How AI Is Cracking A Hard Problem – Giving Computers A Sense Of Smell

Today, smartphones in most people’s pockets provide impressive built-in capabilities based on the sciences of sound and light: voice assistants, facial recognition, and photo enhancement. The science of odor does not offer anything comparable. But that situation is changing, as advances in machine olfaction, also called “digitized smell,” are finally answering Bell’s call to action.

Research on machine olfaction faces a formidable challenge due to the complexity of the human sense of smell. Whereas human vision mainly relies on receptor cells in the retina – rods and three types of cones – smell is experienced through about 400 types of receptor cells in the nose.

Machine olfaction starts with sensors that detect and identify molecules in the air. These sensors serve the same purpose as the receptors in your nose. But to be useful to people, machine olfaction needs to go a step further. The system needs to know what a certain molecule or a set of molecules smells like to a human. For that, machine olfaction needs machine learning.

Machine learning, and particularly a kind of machine learning called deep learning, is at the core of remarkable advances such as voice assistants and facial recognition apps. Machine learning is also key to digitizing smells because it can learn to map the molecular structure of an odor-causing compound to textual odor descriptors. The machine learning model learns the words humans tend to use – for example, “sweet” and “dessert” – to describe what they experience when they encounter specific odor-causing compounds, such as vanillin.

However, machine learning needs large datasets. The web has an unimaginably huge amount of audio, image, and video content that can be used to train artificial intelligence models. This data can be used to teach machines to recognize and interpret smells just as they have learned to recognize faces and voices.