AUDIOBOOK
Not with a Bug, But With a Sticker
Attacks on Machine Learning Systems and What to Do About Them
Ram Shankar Siva Kumar(0)
About
A robust and engaging account of the single greatest threat faced by AI and ML systems.
In Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What to Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour-from inside secretive government organizations to academic workshops at ski chalets to Google's cafeteria-recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.
Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.
In Not with a Bug, But with a Sticker: Attacks on Machine Learning Systems and What to Do About Them, a team of distinguished adversarial machine learning researchers deliver a riveting account of the most significant risk to currently deployed artificial intelligence systems: cybersecurity threats. The authors take you on a sweeping tour-from inside secretive government organizations to academic workshops at ski chalets to Google's cafeteria-recounting how major AI systems remain vulnerable to the exploits of bad actors of all stripes.
Based on hundreds of interviews of academic researchers, policy makers, business leaders and national security experts, the authors compile the complex science of attacking AI systems with color and flourish and provide a front row seat to those who championed this change. Grounded in real world examples of previous attacks, you will learn how adversaries can upend the reliability of otherwise robust AI systems with straightforward exploits.