Siting wind farms more quickly, cheaply
New model predicts wind speeds more accurately with three months of data than others do with 12.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
Cutting cost and power consumption for big data
New network design exploits cheap, power-efficient flash memory without sacrificing speed.
Computer program fixes old code faster than expert engineers
What takes coders months, CSAIL’s “Helium” can do in an hour.
CSAIL report: Giving government special access to data poses major security risks
Whether “backdoor” or “front-door,” government access imperils your data, report authors say.
Helping students stick with MOOCs
New techniques could help identify students at risk for dropping out of online courses.
Automatic bug repair
System fixes bugs by importing functionality from other programs — without access to source code.
Amplifying small motions in large motions
Video-processing algorithm magnifies motions indiscernible to naked eye, even in moving objects.
Centimeter-long origami robot
Controlled by magnetic fields, tiny robot climbs inclines, swims, and carries loads twice its weight.
Robotics competition generated groundbreaking research
MIT team based robotic control algorithms on cutting-edge theory.
Longstanding problem put to rest
Proof that a 40-year-old algorithm is the best possible will come as a relief to computer scientists.
MIT team places sixth at international DARPA Robotics Challenge
CSAIL team just misses winning the grand prize after programming a 400-lb humanoid robot to lift beams, climb stairs, and drive a car.
Robotic challenger
MIT team prepares to meet 24 others in $2 million DARPA competition to develop disaster-assistance robots.
Helping robots put it all together
New algorithm lets autonomous robots divvy up assembly tasks on the fly.
Gauging materials’ physical properties from video
“Visual microphone” technology could lead to noninvasive identification of objects’ structural defects.
To handle big data, shrink it
Algorithm reduces size of data sets while preserving their mathematical properties.