Collaborating Authors

US Air Force funds Explainable-AI for UAV tech


Z Advanced Computing, Inc. (ZAC) of Potomac, MD announced on August 27 that it is funded by the US Air Force, to use ZAC's detailed 3D image recognition technology, based on Explainable-AI, for drones (unmanned aerial vehicle or UAV) for aerial image/object recognition. ZAC is the first to demonstrate Explainable-AI, where various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle. "With our superior approach, complex 3D objects can be recognized from any direction, using only a small number of training samples," said Dr. Saied Tadayon, CTO of ZAC. "For complex tasks, such as drone vision, you need ZAC's superior technology to handle detailed 3D image recognition." "You cannot do this with the other techniques, such as Deep Convolutional Neural Networks, even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," continued Dr. Bijan Tadayon, CEO of ZAC.

Attacking Artificial Intelligence: AI's Security Vulnerability and What Policymakers Can Do About It


Artificial intelligence systems can be attacked. The methods underpinning the state-of-the-art artificial intelligence systems are systematically vulnerable to a new type of cybersecurity attack called an "artificial intelligence attack." Using this attack, adversaries can manipulate these systems in order to alter their behavior to serve a malicious end goal. As artificial intelligence systems are further integrated into critical components of society, these artificial intelligence attacks represent an emerging and systematic vulnerability with the potential to have significant effects on the security of the country. These "AI attacks" are fundamentally different from traditional cyberattacks. Unlike traditional cyberattacks that are caused by "bugs" or human mistakes in code, AI attacks are enabled by inherent limitations in the underlying AI algorithms that currently cannot be fixed. Further, AI attacks fundamentally expand the set of entities that can be used to execute ...

Darpa Wants to Build an Image Search Engine out of DNA


Most people use Google's search-by-image feature to either look for copyright infringement, or for shopping. See some shoes you like on a frenemy's Instagram? Search will pull up all the matching images on the web, including from sites that will sell you the same pair. In order to do that, Google's computer vision algorithms had to be trained to extract identifying features like colors, textures, and shapes from a vast catalogue of images. Luis Ceze, a computer scientist at the University of Washington, wants to encode that same process directly in DNA, making the molecules themselves carry out that computer vision work. And he wants to do it using your photos.

Explainable-AI (Artificial Intelligence) Image Recognition Startup Pilots Smart Appliance with Bosch


Z Advanced Computing, Inc. (ZAC), an AI (Artificial Intelligence) software startup, is developing its Smart Home product line through a paid-pilot for smart appliances for BSH Home Appliances, the largest manufacturer of home appliances in Europe and one of the largest in the world. BSH Home Appliances Corporation is a subsidiary of the Bosch Group, originally a joint venture between Robert Bosch GmbH and Siemens AG. ZAC Smart Home product line uses ZAC Explainable-AI Image Recognition. ZAC is the first to apply Explainable-AI in Machine Learning. "You cannot do this with other techniques, such as Deep Convolutional Neural Networks," said Dr. Saied Tadayon, CTO of ZAC.