In a paper being presented at the International Conference on Learning Representations in May, MIT researchers describe an NAS algorithm that can directly learn specialized convolutional neural networks (CNNs) for target hardware platforms — when run on a massive image dataset — in only 200 GPU hours, which could enable far broader use of these types of algorithms.
Taking a cue from biological cells, researchers from MIT, Columbia University, and elsewhere have developed computationally simple robots that connect in large groups to move around, transport objects, and complete other tasks.
Access the recording and presentation from the lecture under "Agenda".
Abstract:
Phil and his Salesforce colleagues, Sonke Rohde and Steven Tamm, shared cutting-edge ML research as applied to hyper-personalized commerce and enterprise systems. They peeled under the cover of the successful Salesforce "platform" as a triumph of the monolith as a system engineering pattern. He provided an honest assessment of how a silicon valley company approaches innovation while balancing customer demands and diverse ethical concerns around the world.
MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) held a special workshop with Microsoft Research to explore key challenges in creating trustworthy and robust artificial intelligence (AI) systems. The effort focused on addressing concerns about the trustworthiness of AI systems, including rising concerns with the safety, fairness, and transparency of the technologies.
Neural network assimilates multiple types of health data to help doctors make decisions with incomplete information.
MIT researchers have developed a model that can assimilate multiple types of a patient’s health data to help doctors make decisions with incomplete information.
