Intel IoT Technologies
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Intel IoT Technologies
What is IoT ?
IoT stands for Internet of Things.
The Internet of Things is a network of physical objects that are embedded with electronics, software, sensors and connectivity to enable these objects to collect and exchange data.
IoT technology can be used in many ways. For example, it can be used by retailers to improve operational efficiency by tracking inventory levels and better understanding customer buying patterns. It can also be used by healthcare providers for remote patient monitoring or even help farmers monitor their crops remotely.
The IoT market is containing a rapid growth. In fact, it is expected to reach $6 billion by 2020. This is attributed to the sheer number of new applications and innovations that are coming up in this field.
Intel has been a major player in this field for quite some time now, with a wide range of innovative products like smart cards and more importantly, micro-processors which set industry standards for IoT.
Why IoT ?
Internet of Things (IoT) is a trend that has been gaining momentum over recent years and it will continue to grow in the future.
With more and more devices being connected to the internet, there are more opportunities for IoT technologies.
Intel IoT Technologies
Intel is a multinational corporation that manufactures semiconductors, computer hardware, and other technology. Founded in 1968 by Gordon Moore and Robert Noyce, Intel introduced the world's first microprocessor in 1971.
Intel is the world’s largest semiconductor manufacturing company. It has been transforming from a PC-centric company to one that focuses on the data revolution, artificial intelligence and autonomous driving. Intel has developed some IoT technologies in order to provide solutions for various industries like healthcare, manufacturing and retail.
Intel's IoT technologies collect and process data from connected devices to help people make better decisions.
Intel's IoT technologies are comprised of three subsystems: The Platform, Connectivity and Solutions.
The company has also developed several market-leading technologies for data analytics, machine learning and AI as well as the security software which protects the aforementioned technologies from bad actors.
Moreover, Intel's goal is to make all these various subsystems work in perfect harmony.
Intel IoT Technologies use machine learning to deliver a better customer experience. It provides the right solutions for each business and the right data for each customer.
Intel’s IoT technology is in every industry, from healthcare to retail, from travel and hospitality to manufacturing. They can help these industries collect data and insights that they need to grow - delivering better service, smarter marketing, more efficient operations, and healthier lifestyles.
The Intel IoT Technology is the combination of hardware and software capabilities. The technology provides a platform for the development of smart devices and connected intelligent systems or machines.
Intel IoT technologies are designed to allow machines, devices, and other technology to communicate with each other in a world that is getting more connected every day. Intel's IoT technologies include a specialized security subsystem called Intel Secure Device Onboard (SDO).
Artificial Intelligence
The next generation of hardware and software solutions built on Intel® architecture is designed to help organizations unleash IoT innovation in new and transformative ways.
With the recent advancements in artificial intelligence (AI), computers have evolved from simply being able to follow instructions, to now being able to think for themselves in many complex and interesting ways. This is possible because of machine learning-based algorithms that can learn from data and patterns found from other digital systems or by analyzing human behavior. These algorithms can be trained through artificial neural networks, which allows them not just to classify sounds or images but identify patterns.
Artificial Intelligence (AI) refers to a broad class of systems that enables machines to mimic advanced human capabilities. Machine Learning (ML) is a class of statistical methods that uses parameters from existing data and then predicts outcomes on similar novel data, such as with recession , decision trees, state vector machines. Deep learning(DL) is a subset of ML that uses multiple layers and algorithms inspired by the structure and function of the brain, called artificial neural networks, to learn from large amounts of data. DL is used for such projects as computer vision, natural language processing, recommendation engines and others.
Initially, data is created and entered into the system, at which point it goes through preprocessing to ensure consistent data form, type, and quality. When clean data is assured, it goes into a modeling and optimization process to support smarter, faster analytics. Once the AI model is proven, it can be deployed to meet project requirements.
Businesses increasingly look to artificial intelligence (AI) to increase revenue, drive efficiencies, and innovate their offerings. In particular, AI use cases powered by deep learning (DL) generate some of the most powerful and useful insights; some of these use cases can enable advances across numerous industries, for example:
- Image classification, which can be used for concept assignment like facial sentiment
- Object detection, which is utilized by autonomous vehicles for localization of objects
- Image segmentation, which provides the ability to outline organs in a patient’s magnetic resonance imaging (MRI)
- Natural language processing, which enables textual analysis or translation
- Recommender systems, which can be used by online stores to predict customer preferences or suggest up-sell options .
These use cases are only the beginning. As businesses incorporate AI into their operations, they discover new ways of applying AI. However, the business value of all AI use cases is dependent on how quickly answers can be inferenced from models trained by deep neural networks. The resources needed to support inferencing on DL models can be substantial, and they often require organizations to update their hardware to obtain the required performance and speed. However, many customers want to extend their existing infrastructures rather than purchase new single-purpose hardware. The flexibility of the Intel® hardware architecture that your IT department is already familiar with can help protect your IT investments. Intel Select Solutions for AI Inferencing are “turnkey platforms” that provide pre-bundled, verified, and optimized solutions for low-latency, high-throughput inference performed on a CPU, not on a separate accelerator card.
Intel® Distribution of OpenVINO™ Toolkit
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference.
- Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks
- Use models trained with popular frameworks like TensorFlow, PyTorch and more
- Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud.
The OpenVINO toolkit makes it simple to adopt and maintain your code. Open Model Zoo provides optimized, pretrained models and Model Optimizer API parameters make it easier to convert your model and prepare it for inferencing. The runtime (inference engine) allows you to tune for performance by compiling the optimized network and managing inference operations on specific devices. It also auto-optimizes through device discovery, load balancing, and inferencing parallelism across CPU, GPU, and more.
The Intel® Distribution of Open Visual Inference and
Neural Network Optimization toolkit (Intel Distribution of
OpenVINO toolkit) is a developer suite that accelerates high performance AI and DL inference deployments. The toolkit
takes models trained in different frameworks and optimizes
them for multiple Intel hardware options in order to provide
maximum performance for deployment. Using the toolkit’s
Deep Learning Workbench, models can be quantized to a
lower precision, a process in which the toolkit transforms
models from using large, high-precision 32-bit floating-point numbers, which are typically used for training and
occupy more memory, to using 8-bit integers, which optimize
memory usage and performance. Swapping out floating-point numbers for integers leads to significantly faster AI
inference with almost identical accuracy. The toolkit can
convert and execute models built in a variety of frameworks,
including TensorFlow, MXNet, PyTorch, Kaldi, and any
framework supported by the Open Neural Network Exchange
(ONNX) ecosystem. Additionally, pre-trained, public models
are also available that can expedite development and
improve image processing pipelines for Intel processors,
without the need to search for or train your own models.
We can use Intel® Distribution of OpenVINO™ Toolkit to create python projects
This is for developers interested in creating computer vision, AI, IoT, and cloud-based applications using the Intel® Distribution for OpenVINO™ toolkit with Intel® System Studio and the Intel® Distribution for Python*.
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