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.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. It's free, we don't spam, and we never share your email address.

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.

While Part 1 of this 2-part blog series covered Deep Neural Networks and the different accelerators for implementing Deep Neural Network Models, Part 2 will talk about different Deep Learning Frameworks and hardware frameworks provided by FPGA Vendors. Deep learning framework can be considered as a tool or library that helps us to build DNN models quickly and easily without any in-depth knowledge of the underlying algorithms. It provides a condensed way for defining the models using pre-built and optimized components. Some of the important deep learning frameworks are Caffe, TensorFlow, Pytorch, Keras, etc. Caffe is a deep neural network framework designed to improve speed and modularity. It is developed by Berkeley AI Research.

AI is changing every market and enabling new and enhanced use cases across various industries like health and life sciences, retail, industrial, and more. The Seeker is calling on developers around the world to create intelligently, computer vision-based solutions using the Intel Distribution of OpenVINO toolkit. The Intel Distribution of OpenVINO toolkit is a comprehensive tool suite that helps developers harness the full potential of AI and computer vision across multiple Intel architectures. The toolkit enables developers to build innovative applications that can scale to meet real-world challenges. Submissions to this Challenge must be received by 11:59PM PT, October 13, 2020.

This Python*-based command line tool imports trained models from popular deep learning frameworks such as Caffe*, TensorFlow*, and Apache MXNet*, and Open Neural Network Exchange (ONNX*). Standard frameworks are not required when generating IR files for models consisting of standard layers. When processing custom layers in original models, the Model Optimizer provides a flexible mechanism of extensions.

In more ways than one, software has become the last mile between the developers and the underlying hardware infrastructure, enabling them to utilise the optimization capabilities of processors. Analytics India Magazine spoke to Akanksha Bilani, Country Lead -- India, Singapore, ANZ at Intel Software to understand why, in today's world, transformation of software is key to driving effective business, usage models and market opportunity. "Gone are the days where adding more racks to existing platforms helped drive productivity. Moore's law and AI advocates that the way to take advantage of hardware is by driving innovation on software that runs on top of it. Studies show that modernization, parallelisation and optimization of software on the hardware helps in doubling the performance of our hardware," she emphasizes.

The public cloud offers unmatched power to train sophisticated deep learning models. Developers can choose from a diverse set of environments based on CPU, GPU and FPGA hardware. Cloud providers exposing high-performance compute environments through virtual machines and containers provide a unified stack of hardware and software platforms. Developers don't need to worry about getting the right set of tools, frameworks, and libraries required for training the models in the cloud. But training a model is only half of the AI story.