Researchers at MIT have developed an artificial intelligence tool to improve the efficiency of robotic warehouses. The researchers, drawing from their work in moderating traffic congestion, created a deep-learning model that can process information about the warehouse, including robot paths, tasks, and obstacles, to optimize warehouse functionality. By grouping the robots into smaller units, the…
Drug efficacy in humans can be heavily influenced by how it interacts with various digestive system transporters. Researchers at the Massachusetts Institute of Technology (MIT), Duke University, and Brigham and Women’s Hospital have developed a method that identifies the interactions between drugs and these transporters. These interactions can potentially result in adverse effects if two…
In crowded robotic warehouses, managing hundreds of robots to navigate efficiently and avoid collisions is a complex challenge. This has prompted a group of MIT researchers to create a deep-learning model to tackle the problem. Using AI, the team has developed an innovative model that encapsulates details about the warehouse environment - the robots, planned…
Researchers from MIT, Brigham and Women’s Hospital, and Duke University have developed a machine-learning, multipronged strategy to identify which transporter proteins a drug uses to navigate through a patient's digestive tract. Knowledge in this area is key to improving drug efficacy and patient safety as drugs using the same transporter proteins can interfere with each…
As automated warehouses become increasingly popular in various industries, ensuring the efficiency and safety of hundreds of robots navigating such spaces is a significant challenge. Notably, even top-performing algorithms struggle to keep pace with the demands of e-commerce or manufacturing. To address this, a group of MIT researchers, who use AI to mitigate traffic congestion,…
Navigating hundreds of robots in a warehouse without causing accidents is a growing challenge for many industries, from e-commerce to automotive production. To address this issue, a team of researchers at the Massachusetts Institute of Technology (MIT) has developed an AI-based approach that increases efficiency and reduces congestion.
The team devised a deep learning model that…
Discovering the transporters used by specific drugs can have profound impacts on patient care, and can also inform drug development. Drugs taken orally must pass through the digestive tract, and this often happens via transporter proteins. But, it's often unknown which transporter a certain drug uses to exit the digestive tract, and this could potentially…
Researchers from MIT, Brigham and Women’s Hospital, and Duke University have pioneered a multifaceted approach to determine the transporters used by various drugs to exit the digestive tract. Leveraging tissue models and machine-learning algorithms, the team has discovered that doxycycline (an antibiotic) and warfarin (a blood thinner) can interfere with each other’s absorption.
All orally consumed…
MIT researchers have developed a technique to train robots on multiple tasks by combining and optimising data from a variety of sources. At the core of their work is a type of generative AI known as a 'diffusion model', which learns from a specific dataset to complete a task. However, the particular innovation here lies…