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National Institutes of Health (NIH)

Human hearing can potentially be modeled effectively through deep neural networks.

A study from the Massachusetts Institute of Technology (MIT) has advanced the development of computational models based on the structure and function of the human auditory system. Findings from the study suggest these models that are derived from machine learning could be used to improve hearing aids, cochlear implants and brain-machine interfaces. The study, conducted by…

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Deep neural networks exhibit potential as representations of human auditory perception.

A team of researchers from the Massachusetts Institute of Technology (MIT) has been investigating computational models that are designed to mimic the structure and function of the human auditory system. They claim that these models could have future applications in the development of more advanced hearing aids, cochlear implants, and brain-machine interfaces. In a study that…

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Bridging the gap between design and production for optical equipment.

Researchers from MIT and the Chinese University of Hong Kong are using machine learning to close the gap between design and manufacturing processes in photolithography - a method used in the creation of computer chips and optical devices. Photolithography involves using light to etch features onto a surface. However, tiny variations during production often lead…

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Deep neural networks demonstrate potential in simulating human auditory perception.

A study by Massachusetts Institute of Technology (MIT) shows that machine learning-based computational models are making strides towards mimicking the human auditory system, potentially improving the design of devices like hearing aids and cochlear implants. The research indicated that these models’ internal data structures have similarities to those seen in the human brain in response…

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Bridging the gap between design and production in the field of optical devices.

Photolithography, the process of manipulating light to etch features on to a surface, is crucial in making computer chips and optical devices. However, the performance of devices made using this process often falls short of their original designs due to minute deviations during manufacturing. To address this design-to-manufacturing gap, researchers from MIT and the Chinese…

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Bridging the gap between design and production for optical instruments.

Photolithography, a process of etching detailed patterns onto surfaces using light, is a crucial technique in the design and production of computer chips and other optical devices, such as lenses. However, minute deviations during manufacturing can cause a discrepancy between the designer's intentions and the actual produced device. To help bridge this gap between design…

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Deep neural networks demonstrate potential in being models for human auditory systems.

MIT researchers have conducted a study that suggests machine learning models might be used to better design hearing aids, cochlear implants, and brain-machine interfaces. In the largest research project to date involving deep neural networks trained to carry out auditory tasks, the researchers showed that most of these models mimic the human brain’s behaviour when…

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Bridging the gap between design and production for optical equipment.

Researchers at MIT and the Chinese University of Hong Kong have developed a machine learning model to close the gap between design and manufacturing in the field of photolithography. The technique, which involves manipulating light to etch onto surfaces, sees use in the creation of computer chips and optical devices but often falls short of…

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Advanced neural networks display potential in being models for human auditory perception.

Computational models that mirror the structure and functioning of the human auditory system could lead to improvements in hearing aids, cochlear implants, and brain-machine interfaces, researchers at MIT say. The team has conducted the largest study yet of deep neural networks trained to perform auditory tasks, and found that most generate internal representations bearing similarities…

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A novel approach to AI successfully encapsulates ambiguity present in medical imagery.

In the field of biomedicine, segmentation refers to the process of highlighting important structures in a medical image, from organs to cells. Artificial intelligence (AI) models are starting to play a pivotal role in this task, but there are limitations with most existing models, mainly due to the fact that they are unable to factor…

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A novel Artificial Intelligence technique records ambiguity within medical imaging.

A team at MIT, along with the Broad Institute of MIT and Harvard, and Massachusetts General Hospital, has developed an artificial intelligence (AI) tool that can help navigate the uncertainty in medical image analysis. The tool, named Tyche, provides multiple possible interpretations of a medical image rather than the single answer typically provided by AI…

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