Machine learning image classifiers use context clues to help understand the contents of a room, for example, if they manage to identify a dining-room table with a high degree of confidence, that can help resolve ambiguity about other objects nearby, identifying them as chairs.
Read the rest “There's a literal elephant in machine learning's room”
Robot law pioneer Ryan Calo (previously) teamed up with U Washington computer science and law-school colleagues to write Is Tricking a Robot Hacking? -- a University of Washington School of Law Research Paper.
Read the rest “Law professors and computer scientists mull whether America's overbroad "hacking" laws ban tricking robots”
A group of Chinese computer scientists from academia and industry have published a paper documenting a tool for fooling facial recognition software by shining hat-brim-mounted infrared LEDs on the user's face, projecting CCTV-visible, human-eye-invisible shapes designed to fool the face recognition software.
Read the rest “Invisible, targeted infrared light can fool facial recognition software into thinking anyone is anyone else”
Machine learning models use statistical analysis of historical data to predict future events: whether you are a good candidate for a loan, whether you will violate parole, or whether the thing in the road ahead is a stop sign or a moose.
Read the rest “Machine learning models keep getting spoofed by adversarial attacks and it's not clear if this can ever be fixed”
Machine-learning-based image classifiers are vulnerable to "adversarial preturbations" where small, seemingly innocuous modifications to images (including very trivial ones) can totally confound them.
Read the rest “Google's AI thinks this turtle is a rifle”
The Open AI researchers were intrigued by a claim that self-driving cars would be intrinsically hard to fool (tricking them into sudden braking maneuvers, say), because "they capture images from multiple scales, angles, perspectives, and the like." Read the rest “Techniques for reliably fooling AI machine-vision classifiers”
Jigsaw is a "wildly ambitious" Google spin-off research unit that recently released Perspective, a machine-learning system designed to identify argumentative, belittling and meanspirited online conversation. Within days of its release, independent researchers have published a paper demonstrating a way of tricking Perspective into trusting ugly messages, just by introducing human-readable misspellings into their prose. Read the rest “Google's troll-fighting AI can be defeated by typos”
Yahoo has released a machine-learning model called open_nsfw that is designed to distinguish not-safe-for-work images from worksafe ones. By tweaking the model and combining it with places-CNN, MIT's scene-recognition model, Gabriel Goh created a bunch of machine-generated scenes that score high for both models -- things that aren't porn, but look porny. Read the rest “Using Machine Learning to synthesize images that look NSFW but aren't”