Published in June 2009 at a computer vision conference in Florida, ImageNet's open dataset quickly became the basis of an annual challenge to see which algorithm would have the lowest error rate in identifying images.2 In the inaugural competition, held in 2010, every team had an error rate of at least 25%. However, by combining the techniques of deep learning with the massive set of training data available with ImageNet, researchers sent error rates tumbling. By 2017, the last year of the competition, the error rate was less than 3%.3 ImageNet provided a big boost to AI--the dataset is credited with the resurgence of deep learning.4

Qualcomm today at its Snapdragon Summit 2021 announced a collaboration with Google Cloud to bring the latter's Neural Architecture Search to Qualcomm platforms. The move is designed to speed up the development of AI models at the edge. Qualcomm claims the announcement will make it the first system-on-a-chip (SoC) customer to offer the Google Cloud Vertex AI Neural Architecture Search services. It will first be available on the Snapdragon 8, Gen 1 Mobile Platform, followed by the Snapdragon portfolio across mobile, IoT, automotive, and XR platforms. As AI/ML hardware has become more widespread, attention has turned to the software stack, which often consists of point solutions.

Anaconda Inc. announced today it is leveraging Docker containers and Kubernetes clusters to accelerate the development of AI applications built and deployed using graphical processor units (GPUs) from NVIDIA. Previously, Anaconda added support for Docker and Kubernetes to version 5.0 of Anaconda Enterprise, a commercially supported instance of an open source platform for developing, governing and automating data science and AI pipelines on Intel processors. A version 5.2 of Anaconda Enterprise extends that platform to add support for GPUs. Matthew Lodge, senior vice president of products and marketing at Anaconda, says that training AI applications has been proven to be significantly faster on GPUs. But over time, developers of AI applications will be employing a broad range of algorithms across Intel processors, GPUs, field programmable gate arrays and new classes of processors such as the TPU processors developed by Google, which are designed specifically for AI applications.

The maker of an ultra-efficient light-powered AI chip, Lightmatter recently announced that it will be investing $11 million in the production of the industry's first chip to take advantage of the unique properties of light for enabling fast and efficient inference and training engines. Recently Stan Reiss from Matrix and Santo Politi from Spark have joined the company's board of directors. Lightmatter is one of the leading brands innovating in the chip industry and not many companies are looking forward to any new innovation in the chip industry. No other company, for now, can overstate the innovations of Lightmatter. It is constantly working forward to introduce AI into our lives.

"Taiwan has been the epicenter of the PC revolution, and it will serve as a key center for the next industry revolution focused on AI," said NVIDIA founder and CEO Jensen Huang. "We are delighted to be working closely with MOST to ensure that Taiwan fully harnesses the power of this technological wave." "AI is the key to igniting Taiwan's next industrial revolution, building on the long-established strength of our IT manufacturing capabilities," said Dr. Liang-Gee Chen, Minister of Science and Technology. "Our focus is on drawing academics, industry and young talent into our AI Grand Plan to create an ecosystem based on AI innovation." Under the agreement, the National Center for High-Performance Computing will build Taiwan's first AI-focused supercomputer powered by NVIDIA DGX AI computing platforms and Volta architecture-based GPUs.