This kind of algorithm could be used in testing air quality or diagnosing medical conditions
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.