Leverage generative AI tools to speed up work tasks and boost your productivity. Examine the important role humans play in the effective use of AI, and understand the types of workplace tasks you can augment with AI. By the end of this module, you will be able to determine if AI is right for a given task and how to use AI to accelerate workflows.
Learning Objectives
- Recognize how generative AI can be used for several different workplace applications.
- Describe how AI tools are powered by AI models to produce outputs.
- Identify opportunities to leverage AI for increased productivity and optimized work processes
- Explain the importance of providing human oversight when using AI tools.
- Evaluate whether generative AI is an optimal tool to apply to a specific task.
Explore generative AI
Video: Module 2 introduction: Maximize productivity with AI tools
This introduction to generative AI emphasizes its potential to revolutionize work by boosting productivity and offering creative solutions across various fields.
The speaker, Tris, a Director of Product Management at Google DeepMind, shares her personal experience using AI to streamline tasks, brainstorm ideas, and enhance collaboration.
The course promises to explore practical applications of generative AI, teach participants how to leverage these tools effectively, and highlight the importance of responsible AI use through a human-in-the-loop approach.
Essentially, the course aims to equip participants with the knowledge and skills to harness the power of generative AI for a more efficient and innovative workday.
- We’ll explore how to
effectively engage with AI tools to tackle a variety of tasks and boost your productivity. AI tools are revolutionizing
the way we approach work. These tools can offer creative solutions that help organizations and individuals tackle challenges both big and small. Whether you’re in design, finance, or any other field, generative AI can help you streamline processes and maximize productivity. Do you create content? Generative AI can help you write drafts and create images in minutes. Is data analysis more your thing? Generative AI can assist
you with forecasting trends and communicating
insights to stakeholders. Maybe you need help
brainstorming innovative ideas. Generative AI can help with that too. Hi, my name is Tris, and I’m a director of Product Management here at Google DeepMind. Together, my team and I build AI products that simplify complex tasks, making your daily work life smoother and hopefully more efficient. I’ve been at Google for almost 10 years, and I’ve seen so many
amazing projects in AI. I started my career in
AI about 20 years ago in Cambridge, Massachusetts, at a small startup
building, of all things, a natural language search engine. In my job, I use AI to
collaborate with colleagues, research fresh product concepts, and find creative approaches
to problem solving. For example, I often use
AI to brainstorm ideas for how I can make my team
meetings more fun and productive. Essentially, I use AI
a lot to help me boost my productivity and
better achieve my goals. I’m thrilled to be your guide on this tour into the dynamic world of generative AI. In this part of the course, we’ll explore practical
applications of generative AI that can transform the way you work. And along the way,
you’ll learn how to boost your productivity with AI tools. You’ll also learn about
using AI responsibly by applying a human-in-the-loop
approach to AI. So, are you ready to explore how generative AI can
transform your workday? Let’s uncover all the amazing
things you can do with AI.
Video: Discover generative AI applications
Generative AI is a collaborative tool that can accelerate workflow by generating new content such as text, images, audio, video, and code. It has various applications, including:
- Text generators: draft engaging content, research, summarize information, and translate content into multiple languages.
- Image generators: create logos, design visuals for social media, and more.
- Audio and video generators: produce promotional videos and multimedia projects.
- Code generators: ensure website functionality across devices.
These tools can help boost productivity and efficiency in various tasks, such as web design, content creation, and research. The possibilities are endless, and generative AI can help bring ideas to life with just a few clicks.
- Picture this, you’ve got an idea, and with just a few clicks,
you’re bringing it to life, that’s what generative AI is all about. It’s a collaborative tool that can help you
accelerate your workflow. As you might recall, generative AI is AI that can generate new
content like text, images, or other media. This versatility paves the way for endless creative applications. Imagine you’re a web designer and you’re creating a new
website for a local business, you could tap into an array
of generative AI tools to complete various parts of
the project more efficiently. For instance, one of the most common types of generative AI tools
are text generators. These tools can help you
draft engaging content, like crafting a compelling
company profile, or creating detailed product
descriptions for your website. They can also support your
research on topics relevant to the business. Summarizing information
for easy understanding and much more. Text generators can even help
you translate your content into multiple languages,
making the website accessible to a wider audience. Another type of generative
AI tool you can use is an image generator. This tool can be used to create a logo that aligns with the business’s branding, design visuals for social
media posts and more. And why stop at images? An audio and video
generator can assist you with producing promotional videos and other kinds of multimedia projects that showcase your company’s
products and services. Finally, to bring the website to life, you can use a code generator
for tasks like ensuring that the website loads
correctly on desktops, tablets, and mobile devices. There are endless ways to use these types of generative AI tools to boost your productivity,
whether it be to generate text, images, audio, video, or code. Now that you know more about
the potential applications of generative AI, think of
everything you can accomplish with these tools.
Video: Tris: My favorite ways to use AI
Here’s a summary of the text:
Tris, a Product Management Director at Google DeepMind, shares their enthusiasm for working in AI research, comparing it to being part of a Renaissance-like era that will reshape humanity. They believe AI will be the most important invention of our lifetimes. Tris uses AI daily to generate new ideas, collaborate, and improve their work. They cite a study that found generative AI improves productivity, efficiency, quality, and accuracy, which they’ve also experienced personally. Tris also uses AI in their personal life, such as generating birthday party ideas for their wife. They encourage everyone to experiment with AI, emphasizing that anyone can learn to use it effectively, including children.
- I’m Tris. I’m a product management
director at Google DeepMind, which means I work on
Cutting-Edge AI Research. DeepMind is the team that has
the mission to disrupt what’s possible with
computers and to do so in a responsible
way. Every day I come into work and I try to figure out what it
is that we can do to make AI and our research
just a little bit better, to organize better, to make our
team cohere better, to make sure that we get
products out better. I think working in AI right now is a bit like being around
during the Renaissance and watching science unfold and completely reshape what was
possible for humanity. I personally believe that AI will be the most
important invention of our lifetimes. I use AI in my work all the
time to come up with new ideas, to collaborate with my
colleagues, to change the way that I’m
thinking, and to test what I do. But recently, one of the
largest consulting firms in America actually released a study about
using generative AI to improve productivity of its
workforce, and they found that it improved people’s efficiency, quality,
and accuracy. I found that to be the case in
my work as well. It’s an amazingly cool, iterative,
creative process that can improve your life. I also use it for my personal
life. You say, “Oh, you know, my wife
really loves dogs, and cooking, and the
outdoors, and music, especially folk
music.” And you say, “What would be amazing birthday
party ideas for her?” And you can ask follow-up
questions and find really interesting
ideas that I honestly never would’ve
come up with myself. Get out there. Experiment. You can learn this. You can do
this. Anybody can do this. Right? You know, my kids can use
generative AI tools effectively and answer questions and
explore interesting areas. And I know that everybody
watching this course can too.
Video: Understand how AI tools work
AI tools can be effectively integrated into your work routine to streamline workflows. These tools, powered by AI models, come in three main forms: stand-alone tools, integrated features, and custom solutions.
Types of AI Tools:
- Stand-alone AI Tools:
- These are independent applications that can be accessed online or downloaded.
- Example: Speeko, an AI speech coach, helps improve communication skills by analyzing voice and providing feedback on clarity, pacing, and word choice.
- Integrated AI Features:
- Built-in enhancements within existing software.
- Example: Neural Filters in Adobe Photoshop, which allow users to edit images by blurring backgrounds, adjusting colors, and changing facial expressions.
- Custom AI Solutions:
- Tailormade applications designed to solve specific problems.
- Example: An AI system implemented at Johns Hopkins Hospital to detect sepsis, enabling faster diagnosis and treatment.
Understanding AI Models:
- AI models are the engines behind AI tools, trained on data to recognize patterns and perform tasks.
- These models provide the capabilities, while the tools offer interfaces to assist users in completing tasks.
Focus of the Course:
- The course emphasizes stand-alone generative AI tools due to their availability, ease of use, and potential to fit seamlessly into workstreams.
Conclusion:
By understanding and utilizing these different types of AI tools, you can enhance productivity and efficiency in your work.
- There are all kinds of
ways to leverage AI tools and many of them are
readily available to you. Let’s explore the different
ways that AI tools can be integrated into your work routine and how they can
streamline your workflows. As you might recall, an AI
tool is AI powered software that can automate or assist
users with a variety of tasks. In your role, you may come
across generative AI tools in a few different ways,
as stand-alone AI tools, as integrated AI features
or as custom solutions. A stand-alone AI tool
describes AI-powered software that’s designed to be used on its own. Stand-alone AI tools
can be accessed online or downloaded to a device with
little or no technical setup. For example, Speeko is an AI speech coach designed to help you
communicate with confidence. Speeko uses AI to analyze your voice and give you feedback on how to improve your
communication skills in any setting, whether you’re preparing
for online meetings or in-person presentations. Suppose you’re preparing
for a job interview and want to practice answering
common interview questions. With Speeko, you can
record yourself responding to these questions, and
Speeko will give you feedback on aspects, like clarity,
pacing, and word choice, helping you learn how to
communicate more effectively during the actual interview. Speeko also gives you
personalized exercises to help you improve your skills. As another example, let’s say, you want to create a new
image for a design project using a desktop app you already have. Instead of searching for a
stand-alone image generator, the desktop app might have
an integrated AI feature that can help you get the job done. An integrated AI feature refers
to a built-in enhancement to a particular piece of software. For example, Neural Filters is an AI tool that’s built right into Adobe Photoshop. It lets you edit images
by blurring backgrounds, adjusting colors, changing
facial expressions, and much more. Stand-alone tools and integrated features are two common ways to use AI, but custom AI solutions
are also an option. A custom AI solution is an application that’s tailormade to
solve a specific problem. For instance, in 2022,
Johns Hopkins Hospital implemented an AI solution to
help doctors detect sepsis, a condition where the body has an extreme response to an infection. This AI solution allowed
doctors to diagnose and treat patients up to six hours sooner than previously possible, leading to improved patient outcomes. AI models, like this, can
be rewarding to build, but they typically require
dedicated resources and buy-in from your
organization to produce. In this course, we’ll focus mainly on stand-alone generative AI tools because they’re readily
available, often free to use, and can easily fit in your workstream. Before using AI tools,
it’s helpful to understand the technologies that
power them, AI models. An AI model is a computer
program trained on a set of data to recognize patterns and
perform specific tasks. Think of the AI model as the engine and the AI tool as the car. The model provides the
underlying capabilities while the tools interface
assists you in completing tasks. Whether you’re using a stand-alone tool, an integrated feature, or a
custom solution in your work, remember that an AI tool
is powered by an AI model. Next up, we’ll explore
how to effectively engage with AI tools to tackle a variety of tasks and boost your productivity.
Reading: AI models and the training process
Reading
You’ve already explored how AI tools are powered by AI models. In this reading, you’ll gain a deeper understanding of how AI models are developed. You’ll uncover the iterative process that AI designers and engineers use to train AI models from data, ensuring that AI tools work reliably. Learning more about the inner workings of AI tools will help you discuss them more accurately and confidently.
AI tools and AI models
Terms like AI tools and AI models can be confusing because they sound similar but refer to different things. Recall that an AI tool is AI-powered software that can automate or assist users with a variety of tasks. An AI model is a computer program trained on sets of data to recognize patterns and perform specific tasks.
To illustrate this relationship, consider a car and its engine.
The car with its user-friendly interface like a steering wheel and dashboard, represents the AI tool. This tool helps you get to your destination, which could be a completed task or a desired output. Just like car engineers design different features for various needs, AI designers and engineers build AI tools with functionalities specific to their purpose. But under the hood, so to speak, lies the AI model. This unseen engine processes the information you provide, like your input into a photo editing software, and allows the AI tool to function.
Similar to how we choose cars based on their purpose, like a sedan for everyday driving or a pickup truck for hauling heavy loads, AI tools are developed for a wide range of applications. There are AI tools for generating different creative texts, images, videos, or even writing computer code. And regardless of the specific function of the AI tool, it’s the underlying AI model that does the heavy lifting, powering the tool’s capabilities.
Note: Some AI tools leverage multiple AI models, working together as a “family,” to achieve more flexibility and perform a wider range of tasks. Each model within the family might be specialized for a specific subtask, ultimately contributing to the overall functionality of the AI tool. You’ll explore these types of multimodal tools later in this course.
The process of training AI models
AI designers and engineers develop AI models through a process called training. Here’s an example of the typical steps a designer might take in this process, in this case for building a model that predicts rainfall:
- Define the problem to be solved. AI designers and engineers want to predict rain to help people stay dry when commuting to and from work. They start by considering AI’s capabilities and limitations before identifying an AI solution.
- Collect relevant data to train the model. AI designers and engineers gather historical data of days when it rained and days when it didn’t rain over the past 50 years.
- Prepare the data for training. AI designers and engineers prepare the data by labeling important features, such as outdoor temperature, humidity, and air pressure, and then noting whether it rained. It’s also common to separate the data into two distinct sets: a training set and a validation set to test with later.
- Train the model. AI designers and engineers apply machine learning (ML) programs to their rain prediction model, which helps it recognize patterns in its training data that indicate the likelihood of rainfall. Those patterns might include high temperatures, low air pressure, and high humidity.
- Evaluate the model. AI designers and engineers use the validation set they prepared earlier to assess their model’s ability to predict rainfall accurately and reliably. Analyzing a model’s performance can uncover potential issues impacting the model, such as insufficient or biased training data. If any issues exist, the AI designers and engineers may revisit an earlier step in this process to try a different approach. Once the model performs well with its validation set, the process continues to the next step.
- Deploy the model. When the AI designers and engineers are satisfied with their model’s performance, they deploy it in an AI tool—helping people in their city stay dry on their way to work!
Model training is an iterative process. AI designers and engineers can repeat each step as many times as necessary and make adjustments until they create the best model possible.
But the process doesn’t stop at deployment. Once users interact with a model in practical situations, the model might be exposed to new challenges. AI designers and engineers should continuously monitor and collect feedback on their models, ensuring their models continue to perform reliably and to identify areas for improvement. It’s this iterative process of continual refinement that makes AI models precise and versatile, which ultimately leads to effective, reliable AI tools. When you understand how AI models are developed, you can make informed decisions about when and how to use an AI tool to accomplish your goals.