# How AI Learns to Smell with Alex Wiltschko - #771

Page: https://stenobird.com/podcast/twiml-ai-podcast/how-ai-learns-to-smell-with-alex-wiltschko-771
Text version: https://stenobird.com/podcast/twiml-ai-podcast/how-ai-learns-to-smell-with-alex-wiltschko-771.md
Podcast: [The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)](https://stenobird.com/podcast/twiml-ai-podcast)
Published: 2026-07-08T19:36:00+00:00
Episode link: https://twimlai.com/podcast/twimlai/how-ai-learns-smell
Audio file: https://pscrb.fm/rss/p/traffic.megaphone.fm/MLN6387905777.mp3
Processing state: processed
JSON: https://stenobird.com/v1/public/podcasts/twiml-ai-podcast/episodes/how-ai-learns-to-smell-with-alex-wiltschko-771
Duration seconds: 3595

## Resource

Alex Wilchko, CEO of Osmo, explains how to bridge the gap between chemical structures and digital perception. The discussion focuses on building the 'map' required to digitize smell through graph neural networks and multi-dimensional embedding spaces.

## Highlights
- Main idea: Digitizing smell requires a 'map' that translates molecular structures into a digital embedding space, similar to how RGB works for vision
- Technical approach: Graph neural networks are used to process molecular macrostructures and propagate information into a 300-dimensional principle odor map
- Practical takeaway: Building a self-perpetuating data loop involves using chemical sensors to feed proprietary datasets that train predictive models
- Failure mode: The 'structure-odor relation problem' remained unsolved for a century because of the difficulty in mapping complex chemical inputs to human perception
- Future potential: Olfactory intelligence could enable early disease detection, emotion sensing, and the creation of entirely new synthetic fragrances

## Topics

Olfactory Intelligence, Graph Neural Networks, Molecular Modeling, Chemical Sensors, Embedding Spaces, Chemoinformatics, Foundation Models, Digital Scent

## Chapters
- 1:00 — The Chemistry of Intelligence: The importance of training AI on the chemical language used by non-human species like bacteria and fungi.
- 5:00 — The Biological Basis of Smell: Understanding olfactory sensory neurons and the genetic encoding of scent receptors.
- 10:00 — Solving the Structure-Odor Problem: How neural networks can finally bridge the gap between molecular structure and perceived odor.
- 14:00 — Graph Neural Networks in Olfaction: Using GNNs to process molecular graphs and capture multi-dimensional scent characteristics.
- 19:00 — Building Proprietary Datasets: The process of enumerating molecules and creating large-scale olfactory datasets.
- 23:00 — Aligning Sensors with Human Perception: Using chemical sensors to create data that can be mapped to human sensory labels.
- 28:00 — The Self-Perpetuating Data Loop: How fragrance design serves as both a business model and a source of continuous training data.

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## Transcript

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