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Short abstract

In 2023, the Principal Odor Map promised to allow computers to smell and to simultaneously provide insight into how human olfaction works. I examine the contingencies this promise is built upon, how they limit and silo olfaction, and attribute to what tradition made this narrowing of smell possible.

Long abstract

Olfaction as sensory perception has both eluded digitization and understanding. Google Brain's Principal Odor Map (POM) attempted to solve both problems at once with graph neural networks, mapping thousands of chemical features toward perceptual labels. Their promise is that the POM would be an intuitive understanding of how we perceive molecules as smell and provide new gateways toward smell innovation.

Of course, the POM is built upon a genealogy of olfactory experts, usually perfumers, using a limited and private dataset that follows traditions in the fragrance industry. First, I will analyze the ground truths of the POM team's datasets, then compare how their models benchmarked and measured success against trained panelists. Second, I will assess how these models' ground truths are rooted in limited understandings of olfaction but endeavor to standardize said faulty model. Third, I will trace how the POM follows a olfactory heritage bound in materially rich and exclusionary corporate epistemologies and how that motivates the innovation that POM promises.

The story of machine olfaction portrays how arbitrary, quick-to-access datasets of limited human experience acculturated with semantic clues become machine perception: that which affects human sensory perception at large. The standardization of this limited perception however stands opposed to both senses and a body that we do not fully understand, but will compromise to fit our systems. But smells tend to have alternative readings across experts and laypeople, or domains and experience, and olfaction may provide insight into resilient sensory modes in the collapsing futures of machine perception.