Keywords like Embedded AI /Embedded ML /Edge AI, mean the same that encapsulate making an AI algorithm or model run seamlessly on embedded devices. However, due to a massive gap between technologies, C-Suites and even the techies don't know where to start. Artificial intelligence (AI) is witnessed as an imperative technology required for the growth and development of the Internet of Things (IoT), robots and autonomous vehicles. Embedding advanced AI in action in everyday life requires massive data sources to go through multiple high-speed computers in remote server farms. Besides, they are now also being proposed as a way of managing the immensely complex 5G protocols.
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Artificial intelligence used to happen almost exclusively in the cloud, but this introduces delays (latency) for the users and higher costs for the provider, so it's now very common to have on-device AI on mobile phones or other systems powered by application processors. But recently there's been a push to bring machine learning capabilities to even lower-end embedded systems powered by microcontrollers, as we've seen with GAP8 RISC-V IoT processor or Arm Cortex-M55 core and the Ethos-U55 micro NPU for Cortex-M microcontrollers, as well as Tensorflow Lite. Edge Impulse is another solution that aims to ease deployment of machine learning applications on Cortex-M embedded devices (aka Embedded ML or TinyML) by collecting real-world sensor data, training ML models on this data in the cloud, and then deploying the model back to the embedded device. The company collaborated with Arduino and announced support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards last May. The solution supports motion sensing, computer vision, and audio recognition to detect glass breaking, hydraulic shocks, manufacturing defects, and so on.
AI.Reverie, company specializing in synthetic data for improved artificial intelligence (AI), announced that it has won a $1.5 million Phase 2 Small Business Innovation Research (SBIR) contract by AFWERX to build AI algorithms and improve navigation capabilities for the U.S. Air Force. According to the company, AI.Reverie will be supporting the 7th Bomb Wing at Dyess Air Force Base through their Rapid Capabilities office by leveraging synthetic data to train and improve the accuracy of vision algorithms for navigation. The use of synthetic data, or computer-generated images, aims to solve the resource barriers associated with real data: the high cost and slow turnaround of hand-labeled photos stalls deployment of vision algorithms needed to save lives. AI.Reverie's Phase 2 SBIR contract closely follows its co-publication with the IQT Lab CosmiQ Works of a paper highlighting the value of synthetic data to train computer vision algorithms. The research partners also released RarePlanes, the largest open dataset of real and synthetic overhead imagery for academic and commercial use.
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.
Smart appliances are nothing new. As with most consumer products, the ubiquity of built-in WiFi and Bluetooth progresses steadily, promising a bold, yet decidedly unspecific, future of "connectedness." LG's new ProActive Care is attempting to finally make good on some of that promise by leveraging artificial intelligence (AI) to zero in on one of the biggest pain-points in appliance ownership: services and repair. Broken or malfunctioning appliances are a fact of life for consumers. No matter how well built, those big metal boxes that get exceptionally hot, wet, or cold on a daily basis are going to break down eventually.
Samsung and LG are going head-to-head with their new smart fridges that scan the inside of the fridge and offer meal suggestions at CES 2020 this week. The smart fridges use updated AI technology that not only recognises food inside and sends smartphone updates, but also makes meal suggestions based on the available ingredients. The updates mean Samsung and LG smart fridge users will get a little help with planning what to cook for the week. The technology is another step towards the fully-automated kitchen of the future, where users don't even have to think about mealtimes because the machines do it all for you. Samsung's Family Hub features new AI capabilities that suggest recipes and works out meal plans Samsung's next-generation refrigerator in its Family Hub range uses AI-enhanced cameras to scan the contents of the fridge and suggests recipes based on what you have in stock.
Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.
Axiomtek has released the AIE500-901-FL, an advanced artificial intelligence (AI) embedded system for edge AI computing and deep learning applications. The device supports two CAN or two COM interfaces. The embedded system employs an Nvidia Jetson TX2 module which has a 64-bit ARM A57 processor; Nvidia Pascal GPU with 256 CUDA cores; and 8 GiB of 128-bit LPDDR4 memory. To withstand the rigors of day-to-day operation, the product has an operating temperature range of -30 C to 60 C and vibration of up to 3 Grms with its construction. According to the company, this fanless AI edge system is dedicated to achieving smart manufacturing and intelligent edge applications.
The "Artificial Intelligence in IoT (AIoT) Convergence: Technologies, Platforms, Applications and AIoT Services in Industry Verticals 2019 - 2024" report has been added to ResearchAndMarkets.com's offering. This research provides a multi-dimensional view into the AI market including analysis of embedded devices and components, embedded software, and AI platforms. This research also assesses the combined Artificial Intelligence (AI) marketplace including embedded IoT and non-IoT devices, embedded components (including AI chipsets), embedded software and AI platforms, and related services. This research evaluates leading solution providers including hardware, software, integrated platforms, and services. It includes quantitative analysis with forecasts covering AI technology and systems by type, use case, application, and industry vertical.