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MIT Schwarzman College of Computing

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

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.

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.

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

Scientists at Massachusetts Institute of Technology (MIT) have developed a computational model aimed at simplifying the process of protein engineering. The researchers applied mutations to natural proteins with desirable traits, such as the ability to emit fluorescent light, using random mutation to cultivate better versions of the protein. The technique was deployed using the green…

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When should you rely on an AI model?

MIT researchers have developed a technique for improving the accuracy of uncertainty estimates in machine-learning models. This is especially important in situations where these models are used for critical tasks such as diagnosing diseases from medical imaging or filtering job applications. The new method works more efficiently and is scalable enough to apply to large…

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The cognitive abilities of extensive linguistic models are frequently exaggerated.

Artificial intelligence (AI) and particularly large language models (LLMs) are not as robust at performing tasks in unfamiliar scenarios as they are positioned to be, according to a study by researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). The researchers focused on the performance of models like GPT-4 and Claude when handling “default tasks,”…

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“They have the ability to envision influencing the world they reside in.”

A group of New England Innovation Academy students have developed a mobile app that highlights deforestation trends in Massachusetts as part of a project for the Day of AI, a curriculum developed by the MIT Responsible AI for Social Empowerment and Education (RAISE) initiative. The TreeSavers app aims to educate users about the effects of…

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Researchers from MIT present a generative artificial intelligence for databases.

GenSQL, a new AI tool developed by scientists at MIT, is designed to simplify the complex statistical analysis of tabular data, enabling users to readily understand and interpret their databases. To this end, users don't need to grasp what is happening behind the scenes to develop accurate insights. The system's capabilities include making predictions, identifying anomalies,…

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MIT scholars researching generative AI’s implications and uses received the second round of seed fund allocations.

MIT President Sally Kornbluth and Provost Cynthia Barnhart last year issued a call for papers with the aim of developing effective strategies, policy recommendations, and calls to action in the field of generative artificial intelligence (AI). The response was overwhelming, with a total of 75 proposals submitted. Out of these, 27 were selected for seed…

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MIT researchers studying the implications and uses of generative AI receive a second phase of seed funds.

Last summer, MIT President Sally Kornbluth and Provost Cynthia Barnhart issued a call for papers on generative artificial intelligence (AI). They sought effective roadmaps, policy recommendations, and calls for action in the AI field, and received 75 proposals. Out of these, 27 were selected for seed funding. Due to the robust response to this initial funding…

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MIT researchers studying the effects and uses of generative AI have received a second round of seed funding.

MIT President, Sally Kornbluth, and Provost, Cynthia Barnhart, recently solicited research proposals on the topic of generative artificial intelligence (AI). The response was overwhelming, with 75 proposals submitted from across MIT. Consequently, due to the level of interest and quality of the proposals, a second call for papers was announced, which led to an additional…

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MIT researchers studying the effects and usage of generative AI receive another round of seed funding.

In response to their call for papers last summer, MIT President Sally Kornbluth and Provost Cynthia Barnhart received an overwhelming interest from the research community. The call for proposals was made to "articulate effective roadmaps, policy recommendations, and calls for action across the broad domain of generative AI." The response far exceeded expectations, with 75…

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