Symrise creates new fragrances using deep neural networks

"Fragrance makers have about 3,000 raw ingredients to work with to create their concoctions, and each fragrance formula can have anywhere from seven to 100 ingredients. When compounded, those numbers create a nearly infinite number of potential combinations."

"“A fresh, fruity floral with an innovative twist in the top note.” That’s how master perfumer Dave Apel describes the first perfume created by artificial intelligence, which a Brazilian beauty company plans to start selling in 2019. Now, machines can’t smell, at least not like humans can. But as a result of work that fragrance manufacturer Symrise AG did with IBM Research, you can now add “creating great perfumes” to the list of things that AI-powered robots can do as well as humans. Symrise and IBM collaborated to develop Philyra, an AI-based system that’s similar in some respects to Chef Watson, the IBM program that has turned some heads for its capability to create tasty recipes by utilizing interesting combinations of foods and flavors. The companies took that work as a starting point, and tweaked it to handle fragrances and formulas. Richard Goodwin, an IBM Research scientist, says Philyra was trained to recognize similarities among the formulas for 1.5 million existing fragrances, which includes fine fragrance (such as women’s perfume and men’s cologne), as well as lesser scents used for shampoo, laundry detergent, and candles. These formulas were labeled with information about associated human perceptions, as well as “success factors,” such as sales or a client’s initial approval or disapproval of a novel scent. Then a series of algorithms – including deep neural networks, support vector machines, and a random forest — find correlations hidden in the data. The analysis starts with measuring the “distance” between fragrances, Goodwin says. “We train a model to predict how close… two fragrances are going to be, or given three fragrances, which two are going to be perceived as being more similar,” the IBM researcher tells Datanami. “That allows us to identify white spaces, those areas where there are no other fragrances that are in the catalog that’s close to one we’ve generated.” Philyra was able to learn which chemical substances can function as substitutes for others, which substances were good complements, and which ones weren’t."

"A series of algorithms – including deep neural networks, support vector machines, and a random forest — find correlations hidden in the data."

"Formulas for 1.5 million existing fragrances, which includes fine fragrance (such as women’s perfume and men’s cologne), as well as lesser scents used for shampoo, laundry detergent, and candles."