What opportunities have you identified in using data and AI in the design process?

To identify the opportunities, we need to clarify what we mean by AI and design. For me, AI (machine learning and data-driven modeling) is a generic framework that provides the possibility to learn reasonable answers to any question/problem we might have, provided that these problems are generic enough and that one can collect enough data around those problems. If these problems are not generic and repeatable, machine learning is not going to work. Classically, experts are those who know the answers to domain-specific questions, and in my opinion, AI is turning this upside down. Fields that are about problem-solving with domain expertise will be automated in large parts.

While AI is good at learning answers, I don’t believe that AI can come up with “good questions” or to decide where to pay more attention (Even though the recent breakthroughs in natural language understanding are precisely based on the so-called “Attention mechanisms”). Therefore, if we define Human Intelligence (HI) as the ability to develop the right questions, and Artificial Intelligence (AI) as the ability to learn the correct answers, HI and AI can play very nicely together, where the human actor is thinking about the question or the context. The machine takes care of those tedious tasks (e.g., searching in a database of millions of images) or sometimes tasks impossible for HI (e.g., learning a nonlinear function out of a large high dimensional data set). Therefore, I think AI will be massively implemented in applications with clear questions that are repeatable, standardized, and easy to automate. On the other hand, those applications which need more creativity (thinking out of the data!) and continually changing the questions and reframing the problem, or those that rely on strong storytelling skills will remain in the realm of human intelligence.

Now the question is, what do we mean by design? For our discussion here, we can imagine a spectrum where, in one extreme is more about creativity, art, and problematization. On the other extreme, it is mainly about scalability, repeatability, and problem-solving. If we think of engineering design and optimization, where we deal with more standardized products, like in the auto industry and other consumer products, there are plenty of opportunities.

Technically speaking, the first opportunities are definitely in surrogate modeling in design optimization, which is about speeding up the physics-based simulations such as Finite Element Analysis (FEA) in structural and mechanical systems or other Computational Fluid Dynamics (CFD) simulations such as water and energy in buildings or cities.

The second type of application is at the intersection of machine learning and generative tools in structural design. Here, we can use machine learning to learn the complex nature of these generative techniques. For example, a method called Force Density Method (FDM) was developed in the 1970s. Given a particular graph and the loading conditions, it solves a linear system of equations and gives us a geometric form under forces’ equilibrium. This is great as it is very fast, but now the question for the human designer is which system FDM should solve? FDM generates a new geometry and a new form every time you change the topology and other structural parameters such as the force densities on the input graph. Unfortunately, it turns out that these systems are chaotic and highly nonlinear and can quickly go beyond the limits of interactive systems. But now, since these generative methods are very fast in generating large datasets for specific design briefs, we have an opportunity to develop data-driven systems for the interactions between the human designer and the machine to explore the design space and navigate in the space geometric possibilities. Here, the machine acts like a personalized assistant that learns the human designer’s preferences, explores the design space, suggests new options, and gets feedback from the designer.

Another type of application of AI in design is in the field of building systems design and smart homes, which depends on the behavior of the people who will live in a specific building and a particular climate or region. At the moment, many design decisions about these building systems and how they should operate are made in the early stages of the design process, with a lot of assumptions about “normal” human behaviors. These assumptions can be questionable and falsified. Here, machine learning concepts such as Reinforcement Learning (RL) and interactive learning frameworks, together with IoT technologies, promise new building operation paradigms.

Another type of AI applications is emerging at a larger scale in cities. The ever-growing pervasive sensing and diverse urban data streams all over the world have introduced new planetary scale “comparative approaches” where cities can learn from each other. An urban designer can identify complex and high dimensional patterns in a specific city while having a detailed view of all the similar cases worldwide. I believe that these types of applications can be used in real estate applications, which are highly scalable compared to one-of-a-kind architectural design projects.

Finally, we can think about those (architectural) design projects, which are not really generic, repeatable, and scalable. In a way, these projects are interesting because they are “outliers”. For example, we can think of a famous building compared to the majority of buildings in a city. These rare design projects rely on “mastership,” and are heavily dependent on human intelligence, and I am not sure how one can use AI to generate a masterpiece. Because what AI is good at is nonlinear interpolation in the experienced world (data), while the next masterpiece does not exist yet. If it exists, it does not have a lot of similar cases in our datasets. Therefore, I am not personally interested in those applications of AI using state of the art in deep learning such as (GANs) to generate art. I think true art means being outside of (or against) a system, while AI is how to behave within the system and learn to comply with the rules.