Big Data, Big Design: Why Designers Should Care about Artificial Intelligence
- Length: 176 pages
- Edition: 1
- Language: English
- Publisher: Princeton Architectural Press
- Publication Date: 2021-10-19
- ISBN-10: 1616899158
- ISBN-13: 9781616899158
- Sales Rank: #164910 (See Top 100 Books)
Big Data, Big Design provides designers with the tools they need to harness the potential of machine learning and put it to use for good through thoughtful, human-centered, intentional design.
Enter the world of Machine Learning (ML) and Artificial Intelligence (AI) through a design lens in this thoughtful handbook of practical skills, technical knowledge, interviews, essays, and theory, written specifically for designers. Gain an understanding of the design opportunities and design biases that arise when using predictive algorithms. Learn how to place design principles and cultural context at the heart of AI and ML through real-life case studies and examples. This portable, accessible guide will give beginners and more advanced AI and ML users the confidence to make reasoned, thoughtful decisions when implementing ML design solutions.
Cover Title Page Copyright Contents Acknowledgments Preface Chapter One: Peek Inside the Black Box John Zimmerman, PhD, Carnegie Mellon University | Interview Joanna Peña-Bickley, Amazon | Interview Rebecca Fiebrink, PhD, University of the Arts London | Interview Alex Fefegha, Comuzi | Interview “Animistic Design,” Philip van Allen, ArtCenter College of Design | Essay “Machines Have Eyes,” Anastasiia Raina, Lia Coleman, Meredith Binnette, Yimei Hu, Danlei Huang, Zack Davey, Qihang Li, Rhode Island School of Design | Essay Chapter Two: Seize the Data Silka Sietsma, Adobe | Interview Pattie Maes, PhD, Massachusetts Institute of Technology | Interview Patrick Hebron, Adobe | Interview Stephanie Yee, Stitch Fix; Tony Chu, Facebook | Interview “Thinking Design + Conversation,” Paul Pangaro, Carnegie Mellon University | Essay “More than Human-Centered Design,” Anab Jain, Superflux | Essay Chapter Three: Predict the Way Rumman Chowdhury, PhD, Parity | Interview David Carroll, Parsons School of Design | Interview Caroline Sinders | Interview Sarah Gold, IF | Interview “What Is Missing Is Still There,” Mimi Ọnụọha | Essay “Anatomy of an AI System,” Kate Crawford and Vladan Joler, AI Now Institute | Essay Chapter Four: Who’s Afraid of Machine Learning? The Future: Exciting but Fraught | Conclusion Notes Credits Index
Donate to keep this site alive
1. Disable the AdBlock plugin. Otherwise, you may not get any links.
2. Solve the CAPTCHA.
3. Click download link.
4. Lead to download server to download.