Ann-Sophie Barwich, “Nothing to Sneeze at: The Limits of Machine Learning in Olfaction”

Can machine learning crack the code in the nose? Over the past couple of years, several studies tried to solve the relation between chemical structure and sensory quality with Big Data (e.g., Koulakov et al. 2011; Keller et al. 2017). These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools. None of them succeeded, however, and it matters as to why this is the case. This talk argues that we should be deeply skeptical about the trend to black-box the sensory system's biology in our theories of perception. Instead, we need to ground both stimulus models and psychophysical data on real causal-mechanistic explanations of the olfactory system. The central question is: Would knowledge of biology lead to a different understanding of the stimulus in odor coding than the one utilized in current machine learning models? That is indeed the case. Recent studies about receptor behavior have revealed that the olfactory system operates by principles not captured in current stimulus-response models (e.g., Poivet et al. 2017, 2018). This may require a fundamental revision of computational approaches to olfaction, including its psychological effects.

Event is free and open to interested philosophers and scientists! Contact John (jbickle@philrel.msstate.edu) or Antonella (antonella.tramacere2@unibo.it) for login instructions.