What is adversarial machine learning (AML)? AML is the purposeful manipulation of data or code to cause a machine learning (ML) algorithm to misfunction or present false predictions. A popular example of AML is from a team at Google that carried out an experiment on GoogLeNet, a convolutional neural network architecture that won the ImageNet Large Scale Visual Recognition Challenge in 2014. Adding noise to an image of a panda and digitally changing its characteristic led the program to more highly predict that the image was a gibbon. This type of manipulation is relatively easy to execute with just a few bits of code inserted into the original algorithm.
Defining The Line: Why The Department of Defense Should Reconsider United States Space Command’s Area Of Responsibility
In announcing the reestablishment of United States Space Command (USSPACECOM), the geographic combatant command also announced a new area of responsibility (AOR) for its warfighting domain. Typically, publication of a geographic combatant command’s AOR is routine, but, due to unique considerations in space law, USSPACECOM’s AOR is unprecedented. A reexamination of the AOR by USSPACECOM may avoid inadvertent, disadvantageous consequences, including the formation of international law.
Interviews and Podcasts
General Jay Raymond discusses space as a warfighting domain and what the historic Falcon Heavy launch means for the military