complex tasks that were previously impossible for AI due to its Advanced Arch Ure Gemini

is incredibly adaptable and scalable making it suitable for diverse applications model Garden is

continuously updated to include new models and now you know absolutely everything about generative AI okay

maybe you don't know everything but you definitely know the basics thank you for watching our course and make sure to

check out our other videos if you want to learn more about how you can use AI [Music]