Design is a fundamental driver of society’s advancement. The design process allows us to find solutions to increasingly complex problems, and to create ever more advanced physical systems and mental constructs, which in turn enable us to grow and thrive.
Through history, the design process has itself been supported by an evolving set of tools and methods, from pencil, paper and modelling clay, to well defined qualitative methods for helping to understand needs and opportunities.
The diagram above from Stanford d.school lays out the high level process of design as comprising fives steps:
Over the past eighteen months, I have begun to notice a trend across a number of startup teams, taking the design process and applying new computational techniques and algorithmic developments. Through doing this, they are demonstrating their ability to find better solutions faster than a human designer might be able to unaided.
These companies are working on design problems across many areas. Most work has been done to date on applying these techniques to the rapid design of more attractive and intuitive layouts and interfaces, but we are now seeing them applied to design problems in areas including engineering (a component for a specific purpose), architecture (a layout for a new building) and biology (a molecule to target a specific disease).
So what computational approaches are people using here? Let’s look again at the five steps we outlined above:
Summarising this process in somewhat technical language, one could say that design is the process of exploring the solution space specified by a set of rules, evaluating options using heuristics to narrow this space down to a short list, simulating these potential solutions, and then benchmarking their performance to pick an optimal solution. Perhaps this design process then looks more like the below:
We are already seeing commercial applications that use these new design processes, but a number of interest challenges and areas for improvement remain:
A 2017 survey by Adobe of ‘creatives’ (limited to those involved in the kind of design problems that can be solved using Adobe’s suite of tools) reported that 69% of respondents see themselves using more AI in the next five years, but 55% say AI will not take over their responsibilities.
I would say that is a positive response, with designers already understanding the potential these solutions can have to improve their workflows and outputs, and showing willingness to make increasing use of them. A number of great pieces have been written providing deeper exploration on how AI is impacting this kind of design and some of the new tools available here, including “Algorithm-Driven Design: How Artificial Intelligence Is Changing Design” by Yury Vetrov and “How AI has started to impact our work as designers” by Fabricio Teixeira.
Applying these new tools to the design process makes it possible to discover better solutions faster and at lower cost. Taking this to its extreme, we can repeat this process not just for every problem but for every end application, feeding increasingly specific rules and data into the specify and benchmark stages, and creating outputs tailored for a specific user, environment or situation.
Designing in this way, the solutions we use in our everyday lives can become ever more effective, allowing us to become increasingly efficient, and driving society’s progress at ever greater speed.
If you are reinventing a design process, I would love to speak with you.
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