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National Institutes of Health (NIH)

Human hearing models indicate potential through the use of deep neural networks.

A study from MIT has shown that machine learning can be employed to improve the design of hearing aids, cochlear implants, and brain-machine interfaces. These computational models are designed to simulate the function and structure of the human auditory system. The research is the largest of its kind in studying deep neural networks that have…

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Deep neural networks exhibit potential in mimicking the human auditory system.

Researchers at MIT have discovered that computational models derived from machine learning are increasingly mimicking the function and structure of the human auditory system. This finding has significant implications for the design of more effective hearing aids, cochlear implants, and brain-machine interfaces. In the most extensive study to date of deep neural networks used for…

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Deep neural networks demonstrate potential in replicating the human auditory system.

Modern machine learning models are becoming increasingly adept at simulating the structure and function of the human auditory system, a development that could lead to improvements in devices like hearing aids, cochlear implants, and brain-machine interfaces. A team at MIT conducted what is considered the largest study of deep neural networks trained for auditory tasks…

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Bridging the gap between design and production for optical instruments.

Researchers at Massachusetts Institute of Technology (MIT) and Chinese University of Hong Kong have invented a machine learning-based digital simulator to shrink the gap between design intention and actual manufacturing of computer chips and optical devices. The process of photolithography used in creating such devices often leads to tiny deviations between theoretical design and practical…

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Narrowing the divide between designing and manufacturing of optical devices.

Photolithography, a technique for fabricating computer chips and optical devices, frequently encounters problems due to minute deviations during the manufacturing process. To address this, scientists from MIT and the Chinese University of Hong Kong have successfully used machine learning to build a digital simulator that effectively mimics certain photolithography manufacturing processes. The simulator, which utilizes…

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Deep neural networks demonstrate potential in modeling human auditory perception.

A study conducted by a team from MIT offers promising results in the development of computational models that simulate the function and structure of the human auditory system. These models have potential applications for improving hearing aids, cochlear implants, and brain-machine interfaces. Conducted on an unprecedented scale, the study used deep neural networks trained to…

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Bridging the gap between design and manufacturing for optical devices.

Photolithography, a process used to etch features onto surfaces like computer chips and optical lenses, often results in devices that underperform due to tiny variations during manufacturing. To address this, researchers from MIT and the Chinese University of Hong Kong have employed machine learning to create a digital simulator that replicates a specific photolithography manufacturing…

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Promising signs are exhibited by deep neural networks in their potential of modeling human hearing.

In the largest study of deep neural networks that can perform auditory tasks, MIT found that the models mimic human auditory representations when exposed to the same sounds. Neural networks are models that have multiple layers of information-processing units that can be trained to perform particular tasks using large amounts of data. These models are…

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A novel computational method may simplify the process of engineering beneficial proteins.

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The search algorithm uncovers almost 200 novel types of CRISPR systems.

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