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Bridging the gap between designing and manufacturing in the field of optical devices.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 16, 202442Views0Likes0Comments
Photolithography, a process used to create computer chips and optical devices, can often have tiny deviations during production, causing the final product to fall short of the initial design. To address this, researchers from MIT and the Chinese University of Hong Kong have used machine learning to develop a digital simulator that more accurately models…

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Deep neural networks exhibit potential for modelling human auditory processes.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 16, 202440Views0Likes0Comments
A new study from MIT suggests that modern computational models powered by machine learning could potentially aid the design of better hearing aids, cochlear implants, and brain-machine interfaces. These models, specifically deep neural networks, are starting to encompass functions that replicate the structure of the human auditory system.  The study further illuminates how to best train…

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The computational model successfully encapsulates the hard-to-capture transitional stages of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 16, 202438Views0Likes0Comments
In a breakthrough study at MIT, researchers have used machine learning (ML) to calculate the ephemeral transition state in chemical reactions, representing a significant step forward for computational chemistry. The transition state occurs when molecules in a reaction gain energy to the point where the reaction becomes irreversible. Researchers have struggled to observe this pivotal…

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This AI Article by SambaNova introduces a technique for machine learning that refines pretrained LLMs for unfamiliar languages.

AI Paper Summary, AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedApril 16, 202437Views0Likes0Comments
The rapid improvement of large language models and their role in natural language processing has led to challenges in incorporating less commonly spoken languages. Embedding the majority of artificial intelligence (AI) systems in well-known languages inevitably forces a technological divide across linguistic communities that remains mostly unaddressed. This paper introduces the SambaLingo system, a novel…

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A computer science expert explores new limits of geometry.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 16, 202436Views0Likes0Comments
More than 2,000 years after Greek mathematician Euclid laid the groundwork for geometry, Justin Solomon, an associate professor at the MIT Department of Electrical Engineering and Computer Science, is leveraging modern geometric techniques to solve complex problems that seemingly have no connection to shapes. Solomon's work involves using geometrical structures in comparing datasets to predict…

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Bridging the gap between design and production for optical instruments.

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 16, 202440Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong have developed a machine learning technique to bridge the gap between the design and manufacturing processes in photolithography. Photolithography, a technique commonly used in fabricating computer chips and optical devices like lenses, often falls short of the designers' expectations due to minute deviations during manufacturing.…

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

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 16, 202437Views0Likes0Comments
MIT researchers have conducted the largest study to date of deep neural networks trained for auditory tasks. These computational models, which mimic the structure and function of the human auditory system, have the potential to improve hearing aids, cochlear implants, and brain-machine interfaces. The study shows that the majority of the models generate representations which…

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The computational model accurately encapsulates the hard-to-detect transitional phases of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 16, 202439Views0Likes0Comments
Scientists at MIT have devised a machine learning-based method that can rapidly calculate the transitional states of molecules during a chemical reaction. The transient nature of these states has made observation particularly challenging. Understanding these states is key to developing catalysts or deciphering how natural systems induce specific changes. The MIT team constructed their computational approach…

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A computer engineer is expanding the limits of geometric theory.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Computer vision, Electrical Engineering & Computer Science (eecs), Faculty, Machine learning, MIT Schwarzman College of Computing, Profile, School of Engineering, UncategorizedApril 15, 202443Views0Likes0Comments
Justin Solomon, an associate professor at the Massachusetts Institute of Technology (MIT), is applying modern geometric techniques to solve complex problems in data science, computer graphics, and artificial intelligence. He draws upon the principles of geometry— the study of shapes—pioneered over 2,000 years ago by Greek mathematician Euclid. The relevance of geometric principles extends beyond…

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

Artificial Intelligence, Biological engineering, Computer science and technology, Electronics, Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, MIT.nano, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 15, 202436Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong have developed a machine-learning based digital simulator that can more precisely model specific photolithography manufacturing processes used in creating computer chips and optical devices like lenses. The simulator is designed to help close the gap between design and manufacturing, as tiny deviations during the manufacturing…

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Deep neural networks demonstrate potential in being suitable models for studying human auditory perception.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 15, 202441Views0Likes0Comments
A new study by researchers from the Massachusetts Institute of Technology (MIT) has brought us closer to creating computational models that can mimic the human auditory system in the design of better hearing aids, cochlear implants, and brain-machine interfaces. The research, which is the most extensive of its kind, showed that most deep neural network models…

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The computational model successfully captures the hard-to-detect transition states in chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 15, 202442Views0Likes0Comments
During a chemical reaction, molecules gain energy until they reach a point known as the transition state, a pivotal moment where the reaction must proceed. The structures of these states can be determined using quantum chemistry methods, but these calculations are time-intensive. To tackle this issue, a team of MIT researchers developed a machine learning-based…

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