from the world's big
A new AI passes the smell test almost 100% of the time
Our remarkable olfactory senses are modeled in a new research chip.
- Two researchers have created an algorithm that can accurately identify 10 different smells.
- The AI algorithm runs on an Intel chip that has 130,000 silicon "neurons."
- The natural mammalian olfactory bulb grows new neurons even in adults.
Our noses are busy beasts. At any given moment, multiple smells are competing for our attention, and somehow the brain can tell when it's smelling an orange even against a backdrop of other scents, say perfume or soap. The brain's olfactory bulb has hundreds of receptors tracking odors all the time, and yet somehow keeps everything straight. A scientist at Cornell University working with a researcher at Intel has just created an AI algorithm trained to recognize 10 scents by mimicking the mammalian olfactory bulb (MOB). Give the algorithm a computer chip to run on and it can learn to identify new odors.
Simulating the brain
Image source: GiroScience/Shutterstock
Thomas Cleland, senior author of "Rapid Learning and Robust Recall in a Neuromorphic Olfactory Circuit" published in Nature Machine Intelligence says, "This is a result of over a decade of studying olfactory bulb circuitry in rodents and trying to figure out essentially how it works, with an eye towards things we know animals can do that our machines can't."
"We now know enough to make this work," says Cleland speaking with Cornell Chronicle. "We've built this computational model based on this circuitry, guided heavily by things we know about the biological systems' connectivity and dynamics. Then we say, if this were so, this would work. And the interesting part is that it does work."
Cleland's co-author, Intel intern Nabil Imam, has the AI running on an Intel research chip called Loihi. The chip is something of a wonder all by itself. It's designed to perform neuromorphic computing inspired by the brain, sporting some 130,000 silicon "neurons." Like our own neurons, each can fire independently of the others, sending pulsed signals to its neighbors, changing their electrical states. In addition, Loihi can accept inputs from a variety of physical sensors, such as the metal-oxide chemical sensors that Cleland and Imam used, allowing the entire structure to simulate the natural learning process that occurs when the mammalian brain is fed various sensory inputs.
What the AI knows
In the MOB, there are a couple of important neuron types: mitral cells that switch on to signify a smell has been detected but don't identify it, and granule cells that learn to become specialized and pinpoint chemicals in the smell. This is the process Cleland and Imam have programmed Loihi to imitate when odors are presented to the system's sensors.
The researchers have trained the algorithm to identify 10 chemical smells: acetone, acetaldehyde, ammonia, butanol, ethylene, methane, methanol, carbon monoxide, benzene, and toluene. The idea is that in the presence of an odor, Loihi verifies the scent's presence and patterns of firing neurons identify exactly which smell it is.
Over the course of five cycles of exposure to each odor, the AI was eventually able to associate specific neural firing patterns with specific smells. The AI correctly identified eight of the smells 100 percent of the time, and the remaining two with 90 percent accuracy.
Additional simulations were run in which the target odor was masked 80 percent by other smells, the way they'd often be in the real word. In these tests, accuracy dropped down to just 30 percent, though that's still pretty impressive for such a "young" AI. Says Cleland, "The pattern of the signal has been substantially destroyed, and yet the system is able to recover it."
The sweet smell of success
Image source: Patrick J. Lynch, wikimedia
As AI and machine learning do, Cleland's and Imam's algorithm would be expected to get better with more training. Machine smell-detection for use in bomb-sniffing, for example, is clearly not just around the corner. Still, the research offers new insights about learning.
Due to the normally finite number of neurons available for storage of new memories in an adult brain, and the fact that "when you learn something, it permanently differentiates neurons," as Cleland says, storing new information means changing the information already encoded in neurons. However, the mammalian olfactory bulb is able to continually learn smells — hundreds or thousands of them —without losing knowledge of already-learned odors. It's one of the few areas of the brain that can keep creating new neurons through adulthood.
The researchers' experience with Loihi underscores the value of the olfactory bulb's unusual neuronal expandability. "The computational model turns into a biological hypothesis for why adult neurogenesis is important," Cleland says, "because it does this thing that otherwise would make the system not work. So in that sense, the model is feeding back into biology. And in this other sense, it's the basis for a set of devices for artificial olfactory systems that can be constructed commercially."
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Construction of the $500 billion dollar tech city-state of the future is moving ahead.
- The futuristic megacity Neom is being built in Saudi Arabia.
- The city will be fully automated, leading in health, education and quality of life.
- It will feature an artificial moon, cloud seeding, robotic gladiators and flying taxis.
The Red Sea area where Neom will be built:
Saudi Arabia Plans Futuristic City, "Neom" (Full Promotional Video)<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="c646d528d230c1bf66c75422bc4ccf6f"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/N53DzL3_BHA?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
Many of the most popular apps are about self-improvement.
Emotions are the newest hot commodity, and we can't get enough.
Frequent shopping for single items adds to our carbon footprint.
- A new study shows e-commerce sites like Amazon leave larger greenhouse gas footprints than retail stores.
- Ordering online from retail stores has an even smaller footprint than going to the store yourself.
- Greening efforts by major e-commerce sites won't curb wasteful consumer habits. Consolidating online orders can make a difference.
A pile of recycled cardboard sits on the ground at Recology's Recycle Central on January 4, 2018 in San Francisco, California.
Photo by Justin Sullivan/Getty Images<p>A large part of the reason is speed. In a competitive market, pure players use the equation, <em>speed + convenience</em>, to drive adoption. This is especially relevant to the "last mile" GHG footprint: the distance between the distribution center and the consumer.</p><p>Interestingly, the smallest GHG footprint occurs when you order directly from a physical store—even smaller than going there yourself. Pure players, such as Amazon, are the greatest offenders. Variables like geographic location matter; the team looked at shopping in the UK, the US, China, and the Netherlands. </p><p>Sadegh Shahmohammadi, a PhD student at the Netherlands' Radboud University and corresponding author of the paper, <a href="https://www.cnn.com/2020/02/26/tech/greenhouse-gas-emissions-retail/index.html" target="_blank">says</a> the above "pattern holds true in countries where people mostly drive. It really depends on the country and consumer behavior there."</p><p>The researchers write that this year-and-a-half long study pushes back on previous research that claims online shopping to be better in terms of GHG footprints.</p><p style="margin-left: 20px;">"They have, however, compared the GHG emissions per shopping event and did not consider the link between the retail channels and the basket size, which leads to a different conclusion than that of the current study."</p><p>Online retail is where convenience trumps environment: people tend to order one item at a time when shopping on pure player sites, whereas they stock up on multiple items when visiting a store. Consumers will sometimes order a number of separate items over the course of a week rather than making one trip to purchase everything they need. </p><p>While greening efforts by online retailers are important, until a shift in consumer attitude changes, the current carbon footprint will be a hard obstacle to overcome. Amazon is trying to have it both ways—carbon-free and convenience addicted—and the math isn't adding up. If you need to order things, do it online, but try to consolidate your purchases as much as possible.</p><p>--</p><p><em>Stay in touch with Derek on <a href="http://www.twitter.com/derekberes" target="_blank">Twitter</a>, <a href="https://www.facebook.com/DerekBeresdotcom" target="_blank">Facebook</a> and <a href="https://derekberes.substack.com/" target="_blank">Substack</a>. His next book is</em> "<em>Hero's Dose: The Case For Psychedelics in Ritual and Therapy."</em></p>
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