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A computer scientist advances the 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 14, 202436Views0Likes0Comments
Over two thousand years ago, Greek mathematician Euclid revolutionized the world with his groundbreaking work in geometry. Today, MIT Associate Professor Justin Solomon is using contemporary geometric techniques to solve intricate problems, which often don't appear to be related to shapes, albeit heavily correlate with data arrangement in a high-dimensional space. Solomon, who is also a…

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Bridging the gap between the design and manufacturing 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 14, 202437Views0Likes0Comments
Photolithography is a manufacturing process that uses light to precisely etch features onto surfaces, such as producing computer chips and optical devices. However, small imprecisions in the process can sometimes result in devices not being produced to specifications. To close this gap, researchers from MIT and the Chinese University of Hong Kong are employing machine…

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Promising signs of modeling human hearing are displayed 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 14, 202435Views0Likes0Comments
A new study from the Massachusetts Institute of Technology (MIT) has found that modern computational models based on machine learning and structured similarly to the human auditory system could assist researchers in developing better hearing aids, cochlear implants, and brain-machine interfaces. The largest study of its kind on deep neural networks trained for auditory tasks…

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The computational model accurately represents 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 14, 202436Views0Likes0Comments
An MIT research team has developed an approach that quickly calculates the structure of transition states fundamental in chemical reactions - the fleeting and typically unobservable point that determines whether a reaction proceeds. This new machine learning-based model could assist in developing new reactions and catalysts for creating materials like fuels or drugs, and might…

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Google AI Debuts Patchscopes: A Machine Learning Method Teaching LLMs to Yield Natural Language Explanations of Their Concealed Interpretations.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedApril 13, 202436Views0Likes0Comments
To overcome the challenges in interpretability and reliability of Large Language Models (LLMs), Google AI has introduced a new technique, Patchscopes. LLMs, based on autoregressive transformer architectures, have shown great advancements but their reasoning process and decision-making are opaque and complex to understand. Current methods of interpretation involve intricate techniques that dig into the models'…

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A computer technologist is advancing the 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 13, 202438Views0Likes0Comments
Justin Solomon, an associate professor in the MIT Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL), employs modern geometric techniques to solve intricate problems often unrelated to shapes. Using these geometric methods, data sets can be compared and the high-dimensional space in which the…

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A computer scientist stretches the 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 13, 202434Views0Likes0Comments
The Greek mathematician Euclid is renowned for laying the groundwork of geometry more than 2,000 years ago. In present times, Justin Solomon, an Associate Professor at MIT's Department of Electrical Engineering and Computer Science, is deriving inspiration from Euclid's fundamental theories and using modern geometric techniques to solve complex problems. Remarkably, these issues frequently bear…

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Bridging the gap between the design and production stages 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 13, 202438Views0Likes0Comments
Researchers from MIT and the Chinese University of Hong Kong have leveraged machine learning to construct a digital simulator to enhance the precision of photolithography and bridge the gap between design and manufacturing. Photolithography, a crucial manufacturing process in computer chip production and optical device fabrication, suffers from slight deviations that can lead to shortcomings…

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Narrowing the distance 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 13, 202437Views0Likes0Comments
Photolithography, a technique used to etch precise features onto surfaces for the creation of computer chips and optical devices, is often inaccurately executed due to tiny deviations during manufacturing. In an attempt to bridge this gap between design and production, a team of researchers from MIT and the Chinese University of Hong Kong have developed…

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

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 13, 202437Views0Likes0Comments
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.

Artificial Intelligence, Brain and cognitive sciences, Hearing, Machine learning, McGovern Institute, National Institutes of Health (NIH), Research, School of Science, UncategorizedApril 13, 202434Views0Likes0Comments
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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The computational model grasps the hard-to-capture transition phases of chemical reactions.

Artificial Intelligence, Chemical engineering, Chemistry, Machine learning, National Science Foundation (NSF), Research, School of Engineering, School of Science, UncategorizedApril 13, 202431Views0Likes0Comments
During a chemical reaction, molecules gain energy until they reach what is known as the transition state — a point at which the reaction must proceed. This state is extremely short-lived and nearly impossible to observe experimentally. Its structures can be calculated using quantum chemistry techniques, but these methods are very time-consuming. Recently, a team of…

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