It's no secret that artificial intelligence (AI) is one of the hottest topics in the tech world today. Every day, it seems like there's a new story about how AI is being used to improve some aspect of our lives, from personal assistants to driverless cars. Given all the hype, it's no wonder that educators are eager to introduce AI concepts to their students. Now, thanks to resources inside Intel's 5-module teaching kit for AI inference teaching the Intel Distribution of OpenVINO toolkit, it is easier than ever to introduce the concepts of deep learning AI to students. Get your students hands-on coding experience with this teacher kit, which comes with a lesson plan, 5-modules of workbooks, videos, quizzes, and Jupyter* Notebook coding lab tutorials.

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In the last post, I introduced Intel Distribution of Modin and Intel Extension for Scikit-learn, integral parts of the Intel oneAPI AI Analytics Toolkit, and the overall Intel AI Software suite. Let's take a closer look at Modin and Scikit-learn extensions through this tutorial. The objective of this guide is to highlight how Modin and Scikit-learn extensions are a drop-in replacement for stock Pandas and Scikit-learn libraries. You can try this tutorial either in Intel DevCloud or your workstation. For this tutorial, I provisioned an e2-standard-4 VM on Google Compute Engine with 4 vCPUs and 16GB RAM based on the Intel Broadwell platform.

Ahead of MWC 2022, Intel has released a new version of the Intel Distribution of the OpenVINO toolkit which induces major upgrades to accelerate AI inferencing performance. Since the launch of OpenVINO in 2018, the chip giant has enabled hundreds of thousands of developers to accelerate the performance of AI inferencing beginning at the edge and extending to both enterprise and clients. This latest release includes new features built upon three-and-a-half years of developer feedback and also includes a greater selection of deep learning models, more device portability choices and higher inferencing performance with fewer code changes. VP of OpenVINO developer tools in Intel's Network and Edge Group, Adam Burns provided further insight on this latest version of the company's distribution of the OpenVINO toolkit in a press release, saying: "The latest release of OpenVINO 2022.1 builds on more than three years of learnings from hundreds of thousands of developers to simplify and automate optimizations. The latest upgrade adds hardware auto-discovery and automatic optimization, so software developers can achieve optimal performance on every platform. This software plus Intel silicon enables a significant AI ROI advantage and is deployed easily into the Intel-based solutions in your network."

From the depths of the oceans to the blackest outposts of space, robots go where we can't. They do the work that's too dangerous or impossible for people, including maintaining infrastructure in hard-to-reach places. In factories, robots help to increase quality and safety on the assembly line. Robots, especially industrial robotic arms, are great candidates for deep reinforcement learning. Deep reinforcement learning (DRL) uses experimentation to train a deep learning solution.

This will enable users of MVTec software products to benefit from AI accelerator hardware that is compatible with the OpenVINO toolkit from Intel. As a result, significantly faster deep learning inference times can be achieved on Intel processors including CPUs, GPUs and VPUs for key workloads. By expanding the range of supported hardware, users can now harness the performance of a wide range of Intel devices to accelerate their deep learning applications and are no longer limited to a few specific devices. At the same time, the integration works seamlessly and is not bound to certain hardware specifics. Simply by changing parameters, the inference of an existing deep learning application can now be executed on devices supported by the OpenVINO toolkit.

"Fantastic! How fast can we scale?" Perhaps you've been fortunate enough to hear or ask that question about a new AI project in your organization. Or maybe an initial AI initiative has already reached production, but others are needed -- quickly. At this key early stage of AI growth, entesrprises and the industry face a bigger, related question: How do we scale our organizational ability to develop and deploy AI? Business and technology leaders must ask: What's needed to advance AI (and by extension, data science) beyond the "craft" stage, to large-scale production that is fast, reliable, and economical? The answers are crucial to realizing ROI, delivering on the vision of "AI everywhere", and helping the technology mature and propagate over the next five years.

It should be no surprise that Python continues to grow in popularity. Data Scientists, Machine Learning (ML) developers, and all manner of data junkies love the ease of creating Python code – but many are put off by the slow execution that's inherent with most interpreted languages like Python. It was with one goal – accelerating Python execution performance – that lead to the creation of Intel Distribution for Python, a set of tools that let anyone speed Python application performance right out of the box, usually with no code changes required. Intel Distribution for Python* speeds NumPy, SciPy, and scikit-learn by integrating the Intel Math Kernel Library (Intel MKL) and Intel Data Analytics Acceleration Library (Intel DAAL), both written in C and assembler, to speed up Python math functions. Also, Intel Distribution for Python* incorporates the latest advances in vectorization and threading, Numba, and Cython to deliver composable parallelism with Threaded Building Blocks (TBB).

What's New: Samsung Medison and Intel are collaborating on new smart workflow solutions to improve obstetric measurements that contribute to maternal and fetal safety and can help save lives. Using an Intel Core i3 processor, the Intel Distribution of OpenVINO toolkit and OpenCV library, Samsung Medison's BiometryAssist automates and simplifies fetal measurements, while LaborAssist automatically estimates the fetal angle of progression (AoP) during labor for a complete understanding of a patient's birthing progress, without the need for invasive digital vaginal exams. "Samsung Medison's BiometryAssist is a semi-automated fetal biometry measurement system that automatically locates the region of interest and places a caliper for fetal biometry, demonstrating a success rate of 97% to 99% for each parameter1. Such high efficacy enables its use in the current clinical practice with high precision." Why It's Needed: According to the World Health Organization, about 295,000 women died during and following pregnancy and childbirth in 2017, even as maternal mortality rates decreased.

According to the World Health Organization, approximately 295,000 women died during and following pregnancy and childbirth in 2017, even as maternal mortality rates have been decreasing. While every pregnancy and birth is unique, most maternal deaths are preventable. Research from the Perinatal Institute found that tracking fetal growth is essential for good prenatal care and can help prevent stillbirths when physicians are able to recognize growth restrictions. Samsung Medison and Intel are collaborating on new smart workflow solutions to improve obstetric measurements that contribute to maternal and fetal safety and can help save lives. Using an Intel Core i3 processor, the Intel Distribution of OpenVINO toolkit and OpenCV toolkit, Samsung Medison's BiometryAssist automates and simplifies fetal measurements, while LaborAssist automatically estimates the fetal angle of progression (AoP) during labor for a complete understanding of a patient's birthing progress, without the need for invasive digital vaginal exams.