odour. Bell asked his readers to «measure a smell.»
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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 odour does not offer anything comparable. But that situation is changing, as advances in machine olfaction, also called «digitised 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.
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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.
Applying machine learning to smells
Machine learning, and particularly a kind of machine learning called