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The new system detects medications that are unsafe to combine.

Artificial Intelligence, Computer science and technology, Drug delivery, Machine learning, Mechanical engineering, Medicine, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedMay 30, 202439Views0Likes0Comments
A team of researchers at MIT, Brigham and Women’s Hospital, and Duke University have developed a method to identify the transport proteins that specific drugs use to leave the digestive tract. This is particularly important because drugs that use the same transport protein can interfere with each other's function. Understand the workings of these transporters could…

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Empowering individuals facing challenges with the use of artificial intelligence.

Algorithms, Alumni/ae, Artificial Intelligence, Data, Innovation and Entrepreneurship (I&E), Machine learning, Media Lab, School of Architecture and Planning, Startups, UncategorizedMay 30, 202432Views0Likes0Comments
In 2010, Karthik Dinakar and Birago Jones, students at MIT Media Lab, built a tool for their class project aimed at aiding content moderation teams in companies like Twitter and YouTube. The duo was invited to demonstrate their invention at a cyberbullying summit at the White House. The night before the event, Dinakar found that…

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This small, secure identification label can verify nearly anything.

Artificial Intelligence, Computer chips, Computer science and technology, Electrical Engineering & Computer Science (eecs), Electronics, Internet of things, Machine learning, MIT Schwarzman College of Computing, National Science Foundation (NSF), Research, Research Laboratory of Electronics, School of Engineering, Sensors, Supply chains, UncategorizedMay 30, 202438Views0Likes0Comments
MIT researchers have created a microscopic, low-cost cryptographic ID tag, designed to protect products from counterfeiting by providing improved security compared to traditional radio frequency tags (RFIDs). The technology, developed using terahertz waves, can offer a highly secure, low-cost, and easy-to-implement solution in preventing tampering and ensuring product authenticity. RFID tags typically use radio waves to…

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The recent model recognizes medications that are not advisable to be combined.

Artificial Intelligence, Computer science and technology, Drug delivery, Machine learning, Mechanical engineering, Medicine, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedMay 30, 202427Views0Likes0Comments
Researchers from the Massachusetts Institute of Technology (MIT), Brigham and Women’s Hospital, and Duke University have used tissue models and machine-learning algorithms to identify how specific drugs pass through the digestive tract. The knowledge can help improve patient treatments, as certain drugs could interfere with each other if they depend on the same protein transporters.…

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A new method is able to determine which medications should not be combined.

Artificial Intelligence, Computer science and technology, Drug delivery, Machine learning, Mechanical engineering, Medicine, National Institutes of Health (NIH), Research, School of Engineering, UncategorizedMay 30, 202433Views0Likes0Comments
Researchers from the Massachusetts Institute of Technology (MIT), Brigham and Women’s Hospital, and Duke University have developed an innovative strategy to identify the transporter proteins used by different drugs in the body’s gastrointestinal (GI) tract. The method, which employs tissue models and machine-learning algorithms, aims to improve drug administration by enabling predictions of drug interactions…

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Implementing AI for individuals confronted with issues to resolve.

Algorithms, Alumni/ae, Artificial Intelligence, Data, Innovation and Entrepreneurship (I&E), Machine learning, Media Lab, School of Architecture and Planning, Startups, UncategorizedMay 30, 202439Views0Likes0Comments
In 2010, Karthik Dinakar SM ’12, PhD ’17, and Birago Jones SM ’12, students at the Media Lab, embarked on a project aimed at helping content moderation teams at major companies like Twitter (now X) and YouTube. Their work ignited a great deal of interest and they were invited to present their project at a…

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The Research Group of InternLM has launched InternLM2-Math-Plus which is an array of Math-Centric LLMs available in various sizes like 1.8B, 7B, 20B, and 8x22B. They offer improved thought chaining, code understanding, and reasoning capabilities based on LEAN 4.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Language Model, Large Language Model, Machine learning, Staff, Tech News, Technology, UncategorizedMay 29, 202434Views0Likes0Comments
The InternLM research team is dedicated to improving and developing large language models (LLMs) specifically tailored for mathematical reasoning and problem-solving. They aim to strengthen artificial intelligence's performance ability when dealing with mathematically complex tasks, such as formal proofs and informal problem-solving. Researchers from several esteemed institutions have worked together on producing the InternLM2-Math-Plus model…

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A new multi-scale framework named Hierarchical Graph Masked AutoEncoders (Hi-GMAE) is designed to manage the hierarchical structures inherent in the graph.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 29, 202434Views0Likes0Comments
The traditional methods of supervised learning often encounter difficulties when applied to graph analysis as they require labeled data, which is complex and challenging in the case of academic, social, and biological networks. Graph Self-supervised Pre-training (GSP) techniques, classified broadly as contrastive and generative, address these limitations by harnessing the inherent structures and features of…

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Hierarchical Graph Masked AutoEncoders (Hi-GMAE): An Innovative Multi-Level GMAE Structure Engineered to Manage the Hierarchical Configurations within a Graph.

AI Shorts, Applications, Artificial Intelligence, Editors Pick, Machine learning, Staff, Tech News, Technology, UncategorizedMay 29, 202442Views0Likes0Comments
In graph analysis, collecting labeled data for traditional supervised learning methods can be challenging, particularly for academic, social, and biological networks. As a means to navigate this, Graph Self-supervised Pre-training (GSP) techniques have become more prevalent. These methods capitalize on the inherent structures and characteristics of graph data, mining meaningful representations without needing labeled examples…

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