Hidden Hot Battle Lessons of Cold War: All Learning Models Have Flaws, Some Have Casualties

In a pursuit of realistic expectations for learning models can we better prepare for adversarial environments by examining failures in the field? All models have flaws, given any usual menu of problems with learning; it is the rapidly increasing risk of a catastrophic-level failure that is making data /robustness/ a far more immediate concern. This talk pulls forward surprising and obscured learning errors during the Cold War to give context to modern machine learning successes and how things quickly may fall apart in evolving domains with cyber conflict.

Davi Ottenheimer presently is focused on product security at MongoDB. Originally from a remote corner of the vast Konza Prairie in north-eastern Kansas, Davi studied ethics of military intervention and earned a graduate degree in History from the London School of Economics before spending more than two decades in global security operations, compliance assessments and information security product development, as well as incident response and digital forensics. Some of his prior roles include being the "dedicated paranoid" at Yahoo to protect hundreds of millions of customers on mobile and IoT products; and leading the cloud engineering team at VMware through complex security and compliance regulations; and being known as "flyingpenguin" while building a internationally recognized security consulting practice. Over the last several years his research focused on security industry challenges for a new book called "Realities of Securing Big Data", which is a follow-up to the cloud book he co-authored in 2012 called “Securing the Virtual Environment: How to Defend the Enterprise Against Attack".