Helping data centers deliver higher performance with less hardware
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
The AI-powered tool could inform the design of better sensors and cameras for robots or autonomous vehicles.
The “self-steering” DisCIPL system directs small models to work together on tasks with constraints, like itinerary planning and budgeting.
An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data.
By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.
Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.