How AI Learns to Smell

In this episode, Alex Wiltschko, founder and CEO of Osmo, joins the show to discuss his goal of giving computers a sense of smell and what it takes to build olfactory intelligence.

We explore the science behind smell, from the hundreds of olfactory receptors in the human nose to the challenge of mapping the relationship between molecular structure and odor, ensuring safety regulations are met, and building foundation models for smell. Alex explains how graph neural networks and advanced embedding spaces allow AI to capture the multi-dimensional structure of scents, grouping them into perceptual neighborhoods, and creating a machine learning representation that predicts how molecules smell.

We also cover how Osmo built the largest proprietary olfactory dataset from scratch to train a fleet of predictive models, and how olfactory intelligence could eventually power applications far beyond fragrance, including disease detection, emotion sensing, and consumer devices.

🗒️ Full show notes: https://twimlai.com/go/771.

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📖 CHAPTERS
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00:00 – Background
01:52 – Digitizing smell
04:41 – Sense of smell: olfactory sensory neurons, olfactory epithelium, and olfactory receptors
07:08 – How powerful is the human nose?
09:09 – Structure-odor relation problem
12:56 – Nodes and edges of graph neural net
14:22 – Principal odor map
16:41 – Examples
19:13 – Collecting olfactory dataset
23:13 – Regulatory review
24:14 – Manufacturing scents
28:37 – Multimodal embedding and scent generation
33:01 – Similarity of olfactory intelligence with autonomous vehicles
38:14 – Relationship between taste and smell
40:06 – Building olfactory foundation models and future in medical detection
48:14 – "Nose on a chip"
49:35 – Future consumer applications
53:34 – Scent unlocking emotions and aromatic traditions

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