Introduction to Generative AI

Summary

This video script introduces generative AI, a subset of deep learning that uses neural networks to create new content like text, images, and audio. It explains the fundamentals of AI, the difference between AI and machine learning, and the types of machine learning models. The script delves into the capabilities of generative models, the importance of training data, and the use of prompts to guide AI output. It also highlights the potential applications of generative AI in various industries and showcases tools like Vertex AI, Foundation models, and the versatile Gemini model for diverse AI tasks.

Takeaways

• 🧠 Generative AI is a type of AI technology that can create various content including text, images, audio, and synthetic data.
• 🤖 Artificial Intelligence (AI) is a branch of computer science that focuses on creating intelligent agents and systems capable of reasoning, learning, and acting autonomously.
• 📈 Machine Learning is a subset of AI that enables models to learn from input data and make predictions on new, unseen data.
• 🏷️ Supervised learning involves models trained on labeled data, while unsupervised learning deals with unlabeled data, focusing on discovering patterns and grouping.
• 🧠 Deep Learning is a subset of machine learning that uses artificial neural networks to process complex patterns, inspired by the human brain.
• 🔀 Generative AI is a subset of deep learning, capable of using both labeled and unlabeled data through various learning methods.
• 📊 Generative models generate new data instances based on learned probability distributions, unlike discriminative models that classify or predict labels.
• 📚 Large language models, a type of generative AI, learn patterns in language and can generate human-like text in response to prompts.
• 🛠️ Prompts are used to guide the output of a generative AI model, and effective prompt design is crucial for desired results.
• 🖼️ Generative AI models come in various types, such as text-to-text, text-to-image, text-to-video, and text-to-3D, each serving different applications.
• 🌐 Foundation models are large AI models pre-trained on vast data, adaptable for numerous downstream tasks, potentially revolutionizing various industries.

Q & A

What is generative AI?

Generative AI is a type of artificial intelligence technology that can produce various types of content including text, imagery, audio, and synthetic data.

How is AI defined in the context of this script?

AI is described as a branch of computer science that deals with the creation of intelligent agents and systems that can reason, learn, and act autonomously.

What is the relationship between AI and machine learning?

Machine learning is a subfield of AI. It involves programs or systems that train a model from input data, enabling the model to make useful predictions from new, never-before-seen data.

What distinguishes supervised machine learning models from unsupervised ones?

Supervised models use labeled data that comes with a tag, while unsupervised models work with unlabeled data that has no tag, focusing on discovery and grouping within the data.

How does deep learning fit into the AI discipline?

Deep learning is a subset of machine learning methods that uses artificial neural networks to process more complex patterns than traditional machine learning models.

What is the main difference between generative and discriminative models?

Generative models generate new data instances based on a learned probability distribution, while discriminative models classify or predict labels for data points based on learned relationships from labeled data.

What is a prompt in the context of generative AI?

A prompt is a short piece of text given to a large language model as input, which can be used to control the output of the model.

What are the potential issues with Transformer models like hallucinations?

Hallucinations refer to the generation of nonsensical or grammatically incorrect words or phrases by the model, often caused by insufficient data, noisy data, lack of context, or insufficient constraints.

How can generative AI models be used for code generation?

Generative AI models can help in debugging source code, explaining code line by line, crafting SQL queries, translating code from one language to another, and generating documentation and tutorials for source code.

What is the role of Vertex AI Studio in working with generative AI models?

Vertex AI Studio allows developers to quickly explore and customize generative AI models for use in their applications on Google Cloud, providing tools and resources to facilitate the creation and deployment of these models.

What are Foundation models and how can they be utilized?

Foundation models are large AI models pre-trained on vast amounts of data and designed to be adapted or fine-tuned for a wide range of downstream tasks, revolutionizing industries and enabling capabilities like sentiment analysis, image captioning, and fraud detection.

Outlines

00:00

🤖 Introduction to Generative AI

This paragraph introduces the concept of generative AI, a subset of artificial intelligence that can generate various types of content such as text, images, audio, and data. It differentiates AI from machine learning, explaining AI as a broader discipline of creating intelligent agents, while machine learning is a subset that allows models to learn from data. The paragraph also distinguishes between supervised and unsupervised machine learning models, providing examples of how they work and their applications.

05:02

🧠 Deep Learning and Generative Models

This section delves deeper into deep learning, a subset of machine learning that uses artificial neural networks to process complex patterns. It explains how neural networks, inspired by the human brain, can learn from both labeled and unlabeled data through semi-supervised learning. The paragraph further clarifies the difference between generative and discriminative models, with the former generating new data instances and the latter classifying or predicting labels for data points. It also introduces the concept of large language models and their ability to generate human-like text in response to prompts.

10:04

🛠 Generative AI's Process and Models

The paragraph discusses the generative AI process, which involves training on both labeled and unlabeled data to build a foundation model capable of generating new content across various media types. It highlights the evolution from traditional programming to neural networks and generative models, emphasizing the user's ability to generate custom content. The paragraph also introduces different types of generative models, such as text-to-text, text-to-image, text-to-video, and text-to-3D, each with specific applications and methods like diffusion for image generation.

15:05

📚 Understanding Generative AI's Challenges and Tools

This section addresses the challenges faced by generative AI, particularly the issue of 'hallucinations' where models generate nonsensical or incorrect outputs. It also introduces the concept of prompts and their role in controlling the output of generative AI models. The paragraph outlines various model types, such as text-to-task models for performing defined actions based on text input, and discusses the potential of foundation models to revolutionize industries by adapting to a wide range of downstream tasks.

20:05

🌐 Applications and Resources for Generative AI

The final paragraph focuses on the practical applications of generative AI, showcasing its use in code generation and other tasks. It mentions tools like Vertex AI Studio for exploring and customizing AI models, Vertex AI for building AI applications with minimal coding, and the Palm API for experimenting with Google's large language models. The paragraph also touches on the multimodal capabilities of Gemini, a model that can analyze various data types, and the continuous updates to the Model Garden to include new models for diverse applications.

Keywords

💡Generative AI

Generative AI refers to a subset of artificial intelligence that is capable of creating new content, such as text, images, audio, and synthetic data, based on the patterns it has learned from existing content. It is central to the video's theme as it is the main subject being discussed. For instance, the script defines generative AI and explores its various applications, such as generating text, images, and even code.

💡Artificial Intelligence (AI)

Artificial Intelligence, or AI, is a branch of computer science that focuses on creating intelligent agents and systems capable of reasoning, learning, and acting autonomously. The video provides context to AI by differentiating it from machine learning and explaining its role in creating systems that mimic human thought and action.

💡Machine Learning

Machine learning is a subfield of AI that involves training a model on input data so that it can make predictions on new, unseen data. The script explains that machine learning enables computers to learn without explicit programming, which is foundational to understanding how generative AI models are developed and function.

💡Supervised Learning

Supervised learning is a type of machine learning where the model is trained on labeled data, which includes examples with corresponding correct answers. The video uses the example of a restaurant owner using historical data to predict tip amounts, illustrating how supervised learning models learn from past examples to make future predictions.

💡Unsupervised Learning

Unsupervised learning is another class of machine learning where the model works with unlabeled data, discovering patterns and structures without predefined outcomes. The script mentions grouping employees based on tenure and income as an example of unsupervised learning, highlighting its use in data exploration and clustering.

💡Deep Learning

Deep learning is a subset of machine learning that uses artificial neural networks to process complex patterns in data. The video explains that deep learning models, inspired by the human brain, consist of many layers allowing them to learn from both labeled and unlabeled data through supervised, unsupervised, and semi-supervised methods.

💡Neural Networks

Neural networks are computing systems inspired by the human brain, composed of interconnected nodes or neurons that can learn to perform tasks. The script describes how these networks are used in deep learning to enable generative AI models to understand and generate complex patterns in data.

💡Generative Model

A generative model is a type of machine learning model that learns the probability distribution of data and generates new, unseen data instances. The video contrasts generative models with discriminative models, explaining that generative models can create new content, such as images, based on learned patterns.

💡Discriminative Model

Discriminative models are used to classify or predict labels for data points based on learned relationships between features and labels. The script uses the example of a model classifying whether an image is a dog or a cat, showing how discriminative models differ from generative models in their output.

💡Transformers

Transformers are a type of neural network architecture that has revolutionized natural language processing. The video mentions Transformers as the underlying technology that enables generative AI models to process sequences of data and generate human-like text in response to prompts.

💡Prompt

A prompt is a short text input given to a generative AI model to guide its output. The script discusses the importance of crafting effective prompts to generate desired responses from large language models, emphasizing their role in controlling the content creation process.

💡Foundation Models

Foundation models are large AI models pre-trained on a vast amount of data and can be adapted for various downstream tasks. The video introduces foundation models as a way to revolutionize industries by offering capabilities like sentiment analysis and object recognition, highlighting their potential for diverse applications.

💡Vertex AI

Vertex AI is a Google Cloud platform that allows developers to explore, customize, and deploy generative AI models. The script mentions Vertex AI Studio and Vertex AI as tools that facilitate the creation of AI applications with little to no coding experience, showcasing Google Cloud's support for generative AI development.

💡Code Generation

Code generation is an application of generative AI where the model creates or translates code based on user input. The video provides an example of using a generative AI model to convert a pandas data frame into a JSON file, demonstrating how code generation can assist in software development tasks.

Highlights

Introduction to Generative AI by Roger Martinez, a developer relations engineer at Google Cloud.

Generative AI's capability to produce content like text, imagery, audio, and synthetic data.

The distinction between AI, which is a broader discipline, and machine learning, a subset of AI focused on model training from data.

Supervised and unsupervised machine learning models, with examples of their applications.

Deep learning as a subset of machine learning using artificial neural networks to process complex patterns.

The role of semi-supervised learning in training neural networks with both labeled and unlabeled data.

Generative AI as a subset of deep learning that uses neural networks for creating new content.

The difference between generative and discriminative models in AI, with examples of each.

How generative AI learns underlying data structures to create new, similar samples.

The use of prompts in controlling the output of generative AI models.

Types of generative AI models including text-to-text, text-to-image, text-to-video, and text-to-3D.

Foundation models as large AI models pre-trained for adaptation to various downstream tasks.

The potential of foundation models to revolutionize industries and their applications in tasks like sentiment analysis and object recognition.

Google's Vertex AI Studio for exploring and customizing generative AI models.

Vertex AI for building AI search and conversational interfaces with no coding experience.

Palm API for experimenting with Google's large language models and tools.

Gemini, a multimodal AI model capable of understanding text, images, audio, and code.

Model Garden, a continuously updated collection of models for diverse applications.

Transcripts

[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 Y

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

yals 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

Palm or Pathways language model or

Lambda language model for dialogue

applications inest 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 geni 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

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 has learned

from the examples it's been shown and

creat 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

and 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 with

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 text 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 it's 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 prompted design is the

process of creating a prompt that will

generate the 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 but 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 text to text text to

text 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 captioned with a

short text description diffusion is one

method used to achieve this there's also

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 of 3D models

generate threedimensional 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 taxt 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

Amazon 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 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 Healthcare 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 Palm

API for chat and text the vision

Foundation models include stable

diffusion which have 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 feel about your

product or service you can use the

classification task sentiment analys

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 input a code file conversion

problem converting 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

adjon 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 collab so to

summarize Gemini code generation can

help you debug your lines of source code

explain your code to you line by line

craft seq queries for your database

translate code from one language to

another 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 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 search and

conversation formerly gen app builder

build with little or no coding and no

prior machine learning

experience vertex AI can help you create

your own chat Bots digital assistance

custom search engines knowledge bases

training applications and more and

lastly we have Palm API Palm API lets

you test and experiment with Google's

large language models and gen tools to

make prototyping quick and more

accessible developers can integrate Palm

API with maker suite and use it to

access the API using 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 and what do these tools

do I'm so glad you asked 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 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 architecture

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

[Music]

AI