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Artificial Creativity

Released in June 2021, Artificial Creativity explores the emerging field of creativity in artificial intelligence (AI) from a design perspective, bringing together insights from computer science and creative disciplines. In this course, you will survey the history and theories behind today's creative AI, analyze the unorthodox approaches that have advanced the field, and experience cutting-edge creative AI tools. At the same time, you will learn design thinking research methods, how to implement them for your creative insights, and how to translate them to professional and business contexts. Throughout the course, you will step away from your computer to conduct design research, applying the techniques and theories you learn each week to a topic of your choice. You will also engage with other learners through discussions while challenging and expanding your understanding of creativity.

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What's inside

Syllabus

Can Machines Be Creative?

In this module, you will learn what to expect throughout this course, explore machine creativity, and start your first design research observation.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Appropriate for students looking to understand the fundamentals of using AI for creative applications and products

Introduces students to key principles and methodologies from both the design and AI disciplines

Projects require learners to think creatively, conduct research, and develop prototypes using modern creative AI tools

Taught by industry experts with experience in creative AI research and development

Requires students to engage with classmates through discussions and peer feedback, creating opportunities for diverse perspectives and collaboration

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Reviews summary

Exploring ai's creative side through design

According to learners, 'Artificial Creativity' offers a largely positive exploration of AI's intersection with creative disciplines. Students particularly commend its unique design-centric approach, which effectively blends computer science insights with design thinking research methods. The course is noted for its thought-provoking lectures and accessible explanations of complex topics like GPT-2 and GANs. While many found it invaluable for creative professionals seeking conceptual understanding and new integration ideas, some technical learners found it less focused on practical coding or building AI systems. Overall, it provides a solid theoretical foundation and challenges traditional views of creativity, proving inspiring for many.

Explores modern concepts like GPT-2 and GANs.

"The modules on GPT-2 and GANs were fascinating..."

"The GAN experiments were especially eye-opening."

"It was very current for its time, covering exciting topics like GANs."

Complex ideas are explained accessibly by the instructor.

"The instructor did a fantastic job of explaining complex ideas in an accessible way."

"The historical context and exploration of different AI approaches... were really well done."

"The course content is well-structured and highly engaging."

Encourages real-world application through off-screen research.

"The course encourages you to step away from the screen and apply design research, which was a refreshing change."

"I appreciated the focus on design research methods, which is quite unique."

"The design research activities were my favorite part, forcing me to apply concepts in the real world."

"The design research framework is unique and valuable."

Blends AI theory with practical design thinking methods.

"This course was a fascinating dive into the intersection of AI and design. I appreciated the focus on design research methods, which is quite unique."

"As a designer, I found the blend of AI concepts with practical design thinking invaluable."

"For someone with a background in traditional design, this course was an excellent bridge to AI."

"The blend of art, design, and computer science is perfectly balanced."

Strong in theory, less on practical coding or building.

"I wished for a bit more hands-on experimentation with the tools beyond just theory."

"It's very conceptual and theoretical, which is fine, but I was hoping for more practical coding examples or detailed walkthroughs."

"I enrolled hoping for more technical insights into building creative AI... it lacked practical depth for someone with a CS background."

"It's more about understanding the 'why' than the 'how' from a technical standpoint."

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 Artificial Creativity with these activities:

Create a Comprehensive Course Resource Compilation

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Organize and expand on course materials by creating a central repository for notes, assignments, and other resources.

Browse courses on AI

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Gather all relevant materials from the course, including lectures, readings, and assignments.
Review the materials and identify key concepts and ideas.
Create a structured and organized system for storing and accessing the materials.

Refine Python Programming Skills

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Ensure you have a solid understanding of Python programming, which is used extensively in AI and ML applications.

Browse courses on AI

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Review the basics of Python syntax and data structures.
Practice writing simple Python programs.
Complete coding challenges or exercises to refine your skills.

Review Advanced Mathematics Concepts

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Strengthen your foundation in advanced mathematics, which is essential for understanding AI algorithms and techniques.

Browse courses on AI

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Identify the specific mathematical concepts you need to review.
Find resources such as textbooks, online courses, or tutorials to refresh your knowledge.
Practice solving problems and exercises.

Six other activities

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Show all nine activities

Review 'Machine Learning: A Probabilistic Perspective'

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Familiarize yourself with a practical text on AI and Machine Learning, a foundational topic for this course.

View Probabilistic Machine Learning: Advanced Topics... on Amazon

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Read each chapter, taking notes on key concepts and equations.
Solve the exercises at the end of each chapter to test your understanding of the material.

Connect with AI and Creativity Experts

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Seek guidance and mentorship from experienced professionals in the field of AI and creativity.

Browse courses on AI

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Identify potential mentors through online platforms, research, or personal connections.
Reach out to your chosen mentors and express your interest in connecting.
Set up meetings or calls to discuss your career goals and learning needs.

Follow Tutorials on Creative AI Techniques

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Expand your knowledge of creative AI techniques by exploring online tutorials and resources.

Browse courses on AI

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Identify specific creative AI techniques you want to learn.
Find high-quality tutorials or online courses that cover these techniques.
Follow the tutorials, complete the exercises, and apply the techniques in your own projects.

Design Thinking Exercises

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Practice design thinking techniques to enhance your understanding of AI concepts related to creativity.

Browse courses on Design Thinking

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Identify a problem or challenge related to AI and creativity.
Brainstorm multiple solutions to the problem.
Select the most viable solution and develop a prototype.

Attend an AI and Creativity Workshop

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Gain practical insights and connect with experts in the field of AI and creativity through workshop participation.

Browse courses on AI

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Research and identify relevant AI and creativity workshops.
Register and attend the chosen workshop.
Actively participate in discussions and hands-on activities.

Develop an AI-Generated Creative Artifact

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Apply your knowledge of AI and creativity by designing and developing an original artifact using AI-powered tools.

Browse courses on AI

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Define the concept and creative intent for your AI-generated artifact.
Select an appropriate AI model or platform to generate the artifact.
Train and fine-tune the AI model based on your creative vision.
Generate and refine the creative artifact using the AI model.
Present your artifact and reflect on the creative process and insights gained.

Career center

Learners who complete Artificial Creativity will develop knowledge and skills that may be useful to these careers:

Computational Creativity Engineer

Computational Creativity Engineers develop and use computational tools and techniques to create innovative products and solutions. This course is highly relevant, as it looks at the emerging field of computational creativity and provides practical experience with cutting-edge AI tools.

See salaries and explore the career path for Computational Creativity Engineer

Interaction Designer

Interaction Designers design and develop interactive digital products and experiences. This course is highly relevant, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more intuitive and user-friendly interfaces.

See salaries and explore the career path for Interaction Designer

Product Designer

Product Designers create and design physical and digital products. This course is highly relevant, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more innovative and user-centric products.

See salaries and explore the career path for Product Designer

User Experience Designer

User Experience Designers design and develop digital products and experiences. This course is highly relevant, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more intuitive and user-friendly interfaces.

See salaries and explore the career path for User Experience Designer

Machine Learning Engineer

Machine Learning Engineers design, develop, and maintain machine learning models and systems. This course may be useful, as it provides a foundation in the theories and techniques behind Artificial Creativity, which can help Machine Learning Engineers develop and apply more creative approaches to their work.

See salaries and explore the career path for Machine Learning Engineer

Software Engineer

Software Engineers design, develop, and maintain software systems. This course may be useful, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to develop more innovative and efficient software solutions.

See salaries and explore the career path for Software Engineer

Game Designer

Game Designers create and design video games. This course may be useful, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more engaging and immersive gaming experiences.

See salaries and explore the career path for Game Designer

Technical Artist

Technical Artists create and use digital tools and techniques to create visual content for video games and other interactive media. This course may be helpful, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more realistic and immersive visual effects.

See salaries and explore the career path for Technical Artist

Digital Artist

Digital Artists use digital tools and techniques to create visual content. This course may be helpful, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to enhance and augment creativity in the arts.

See salaries and explore the career path for Digital Artist

Web Developer

Web Developers design and develop websites. This course may be useful, as it provides insight into the emerging field of Artificial Creativity and explores how AI can be used to create more dynamic and engaging web experiences.

See salaries and explore the career path for Web Developer

Computer Scientist

Computer Scientists conduct research and investigate problems that can be solved with computing technology. This course may be helpful to those in this role, as it provides insight into the history and theories behind creative AI and explores unorthodox approaches that have advanced the field.

See salaries and explore the career path for Computer Scientist

Researcher

Researchers conduct research and investigate problems in a variety of fields. This course may be useful, as it provides insight into the history and theories behind creative AI, which can help Researchers develop and apply more creative approaches to their work.

See salaries and explore the career path for Researcher

Systems Analyst

Systems Analysts analyze and design computer systems. This course may be useful, as it provides insight into the history and theories behind creative AI, which can help Systems Analysts develop and apply more creative approaches to their work.

See salaries and explore the career path for Systems Analyst

Artificial Intelligence Architect

Artificial Intelligence Architects work on the design of AI capabilities and systems. This course may be useful to those in this role, as it provides insight into advanced theories such as symbolic and subsymbolic approaches used in the field of Artificial Intelligence.

See salaries and explore the career path for Artificial Intelligence Architect

Data Scientist

Data Scientists analyze and interpret data to extract meaningful insights and patterns. This course may be useful, as it provides a foundation in the theories and techniques behind Artificial Creativity, which can help Data Scientists develop and apply more creative approaches to their work.

See salaries and explore the career path for Data Scientist

Reading list

We've selected 14 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 Artificial Creativity.

Deep Learning

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Comprehensive guide to deep learning, a subfield of machine learning that has been responsible for many recent advances in AI. It valuable resource for anyone who wants to learn more about deep learning.

Deep Learning

Paperback

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