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 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.

Counting search queries isn’t easy, but MIT CSAIL’s new LearnedSketch system for “frequency-estimation” aims to help.

Researchers combine statistical and symbolic artificial intelligence techniques to speed learning and improve transparency.

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.