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An AI That Mimics How Mammals Smell Recognizes Scents Better Than Other AI

Maria Temming, Science News

Using the mammalian brain as a blueprint, scientists designed an artificial neural network that can keep learning new aromas without forgetting others.

When it comes to identifying scents, a “neuromorphic” artificial intelligence beats other AI by more than a nose.

The new AI learns to recognize smells more efficiently and reliably than other algorithms. And unlike other AI, this system can keep learning new aromas without forgetting others, researchers report online March 16 in Nature Machine Intelligence. The key to the program’s success is its neuromorphic structure, which resembles the neural circuitry in mammalian brains more than other AI designs.

This kind of algorithm, which excels at detecting faint signals amidst background noise and continually learning on the job, could someday be used for air quality monitoring, toxic waste detection or medical diagnoses.

The new AI is an artificial neural network, composed of many computing elements that mimic nerve cells to process scent information (SN: 5/2/19). The AI “sniffs” by taking in electrical voltage readouts from chemical sensors in a wind tunnel that were exposed to plumes of different scents, such as methane or ammonia. When the AI whiffs a new smell, that triggers a cascade of electrical activity among its nerve cells, or neurons, which the system remembers and can recognize in the future.

Like the olfactory system in the mammal brain, some of the AI’s neurons are designed to react to chemical sensor inputs by emitting differently timed pulses. Other neurons learn to recognize patterns in those blips that make up the odor’s electrical signature.

This brain-inspired setup primes the neuromorphic AI for learning new smells more than a traditional artificial neural network, which starts as a uniform web of identical, blank slate neurons. If a neuromorphic neural network is like a sports team whose players have assigned positions and know the rules of the game, an ordinary neural network is initially like a bunch of random newbies.

As a result, the neuromorphic system is a quicker, nimbler study. Just as a sports team may need to watch a play only once to understand the strategy and implement it in new situations, the neuromorphic AI can sniff a single sample of a new odor to recognize the scent in the future, even amidst other unknown smells.

Read the full story on Science News

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