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Center for Academic Innovation Experts
With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.
Read more
With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.
Read more
With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like. This course brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI on our current society and its implications for the future.
This course is licensed CC BY-SA 4.0 with the exclusion of the course image.
Register for this course and see more details by visiting:
OpenCourser.com/course/quktdq/generative
What's inside
Syllabus
Generative AI Essentials: Overview and Impact
This short course introduces learners to generative artificial intelligence (GenAI) and explains how large language models and generative AI tools, like ChatGPT, work. Explore the ethical use of GenAI, the implications of authorship, and how these tools might be utilized and regulated moving forward. It brings together University of Michigan experts on communication technology, the economy, artificial intelligence, natural language processing, architecture, and law to discuss the impacts of generative AI now and into the future.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Taught by Center for Academic Innovation Experts
Explores generative artificial intelligence (GenAI) and explains how large language models and generative AI tools work
Examines the ethical use of GenAI, the implications of authorship, and how these tools might be utilized and regulated moving forward
Discusses the potential impacts of generative AI on various aspects of society
Brings together experts from multiple disciplines to provide a comprehensive understanding of generative AI
Save this course
Save Generative AI Essentials: Overview and Impact to your list so you can find it easily later:
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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 Essentials: Overview and Impact with these
activities:
Review Natural Language Processing
Show steps
Mastering Natural Language Processing (NLP) is vital for building a strong foundation in Generative AI and its applications.
Browse courses on
Natural Language Processing
Show steps
-
Revisit core concepts of NLP, including tokenization, stemming, and parts of speech tagging.
-
Review different types of language models, such as n-grams, recurrent neural networks (RNNs), and transformers.
-
Explore pre-trained NLP models and their capabilities.
Review generative AI concepts
Show steps
Deepen your existing knowledge by going over the basic concepts of generative AI and large language models.
Browse courses on
Generative AI
Show steps
-
Review course materials from previous courses on AI
-
Read articles and watch videos on generative AI
-
Experiment with generative AI tools like ChatGPT
Read 'Deep Learning with Python'
Show steps
Gain a deeper understanding of the underlying principles of Generative AI by exploring a comprehensive reference book on Deep Learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Acquire a copy of 'Deep Learning with Python'.
-
Read and study the chapters relevant to Generative AI.
-
Apply the concepts learned to your Generative AI projects.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Explore ChatGPT tutorials and demos
Show steps
Getting hands-on experience with ChatGPT through tutorials and demos will help you understand its capabilities and limitations, which is part of the focus of this course.
Show steps
-
Identify a few ChatGPT tutorials that cover different use cases, such as text generation, question answering, and code generation.
-
Follow the instructions in each tutorial and experiment with ChatGPT.
-
Attend online demos of ChatGPT to see it being applied in different domains.
Follow tutorials on generative AI tools
Show steps
Develop your practical skills by following guided tutorials on how to use generative AI tools effectively.
Browse courses on
Generative AI Tools
Show steps
-
Find tutorials on generative AI tools like ChatGPT
-
Follow the steps in the tutorials to learn how to use the tools
-
Practice using the tools on your own projects
Experiment with Generative AI Tools
Show steps
Hands-on experience with Generative AI tools will enhance your understanding of their potential and limitations.
Show steps
-
Identify a few popular Generative AI tools, such as ChatGPT, DALL-E 2, or Stable Diffusion.
-
Follow guided tutorials to create your first projects using these tools.
Attend a Generative AI Workshop
Show steps
Immerse yourself in Generative AI through hands-on workshops and expert insights.
Show steps
-
Identify and register for a Generative AI workshop that aligns with your interests.
-
Participate actively in the workshop, asking questions and engaging in discussions.
Participate in a study group or discussion forum on generative AI
Show steps
Expand your perspectives and engage in meaningful discussions by participating in a study group or discussion forum on generative AI.
Browse courses on
Generative AI
Show steps
-
Find a study group or discussion forum on generative AI
-
Attend the sessions regularly
-
Participate in discussions and share your insights
Join a Generative AI Study Group
Show steps
Participating in discussions with peers will foster a deeper understanding of Generative AI and its implications.
Show steps
-
Find a study group or online forum dedicated to Generative AI.
-
Engage in discussions, share ideas, and learn from others' perspectives.
Join a study group for this course
Show steps
Engaging in discussions with peers will enhance your understanding of the course material, especially in regards to the ethical implications of AI.
Show steps
-
Connect with classmates via the course forum or social media.
-
Organize regular study sessions to discuss lecture content, readings, and assignments.
-
Take turns presenting on specific topics to reinforce your understanding and engage others.
Practice Generative AI Problem-Solving
Show steps
Regularly solving Generative AI problems will enhance your analytical and problem-solving skills.
Show steps
-
Identify online platforms or resources that provide Generative AI problem sets.
-
Attempt to solve the problems, focusing on both accuracy and efficiency.
-
Review your solutions and learn from your mistakes.
Provide mentorship to other students in this course
Show steps
Mentoring others will not only help them succeed but also reinforce your own understanding of the course material.
Show steps
-
Identify a student who is struggling with the course material or a specific topic.
-
Offer to help and provide guidance and support.
-
Meet regularly to review concepts, answer questions, and provide encouragement.
Write a blog post or article about generative AI
Show steps
Demonstrate your understanding and critical thinking by writing about the implications and potential applications of generative AI in different fields.
Browse courses on
Generative AI
Show steps
-
Research the topic of generative AI
-
Develop an outline for your blog post or article
-
Write the first draft of your content
-
Revise and edit your content
-
Publish your content on a blog or platform
Develop a Generative AI Use Case
Show steps
Applying Generative AI to solve real-world problems will solidify your grasp of its practical applications.
Show steps
-
Identify a specific problem or challenge that Generative AI could potentially address.
-
Design a solution that leverages the capabilities of Generative AI.
-
Create a prototype or demonstration of your solution.
Contribute to Open-Source Generative AI Projects
Show steps
Contributing to open-source projects will provide valuable hands-on experience and deepen your understanding of Generative AI.
Show steps
-
Identify open-source Generative AI projects that align with your interests.
-
Review the project documentation and codebase.
-
Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Develop a project proposal using a generative AI tool
Show steps
Applying your understanding of generative AI tools to a project will help you think critically about their potential applications and limitations.
Show steps
-
Identify a project idea that aligns with your interests and the course objectives.
-
Use a generative AI tool, such as ChatGPT or Jasper, to generate a draft proposal.
-
Refine the proposal by adding original research, analysis, and your own insights.
-
Present your project proposal to the class or a group of peers.
Participate in a hackathon or competition related to generative AI
Show steps
Challenge yourself and showcase your skills by participating in a hackathon or competition related to generative AI.
Browse courses on
Generative AI
Show steps
-
Find a hackathon or competition that aligns with your interests
-
Form a team or work individually
-
Develop a solution to the challenge
-
Submit your solution and present it to the judges
Contribute to an open-source Generative AI project
Show steps
Contributing to an open-source project will provide you with hands-on experience with generative AI and its applications, deepening your understanding of this topic.
Show steps
-
Identify an open-source Generative AI project that aligns with your interests and skill set.
-
Review the project documentation and familiarize yourself with its codebase.
-
Propose a new feature or improvement to the project and get it approved by the maintainers.
-
Implement your proposed change and submit a pull request.
Review Natural Language Processing
Show steps
Mastering Natural Language Processing (NLP) is vital for building a strong foundation in Generative AI and its applications.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Revisit core concepts of NLP, including tokenization, stemming, and parts of speech tagging.
- Review different types of language models, such as n-grams, recurrent neural networks (RNNs), and transformers.
- Explore pre-trained NLP models and their capabilities.
Review generative AI concepts
Show steps
Deepen your existing knowledge by going over the basic concepts of generative AI and large language models.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Review course materials from previous courses on AI
- Read articles and watch videos on generative AI
- Experiment with generative AI tools like ChatGPT
Read 'Deep Learning with Python'
Show steps
Gain a deeper understanding of the underlying principles of Generative AI by exploring a comprehensive reference book on Deep Learning.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
Show steps
Show steps
- Acquire a copy of 'Deep Learning with Python'.
- Read and study the chapters relevant to Generative AI.
- Apply the concepts learned to your Generative AI projects.
15 other activities
Expand to see all activities and additional details
Show all 18 activities
Explore ChatGPT tutorials and demos
Show steps
Getting hands-on experience with ChatGPT through tutorials and demos will help you understand its capabilities and limitations, which is part of the focus of this course.
Show steps
Show steps
Show steps
- Identify a few ChatGPT tutorials that cover different use cases, such as text generation, question answering, and code generation.
- Follow the instructions in each tutorial and experiment with ChatGPT.
- Attend online demos of ChatGPT to see it being applied in different domains.
Follow tutorials on generative AI tools
Show steps
Develop your practical skills by following guided tutorials on how to use generative AI tools effectively.
Browse courses on
Generative AI Tools
Show steps
Show steps
Show steps
- Find tutorials on generative AI tools like ChatGPT
- Follow the steps in the tutorials to learn how to use the tools
- Practice using the tools on your own projects
Experiment with Generative AI Tools
Show steps
Hands-on experience with Generative AI tools will enhance your understanding of their potential and limitations.
Show steps
Show steps
Show steps
- Identify a few popular Generative AI tools, such as ChatGPT, DALL-E 2, or Stable Diffusion.
- Follow guided tutorials to create your first projects using these tools.
Attend a Generative AI Workshop
Show steps
Immerse yourself in Generative AI through hands-on workshops and expert insights.
Show steps
Show steps
Show steps
- Identify and register for a Generative AI workshop that aligns with your interests.
- Participate actively in the workshop, asking questions and engaging in discussions.
Participate in a study group or discussion forum on generative AI
Show steps
Expand your perspectives and engage in meaningful discussions by participating in a study group or discussion forum on generative AI.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Find a study group or discussion forum on generative AI
- Attend the sessions regularly
- Participate in discussions and share your insights
Join a Generative AI Study Group
Show steps
Participating in discussions with peers will foster a deeper understanding of Generative AI and its implications.
Show steps
Show steps
Show steps
- Find a study group or online forum dedicated to Generative AI.
- Engage in discussions, share ideas, and learn from others' perspectives.
Join a study group for this course
Show steps
Engaging in discussions with peers will enhance your understanding of the course material, especially in regards to the ethical implications of AI.
Show steps
Show steps
Show steps
- Connect with classmates via the course forum or social media.
- Organize regular study sessions to discuss lecture content, readings, and assignments.
- Take turns presenting on specific topics to reinforce your understanding and engage others.
Practice Generative AI Problem-Solving
Show steps
Regularly solving Generative AI problems will enhance your analytical and problem-solving skills.
Show steps
Show steps
Show steps
- Identify online platforms or resources that provide Generative AI problem sets.
- Attempt to solve the problems, focusing on both accuracy and efficiency.
- Review your solutions and learn from your mistakes.
Provide mentorship to other students in this course
Show steps
Mentoring others will not only help them succeed but also reinforce your own understanding of the course material.
Show steps
Show steps
Show steps
- Identify a student who is struggling with the course material or a specific topic.
- Offer to help and provide guidance and support.
- Meet regularly to review concepts, answer questions, and provide encouragement.
Write a blog post or article about generative AI
Show steps
Demonstrate your understanding and critical thinking by writing about the implications and potential applications of generative AI in different fields.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Research the topic of generative AI
- Develop an outline for your blog post or article
- Write the first draft of your content
- Revise and edit your content
- Publish your content on a blog or platform
Develop a Generative AI Use Case
Show steps
Applying Generative AI to solve real-world problems will solidify your grasp of its practical applications.
Show steps
Show steps
Show steps
- Identify a specific problem or challenge that Generative AI could potentially address.
- Design a solution that leverages the capabilities of Generative AI.
- Create a prototype or demonstration of your solution.
Contribute to Open-Source Generative AI Projects
Show steps
Contributing to open-source projects will provide valuable hands-on experience and deepen your understanding of Generative AI.
Show steps
Show steps
Show steps
- Identify open-source Generative AI projects that align with your interests.
- Review the project documentation and codebase.
- Identify an area where you can contribute, such as bug fixes, feature enhancements, or documentation improvements.
Develop a project proposal using a generative AI tool
Show steps
Applying your understanding of generative AI tools to a project will help you think critically about their potential applications and limitations.
Show steps
Show steps
Show steps
- Identify a project idea that aligns with your interests and the course objectives.
- Use a generative AI tool, such as ChatGPT or Jasper, to generate a draft proposal.
- Refine the proposal by adding original research, analysis, and your own insights.
- Present your project proposal to the class or a group of peers.
Participate in a hackathon or competition related to generative AI
Show steps
Challenge yourself and showcase your skills by participating in a hackathon or competition related to generative AI.
Browse courses on
Generative AI
Show steps
Show steps
Show steps
- Find a hackathon or competition that aligns with your interests
- Form a team or work individually
- Develop a solution to the challenge
- Submit your solution and present it to the judges
Contribute to an open-source Generative AI project
Show steps
Contributing to an open-source project will provide you with hands-on experience with generative AI and its applications, deepening your understanding of this topic.
Show steps
Show steps
Show steps
- Identify an open-source Generative AI project that aligns with your interests and skill set.
- Review the project documentation and familiarize yourself with its codebase.
- Propose a new feature or improvement to the project and get it approved by the maintainers.
- Implement your proposed change and submit a pull request.
Career center
Learners who complete Generative AI Essentials: Overview and Impact will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new AI algorithms and techniques. This course can help Artificial Intelligence Researchers learn about the latest advances in generative AI, which is a rapidly growing field with many potential applications.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers build and deploy NLP models, which are used to understand and generate human language. This course can help Natural Language Processing Engineers learn about the latest advances in generative AI, which can be used to improve the performance of NLP models.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and deploy machine learning models, which are used to make predictions and decisions based on data. This course can help Machine Learning Engineers learn about the latest advances in generative AI, which can be used to improve the performance of machine learning models.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers build and deploy computer vision models, which are used to interpret images and videos. This course can help Computer Vision Engineers learn about the latest advances in generative AI, which can be used to improve the performance of computer vision models.
Data Scientist
Data Scientist
Data Scientists work to make sense of large datasets, identifying trends and patterns that can be used to make better decisions. This course can help Data Scientists build a foundation in generative AI, which is a rapidly growing field with many potential applications in data science.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, build, and deploy robots. This course can help Robotics Engineers learn about the latest advances in generative AI, which can be used to improve the performance of robots.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to identify trends and patterns. This course can help Data Analysts learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of data analysis.
Software Engineer
Software Engineer
Software Engineers design, build, and deploy software. This course can help Software Engineers learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of software development.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. This course can help Product Managers learn about the latest advances in generative AI, which can be used to improve the design and development of new products.
Marketing Manager
Marketing Manager
Marketing Managers are responsible for the development and execution of marketing campaigns. This course can help Marketing Managers learn about the latest advances in generative AI, which can be used to improve the effectiveness of marketing campaigns.
Sales Manager
Sales Manager
Sales Managers are responsible for the development and execution of sales strategies. This course can help Sales Managers learn about the latest advances in generative AI, which can be used to improve the effectiveness of sales strategies.
Operations Manager
Operations Manager
Operations Managers are responsible for the planning and execution of business operations. This course can help Operations Managers learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of business operations.
Human Resources Manager
Human Resources Manager
Human Resources Managers are responsible for the development and execution of human resources strategies. This course can help Human Resources Managers learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of human resources strategies.
Financial Analyst
Financial Analyst
Financial Analysts are responsible for the analysis of financial data. This course can help Financial Analysts learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of financial analysis.
Management Consultant
Management Consultant
Management Consultants advise businesses on how to improve their operations. This course can help Management Consultants learn about the latest advances in generative AI, which can be used to improve the efficiency and effectiveness of management consulting.
For more career information including salaries, visit:
OpenCourser.com/course/quktdq/generative
Reading list
We've selected 11 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 Essentials: Overview and Impact.
Explores the ethical implications of algorithms and provides a framework for designing and evaluating algorithms that are fair, accountable, and transparent. It is essential reading for anyone interested in the ethical implications of AI.
Save
Classic reference on deep learning, providing a comprehensive overview of the field. It covers the fundamental concepts, algorithms, and applications of deep learning, and is essential reading for anyone interested in the technical details of deep learning.
Provides a comprehensive overview of generative AI, covering the fundamental concepts, techniques, and applications of this rapidly evolving field. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Classic reference on statistical learning, providing a comprehensive overview of the field. It covers the fundamental concepts, algorithms, and applications of statistical learning, and is essential reading for anyone interested in the technical details of statistical learning.
Practical guide to machine learning using Python, providing a step-by-step approach to building and deploying machine learning models. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Practical guide to machine learning using Scikit-Learn, Keras, and TensorFlow, providing a step-by-step approach to building and deploying machine learning models. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Practical guide to machine learning using R, providing a step-by-step approach to building and deploying machine learning models. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Practical guide to deep learning using Python, providing a step-by-step approach to building and deploying deep learning models. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Practical guide to natural language processing using Python, providing a step-by-step approach to building and deploying natural language processing models. It is written in a clear and concise style, making it accessible to readers with a wide range of backgrounds.
Classic reference on speech and language processing, providing a comprehensive overview of the field. It covers the fundamental concepts, algorithms, and applications of speech and language processing, and is essential reading for anyone interested in the technical details of speech and language processing.
Save
Classic reference on computer vision, providing a comprehensive overview of the field. It covers the fundamental concepts, algorithms, and applications of computer vision, and is essential reading for anyone interested in the technical details of computer vision.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/quktdq/generative
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Instructor
Center for Academic Innovation Experts
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Good to know
Taught by Center for Academic Innovation Experts
Explores generative artificial intelligence (GenAI) and explains how large language models and generative AI tools work
Examines the ethical use of GenAI, the implications of authorship, and how these tools might be utilized and regulated moving forward
Discusses the potential impacts of generative AI on various aspects of society
Brings together experts from multiple disciplines to provide a comprehensive understanding of generative AI
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