Lenovo Group Ltd. is capitalizing on two booming markets, Chinese stocks and the global PC industry, to list in Shanghai. The company is the world's largest maker of personal computers and is well-known for acquiring IBM's ThinkPad unit and the Motorola Mobility smartphone business. The news that Lenovo would join the STAR Market, China's answer to the Nasdaq, boosted its Hong Kong-traded shares, which on Wednesday hit their highest level since 2015. A series of Chinese technology companies have recently listed in mainland China or in Hong Kong, amid heightened tensions with the U.S. Beijing has also encouraged companies to join the fledgling STAR Market, also known as the Science and Technology Innovation Board, by introducing more relaxed listing rules and other requirements compared with other Chinese markets. Lenovo and Megvii Technology Ltd., an artificial-intelligence startup specializing in facial recognition, will be among the first companies to make use of a structure known as a Chinese depositary receipt to raise funds.
Last year, we compiled a list of top chips for accelerating ML tasks. We talked about the rising demand of AI-based systems on Chips and the year 2020 is no different -- the trend continued. While few chipmakers capitalised on this trend, chip giants like Intel had to undergo a tough period. They even had to sell their NAND division to South Korean chipmaker SK Hynix. Even Apple announced their separation from Intel processors and opened a new chapter of Apple Silicon.
Manufacturer Nvidia recently welcomed the 1000th health care AI startup company to its Nvidia Inception program, a fast-track support for companies seeking to use machine learning for their ventures. Today the firm officially declared The Nvidia Inception Alliance for Healthcare, which gives members access to resources, particularly for the healthcare business, the GE Healthcare Edison Developer Program. Nvidia Inception is thought of as personalized service for startup companies working with machine learning, providing training and technical assistance with artificial intelligence, in addition to early access to the manufacturer's cutting edge hardware. The Alliance for Healthcare specifically introduces a host of benefits for FDA-approved Premier members, namely access to Nuance AI Marketplace for Diagnostic Imaging, a database of confirmed medical models for coaching algorithms. Nvidia will probably be hosting a particular address open to the public tomorrow at 5 pm CT (11 am Monday GMT) to discuss how the program is working with healthcare professionals in fields ranging from radiology and information science, which has the capacity to automate mundane, repetitive jobs in medical labs and much more rapidly interpret complex information, to the growth of medical devices, including prosthetic limbs that accurately forecast the user's moves.
Today was the release of the second round (version 0.7) of MLPerf Inference benchmark results. Like the latest training results, which were announced in July, the new inference numbers show an increase in the number of companies submitting and an increased number of platforms and workloads supported. The MLPerf inference numbers are segmented out into four categories – Data Center, Edge, Mobile, and Notebook. The number of submissions increased from 43 to 327 and the number of companies submitting increased from just nine to 21. The companies submitting included semiconductor companies, device OEMs, and several test labs.
This is the 2nd part of a 3-part series on building and deploying a custom object detection model to a Raspberry Pi 3. To get caught up,I'd suggest reading part 1 here: Part 2 will be all about training our object detection network using Google Colab . First and foremost, before training, we'll dig into the network architecture we plan to use. EfficientDet is a neural network architecture for object detection. It's one of the TensorFlow object detection APIs from the various model zoos, like CenterNet, MobileNet, ResNet, and Fast R-CNN. EfficientDets are a family of object detection models that achieve state-of-the-art 55.1mAP (mean average precision) on COCO test-dev, while also being 4x -- 9x smaller and using 13x -- 42x fewer FLOPs than previous detectors.
AI processing is changing the world order among CPU, GPU, and FPGA companies, with a host of AI processor startups joining the fray. The fight was once mostly in data centers, but they've all had to decamp to a new battlefield at the network edge. Driven by that premise, Blaize, an AI processor startup in El Dorado Hills, Calif., is heading straight to the edge with its just-announced AI hardware and software. The market forces sending AI inference to the edge are well understood. Privacy concerns, bandwidth issues (going back and forth between edge to cloud), latency and cost worries drive AI processing more and more edgeward.
Over the past few decades, software has been the engine of innovation for countless applications. From PCs to mobile phones, well-defined hardware platforms and instruction set architectures (ISA) have enabled many important advancements across vertical markets. The emergence of abundant-data computing is changing the software-hardware balance in a dramatic way. Diverse AI applications in facial recognition, virtual assistance, autonomous vehicles and more are sharing a common feature: They rely on hardware as the core enabler of innovation. Since 2017, the AI hardware market has grown 60-70% annually, and is projected to reach $65 billion by 2025.
Apple is refreshing its 27-inch iMac, though you'll need a keen eye to spot the differences. The new model doesn't look any different from its predecessors, sporting the same classic look Apple has used for several years now with thick bezels surrounding the 5K display. You won't find any radically new features here either. There's still no biometric authentication, meaning there's no Face ID or Touch ID, and the screen uses the exact same panel and pixel resolution as before. Most of the changes are on the inside, and impact performance.
To improve how sporting events are covered in the news, a new AI 3D pose estimation model was recently developed by a group of researchers from The New York Times R&D group, and wrnch, an AI-computer vision company and member of the NVIDIA Inception program. The 3D pose estimation model can help extract data the human eye can easily miss and help journalists tell a story with more concrete data. "Traditional motion capture techniques require an athlete to wear physical markers. But this isn't possible during live sporting events. Instead, we built a solution that uses our photographers' cameras, machine learning and computer vision to capture this data as an event unfolds," the researchers stated in their article, Estimating 3D Poses of Athletes at Live Sporting Events.
In the vast field of artificial intelligence, computer vision and image recognition is perhaps what most people take interest in. If you are interested in this field, you must have heard of OpenCV. OpenCV is a popular open source project aimed at real-time computer vision. The OpenCV project has announced its hardware project: OpenCV Artificial Intelligence Kit (OAK). It is basically a Raspberry Pi like single board computer specially focused on Computer Vision.