Neural Networks can learn to solve all sorts of problems, from identifying cats in photographs to steering a self-driving car. But whether these powerful, pattern-recognizing algorithms actually understand the tasks they are performing remains an open question.
If you were working as a law clerk in the federal courthouse in Manhattan in the early 1980’s, you might have seen current Microsoft President and Vice Chair Brad Smith edging his way through the doors with a medium-sized, clunky machine, also known as a Personal Computer.
This new generation of GelSight’s mobile device offers a sleek form-factor that is one-third lighter and less than half the volume of its predecessor, allowing it to scan surfaces in tighter spaces, while maintaining accuracy, speed, and field of view.
To get ahead of the uncertainty inherent to crashes, scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Qatar Center for Artificial Intelligence (QCAI) developed a deep learning model that predicts very high-resolution crash risk maps.
