You can read the full 2018 Design A.I. Report English version here. 

Key insights from 2018 Design A.I. Report

If 2017 report was to bring machine intelligence-as technological and social condition- into design discourse (aka. the side A of DesignAI), then 2018 report would be an attempt to bring design mindset and craftsmanship into the discussion of machine intelligence (the side B of DesignAI). As such, in the making of this report, we built a knowledge network of DesignAI, consists of experts and practioners in the field. The viewpoints emerge from the discussions in the network. By doing so, we avoid making DesignAI a subject only about how to make machine replace human designers. Instead, the viewpoints in the report are dialectics between design as a discipline of humanities, cultures, societies and imagination, and the evolution of machine intelligence. 

🤖 Complex System with Diversified Values

  • The optimism about technological singularity and exponential growth over-simplify the complexity of problem solving. The process of problem solving in complex systems is non-linear and irreducible.
  • The understanding of design should be both an “object” and a “system”. It marks the equal weight of big data research and unstructured data research.

💭 Design as Unstructured Data

  • In design practice, computational design has already significantly diverged from classical design. Computational and classical design are dealing with different problem in number of users, time needed to deploy completed product, achievability of “perfection”, designers’ level of confidence, production materials, output, and ROI/KPI. Enterprises (like Alibaba) are redefining the career track of designers.
  • Computational design empowers design to transform from a functional department of which the output is unquantifiable to one that directly accounts for company’s KPI. Business strategies embrace data intelligence more and more to engage with consumer in mass personalized manner, which has exponentially increased the demand of highly personalized design needs.
  • The data generated in the design process is unprocessed resource, despite the longstanding application of design digital tools. 
  • 89% of the design-related data is unstructured data. The mapping and migration between business logic and design logic through computational means has great potential. The Design & AI Lab are making an earnest endeavor towards such mapping and migration.

🧠 Brain Machine Ratio

  • Brain Machine Ratio conceptualizes the synergic relationship between human and machine in design. Human’s acceptance to utilize machine is added to Brain Machine Ratio in 2018 as a new dimension. For instance, though machines exceed human at data processing, they are still uncapable of extracting implications behind data. Designers’ acceptance to use machines in data processing is low because only humans can be illuminated and inspired by the process of data.
  • AI has profound influence on design practice. AI technology unlocks new types of design like smart devices, while reforming old paradigms like graphic design and cities.

❣ Designing Machine EQ

  • AI, data analysis and other related skills are the most needed requirements of companies for design students, both in short term and long term.
  • The values and limits of AI make us rethink the design intelligence. Design education in China, U.S and Europe have added AI-related courses to the curriculum, and have balanced object-oriented design and system-oriented design.
  • Indroducing AI to social design enables us to benefit from the emergent properties of the social system as the collcetive of individual’s behavior. In the meantime, AI restructures the society and adds complexity to the system, which induces new social, moral and ethic problems (like data gap, AI morality, etc.) as new social design subjects.
  • EQ differentiates human from machine. Design’s next frontier is the EQ aspect of machine.

🕵️‍♂️ Dr. Ling Fan is the initiator for Design A.I. Report. He is a professor, entrepreneur, and catalyst to bridge design, technology and business. He is the founder of Tezign.com and founding chair of Tongji University Tezign Design & Artificial Intelligence Laboratory. Ling is a World Economic Forum Global Young Leader. Hong Kong M+/Design Trust awarded him as the inaugural design fellow in 2014. Ling is the Aspen Institute China Fellow and a member of Aspen Global Leadership Network. Ling received his doctoral degree from Harvard University and a master from Princeton University. He held teaching position at UC Berkeley and Central Academy of Fine Arts.

🤖 Core Team:
Ling Fan, Shuyu Gong, Sharon Yan, Yihong Liu, Yifang Bao, Xiang Li

⛩ The Lab:
Tongji University x Tezign Design and Artificial Intelligence Laboratory – sheji.ai

🤝 English Editorial:
Translators: Yifang Bao, Lucy Chen, Shuyu Gong, Yanyan Tong, Sharon Yan
Proofreaders: Mingzhu He, Leila Lin, Esther Ng, Kevin Sun, Ningyi Xi, Lei Xia

🧐 External Support:(In alphabetical order)
Zhiyong Fu (Tsinghua University);Rui Guo (Renmin University of China)
Renke He (Hunan University);Gonglue Jiang (ROKID)
Tat Lam (shanzhai.city); Jiaojiao Li (World Economic Forum)
Mengxi Liu (FUTUREFORM); Shanmu (Alibaba); UX COFFEE
Lingyun Sun (Zhejiang University); Zhuohao Wu (Sinovation Ventures)
Lecheng (Alibaba); Xin Zhang (KPF)

💗 Special Thanks:
Tezign.com,Tongji University College of Design And Innovation
Alibaba UED,World Economic Forum,Shenzhen C Foundation
Yongqi Lou (Tongji University),John Maeda(AUTOMATTIC)
Kaixi Fan (China Academy of Art),Jian Wang (Yunqi 2050 / Alibaba),Ming Leung (CAFA / Tongji)
6000+ responses to the annual questionnaire from designers, entrepreneurs, scholars, students, engineers, lawyers, etc.

(DesignAI.Report is brought you by Tezign.com and Tongji University Tezign Design A.I. Lab. The Chinese version of this post can be found here.)