A new learning system developed by MIT researchers improves robots’ abilities to mold materials into target shapes and make predictions about interacting with solid objects and liquids. The system, known as a learning-based particle simulator, could give industrial robots a more refined touch — and it may have fun applications in personal robotics, such as modelling clay shapes or rolling sticky rice for sushi.
A team led by researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) has developed a robotic system that can detect if an object is paper, metal, or plastic.
Algorand, an open-source software and blockchain technology company, announced today the opening of its TestNet to the public at large. After a successful private TestNet period with several hundred participants, the company is now inviting all businesses, developers and users to engage with TestNet and provide feedback on the quality, function, and overall experience of the TestNet protocol.
The American Academy of Arts and Sciences (AAAS) announced that MIT professor David Karger was among their new 2019 members. The new class of more than 200 members recognizes the outstanding achievements of individuals in academia, the arts, business, government, and public affairs.
MIT researchers have designed a novel flash-storage system that could cut in half the energy and physical space required for one of the most expensive components of data centers: data storage.
MIT CSAIL researchers have devised a new way to find such patterns using machine learning.
Their system uses a neural network to automatically predict if a specific element will appear frequently in a data stream. If it does, it’s placed in a separate bucket of so-called “heavy hitters” to focus on; if it doesn’t, it’s handled via hashing.
A machine-learning model from MIT researchers computationally breaks down how segments of amino acid chains determine a protein’s function, which could help researchers design and test new proteins for drug development or biological research.
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.
