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.
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.
Z Advanced Computing, Inc. (ZAC), the pioneer startup on Explainable-AI (Artificial Intelligence) (XAI), is developing its Smart Home product line through a paid-pilot for Smart Appliances for BSH Home Appliances (a subsidiary of the Bosch Group, originally a joint venture between Bosch and Siemens), the largest manufacturer of home appliances in Europe and one of the largest in the world. ZAC just successfully finished its Phase 1 of the pilot program. "Our cognitive-based algorithm is more robust, resilient, consistent, and reproducible, with a higher accuracy, than Convolutional Neural Nets or GANs, which others are using now. It also requires much smaller number of training samples, compared to CNNs, which is a huge advantage," said Dr. Saied Tadayon, CTO of ZAC. "We did the entire work on a regular laptop, for both training and recognition, without any dedicated GPU. So, our computing requirement is much smaller than a typical Neural Net, which requires a dedicated GPU," continued Dr. Bijan Tadayon, CEO of ZAC.
The recognition of objects is one of the main goals for computer vision research. Some of the applications include: the automation on the assembly line, inspection of integrated circuit chips to detect defects in them, security in face and fingerprint recognition, medical diagnosis and detection of abnormal cells that may indicate cancer, remote sensing for automated recognition of possible hostile terrain to generate maps and aids for the visually impaired of mechanical guide dogs. However, 3D object recognition has been one of the challenging processes facing computer vision systems. One Maryland-based startup may finally have the answer to the problem. The startup, Z Advanced Computing, announced today that it has made technical and scientific breakthrough towards Machine Learning and Artificial Intelligence (AI), where the various attributes and details of 3D (three dimensional) objects can be recognized from any view or angle, using its novel General-AI techniques.
Disclaimer: I'll be talking mainly about logistic-regression and basic feed-forward neural networks, so its helpful to have programmed with those 2 models before reading this piece. OK -- before statisticians and ML folks come running after me after reading the title, I'm not talking about linear regression, for example. Yes, in linear regression, you can use the R-squared (or adjusted R-squared statistic) to talk about explained variance, and since linear regression only involves addition between independent variables (or predictors), they're pretty interpretable. But when it comes to more complex prediction models like Logistic Regression and neural networks, everything about the predictors (or called "features" in ML) becomes more confusing. And logistic-regression & neural networks fall under supervised learning, because they basically just estimate a complicated black-box function from data - labels.