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Generative AI

Fundamentals, Applications, and Challenges

Merve Hickok

Rapidly evolving landscape of generative artificial intelligence (AI) means that both novice and skilled users alike should be aware of the proper applications and challenges of these powerful tools.

In “Generative AI: Fundamentals, Applications, and Challenges,” you’ll discuss the potential benefits, uses, and challenges of a variety of applications for generative AI. Explore the impact these tools could have on business, operations, consumers, society, and the environment. Aspects of the course also dive into the general risks associated with this technology, including issues such as copyright infringement, outdated data, malicious attack surfaces, bias, and more. By participating in this course, you'll gain a strong baseline of knowledge for generative AI, one that will allow you to explore further considerations about its impact on businesses and society.

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What's inside

Syllabus

Introduction to the Course

Use Cases of Generative AI

Responsible Generative AI Concepts

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Introduces applications of generative AI, which are in high demand in industry

Informs learners of emerging trends in generative AI, which are important to know about for all levels of experience

Examines common challenges associated with the use of generative AI, which can help students think critically about potential drawbacks

Teaches responsible use of generative AI, which is important to avoid unethical or harmful applications

Requires no prerequisite knowledge, making it a useful course for those with little to no experience in generative AI

Taught by Merve Hickok, an expert in the field, which signifies course quality and relevance

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Reviews summary

Generative ai: foundational overview & ethical insights

According to learners, this course provides a strong foundational understanding of Generative AI, especially for non-technical professionals and those new to the field. Many appreciate how it demystifies complex concepts, making them accessible. The sections on responsible AI concepts, including ethical considerations, challenges like bias and copyright, and real-world use cases, are frequently highlighted as highly valuable and timely. While some find the course too superficial or lacking technical depth for advanced learners, it is widely considered an excellent starting point for understanding the broad landscape and societal impact of Generative AI.

Content is current, but some topics may need frequent updates.

"The field moves so fast, some content might become outdated quickly. Perhaps more frequent updates would be beneficial."

"The modules on challenges and risks were particularly strong and well-explained, demonstrating current relevance."

"I particularly liked the module on responsible AI, which felt very timely given recent developments."

Provides practical examples of how Generative AI is applied.

"The use cases were incredibly relevant."

"The sections on use cases were very helpful in seeing real-world applicability."

"It highlights important considerations and practical tools that can be immediately understood."

Provides a clear, accessible baseline for Generative AI for beginners.

"Excellent course for anyone looking for a high-level overview of Generative AI. The instructors explain complex concepts in an accessible way."

"This course demystified Generative AI for me... The pacing is just right, and the content is extremely well-structured."

"As a non-technical professional, this course was exactly what I needed. It broke down complex topics into digestible insights."

"A very good introductory course. It helps to clarify many misconceptions about Generative AI and provides a solid conceptual framework."

Highlights ethical considerations, risks, and societal impact.

"The focus on responsible AI is particularly valuable given the rapid advancements."

"I appreciated the modules on responsible Generative AI concepts... The discussions on challenges like bias and copyright were eye-opening and critical."

"I particularly liked the module on responsible AI, which felt very timely."

"The emphasis on ethical use and potential societal impact was very important to me."

Not for deep technical insights or hands-on coding skills.

"It doesn't dive deep into technical implementation, which suited my needs as a business analyst."

"I have a background in machine learning, and I found this course to be too superficial. It barely scratches the surface of the underlying technologies."

"Completely lacked depth. I already knew most of what was taught from news articles. There was no technical insight whatsoever."

"The course content is too basic and very general. I learned very little new information."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Generative AI: Fundamentals, Applications, and Challenges with these activities:

Review "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville

Show steps

Provide a foundational understanding of deep learning, a crucial component of generative AI.

View Deep Learning on Amazon

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Read Chapter 1: Introduction
Complete the exercises in Chapter 1
Summarize the main concepts in Chapter 1

Follow online tutorials on responsible AI practices

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Enhance understanding of ethical considerations and best practices in generative AI development.

Browse courses on Responsible AI

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Identify reputable sources for online tutorials on responsible AI
Select and follow tutorials that cover topics such as AI bias, fairness, and transparency
Summarize and reflect on the key takeaways from the tutorials

Create a resource list on generative AI tools and techniques

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Foster a deeper understanding of available resources and tools for generative AI.

Browse courses on Generative AI Tools

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Research and identify generative AI tools and techniques
Categorize and organize the resources into a comprehensive list
Share the resource list with others in the learning community

Four other activities

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Complete hands-on exercises on generative AI frameworks

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Develop practical skills in implementing and training generative AI models.

Browse courses on TensorFlow

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Choose a generative AI framework (e.g., TensorFlow, PyTorch)
Follow tutorials and complete exercises on using the framework
Create a small generative AI project using the framework

Write a blog post on a generative AI application

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Demonstrate comprehension and ability to apply knowledge of generative AI applications.

Browse courses on Generative AI Applications

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Choose a specific generative AI application
Research the application and its benefits and challenges
Write a blog post that clearly explains the application, its potential impact, and any ethical considerations

Participate in a study group to discuss generative AI use cases

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Engage in collaborative learning and exchange ideas on practical applications of generative AI.

Browse courses on AI Applications

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Join or form a study group with peers
Prepare talking points and examples related to generative AI use cases
Facilitate or participate in discussions within the group

Contribute to open-source generative AI projects

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Gain hands-on experience and contribute to the advancement of generative AI through open-source collaboration.

Browse courses on Generative AI

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Identify open-source generative AI projects
Choose a project that aligns with interests and skill level
Read the project documentation and contribute code or other resources

Career center

Learners who complete Generative AI: Fundamentals, Applications, and Challenges will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Generative AI: Fundamentals, Applications, and Challenges.

The Oxford Handbook of Ethical Theory

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Provides a comprehensive overview of the field of ethics. It covers a wide range of topics, including the history of ethics, the different theories of ethics, and the application of ethics to real-world problems.

Generative AI

Two deals to help you save

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Generative ai: foundational overview & ethical insights

Activities

Career center

Reading list

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