All language subtitles for [English] Introduction to Generative AI [DownSub.com]

GWENDOLYN STRIPLING: Hello.

And welcome to Introduction
to Generative AI.

My name is Dr.
Gwendolyn Stripling.

And I am the
artificial intelligence

technical curriculum developer
here at Google Cloud.

In this course, you learn
to define generative AI,

explain how generative AI works,
describe generative AI model

types, and describe
generative AI applications.

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?

Well, since we are
going to explore

generative artificial
intelligence,

let's provide a bit of context.

So two very common
questions asked

are what is artificial
intelligence

and what is the difference
between AI and machine

learning.

One way to think about it
is that AI is a discipline,

like physics for example.

AI is a branch of
computer science

that deals with the creation
of intelligence agents, which

are systems that can reason,
and learn, and act autonomously.

Essentially, AI has to do
with the theory and methods

to build machines that
think and act like humans.

In this discipline, we
have machine learning,

which is a subfield of AI.

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.

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.

This graph is an
example of the problem

that a supervised model
might try to solve.

For example, let's say you
are the owner of a restaurant.

You have historical
data of the bill amount

and how much different people
tipped based on order type

and whether it was
picked up or delivered.

In supervised learning, the
model learns from past examples

to predict future values,
in this case tips.

So here the model uses
the total bill amount

to predict the future tip amount
based on whether an order was

picked up or delivered.

This is an example
of the problem

that an unsupervised
model might try to solve.

So here you want to look
at tenure and income

and then group or
cluster employees

to see whether someone
is on the fast track.

Unsupervised problems
are all about discovery,

about looking at the raw data
and seeing if it naturally

falls into groups.

Let's get a little deeper
and show this graphically

as understanding
these concepts are

the foundation for your
understanding of generative AI.

In supervised learning,
testing data values or x

are input into the model.

The model outputs a prediction
and compares that prediction

to the training data
used to train the model.

If the predicted test data
values and actual training data

values are far apart,
that's called error.

And the model tries
to reduce this error

until the predicted and actual
values are closer together.

This is a classic
optimization problem.

Now that we've
explored the difference

between artificial intelligence
and machine learning,

and supervised and
unsupervised learning,

let's briefly explore
where deep learning

fits as a subset of
machine learning methods.

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.

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.

And 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 task

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.

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 a 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.

Thus generative models
generate new content.

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.

The generative model learns the
joint probability distribution

or the probability of
x and y and predicts

the conditional probability
that this is a dog

and can then generate
a picture of a dog.

So to summarize,
generative models

can generate new data instances
while discriminative models

discriminate between different
kinds of data instances.

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.

A good way to distinguish
what is gen AI and what is not

is shown in this illustration.

It is not gen AI when the
output, or y, or label is

a number or a class, for
example spam or not spam,

or a probability.

It is gen AI when the output is
natural language, like speech

or text, an image or
audio, for example.

Visualizing this mathematically
would look like this.

If you haven't seen
this for a while,

the y is equal to f of
x equation calculates

the dependent output of a
process given different inputs.

The y stands for
the model output.

The f embodies the function
used in the calculation.

And the x represents the input
or inputs used for the formula.

So the model output is a
function of all the inputs.

If the y is the number,
like predicted sales,

it is not gen AI.

If y is a sentence,
like define sales,

it is generative as the question
would elicit a text response.

The response would be based
on all the massive large data

the model was
already trained on.

To summarize at a high level,
the traditional, classical

supervised and unsupervised
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.

It can classify something
or cluster something.

We use this example to show
you how much more robust

the gen AI process is.

The gen AI process can take
training code, label data,

and unlabeled data
of all data types

and build a foundation model.

The foundation model can
then generate new content.

For example, text, code,
images, audio, video, et cetera.

We've come a long away from
traditional programming

to neural networks
to generative models.

In traditional
programming, we used

to have to hard code the rules
for distinguishing a cat--

the type, animal; legs,
four; ears, two; fur, yes;

likes yarn and catnip.

In the wave of
neural networks, we

could give the network
pictures of cats and dogs

and ask is this a cat and
it would predict a cat.

In the generative
wave, we as users

can generate our own
content, whether it

be text, images, audio,
video, et cetera, for example

models like PaLM or
Pathways Language Model,

or LAMBDA, Language Model
for Dialogue Applications,

ingest very, very large data
from the 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
has learned about a cat.

Now we come to our
formal definition.

What is generative AI?

Gen AI 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.

AI uses the model to predict
what an expected response might

be and this generates
new content.

Essentially, it learns
the underlying structure

of the data and
can then generate

new samples that are similar
to the data it was trained on.

As previously mentioned, 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.

Large language models are
one type of generative AI

since they generate novel
combinations of text

in the form of natural
sounding language.

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 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.

We've stated that generative
language models learn

about patterns and language
through training data,

then, given some text, they
predict what comes next.

Thus generative language models
are pattern matching systems.

They learn about patterns
based on the data you provide.

Here is an example.

Based on things it's learned
from its training data,

it offers predictions of how
to complete this sentence,

I'm making a sandwich with
peanut butter and jelly.

Here is the same
example using Bard,

which is trained on a
massive amount of text data

and is able to
communicate and generate

humanlike text in response
to a wide range of prompts

and questions.

Here is another example.

The meaning of life is--

and Bart 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
a 2018 revolution

in natural language processing.

At a high level, a
transformer model

consists of an
encoder and decoder.

The encoder encodes
the input sequence

and passes it to
the decoder, which

learns how to decode
the representation

for a relevant task.

In transformers, hallucinations
are words or phrases

that are generated
by the model that

are often nonsensical or
grammatically incorrect.

Hallucinations can be caused
by a number of factors,

including the model is not
trained on enough data,

or the model is trained
on noisy or dirty data,

or the model is not
given enough context,

or the model 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.

A prompt is a
short piece of text

that is given to the large
language model 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 the desired output

from a large language model.

As previously mentioned,
gen AI depends a lot

on the training data that
you have fed into it.

And it analyzes the patterns
and structures of the input data

and thus learns.

But with access to a browser
based prompt, you, the user,

can generate your own content.

We've shown illustrations of the
types of input based upon data.

Here are the
associated model types.

Text-to-text.

Text-to-text models take
a natural language input

and produces a text output.

These models are trained
to learn the mapping

between a pair of text, e.g.

for example, translation
from one language to another.

Text-to-image.

Text-to-image models are trained
on a large set of images,

each captioned with a
short text description.

Diffusion is one method
used to achieve this.

Text-to-video and text-to-3D.

Text-to-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, text-to-3D
models generate

three dimensional objects that
correspond to a user's text

description.

For example, this can be used
in games or other 3D worlds.

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
text-to-task model

could be trained to navigate a
web UI or make changes to a doc

through the GUI.

A foundation model is a
large AI model pre-trained

on a vast quantity of data
designed to be adapted or fine

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

health care, finance,
and customer service.

They can be used to
detect fraud and provide

personalized customer support.

Vertex AI offers a
model garden that

includes foundation models.

The language foundation
models include

PaLM API for chat and text.

The vision foundation models
includes stable diffusion,

which has been shown to
be effective at generating

high quality images
from text descriptions.

Let's say you have
a use case where

you need to gather sentiments
about how your customers are

feeling about your
product or service.

You can use the classification
task sentiment analysis task

model for just that purpose.

And what if you needed to
perform occupancy analytics?

There is a task model
for your use case.

Shown here are gen
AI applications.

Let's look at an example
of code generation

shown in the second block
under code at the top.

In this example, I've input a
code file conversion problem,

converting from Python to JSON.

I use Bard.

And I insert into the
prompt box the following.

I have a Pandas DataFrame with
two columns, one with the file

name and one with the hour
in which it is generated.

I'm trying to convert
this into a JSON file

in the format shown onscreen.

Bard returns the steps I need
to do this and the code snippet.

And here my output
is in a JSON format.

It gets better.

I happen to be using Google's
free, browser-based Jupyter

Notebook, known as Colab.

And I simply export the
Python code to Google's Colab.

To summarize, Bart
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.

Generative AI Studio lets you
quickly explore and customize

gen AI models that you can
leverage in your applications

on Google Cloud.

Generative AI Studio helps
developers create and deploy

Gen AI models by providing a
variety of tools and resources

that make it easy
to get started.

For example, there's a
library of pre-trained models.

There is a tool for
fine tuning models.

There is a tool for deploying
models to production.

And there is a community
forum for developers

to share ideas and collaborate.

Generative AI App
Builder lets you

create gen AI apps without
having to write any code.

Gen AI App Builder has a
drag and drop interface

that makes it easy to
design and build apps.

It has a visual
editor that makes

it easy to create
and edit app content.

It has a built-in
search engine that

allows users to search for
information within the app.

And it has a
conversational AI Engine

that helps users to
interact with the app using

natural language.

You can create your own digital
assistants, custom search

engines, knowledge bases,
training applications,

and much more.

PaLM API lets you
test and experiment

with Google's large language
models and gen AI tools.

To make prototyping quick
and more accessible,

developers can integrate
PaLM API with Maker suite

and use it to access the
API using a graphical user

interface.

The suite includes a number of
different tools such as a model

training tool, a model
deployment tool, and a model

monitoring tool.

The model training tool helps
developers train ML models

on their data using
different algorithms.

The model deployment tool helps
developers deploy ML models

to production with a number of
different deployment options.

The model monitoring
tool helps developers

monitor the performance
of their ML models

in production using a
dashboard and a number

of different metrics.

Thank you for watching
our course, Introduction

to Generative AI.