How AI Will Enable Designers to Do More
Written by Microdesk’s Senior Technology Evangelist Peter Marchese
Originally published in Construction Best Practices, Copyright 2020, Knighthouse Publishing. Reprinted with permission.
While the idea of artificial intelligence (AI) can be a scary one to many people, the main thing to keep in mind is that it is still just a tool. Like many things in our history, we have consistently found ways to do more with less; to become more efficient and better than we were before. The development of AI is a big step towards creating many new applications that could drive several industries forward.
For example, designers in manufacturing and healthcare have taken the lead in applying AI and generative design to benefit their trades. There have been some great examples of cutting-edge use cases from racing teams to companies like Airbus that are using these tools, as well as others like IoT and VR which are also starting to gain more traction in our industry.
Healthcare has been taking advantage as well, both for research as well as in applications. Because the tools can process data and work much faster than humans or other manual processes, they can be very useful to do predictive analysis or run through screenings and samples. With a history of being used in cancer research, AI is now being leveraged in the fight against the current COVID-19 pandemic and bearing fruit. Just recently a researcher from startup BenevolentAI in London found a potential treatment that is now being tested.
Those examples mirror some of the use cases we would see in architecture. Using AI to help review and crunch large amounts of data and opposing project needs does return results faster, helping us find solutions that can direct the design. This is particularly beneficial when we consider the length of most projects or the time available to dive into design options before choosing a direction.
And that’s one of the key advantages we need to understand when it comes to AI. It’s still just a tool to leverage the project. It’s not an end-all-be-all technology that will eliminate jobs or do the work for the designer. At the end of the day, it still takes a trained person to input the requirements and interpret the results.
Looking at it from the standpoint of an architect, we can tell the “AI” we are querying to design a space, give it the code we are working with, share the desires of the end users and clients, program, and any constraints we know of, and let it go. It will compute the input and then come back with many iterations, but the most mathematically perfect solution might also be terrible from an aesthetics or comfort standpoint. Those results will still need to be reviewed and then worked upon by the designer.
The one thing that is certain is the usage of AI or generative design will become common in many industries as it will enable designers to do more, not less. Designers already use tools that cover some aspects of these features. For instance, energy analysis AI gives options or advice on glazing sizes or materials. There are also structural analysis algorithms that recommend sizing and shapes for different loads.
As designers, the tools we have at our disposal are both varied and powerful, yet many of them are still in separate silos. The promise of generative design or more advanced AI tools is they can look at many paths at once, rather than one at a time.
Several years ago, Airbus showed how they used generative design tools to create a new, lighter partition which was just as strong as the traditional one, and could save tons of CO2 per year if implemented. The design has been updated and refined over time using similar tools focused on the fabrication process as well as streamlining the approval and airworthiness qualifications. However, it was determined that a new fabrication process would be needed, and the generative design tool was called upon to help with the design of the partition as well as streamlining the workflow.
Think of this example like the work triangle we as designers consider when laying out a usable kitchen space. But now consider that triangle also includes multiple workers, parts, tools and moving components, and a difficult triangular-shaped site footprint to work with and more.
Notably, one significant aspect of AI that sets it apart from generative design is it can learn what is better. Consider this: your photos being uploaded to some services are automatically tagged as buildings or cats. Someone had to “teach” the computers how to “read” what the photo contained so it could work on its own.
As we progress with these technologies, we can teach them what we as the designers prefer, more light at the expense of HVAC efficiency for example. With these tools being used more, the algorithms within AI will become more and more refined, offering better results and improved building blocks for our design projects.