In today's fast-paced world,

AI is quickly becoming a standard tool

in every company's toolkit.

Navigating the emergence of AI in the workplace is similar

to driving down a busy street.

Just as knowing the rules of the road can make you a better

and more effective driver,

understanding the basics of AI can help you

to reach your destination safely

and to avoid potential roadblocks.

To begin this AI journey, let's start by examining

what the term artificial intelligence really means.

In this context, intelligence refers

to the human ability to perform cognitive tasks.

A cognitive task is any mental activity such as thinking,

understanding, learning, and remembering.

As humans, we have cognitive abilities that allow us

to make decisions and solve problems.

However, there are also limits

to how much information we can process at a time.

AI is capable of extending our cognitive abilities,

helping us to make better decisions

and solve problems faster.

With that understanding, the term artificial intelligence

refers to computer programs

that can complete cognitive tasks typically associated

with human intelligence.

Simply put, AI programs can assist us with tasks

by using math to learn from data.

As you'll soon discover, AI has the potential

to greatly improve the quality of our work lives

and streamline business operations.

However, AI isn't a magic solution

to every business problem.

As with any advanced tool,

the key is to use AI to its strengths,

and doing so requires careful thought

and consideration of the technology's capabilities.

Are you curious to discover how AI works?

Want to find out how AI can enhance your work?

Then get ready to explore these questions with me

and Google AI Essentials.

- Across every industry,

AI is introducing new approaches to work.

Companies and organizations

of every type are developing innovative solutions

to a variety of workplace tasks and challenges,

all with the help of AI.

Let's explore some examples of how AI

is reshaping the way people work all across the globe.

Consider UKG,

a provider of HR and workforce management solutions.

By integrating AI into its product suite,

UKG is improving how its employees analyze information

and insights.

This can make it easier

and faster for employees to receive answers

to their work-related questions.

UKG's AI integration also enables managers

to gain advanced analytics from user interactions,

helping them make more informed business decisions.

Now, let's consider one of the oldest industries

in the world, farming.

AI is also being used

to address many challenges in this industry.

For example, a common challenge

that rural farmers face each year

is economic uncertainty due to a variety of factors.

One is frequent changes with crop and livestock yields.

Other factors include unpredictable weather conditions

and limited access to advanced farming techniques.

Play video starting at :1:15 and follow transcript1:15

Uncertainties like these can make it difficult for farmers

to make effective business decisions.

Jiva is an agricultural company focused

on helping rural farmers solve these types of challenges.

As part of their mission,

Jiva provides farming communities with AI solutions

that can assist them in achieving sustainable

and reliable farming practices.

Jiva uses AI tools that can diagnose crop diseases

and suggest remedies.

Farmers can also receive relevant AI-powered advice

that helps them produce better quality crops

and increase yields.

With the help of AI, Jiva helps rural farmers stay informed

and access more insights that can improve their business.

Overall, AI can be a powerful tool

for all kinds of business activities.

And no matter the industry,

AI has the potential to transform how companies

and organizations develop innovative,

forward-thinking solutions.

As you continue your journey into the world of AI,

consider this.

In what ways can AI positively impact your industry,

and how can you be part of that change?

Maya: The exciting world of AI

I'm Maya, I'm Vice President of Strategy

and Operations at Google Research.

Day to day, Google Research is trying to figure out

what are the things that can change society and our business

today, tomorrow, and in the future.

We start by saying, you know,

what's impossible in people's minds?

What are problems that need to be solved?

And how can we apply the expertise that we have

to those problems to provide a solution

that benefits all of us as people and also our business?

One piece of technology

at Google Research we're very proud of,

at this moment because there's so many

that we've had in the past that utilize AI,

is around contrails,

which is when you look up at the sky

and you see the white trail from behind a jet.

And utilizing AI to try to understand

how can we alleviate that

because it has a very negative impact on climate.

Most technologies nowadays have either

an AI component or an adjacency to AI.

I'm very hopeful that AI tools will have

a tremendous impact in a positive way on us as people.

I think that we're just at the beginning of that journey.

AI is one tool that I consider a powerful one

in achieving my life's purpose,

which is to alleviate suffering

and to help us progress as people to a better place,

to have a better society, have a better world.

It's not something that's not approachable

and something that people need to be intimidated by.

We are all welcome on this journey

Explore how AI uses machine learning

- All the buzz surrounding AI

can make it seem like the hottest new tech trend.

But the truth is, AI has been around for a while.

For example, have you ever wondered

how streaming platforms recommend videos you might like?

This feature is brought to you by AI.

For years, streaming platforms have used AI tools

to offer services like recommendation systems

that enhance the user experience.

An AI tool refers to AI-powered software

that can automate or assist users with a variety of tasks.

Examples of AI tools are everywhere,

from GPS systems that suggest quick routes

to translation systems

that interpret conversations in real time.

Companies of all shapes and sizes use AI tools

to streamline operations

and improve the quality of their products and services.

While these AI tools can seem naturally smart,

it's important to recognize that they're not self-taught.

Instead, they're powered by

what's known as machine learning.

Machine learning, or ML, is a subset of AI

focused on developing computer programs

that can analyze data to make decisions or predictions.

ML is a specialized layer

under the broader category of AI technology.

It's often used by AI tools

to make sense of data quickly and efficiently.

AI designers build ML programs using a training set,

which is a collection of data used to teach AI.

Basically, training sets provide ML programs with examples

of what to expect and how to respond appropriately.

For example, consider a food distributor

that uses an AI tool

to sort and pack ripe apples in their factory.

For this tool to work, an AI designer must first train

an ML program to identify ripe apples.

They would do this by providing their ML program

with a training set that includes thousands of images

of ripe and unripe apples.

As the ML program processes these images,

it eventually learns to identify the features

of ripe apples.

Having learned to do this with ML,

the AI tool can then identify ripe apples

that weren't in its training set

and help factory employees work more efficiently.

As I mentioned earlier, many AI tools use ML

to learn and improve their performance.

However, for ML programs to perform effectively,

the quality and relevance of their training data matter.

A fundamental issue to be aware of

is the potential for bias within training data.

This could unintentionally cause an AI tool

to produce inaccurate or unintended outputs.

For example, the AI tool that was used to sort ripe apples

might have learned from training data

that only contain images of specific types of red apples.

This would unintentionally make the AI less accurate

at identifying ripe apples

of varying sizes, shapes, or colors.

The food producer might end up sorting apples incorrectly,

causing them to lose money and waste perfectly good apples.

When used appropriately,

ML plays a key role in advancing AI into the future.

It's truly an incredible and sophisticated technique

with endless applications.

Bài test
A health care company wants to use AI to predict patient
outcomes after surgery. They provide the AI with a training set of
historical patient data that contains details of previous surgical
procedures and outcomes. What role does the training set play in
this scenario?
It is used to schedule future surgeries for patients.
It serves as a database of patient contact information.
It teaches the AI to help predict patient outcomes after surgery.
It suggests other procedures that might be relevant for patients.
Status: [object Object]
1 point
2.
Question 2
Which of the following tasks can a manufacturer accomplish using
generative AI? Select three answers.
Create preview images of new products
Receive incoming shipments of new inventory.
Draft new production guidelines
Brainstorm ways to optimize resource allocation
Status: [object Object]
1 point
3.
Question 3
What are some of the limitations of AI? Select three answers.
AI can reflect or amplify bias.
AI can't answer questions in a detailed and nuanced way.
AI output can contain inaccuracies.
AI can't learn independently.
Status: [object Object]
1 point
4.
Question 4
A real estate agent uses AI to simplify the task of creating
descriptions of new homes listed for sale. First, the agent uses an
AI tool to draft inviting descriptions of a home’s features and
benefits. Then, the agent adjusts the AI-generated descriptions as
needed. What does this scenario describe?
Cross-team collaboration
AI augmentation
AI automation
Replacement
Status: [object Object]
1 point
5.
Question 5
What is one reason human oversight is important when using AI in
decision-making processes?
It focuses on the technical aspects of AI without considering
broader goals.
It ensures that decisions are made with ethical considerations and
accountability.
It reduces operational costs by automating the decision-making
process.
It increases the speed with which AI decisions are made.
Status: [object Object]
1 point
Explore how AI uses machine learning
- All the buzz surrounding AI
can make it seem like the hottest new tech trend.
But the truth is, AI has been around for a while.
For example, have you ever wondered
how streaming platforms recommend videos you might like?
This feature is brought to you by AI.
For years, streaming platforms have used AI tools
to offer services like recommendation systems
that enhance the user experience.
An AI tool refers to AI-powered software
that can automate or assist users with a variety of tasks.
Examples of AI tools are everywhere,
from GPS systems that suggest quick routes
to translation systems
that interpret conversations in real time.
Companies of all shapes and sizes use AI tools
to streamline operations
and improve the quality of their products and services.
While these AI tools can seem naturally smart,
it's important to recognize that they're not self-taught.
Instead, they're powered by
what's known as machine learning.
Machine learning, or ML, is a subset of AI
focused on developing computer programs
that can analyze data to make decisions or predictions.
ML is a specialized layer
under the broader category of AI technology.
It's often used by AI tools
to make sense of data quickly and efficiently.
AI designers build ML programs using a training set,
which is a collection of data used to teach AI.
Basically, training sets provide ML programs with examples
of what to expect and how to respond appropriately.
For example, consider a food distributor
that uses an AI tool
to sort and pack ripe apples in their factory.
For this tool to work, an AI designer must first train
an ML program to identify ripe apples.
They would do this by providing their ML program
with a training set that includes thousands of images
of ripe and unripe apples.
As the ML program processes these images,
it eventually learns to identify the features
of ripe apples.
Having learned to do this with ML,
the AI tool can then identify ripe apples
that weren't in its training set
and help factory employees work more efficiently.
As I mentioned earlier, many AI tools use ML
to learn and improve their performance.
However, for ML programs to perform effectively,
the quality and relevance of their training data matter.
A fundamental issue to be aware of
is the potential for bias within training data.
This could unintentionally cause an AI tool
to produce inaccurate or unintended outputs.
For example, the AI tool that was used to sort ripe apples
might have learned from training data
that only contain images of specific types of red apples.
This would unintentionally make the AI less accurate
at identifying ripe apples
of varying sizes, shapes, or colors.
The food producer might end up sorting apples incorrectly,
causing them to lose money and waste perfectly good apples.
When used appropriately,
ML plays a key role in advancing AI into the future.
It's truly an incredible and sophisticated technique
with endless applications.
Foundations of generative AI
- Advancements in AI technology are reshaping how we work.
Let's explore one of the key developments
at the center of this transformation, generative AI.
As the name suggests, generative AI
is AI that can generate new content
such as text, images, or other media.
A unique quality of generative AI tools
is that you can use them with natural language.
Natural language refers to the way people talk
or write when communicating with each other.
Here's a simplified overview
of how a generative AI tool works with natural language.
First, you provide input.
Input refers to any information
or data that's sent to a computer for processing.
Many generative AI tools, accept text and speeches input,
and some also accept images or video files.
Next, the data is processed by the AI tool.
Then an output is generated in the form of text,
images, audio, or video.
Generative AI and the ability to interact
with computers using natural language has introduced a world
of possibilities for what people can create with AI.
For example, you might be marketing a new business.
You need fresh, engaging content like a promotional poster
to advertise a new product,
but you don't have a creative team
to bring your ideas to life.
No need to stress.
With a few instructions,
generative AI can help you create a poster.
If the generated content doesn't meet your expectations,
you can provide additional instructions
until it produces something that meets your needs.
This is just one example
of how generative AI can complement your skills,
but there are many other ways
it can benefit you and your work.
For example, generative AI can boost your productivity
by helping you with tasks like drafting replies to emails.
It can help you avoid mistakes,
and it can improve your decision-making process
by answering questions and brainstorming ideas with you.
Whether you work in healthcare, education, finance, retail,
or any other field, there are a wide variety
of generative AI tools that can cater to your needs.
One example is a conversational AI tool.
A conversational AI tool is a generative AI tool
that processes text requests and generates text responses.
You can use it to brainstorm ideas, answer questions,
and boost your productivity.
Throughout Google AI Essentials,
you'll gain practical experience
using a conversational AI tool by Google called Gemini.
Gemini can be used
to get some creative inspiration when you're feeling stuck,
build on your ideas,
and provide detailed explanations
that help you explore topics easily.
For example, let's ask Gemini to brainstorm a list
of team-bonding activities for our summer work retreat.
The AI tool responds with a wide range of ideas
from a fun beach party to a relaxed pottery class.
Gemini also share some additional tips
to consider when planning a successful work retreat.
Generative AI has paved the way to exciting new frontiers,
but before we can tap into what this technology
has to offer, it's essential to investigate the capabilities
and limitations of AI as a whole.
Continue to the next part of this lesson to get started.
In this reading, you'll explore some of the ML techniques AI
designers use to build AI programs, deepening your
understanding of how ML leverages data to make decisions and
perform tasks. You'll also explore how ML techniques have paved
the way for generative AI.

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AI development techniques
Artificial intelligence refers to computer programs that can
complete cognitive tasks typically associated with human
intelligence. There are two main techniques used to design AI
programs:
 Rule-based techniques involve creating AI programs that
strictly follow predefined rules to make decisions. For
example, a spam filter using rule-based techniques might
block emails that contain specific keywords using its
predefined logic.
 Machine learning techniques involve creating AI
programs that can analyze and learn from patterns in data to
make independent decisions. For example, a spam filter
using these techniques might flag potential spam for the
recipient to review, preventing automatic blocking. If the
recipient marks emails from trusted sources as safe, the
spam filter learns and adapts its logic to include similar
emails from that sender in the future.
AI tools can use either rule-based or ML techniques, or even a
combination of both. In general, rule-based techniques are
commonly used for tasks that require rigidity, such as blocking
messages from untrusted senders that are obviously spam, like
requests for bank transfers or private information. Conversely, ML
techniques are better suited for tasks demanding flexibility and
adaptability, like learning to recognize that messages from
trusted senders containing typos are not spam.
Approaches to training ML programs
Recall that machine learning is a subset of AI focused on
developing computer programs that can analyze data to make
decisions or predictions. AI designers often use ML in their AI
programs because it doesn’t have the limitations of rule-based
techniques.

There are three common approaches to training ML programs:

 Supervised learning
 Unsupervised learning
 Reinforcement learning
Supervised learning
In this approach, the ML program learns from a labeled training
set. A labeled training set includes data that is labeled or tagged,
which provides context and meaning to the data. For instance, an
email spam filter that's trained with supervised learning would
use a training set of emails that are labeled as “spam” or “not
spam.” Supervised learning is often used when there's a specific
output in mind.
Unsupervised learning
In this approach, the ML program learns from an unlabeled
training set. An unlabeled training set includes data that does not
have labels or tags. For instance, ML might be used to analyze a
dataset of unsorted email messages and find patterns in topics,
keywords, or contacts. In other words, unsupervised learning is
used to identify patterns in data without a specific output in mind.
Reinforcement learning
In this approach, the ML program uses trial-and-error to learn
which actions lead to the best outcome. The program learns to do
this by getting rewarded for making good choices that lead to the
desired results. Reinforcement learning is commonly used by
conversational AI tools. As these tools receive feedback from
users and AI designers, they learn to generate effective
responses.
Each ML technique has its own strengths and weaknesses.
Depending on the type of data that's available and what's needed
to solve the particular problem, AI designers may use one, two, or
all three of these techniques to produce an AI-powered solution.
Generative AI
Advancements in machine learning have helped pave the way for
generative AI—AI that can generate new content, like text,
images, or other media. The content is generated based on user
input. Those inputs are known as prompts. When entering a
prompt while using this type of AI—known as prompting—the tool
often uses a combination of supervised, unsupervised, and
reinforcement learning to generate original content, or output.
For instance, all three approaches play distinct roles in
conversational AI tools. Supervised learning equips conversational
AI tools with foundational dialogue data, enabling them to
respond to common conversational cues appropriately.
Unsupervised learning enables them to interpret nuances in
language, like colloquialisms, that occur naturally in conversation.
Reinforcement learning further strengthens these tools by
allowing them to improve their responses in real-time based on
user feedback. This enables them to adapt to the conversational
context and engage in natural conversations.
Generative AI's ability to create and innovate offers a range of
benefits to all sorts of workplaces and professions, such as
marketing, product development, engineering, education,
manufacturing, and research and development. These benefits
include:
 Greater efficiency: Generative AI can automate or
augment routine tasks, allowing workers to focus on other
work priorities.
 Personalized experiences: Generative AI can tailor its
interactions to individual preferences and needs.
 Better decisions: Generative AI can quickly analyze vast
amounts of data to uncover useful insights.
These are just some of the ways that generative AI can enhance
your work.
For more information
PAIR Explorables is an optional resource for anyone who wants to
learn more about AI. It is a collection of interactive articles that
are designed to make key AI concepts more accessible and
understandable. PAIR Explorables covers a wide range of topics,
including:
 Machine learning basics
  Fairness and bias in AI programs
 Data and privacy considerations in AI
 Potential risks and benefits of AI
Each article features visualizations and interactive controls that
can help you explore different AI concepts and experience how
they work.
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Understand the capabilities and limitations of AI
- Just as you don't need to be a carpenter to use a hammer,
you don't need to be a computer expert
to use AI effectively.
That said, understanding the basics of what AI can do
will help you make the most of the technology.
Today's AI tools can do a lot to enhance your work.
They can generate content,
like assisting a marketing team
by making a promotional video for a new product.
They can analyze information quickly,
like highlighting the key points of a long email thread.
They can answer questions in a detailed and nuanced way.
And overall, they can simplify your day-to-day
and allow you to focus on other aspects of your work.
While AI can complete a variety of tasks,
there are some tasks that require a human touch,
such as handling sensitive issues.
These limitations can be critical in certain contexts.
For example, AI can't learn independently.
It needs people to continually update its training.
Shortcomings in an AI tool's training data
can also potentially reflect or amplify biases,
leading to skewed or unfair outcomes.
Another major limitation
is that AI output can sometimes contain inaccuracies,
otherwise known as hallucinations.
Hallucinations are AI outputs that are not true.
These inaccuracies can range from minor errors,
such as a sentence that doesn't make sense,
to significant distortions.
For instance, consider a sales manager
who's using an AI tool to analyze quarterly sales data.
The AI tool might identify declining sales
of a particular product,
and flag the item
as something that should be removed from stores.
However, what if there were a seasonal factor
affecting sales
that hadn't been accounted for in the AI tool's analysis?
Hallucinations like this one can lead to misguided decisions
if the user doesn't carefully review the AI tool's output.
Considering AI's limitations,
human oversight over AI generated output is crucial
to ensure that the information is accurate and ethical.
Effective management of AI in the workplace
requires teamwork from technical to non-technical roles
to ensure that AI's output and decision making processes
are aligned with values that benefit people.
Ultimately, an inclusive approach
that maintains human oversight over these tools
is the key to shaping a brighter future
where AI works for everyone.
Vint: Use AI for positive change
- Hi, I'm Vint, vice president
and chief internet evangelist at Google.
Some people think of me as famous.
I don't. I think of me as lucky.
I had the good fortune of being the one of the people
who started the design of the internet in 1973.
And since that time,
my entire career has been strongly related
to internet and its evolution.
So people ask me, what is a chief internet evangelist
and why does the internet need an evangelist at all?
And the answer to that is that
only about two thirds of the world's population
are actually online and able to access
all the treasures and riches of the internet.
And so, the chief internet evangelist
is still running around the world,
encouraging people to build more internet
so everyone can have access.
I've been around in the AI space
for almost all of its existence
because the notion of artificial intelligence
goes all the way back to the 1960s.
So, let's talk a little bit about the way in which AI
can be used in crisis situations
like wildfires and things like that.
In nine different countries,
Google's services are being used, the SOS alerts,
to detect and to alert geographically about wildfires.
So, we are starting to see raw material
going into the artificial intelligence algorithms
to help us anticipate what a fire is going to do.
And that will help the forest fire response
and will certainly help get people out of buildings
if they are, in fact, at risk.
Another example comes from one of our sister organizations,
DeepMind, which trained a machine learning system
to learn how to fold proteins
that are generated by the interpretation of DNA.
And the reason that's so important is that
those molecules may turn out to be the key
to solving various kinds of genetic diseases.
I mean, I get really excited about stuff like that.
AI literacy is really about not the absolute details,
exactly how does this work?
Not everyone needs to know
how a car is constructed to use it.
We want people to have a conscious sense
of both the power and peril of artificial intelligence.
I think in the long run,
as we learn how to use these techniques better and better
and more reliably, that it will have a dramatic impact
on almost all walks of life.
It will enhance our ability to do research.
Every student might have a personal tutor, in fact.
Creative people, who are trying to create text
and film, video and imagery, will have a palette and a tool
far more capable than a paintbrush
in order to invent and create.
And so, in the same way that the worldwide web
has turned out to be such a huge platform
for creativity and innovation,
I think the AI world will induce a similar kind of effect.
Activity: Use AI to create a work email
Use AI as a collaborative tool
Ever wondered how to turn your busy workday into an orderly
operation? Let's consider the incredible ways AI can enhance your
day-to-day processes. When we use AI at work, embracing a
people-first strategy is essential. AI enhances our unique human
skills and simplifies many of the tasks that we
perform. Essentially, AI augments our own capabilities. In this
context, AI augmentation refers to the process of using AI to
improve a work product, whether by making it easier to do or
higher in quality. For example, think of your own job or a job you
might have held in the past. Like mine, your job probably
consists of a variety of tasks and duties. Some of the tasks might
be simple, like manually responding to routine questions over
email. Other tasks might be more complex, like brainstorming
new ideas with coworkers. AI can help you with many of these
things by augmenting the work you do, helping you complete the
task quicker and more efficiently. Another way AI can help you to
do your work is through automation. AI automation refers to the
process of using AI to accomplish tasks without any action on the
user's part. For example, maybe a customer support
representative receives hundreds of emails every day from
customers asking for help. They might spend a lot of time reading
each email and typing responses one at a time. Using an AI tool,
they could automatically sort incoming emails by priority. For
emails with low priority, they could use AI to draft replies. With
some guidance from other customer support agents or training
data containing past replies, the AI could learn to quickly
generate quality email responses. By automating this task, the
representative could then focus on high-priority messages, like
ones reporting complex issues that require personal
attention. Striking the right balance between augmentation and
automation takes time, practice, and thoughtful
consideration. Successful businesses can set themselves apart by
applying apeople-first approach to AI and their products, services,
and jobs. Think of AI as a collaborative workspace that thrives on
diverse perspectives. Consider a public relations professional who
wants to use AI to assist in the creation of press releases. To make
sure they get an optimal result, they need to provide human
oversight of the AI tool. But it shouldn't stop there. It's essential
that they also collaborate with other members of their team, such
as managers who can coordinate resources for the project,
members of the editorial team to ensure that the releases are
representative of the brand's voice, and guidance from the legal
department to ensure appropriate rules and regulations are
followed. Everyone has to cooperate to integrate AI
successfully and to make sure the tools' generated output meets
expectations. Integrating AI at work must be an inclusive
process. It requires diverse perspectives from people across
different roles and departments to achieve the best results. It's
critical to learn and adapt as AI is introduced into the
workplace. By taking the time to understand AI and how it can
provide benefits in your work, you can effectively contribute to
the discussion.
Aleck: Make daily tasks easier with AI

- Hi, my name is Aleck and I'm an engineering program manager here at

Google and I'm on the central knowledge management team. The aim of this
department is to ensure that knowledge is not stale, that Googlers in
particular are not reading documents or working with tools that are out of
date, but just to ensure that, you know, the information they're exposed to is
current, up to date, and truly reflects on what's going on internally. I
definitely have a very non-traditional path. Studied architecture in college. I
was working in architecture field for about five years. I think architecture
helped me get better at establishing frameworks, applying frameworks, and
it's something that I can use now with my job as a program manager at
Google to just making sure that things are set up appropriately. And AI helps
with that a lot, too, to help me analyze different things to ensure that
projects can be successful. First thing I used it for was to better understand
AI. (chuckles) I used AI to learn about AI (chuckles) and asked the AI for
recommended material to read as well. That was very useful. The advice I
would have for people that want to use AI tools for the first time is don't be
afraid to use it for something small and then, you know, work your way
up. Try to find a task that might be hard for you or maybe somewhat
stressful or you're not really looking forward to it and see how you can
leverage it. One example of how I use AI tools to make my life more easier,
especially with work, is starting off a document. That's like the hardest thing
for me. Just getting the template started, having specific sections and
headings in there, leveraging AI is very useful and helpfulPlay video starting
at :1:44 and follow transcript1:44and kind of helps me create a more concise
document as well. I use AI for ways to make my work more efficient, whether
it's me taking notes, email summarization, organizing emails, providing key
points on documents, planning for the week. During every meeting, I create
transcripts for meetings, and then I then use AI to help me create immediate
action items, summarize the meeting, and provide me key takeaways from
the meeting. Saves me an hour to an hour and a half of my time. And when
things are efficient and they move well, it makes work easier, it makes it
more fulfilling, and that's something that is amazing and something
that is very, very powerful. AI isn't just about adopting the latest
tech.
Wrap-up
The goal should be to thoughtfully incorporate AI in a way that
puts people first and adds value to your work. In this section, we
discovered how AI works, including the fundamentals of AI and
machine learning. We investigated the capabilities and limits of
AI. We also examined how AI can enhance your work and help you
become more efficient. The AI field is constantly changing. New
advancements are made every day, and the technology is
continuously improving. I hope this experience serves as an
exciting introduction into new AI powered possibilities. To continue
learning, I encourage you to explore how to transform the way
you work with AI as a part of Google AI Essentials.
Go hands on - A Coach Dialogue

This Dialogue activity is a dynamic learning experience that will help you
solidify what you learned from Module 1 of Google AI Essentials and help
prepare you for the graded assessment. In this activity, you’ll engage in a
question-based discussion with our AI-powered guide, Coach.

It’s like a chat with your own mentor: have a question, need clarification, or
want a hint or a different type of example? Just ask!

Here are some guidelines to help you engage with Coach:

What if I get stuck?

If you’re struggling with any part of the Dialogue, look for the hand-raise icon
labeled “I’m stuck” in the top right corner. Clicking this will have Coach
reframe the question or topic. You can also request a hint directly in the chat,
and Coach will provide additional guidance to help you through the
experience.

How can I tell if I’m making progress?

This activity is designed to help you practice and assess your understanding.
At any time, you can click the light-bulb icon labeled “How am I doing?” in
the top right corner, to see how close you are to completing the activity.
Once you achieve 100% progress, you’ll receive a summary highlighting your
strengths and areas for improvement.

What if Coach provides inaccurate information?

Coach is programmed with course materials and instructor input as its
primary sources. However, if Coach ever provides incorrect information or
formats a message improperly, please use the “thumbs-down” icon next to
the message to report it. Provide some context for the error, and we’ll
address the issue promptly to prevent future occurrences.

Open Dialogue

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