![]() ![]() First, the researchers had to make due with “small” data: 7,000 images was not nearly enough to train a machine to reliably and accurately predict how images of designs translate into human ratings. GM had provided them with 7,000 images of 203 vehicles, plus consumer ratings from focus groups where those vehicles had been evaluated, which they could use to train the neural net.īut there were some major obstacles. To create the algorithm, the researchers set up a deep neural network to determine how features of an image translate into ratings. “Product Aesthetic Design: A Machine Learning Augmentation”. ![]() ![]() The idea has two parts, he explains: first, train an algorithm to predict how a human focus group would rate a given design, allowing designers to eliminate the less viable concepts before the theme clinic, “so that you don’t have to kill them further down the process,” and second, use the algorithm to also generate new approaches, to help designers creatively explore the space of possible designs. While working with design teams, Burnap saw an opportunity for AI to improve this process. Yet given all of the preliminary work required to develop and flesh out designs, and with each of the hundreds of theme clinics run each year, across different products and market segments, costing around $100,000, companies inevitably spend huge amounts of cash and manpower developing designs that fall flat with the focus groups and are then scrapped. Input from these clinics can tell the marketers whether consumers view a particular concept as “aggressive,” “modern,” or “luxurious,” for instance, which in turn helps them choose a design that will appeal to their desired market segment. Instead, they rely on “theme clinics,” extensive focus-groups wherein hundreds of highly-targeted consumers are brought in to evaluate vehicle designs on paper. The machine-learning model they developed has two components: one that could empower designers to more nimbly experiment with new design concepts, and one that could help marketers choose where to focus their efforts.Īfter all, given the immense cost of producing a life-size vehicle model, product and market researchers cannot simply A/B test different prototypes to see which resonate best with would-be customers. In a new paper, Burnap, along with John Hauser of MIT and Artem Timoshenko of Northwestern University, explore how AI can help to augment certain time- and cost-intensive parts of the vehicle design process. “Could we do it better, faster, cheaper?” It left him wondering whether machine learning could help streamline and augment that process. ”On average, it takes up to five years and about 3 billion dollars to develop a redesign or new model,” he explains. Working in product research at General Motors earlier in his career, Burnap saw how vehicles evolve from rough sketches on a designer’s notepad to 2D image renderings to life-size clay models complete with functional headlights-and he saw the enormous amount of time and money that companies pour into this design pipeline. Yet designing a new model is both time-consuming and extremely costly, as Alex Burnap, an assistant professor of marketing at Yale SOM, knows firsthand. ![]()
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