Intel’s Neuromorphic Nose Learns Scents in Just One Sniff

Loihi chip programmed to mimic neural structure of mammalian scent organ

2 min read
Intel Labs' Nabil Imam holds a Loihi neuromorphic test chip.
Intel Labs' Nabil Imam holds a Loihi neuromorphic test chip. The team is building algorithms on computer chips to mimic what happens in your brain's neural network when you smell something.
Photo: Walden Kirsch/Intel

Researchers at Intel and Cornell University report that they’ve made an electronic nose that can learn the scent of a chemical after just one exposure to it and then identify that scent even when it’s masked by others. The system is built around Intel’s neuromorphic research chip, Loihi and an array of 72 chemical sensors. Loihi was programmed to mimic the workings of neurons in the olfactory bulb, the part of the brain that distinguishes different smells. The system’s inventors say it could one day watch for hazardous substances in the air, sniff out hidden drugs or explosives, or aid in medical diagnoses.

Loihi’s chip architecture is meant to more closely match the way the brain works than the architectures of CPUs or even new accelerator chips designed to speed deep learning. Researchers hope that such neuromorphic chips will be able do things that today’s AI systems can’t do, or at least can’t do without consuming a lot of power or taking too much time.

One of those things is called “one-shot” learning. Your nose can smell something once, and your brain will immediately recognize it again. But today’s AI systems, which often use deep learning artificial neural networks, must be trained using a huge number of previously identified examples. That makes training a time-consuming, power-hungry process. Even worse, most previously trained AI cannot easily learn a new category without damaging its memory of the old ones, meaning it needs to be completely retrained with all the categories.

Unlike the artificial neurons in today’s AI, Loihi’s neurons carry information in the timing of digitally-represented spikes, which is more analogous to what goes on in your brain.  

The scent-learning experiments required only one Loihi chip, but Intel designed them to be seamlessly linked together in much larger systems. The company reported this week that it had produce a multi-board, 768-chip, 100-million neuron system. The largest Loihi system prior to that comprised 64 chips and the equivalent of 8 million neurons

According to Intel senior research scientist Nabil Imam, the next step is “to generalize this approach to a wider range of problems—from sensory scene analysis (understanding the relationships between objects you observe) to abstract problems like planning and decision-making. Understanding how the brain’s neural circuits solve these complex computational problems will provide important clues for designing efficient and robust machine intelligence.”

However, there are challenges to overcome first. In particular, the system needs to be able to group different, closely related aromas, into a common category. For example, it needs to be able to tell that strawberries from California and strawberries from Europe are the same fruit. “These are challenges in olfactory signal recognition that we're working on and that we hope to solve in the next couple of years before this becomes a product that can solve real-world problems beyond the experimental ones we have demonstrated in the lab,” Imam said in a press release.

Imam and Cornell University olfactory expert Thomas A. Cleland reported the new system this week in Nature Machine Intelligence.

This post was updated on 19 March to include mention of the new 100-million neuron Loihi system.

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Will AI Steal Submarines’ Stealth?

Better detection will make the oceans transparent—and perhaps doom mutually assured destruction

11 min read
A photo of a submarine in the water under a partly cloudy sky.

The Virginia-class fast attack submarine USS Virginia cruises through the Mediterranean in 2010. Back then, it could effectively disappear just by diving.

U.S. Navy

Submarines are valued primarily for their ability to hide. The assurance that submarines would likely survive the first missile strike in a nuclear war and thus be able to respond by launching missiles in a second strike is key to the strategy of deterrence known as mutually assured destruction. Any new technology that might render the oceans effectively transparent, making it trivial to spot lurking submarines, could thus undermine the peace of the world. For nearly a century, naval engineers have striven to develop ever-faster, ever-quieter submarines. But they have worked just as hard at advancing a wide array of radar, sonar, and other technologies designed to detect, target, and eliminate enemy submarines.

The balance seemed to turn with the emergence of nuclear-powered submarines in the early 1960s. In a 2015 study for the Center for Strategic and Budgetary Assessment, Bryan Clark, a naval specialist now at the Hudson Institute, noted that the ability of these boats to remain submerged for long periods of time made them “nearly impossible to find with radar and active sonar.” But even these stealthy submarines produce subtle, very-low-frequency noises that can be picked up from far away by networks of acoustic hydrophone arrays mounted to the seafloor.

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