US Air Force funds Explainable-AI for UAV tech

#artificialintelligence

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.


Ethical AI: 7 Artificial Intelligence Research Organisations to Watch 2017

#artificialintelligence

Christina is audience development editor. After graduating from the University of Nottingham reading philosophy and theology in 2013, Christina joined a tech start-up specialising in mobile apps. She has a keen interest in the mobile platform and innovative tech. In recent years AI has brought us some pretty impressive and widely used tech, from the image recognition being used by Facebook to speech recognition technology at work in Amazon's Alexa or Apple's Siri. It's these breakthroughs in deep learning and neural networks that have led to some of the most exciting yet also worrying times in tech.


Google Brain Co-Founder Teams With Foxconn to Bring AI to Factories

#artificialintelligence

Consumers now experience AI mostly through image recognition to help categorize digital photographs and speech recognition that helps power digital voice assistants such as Apple Inc's Siri or Amazon.com But at a press briefing in San Francisco two days before Ng's Landing.ai In many factories, workers look over parts coming off an assembly line for defects. Ng showed a video in which a worker instead put a circuit board beneath a digital camera connected to a computer and the computer identified a defect in the part. Ng said that while typical computer vision systems might require thousands of sample images to become "trained," Landing.ai's


Darpa Wants to Build an Image Search Engine out of DNA

WIRED

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.


U.S. Air Force invests in Explainable-AI for unmanned aircraft

#artificialintelligence

Software star-up, Z Advanced Computing, Inc. (ZAC), has received funding from the U.S. Air Force to incorporate the company's 3D image recognition technology into unmanned aerial vehicles (UAVs) and drones for aerial image and object recognition. ZAC's in-house image recognition software is based on Explainable-AI (XAI), where computer-generated image results can be understood by human experts. ZAC – based in Potomac, Maryland – is the first to demonstrate XAI, where various attributes and details of 3D 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," says Dr. Saied Tadayon, CTO of ZAC. "You cannot do this with the other techniques, such as deep Convolutional Neural Networks (CNNs), even with an extremely large number of training samples. That's basically hitting the limits of the CNNs," adds Dr. Bijan Tadayon, CEO of ZAC.