This Chinese Facial Recognition Surveillance Company Is Now the World’s Most Valuable AI Startup

The Case Against an Autonomous Military

The potential harm of A.I.s deliberately designed to kill in warfare is much more pressing. The U.S. and other countries are working hard to develop military A.I., in the form of automated weapons, that enhance battlefield capabilities while exposing fewer soldiers to injury or death. For the U.S., this would be a natural extension of the existing imperfect drone warfare program—failures in military intelligence have led to the mistaken killing of non-combatants in Iraq.

More Power, Less Tower: AI May Make Aircraft Control Towers Obsolete

Differentiable Plasticity: A New Method Learning to Learn

Neural networks, which underlie many of Uber’s machine learning systems, have proven highly successful in solving complex problems, including image recognition, language understanding, and game-playing. However, these networks are usually trained to a stopping point through gradient descent, which incrementally adjusts the connections of the network based on its performance over many trials. Once the training is complete, the network is fixed and the connections can no longer change; as a result, barring any later re-training (again requiring many examples), the network in effect stops learning at the moment training ends.