Now the Machines Are Learning How to Smell
Researchers are training neural networks to predict how a molecule smells
Google has its own perfume—or at least one team of the company’s researchers does. Crafted under the guidance of expert French perfumers, the mixture has notes of vanilla, jasmine, melon and strawberries. “It wasn’t half bad,” said Alex Wiltschko, who keeps a vial of the perfume in his kitchen.
Google’s not marketing that scent anytime soon, but it is sticking its nose into yet another aspect of our lives: smell. On Thursday, researchers at Google Brain released a paper on the preprint site Arxiv showing how they trained a set of machine-learning algorithms to predict molecules’ smell based on their structures. Is this as useful as providing maps for most of the world? Maybe not. But for the field of olfaction, it could help puzzle out some big and long-standing questions.
The science of smell lags behind many other fields. Light, for example, has been understood for centuries. In the 17th century, Isaac Newton used prisms to divide the white light of the sun into our now familiar red, orange, yellow, green, blue, indigo and violet rainbow. Subsequent research revealed that what we perceive as different colors are actually different wavelengths. Glance at a color wheel and you get a simple representation of how those wavelengths compare, the longer reds and yellows transitioning into the shorter blues and purples. But smell has no such guide.
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