The research is a collaboration between computer scientists at MIT-IBM Watson AI Lab and mechanical engineers in MIT’s DeCoDe Lab. The team’s new study appeared recently online, and will be in the December print edition of the journal Computer Aided Design. “To do that, we have to first understand the requirements. “This is about how AI can help engineers be better and faster at creating innovative products,” Ahmed says. But, as the researchers also highlight in their study, with some careful planning of task-appropriate metrics, AI models could be an effective design “co-pilot.”

The team’s results show that similarity-focused AI models don’t quite translate when applied to engineering problems. When the researchers presented the same bicycle frame problem to DGMs that they specifically designed with engineering-focused objectives, rather than only statistical similarity, these models produced more innovative, higher-performing frames. In a case study of bicycle frame design, the team shows that these models end up generating new frames that mimic previous designs but falter on engineering performance and requirements. In their study, Ahmed and Regenwetter reveal the pitfalls of deep generative models when they are tasked with solving engineering design problems. “But in design, being different could be important if you want to innovate.” “The performance of a lot of these models is explicitly tied to how statistically similar a generated sample is to what the model has already seen,” says co-author Faez Ahmed, assistant professor of mechanical engineering at MIT. He and his colleagues make the case that if mechanical engineers want help from AI to generate novel ideas and designs, they will have to first refocus those models beyond “statistical similarity.” But as engineers and designers, we often don’t want to create a design that’s already out there.” “The objective of these models is to mimic a dataset. “Deep generative models (DGMs) are very promising, but also inherently flawed,” says study author Lyle Regenwetter, a mechanical engineering graduate student at MIT. These enormously powerful and versatile tools excel at generating new content that resembles everything they’ve seen before.īut as MIT engineers say in a new study, similarity isn’t enough if you want to truly innovate in engineering tasks. These AI supermodels can churn out poems, finish symphonies, and create new videos and images by automatically learning from millions of examples of previous works. ChatGPT and other deep generative models are proving to be uncanny mimics.

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