This content introduces Generative AI as a powerful subset of Artificial Intelligence that creates new content by learning from existing data, distinguishing it from traditional AI models that classify or predict.
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hello and welcome to introduction to
generative AI my name is Dr gwendelyn
stripping 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 like physics for example
AI is a branch of computer science that
deals with the creation of intelligent
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 the trained model can
make useful predictions from new or
neverbe 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 supervised
and supervised ml models the key
difference between the two is that with
supervised models we have labels label
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 sort
of 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 whether it was picked up
or delivered in supervised learning the
the model learns from past examples to
predict future values in 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 sort
of problem that an un supervised 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 trck 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 n 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 UNL 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 got to where
generative AI fits into this AI discipline
discipline
gen is a subset of deep learning which
means it uses artificial n 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
Genera models generate new
content take this example here the
discriminative model learns the
conditional probability distribution or
the probability of why 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 and what
is not is shown in this
illustration it is not gen when the
output or Y or label is a number or a
class of for example spam or not spam or
a probability it is Gen 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 ofx
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 a
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 a number like
predicted sales it is not gen 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
process is the Gen process can take
training code label data and unlabel
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 Etc
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 cap 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 etc for
example models like Palm or Pathways
language model or Lambda language model
for dialogue application
in just 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 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
jni 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 is 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
text
question answering is generated and
under output image a video is
generated we've stated that generative
language models learn about patterns in
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 Bard gives you a contextual
answer and then shows the highest probability
probability
response the power of generative AI
comes from the use of Transformers
Transformers produce the 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
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
misleading
information a pumpt 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 designning is the
process of creating a prompt that will
generate the desired output from a large language
language
model as previously mentioned J 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
textto 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 EG
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 texted 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 train to navigate a web
UI or make changes to a doc through the
guei 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 sentimental analysis image
captioning and object recognition
Foundation models have the potential to
revolutionize many Industries and
including healthare 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 include stable diusion
which has been shown to be effective at
generating high quality images from text description
description
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 aai
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 bar and I insert
into the prompt box the following I have
a pandas data frame 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 on
screen B 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
Jupiter notebook known as collab and I
simply export the python code to
Google's collab to summarize bar 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 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 apps without having
to write any code 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 assistance
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
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
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