Tuesday, October 19, 2021

g-f(2)580 THE BIG PICTURE OF THE DIGITAL AGE (10/19/2021), MIT News, Artificial networks learn to smell like the brain


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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Machine Learning (10/19/2021)  g-f(2)426 


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Artificial networks learn to smell like the brain, MIT News 


  • Using machine learning, a computer model can teach itself to smell in just a few minutes. When it does, researchers have found, it builds a neural network that closely mimics the olfactory circuits that animal brains use to process odors. 
  • Animals from fruit flies to humans all use essentially the same strategy to process olfactory information in the brain. But neuroscientists who trained an artificial neural network to take on a simple odor classification task were surprised to see it replicate biology’s strategy so faithfully. 
  • Guangyu Robert Yang, an associate investigator at MIT’s McGovern Institute for Brain Research, and his collaborators, who reported their findings Oct. 6 in the journal Neuron, say their artificial network will help researchers learn more about the brain’s olfactory circuits. The work also helps demonstrate artificial neural networks’ relevance to neuroscience. 
  • The surprising convergence provides strong support that the brain circuits that interpret olfactory information are optimally organized for their task, he says. Now, researchers can use the model to further explore that structure, exploring how the network evolves under different conditions and manipulating the circuitry in ways that cannot be done experimentally.


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                      Lessons learned, MIT News 


                      • “The algorithm we use has no resemblance to the actual process of evolution,” says Guangyu Robert Yang, an associate investigator at MIT’s McGovern Institute for Brain Research, who led the work as a postdoc at Columbia University. The similarities between the artificial and biological systems suggest that the brain’s olfactory network is optimally suited to its task.
                      • For Yang, a computational neuroscientist, and Columbia University graduate student Peter Yiliu Wang, this knowledge of the fly’s olfactory system represented a unique opportunity. Few parts of the brain have been mapped as comprehensively, and that has made it difficult to evaluate how well certain computational models represent the true architecture of neural circuits, they say.



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                      Building an artificial smell network, MIT News 


                      • Neural networks, in which artificial neurons rewire themselves to perform specific tasks, are computational tools inspired by the brain. They can be trained to pick out patterns within complex datasets, making them valuable for speech and image recognition and other forms of artificial intelligence. There are hints that the neural networks that do this best replicate the activity of the nervous system. But, says Wang, who is now a postdoc at Stanford University, differently structured networks could generate similar results, and neuroscientists still need to know whether artificial neural networks reflect the actual structure of biological circuits. With comprehensive anatomical data about fruit fly olfactory circuits, he says, “We’re able to ask this question: Can artificial neural networks truly be used to study the brain?”
                      • Collaborating closely with Columbia neuroscientists Richard Axel and Larry Abbott, Yang and Wang constructed a network of artificial neurons comprising an input layer, a compression layer, and an expansion layer — just like the fruit fly olfactory system. 
                      • It took the artificial network only minutes to organize itself. The structure that emerged was stunningly similar to that found in the fruit fly brain. Each neuron in the compression layer received inputs from a particular type of input neuron and connected, seemingly randomly, to multiple neurons in the expansion layer. What’s more, each neuron in the expansion layer receives connections, on average, from six compression-layer neurons — exactly as occurs in the fruit fly brain.


                      Opportunity

                      Artificial Intelligence Is Developing A Sense Of Smell: What Could A Digital Nose Mean In Practice?, Forbes 


                      • We already know we can teach machines to see. Sensors enable autonomous cars to take in visual information and make decisions about what to do next when they’re on the road.
                      • Aryballe, a startup that uses artificial intelligence and digital olfaction technology to mimic the human sense of smell, helps their business customers turn odor data into actionable information.
                      • Practical Use Cases for Digital Olfaction
                        • So how does all this digital olfaction data turn into valuable insights for companies?
                        • Odor analytics can help companies do things like:
                        • Engineer the perfect “new car” smells in the automotive industry
                        • Predict when maintenance needs to be done in industrial or automotive equipment
                        • Automatically detect food spoilage in consumer appliances
                        • Reject or approve raw material supply
                        • Reduce R&D time for new foods and beverages
                        • Ensure fragrances of personal care products like deodorants and shampoos last for a long time
                        • Give riders peace of mind on public transportation by emitting an ambient smell
                        • Create personal care devices and health sensors that use odors to detect issues and alert users

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                      • Category 2: The Big Picture of the Digital Age
                      • [genioux fact deduced or extracted from MIT News]
                      • This is a “genioux fact fast solution.”
                      • Tag Opportunities those travelling at high speed on GKPath
                      • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
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                        • Inherited from sources + Supported by the knowledge of one or more experts.


                      References


                      “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)580, Fernando Machuca, October 19, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.com Corporation.


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