Familiarize yourself with the concept of generative AI and its ability to create content is introduced, along with its real-world applications and limitations. You'll delve into the differences between traditional machine learning models, generative AI, and artificial general intelligence (AGI), and explore the key factors driving the development of generative AI.

What is generative AI?

ChatGPT

Getting to know generative AI

Using a model

Working with a generative AI model

Applications of generative AI

Generative AI in the machine learning landscape

Options

Discriminative models

Generative AI

Picking the right tool for the job

Differentiating generative AI from traditional ML

More dogs and GANs

The evolution of generative AI

Driving the field forward

Transformers

GAN architecture

Transformers architecture

RLHF

Generative AI breakthroughs

Introduction to Generative AI

In this chapter, we cover the essential steps in creating generative AI models: research and design, data collection, model training, and evaluation. We examine the significance of diverse datasets and advanced training techniques, as well as various evaluation methods, while discussing their strengths and limitations.

Model design and data collection

Research and design

A dataset windfall?

Model training

Factors impacting model training

Techniques for developing specialized models

Model evaluation

PixAIr animation

Helping Intellibrand evaluate

Developing Generative AI Models

This chapter focuses on the responsible use of generative AI. We discuss the challenges and strategies to mitigate social bias, intellectual property and privacy issues, and ethical considerations to prevent misuse. We conclude by exploring the immense potential and risks of Artificial Generative Intelligence (AGI), along with the approaches to control its outcomes.

Evaluating and mitigating social bias

Sources of bias

Detecting and mitigating bias

Copyright and ownership

Legal and privacy considerations

Identifying IP ownership claims

Responsible generative AI applications

Usage principles

Implementing responsible AI practices

Artificial general intelligence (AGI)

AGI characteristics

AGI outcomes

Using AI Models and Generated Content Responsibly

Chapter 4 examines the potential, impact, and integration of generative AI into human workflows. It discusses key contributors to AI development, from universities to companies, and explores societal adaptations to AI. It delves into AI's implications for productivity, job dynamics, education, media, entertainment, scientific advancements, and ethical considerations.

Bringing new AI into old workflows

Leveraging generative AI for data science

Designing a new marketing campaign

Progress in generative AI

To open source or not to open source

Super Duper GANs

Preparing for a future of generative AI

Generative AI in education

The value of skills in an AI world

You made it!

Getting Ready for the Age of Generative AI

Course Glossary: Generative AI Concepts

Artificial Intelligence (AI) is reshaping our world like never before. Generative AI is a type of AI that can create new content from text, images, and more. In this no-coding course, you'll learn about the exciting concepts in this emerging field and how to prepare for a future where AI becomes widely adopted. You'll start by comprehending how these models create content, where they fit into the machine learning landscape, and how they are developed. Given the power of generative AI, you will also consider how laws and ethics play with these models, learning how to avoid bias and use generative AI responsibly. Finally, you’ll look at the future of generative AI and how we can leverage and collaborate effectively with these tools. After this course, you, too, will be able to unlock the creative capabilities of generative AI. Let's go!

The videos contain live transcripts, which can be accessed by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Get Acquainted with Generative AI</h2>
Artificial Intelligence is reshaping our world like never before. Generative AI is a type of AI model that can create new content, from text to images and more. In this non-technical course, you will learn this emerging field's key concepts and how to prepare for a future where such AIs are pervasive.
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<h2>Understand How Generative AI Works</h2>
You'll start by comprehending how these models create content, where they fit into the machine learning landscape, and how they are developed. From gathering training data to evaluating and improving models, you'll learn what AI companies must consider and do to bring generative AI tools into the world.
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<h2>Explore Key Legal and Ethical Considerations</h2>
Given the power of generative AI, we will also consider how laws and ethics play into building and using these models. You'll learn how to avoid bias and apply generative AI in a responsible way.
<br><br>
<h2>Get the Right Mindset for Utilizing These Tools</h2>
Finally, you’ll look at the future of generative AI and how we can leverage and collaborate effectively with these tools. After this course, you, too, will be able to unlock the creative capabilities of generative AI. Let's go!

Understanding Machine Learning

Discover how to begin responsibly leveraging generative AI. Learn how generative AI models are developed and how they will impact society moving forward.

Generative AI Concepts

AI Temelleri

Data Lead, Google

Models that analyze

Bagels and puppies

Guessing with confidence

Models that imagine

Mixing for effect

Generative adversarial networks (GANs)

Bagel Puppy GAN

Use the right tool for the job

Let's practice!

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Introduction to Generative AI - Generative AI in the machine learning landscape

Generative AI in the machine learning landscape

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