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The AI technique dramatically accelerates the prediction of thermal characteristics of materials.

Alternative energy, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Electrical Engineering & Computer Science (eecs), Energy, Machine learning, Materials science and engineering, MIT Schwarzman College of Computing, Nuclear science and engineering, Renewable energy, Research, School of Engineering, UncategorizedJuly 17, 202435Views0Likes0Comments
An international team of researchers, including members from MIT (Massachusetts Institute of Technology), has developed a machine learning-based approach to predict the thermal properties of materials. This understanding could help improve energy efficiency in power generation systems and microelectronics. The research focuses on phonons - subatomic particles that carry heat. Properties of these particles affect…

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An improved, quicker method to restrict an AI chatbot from delivering harmful replies.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 17, 202437Views0Likes0Comments

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An improved and quicker method to stop an AI chatbot from providing harmful reactions.

Algorithms, Artificial Intelligence, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), Electrical Engineering & Computer Science (eecs), Human-computer interaction, Machine learning, MIT Schwarzman College of Computing, MIT-IBM Watson AI Lab, Research, School of Engineering, UncategorizedJuly 17, 202434Views0Likes0Comments
Artificial intelligence (AI) advancements have led to the creation of large language models, like those used in AI chatbots. These models learn and generate responses through exposure to substantial data inputs, opening the potential for unsafe or undesirable outputs. One current solution is "red-teaming" where human testers generate potentially toxic prompts to train chatbots to…

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Methods for evaluating the dependability of a multi-functional AI model prior to its implementation.

Algorithms, Artificial Intelligence, Computer science and technology, Data, Human-computer interaction, IDSS, Laboratory for Information and Decision Systems (LIDS), Machine learning, Mechanical engineering, MIT Schwarzman College of Computing, Research, School of Engineering, UncategorizedJuly 16, 202434Views0Likes0Comments
Foundation models, or large-scale deep-learning models, are becoming increasingly prevalent, particularly in powering prominent AI services such as DALL-E, or ChatGPT. These models are trained on huge quantities of general-purpose, unlabeled data, which is then repurposed for various uses, such as image generation or customer service tasks. However, the complex nature of these AI tools…

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A novel computational method could simplify the process of creating beneficial proteins.

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 16, 202440Views0Likes0Comments
MIT researchers have developed a computational model that helps predict mutations leading to better proteins, based on a relatively small dataset. In the current process of creating proteins with useful functions, scientists usually start with a natural protein and put it through numerous rounds of random mutation to generate an optimized version. This process has led…

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

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 15, 202436Views0Likes0Comments
In a search to create more effective proteins for various purposes, including research and medical applications, researchers at MIT have developed a new computational approach aimed at predicting beneficial mutations based on limited data. Modeling this technique, they produced modified versions of green fluorescent protein (GFP), a protein found in certain jellyfish, and explored its…

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

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 15, 202435Views0Likes0Comments
Scientists at the Massachusetts Institute of Technology (MIT) have developed a computational tool that can predict mutations to help create better proteins. The tool facilitates the creation of improved versions of proteins through strategic mutations and could offer significant advancements in neuroscience research and medical applications. One common procedure for producing improved proteins involves introducing…

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A novel computational method could simplify the process of designing beneficial proteins.

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 14, 202433Views0Likes0Comments
Researchers at MIT have developed a computational method to hasten the process of generating optimized versions of proteins, using only a small amount of data. The researchers have generated proteins with mutations capable of improving Green Fluorescent Protein (GFP) and a protein used to deliver DNA for gene therapy from an adeno-associated virus (AAV). The process…

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A novel computational method might simplify the process of designing beneficial proteins.

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 14, 202438Views0Likes0Comments
Protein engineering is a complicated process, typically involving the random mutation of a natural protein with a desirable function, repeated until an optimal version of the protein is developed. This process has proven successful for proteins like the green fluorescent protein (GFP), but this isn't the case for all proteins. Researchers at MIT have developed…

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A novel computational method could simplify the process of designing beneficial proteins.

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 13, 202433Views0Likes0Comments
MIT researchers have developed a computational approach to help predict mutations that can create optimized versions of certain proteins, working with a relatively small amount of data. The team believes the system could lead to potential medical applications and neuroscience research tools. Usually, protein engineering begins with a natural protein that already has a desirable function,…

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A novel computational algorithm could simplify the process of creating beneficial proteins.

Artificial Intelligence, Biological engineering, Brain and cognitive sciences, Computer Science and Artificial Intelligence Laboratory (CSAIL), Computer science and technology, Defense Advanced Research Projects Agency (DARPA), DNA, Electrical Engineering & Computer Science (eecs), McGovern Institute, MIT Schwarzman College of Computing, National Institutes of Health (NIH), National Science Foundation (NSF), Proteins, Research, School of Engineering, School of Science, UncategorizedJuly 13, 202435Views0Likes0Comments
MIT researchers have developed a computational approach that predicts protein mutations, based on limited data, that would enhance their performance. The researchers used their model to create optimized versions of proteins derived from two naturally occurring structures. One of these was the green fluorescent protein (GFP), a molecule used to track cellular processes within the…

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Celebrating a significant event: A dedication ceremony applauds the inauguration of the new Schwarzman College of Computing building at MIT.

Artificial Intelligence, Boston and region, Cambridge, Campus buildings and architecture, Community, Computer science and technology, Electrical Engineering & Computer Science (eecs), Machine learning, MIT Schwarzman College of Computing, President L. Rafael Reif, President Sally Kornbluth, School of Engineering, Special events and guest speakers, Technology and society, UncategorizedJuly 13, 202440Views0Likes0Comments
The MIT Stephen A. Schwarzman College of Computing recently celebrated the completion of its new Vassar Street building. The dedication ceremony was attended by members of the MIT community, distinguished guests, and supporters, reflecting on the transformative gift from Stephen A. Schwarzman that initiated the biggest change to MIT’s institutional structure in over 70 years.…

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