Laguna Hills, Calif. – November 22, 2022 –BrainChip Holdings Ltd(ASX: BRN, OTCQX: BRCHF, ADR: BCHPY), the world's first commercial producer of ultra-low power neuromorphic AI IP, today announced that the Rochester Institute of Technology (RIT) has joined the University AI Accelerator Program to ensure students have the tools and resources needed to encourage development of cutting-edge technologies that will continue to usher in an era of essential AI solutions. Rochester Institute of Technology (RIT) is a highly accredited technology institute with AI engineering programs that conduct research on fundamental and applied topics in artificial intelligence. These include algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. BrainChip's University AI Accelerator Program provides hardware, training and guidance to students at higher education institutions with existing AI engineering programs. Students participating in the program will have access to real-world, event-based technologies offering unparalleled performance and efficiency to advance their learning through graduation and beyond.

This article has been accepted for publication in the IEEE International Conference on Sensing, Communication, and Networking (SECON Demo), 2022. Abstract--Digital Twin technology is playing a pivotal role in the modern industrial evolution. Especially, with the technological progress in the Internet-of-Things (IoT) and the increasing trend in autonomy, multi-sensor equipped robotics can create practical digital twin, which is particularly useful in the industrial applications for operations, maintenance and safety. Herein, we demonstrate a real-world digital twin of a safety-critical robotics applications with a Franka-Emika-Panda robotic arm. We develop and showcase an edge-assisted collaborative digital twin for dynamic obstacle avoidance which can be useful in realtime adaptation of the robots while operating in the uncertain and dynamic environments in industrial IoT.

For years, there has been a buzz around AI and how it would revolutionize industrial IoT, changing our lives forever. Organizations would realize enormous gains in productivity, do much more with less, improve working conditions, and reduce waste. AI would usher in an age of reliable devices that do our bidding without issue. This begs the question, given all the potential gains in the industrial space and the oceans of data being created, where's the AI in the land of industrial IoT? The answer is not as complicated as you might think.

This article considers the use of artificial intelligence to help security professionals protect IoT systems. The Internet of Things (IoT) is still in its infancy, but threats to IoT systems and their potential for harm have become quite sophisticated. There are two reasons for this: the value of data and systems that IoT vulnerabilities can give access to; and the high number of potential attack vectors – discrete elements of IoT networks that are vulnerable to foul play. Artificial intelligence (AI) software and algorithms help security professionals to wrest control of this technological battleground back from hackers and protect the IoT as it reaches maturity. Only introduced in 2008, the Internet of Things and IoT systems are still fairly nebulous concepts, subjects of numerous and sometimes conflicting definitions.

This article considers the use of artificial intelligence to help security professionals protect IoT systems. The Internet of Things (IoT) is still in its infancy, but threats to IoT systems and their potential for harm have become quite sophisticated. There are two reasons for this: the value of data and systems that IoT vulnerabilities can give access to; and the high number of potential attack vectors – discrete elements of IoT networks that are vulnerable to foul play. Artificial intelligence (AI) software and algorithms help security professionals to wrest control of this technological battleground back from hackers and protect the IoT as it reaches maturity. Only introduced in 2008, the Internet of Things and IoT systems are still fairly nebulous concepts, subjects of numerous and sometimes conflicting definitions.

We can turn on the lights in our homes from a desk in an office miles away. The built-in cameras and sensors embedded in our refrigerator let us easily keep tabs on what is present on the shelves and when an item is close to expiration. When we get home, the thermostat has already adjusted the temperature so that it's lukewarm or brisk, depending on our preference. These are not examples from a futuristic science fiction story. These are only a few of the millions of frameworks part of Internet of Things (IoT) being deployed today.

AIoT, the confluence of AI and Industrial IoT technological forces, gives rise to a new digital solution category – the Artificial Intelligence of Things (AIoT). AIoT is built for industrial companies looking for better ways to connect their evolving workforce to data-driven decision tools and digitally augment work and business processes and making better use of industrial data already collected. ARC Advisory Group has observed that the convergence and overlap of IT and OT groups, driven largely by the digital transformation of industry in recent years has created organizational confusion and a significant "gray-space" of common technologies between each area, one area being AI. However, leveraging AI requires data science capability, which adds additional complexity to an already complex environment. While engineering roles are skilled in analyzing large amounts of data, setting up and creating production grade machine learning environments is not easily accomplished.

In an IIoT scenario, an attacker could assume control of a smart actuator and knock an industrial robot out of its designated lane and speed limit – potentially damaging an assembly line or injuring operators. Device hijacking: The attacker hijacks and effectively assumes control of a device. These attacks are quite difficult to detect because the attacker does not change the basic functionality of the device. Moreover, it only takes one device to potentially re-infect others, for example, smart meters connected to a grid. In an IIoT scenario, a hijacker could assume control of a smart meter and use the compromised device to launch ransomware attacks against Energy Management Systems (EMSs) or illegally siphon unmetered power lines.

Most of the research on Federated Learning (FL) has focused on analyzing global optimization, privacy, and communication, with limited attention focusing on analyzing the critical matter of performing efficient local training and inference at the edge devices. One of the main challenges for successful and efficient training and inference on edge devices is the careful selection of parameters to build local Machine Learning (ML) models. To this aim, we propose a Particle Swarm Optimization (PSO)-based technique to optimize the hyperparameter settings for the local ML models in an FL environment. We evaluate the performance of our proposed technique using two case studies. First, we consider smart city services and use an experimental transportation dataset for traffic prediction as a proxy for this setting. Second, we consider Industrial IoT (IIoT) services and use the real-time telemetry dataset to predict the probability that a machine will fail shortly due to component failures. Our experiments indicate that PSO provides an efficient approach for tuning the hyperparameters of deep Long short-term memory (LSTM) models when compared to the grid search method. Our experiments illustrate that the number of clients-server communication rounds to explore the landscape of configurations to find the near-optimal parameters are greatly reduced (roughly by two orders of magnitude needing only 2%--4% of the rounds compared to state of the art non-PSO-based approaches). We also demonstrate that utilizing the proposed PSO-based technique to find the near-optimal configurations for FL and centralized learning models does not adversely affect the accuracy of the models.

The National Safety Council (NSC) reports that the top three leading causes of work-related injuries in the U.S. are: overexertion; slips, trips and falls; and contact with objects and equipment. But what if these incidents could be prevented before they occur? Emerging technologies like computer vision, advanced sensors, augmented reality and Artificial Intelligence (AI) are creating new opportunities to do just that. Working together as a connected IIoT system, these solutions provide the visibility and intelligence to identify hazards, prevent injury and reduce overall risk -- empowering organizations to build a virtual safety net for their employees. Let's examine a few ways the industrial Internet of Things (IoT) is helping to keep workers safe.