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Deci Launches Version 2.0 of its Deep Learning Development Platform, to Democratize the Power of NAS and Eliminate the AI Efficiency Gap 

Deci, the deep learning company harnessing AI to build AI, today launched Version 2.0 of its deep learning development platform, making it easier than ever before for AI developers to build, optimize, and deploy computer vision models on any hardware and environment including cloud, edge and mobile with outstanding accuracy and runtime performance.

Deci Boosts Computer Vision & NLP Models’ Performance at MLPerf 

Deci, the deep learning company harnessing Artificial Intelligence (AI) to build AI, announced its results for both Computer Vision (CV) and Natural Language Processing (NLP) inference models that were submitted to the MLPerf v2.0 Datacenter Open division. These submissions demonstrated the power of Deci’s Automated Neural Architecture Construction (AutoNAC) technology, which automatically generated models dubbed DeciNets and DeciBERT, thus delivering breakthrough accuracy and throughput performance on Intel’s CPUs.

Research Highlights: Generative Adversarial Networks

In this regular column, we take a look at highlights for important research topics of the day for big data, data science, machine learning, AI and deep learning. It’s important to keep connected with the research arm of the field in order to see where we’re headed. In this edition, we feature a new paper on Generative Adversarial Networks. Enjoy!

The Decade of Synthetic Data is Underway

In this contributed article, Gil Elbaz, CTO and Co-founder of Datagen, sheds light on new developments that will make the ‘20s the decade when synthetic data finally breaks through to become a mainstream part of practical AI applications. Artificial intelligence algorithms are only as good as the data that train them. In the shift to data-centric models of AI, we are seeing a boom in interest in synthetic data over the past few years. Using synthetic data is one way to overcome the drawbacks to using real data for machine learning training sets.

Faces as the Future of AI

In this contributed article, Dr. Sergey I. Nikolenko, Head of AI at Synthesis AI, discusses how in AI, problems related to human faces are coming to the forefront of computer vision. The article considers some of them, discusses the current state of the art, and introduces a common solution that might advance it in the near future.

Understanding “Human Intent and Behavior” with Computer Vision

In this contributed article, editorial consultant Jelani Harper discusses how computer vision is one of the most eminent forms of statistical Artificial Intelligence in use today. Comprised of varying facets of object detection, facial recognition, image classification, and other techniques, it supports a range of pressing use cases from contact-less shopping to video surveillance.

AI Under the Hood: Flippy the Robot

In this installment of “AI Under the Hood” I introduce “Flippy” by Miso Robotics. Flippy works in fast-food kitchens, operating a frying station for example. The product was decades in the making in terms of research in robotics and machine learning. Flippy is an amalgamation of motors, sensor, chips and processing power that wasn’t possible until just the past few years. The robotic arm is poised to become a regular fixture in high-volume kitchens nationwide in the coming year.

Develop Multiplatform Computer Vision Solutions with Intel® Distribution of OpenVINO™ Toolkit

Realize your computer vision deployment needs on Intel® platforms—from smart cameras and video surveillance to robotics, transportation, and much more. The Intel® Distribution of OpenVINO™ Toolkit (includes the Intel® Deep Learning Deployment Toolkit) allows for the development of deep learning inference solutions for multiple platforms.

Developing Perceptive Machines that See and Reason Like Humans

The National Science Foundation has awarded computer scientist Subhransu Maji at the University of Massachusetts Amherst its Faculty Early Career Development (CAREER) award, a five-year, $545,586 grant, to support his work in computer vision and artificial intelligence (AI).

Where Are We with Computer Vision?

In this article, I’d like to share a nice summary of the state of computer vision from Course 4 “Convolutional Neural Networks” from the new Deep Learning Specialization series on Coursera. Dr. Andrew Ng provides some compelling observations about deep learning and computer vision with the goal of mapping out the future of this increasingly popular technology.