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Bridging the gap between design and production for optical equipment.

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 12, 202433Views0Likes0Comments
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.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 12, 202433Views0Likes0Comments
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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The computational model successfully encapsulates the hard-to-catch transition stages of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 12, 202439Views0Likes0Comments
During a chemical reaction, molecules move towards a transition state, a high-energy state that dictates how the reaction will proceed. However, this transition state is difficult to predict and observe due to its fleeting nature. Traditionally, scientists use quantum chemistry methods like density functional theory to evaluate these transition states, though these calculations tend to…

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Computational model successfully identifies the elusive transitional phases of chemical processes.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 12, 202436Views0Likes0Comments
MIT researchers have developed an approach based on machine learning that can calculate transition states of chemical reactions within seconds. The structures of these transition states, a temporary condition in the middle of a chemical reaction, can typically only be calculated using techniques based on quantum chemistry – a process that can be extremely time-consuming.…

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

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 202437Views0Likes0Comments
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.

Artificial Intelligence, Broad Institute, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Health care, Imaging, Machine learning, MIT Schwarzman College of Computing, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedApril 12, 202439Views0Likes0Comments
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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Deep neural networks demonstrate potential as representations of human auditory mechanisms.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 11, 202442Views0Likes0Comments
An MIT study has been making strides towards developing computational models that mimic the human auditory system, which could enhance the design of hearing aids, cochlear implants, and brain-machine interfaces. These computational models stem from advances in machine learning. The study found that internal representations generated by deep neural networks often mirror those within the…

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A computational model successfully depicts 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 11, 202439Views0Likes0Comments
A group of MIT researchers has developed a new machine learning model which rapidly calculates the structure of transition states during chemical reactions. This fleeting moment is a crucial "point of no return" in reactions. Although this transition state is vital to understanding the pathway of the reaction, it has been notoriously difficult to observe…

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Bridging the gap between design and production for 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 11, 202433Views0Likes0Comments
Researchers from Massachusetts Institute of Technology (MIT) and the Chinese University of Hong Kong have developed a digital simulator that mimics the photolithography process, a technique used to manufacture computer chips and optical devices. The project marks the first use of actual data from a photolithography system in the construction of a simulator. This advancement could…

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Deep neural networks exhibit potential in serving as models for human auditory processing.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 11, 202432Views0Likes0Comments
A study by Massachusetts Institute of Technology (MIT) researchers has indicated that computational models that perform auditory tasks could speed up the development of improved hearing aids, cochlear implants, and brain-machine interfaces. In the study, the largest ever conducted into deep neural network-based models trained to perform hearing-related functions, it was found that most mimic…

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