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 ...

Artificial intelligence (AI) is thrown into conversations about the future of business tech with increasing frequency. Many enterprises now have programmers beavering away on bespoke algorithms to automate tasks or services, which they hope will give them a competitive advantage. These algorithms are trained on vast data sets and eventually learn how to correctly identify common patterns without human intervention. They take time to design, and they don't come cheap. But that doesn't mean AI is purely for the big beasts of the business world.

Andrew Weinert and his colleagues were deeply frustrated. After Hurricane Maria struck Puerto Rico, the researchers from MIT's Lincoln Laboratory were hard at work trying to help the Federal Emergency Management Agency (FEMA) assess the damage. In hand they had the perfect data set: 80,000 aerial shots of the region taken by the Civil Air Patrol right after the disaster. But there was an issue: there were too many images to sort through manually, and commercial image recognition systems were failing to identify anything meaningful. In one particularly egregious example, ImageNet, the golden standard for image classification, recommended labeling an image of a major flooding zone as a toilet.

AI hype is coming to a point where action, not talk, is needed. Scaremongering stories that AI is a faceless mechanism to cut jobs to improve the bottom line have done little for its reputation. There are exaggerated fears on one hand, and inflated expectations of what it can do on the other, but this is a far cry from what it can actually achieve today. AI is a set of general purpose technologies, ranging from Natural Language Processing (NLP), to image recognition, to the application of machine learning to data and large quantities of unstructured data. Customer experience, along with automotive and health, are key applications, taking AI beyond its hype. The key reason for organisations to consider deploying AI is its inherent ability to transform the customer experience.

They also spent a significant amount of time figuring out the best way to annotate the images. They wanted the annotations to offer emergency responders useful context for their missions, and also needed the annotation scheme to be simple enough for data labelers to perform quickly with minimal errors. Rather than object categories, however, the researchers clustered photos based on increasingly specific disaster characteristics: Is there damage? Should the water be there?

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.

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.

Our sole purpose as data scientists is to create value from data. More specific to machine learning (ML), we use algorithms to learn from data so that we can recognize patterns and use them to build generalizable models that ultimately benefit the top or bottom lines. That benefit--or value--is defined by the need that is driving our work. If working for a biotech company, that need might be to discover a new treatment. In marketing, value might come from a model that attributes revenue-generation to specific marketing programs.

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more challenging because: i) new classes unseen in the training phase can appear when predicting; ii) discriminative features need to evolve when new classes emerge in real time; and iii) instances in new classes may not follow the "independent and identically distributed" (iid) assumption. Most existing work only aims to detect the unknown classes and is incapable of continuing to learn newer classes. Although a few methods consider both detecting and including new classes, all are based on the predefined handcrafted features that cannot evolve and are out-of-date for characterizing emerging classes. Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features. The proposed method injects dynamic elements into the system by detecting instances from unknown classes, while at the same time incrementally updating the model to include the new classes. The resulting cascade tree grows by adding a new leaf node classifier once a new class is detected, and the discriminative features are updated via an end-to-end learning strategy. Experiments on two real-world datasets demonstrate that our proposed method outperforms existing state-of-the-art methods.

The Image Recognition Technology Is, Usually, Associated with an Array of Security and Surveillance-Related Uses and the Rapidly Developing Autonomous Vehicle Niche. Can Image Recognition Apps Help Businesses in Other Verticals? With Reuters' predictions for the not-so-far-off year of 2022 being in the region of a hefty $43-57 billion, Image Recognition is one big lure for AI outfits, and, simultaneously, a lot of hope for businesses and organizations that depend upon it for their survival and success. These include entities as diverse, as manufacturers of autonomous cars and security systems, national nature parks, border security forces, and companies that produce drones. Be it monitoring the state of a much cherished rainforest or sending drones to remote oil rigs to check if all one's assets are in one piece, almost all of the widely known uses of Image Recognition seem to be related to security and surveillance.