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Deep Learning market report contains detailed information on factors influencing demand, growth, opportunities, challenges, and restraints. It provides detailed information about the structure and prospects for global and regional industries. In addition, the report includes data on research & development, new product launches, product responses from the global and local markets by leading players. The structured analysis offers a graphical representation and a diagrammatic breakdown of the Deep Learning market by region.

Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023.

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Global Deep Learning Market Global Drivers Restraints Opportunities Trends and Forecasts up to 2023

Market OverviewDeep learning can be considered as a subset of machine learning and consists of algorithms that allow a software to self-train to execute tasks such as image and speech recognition by exposing multilayered neural networks to bulk data. It can have a profound impact on various industries such as finance automotive aerospace telecommunication and information technology oil and gas industrial defense media and advertising medical and others. The increasing research and development activities in this domain is expanding the end use areas for the technology.

The factors that contribute to the high market share are parallelization high computing power swift improvements in information storage capacity in automotive and healthcare industries. A few major applications for deep learning systems are in autonomous cars data analytics cyber security and fraud detection. It has become imperative for both small and big organizations to analyze and extract meaningful information from visual content. Advanced technologies such as graphic processing units are highly accepted in scientific disciplines such as deep learning and data sciences.

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Valuable insights are extracted from bulk data by using deep learning neural networks to improve customer experience and generate innovative products. The development in artificial intelligence capabilities in natural language processing computer vision areas and image and speech recognition are driving the growth for deep learning.The use cases for deep learning is diverse ranging from detecting gene abnormalities and predicting weather patterns to identifying fraudulent insurance claims stock market analysis robotics drones finance agriculture. Deep learning systems have wide applications in the banking and financial sector.

It helps bank employees expand their capabilities so that they can focus more on customer interactions rather than regular banking transactions. The deep learning software can offer solutions based on a clients background and history and thus can provide evidence and context-based reasoning for every problem. Industries worldwide are generating enormous data which require high processing power and this data is being generated at an unprecedented rate and volume. This has created an enormous opportunity for deep learning powered applications. A plethora of start-ups are coming up with vertical specific solutions and global corporations are supporting these start-ups to innovate faster.

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Market AnalysisAccording to Reportocean Research the Global Deep Learning market is expected to grow at a CAGR of 49.93% during the forecast period 2017-2023. The market is driven by factors such as faster processor performance large training data size and sophisticated neural nets. The future potential of the market is promising owing to opportunities such as development in big data technologies expanding end-user base and extensive R&D. The market growth is curbed by restraining factors such as implementation challenges rigid business models dearth of skilled data scientists affordability of organizations and data security concerns and inaccessibility.

Segmentation by SolutionsThe market has been segmented and analyzed by the following components: Software and Hardware.

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Segmentation by End-UsersThe market has been segmented and analyzed by the following end-users: Medical Automotive Retail Finance IT & Telecommunications Industrial Aerospace and Defence Media and Advertising Oil Gas and Energy and Others.

Segmentation by RegionsThe market has been segmented and analyzed by the following regions: North America EMEA Latin America APAC and Latin America.Segmentation by ApplicationsThe market has been segmented and analyzed by the following applications: Image Recognition Voice Recognition Video Surveillance and Diagnostics Data mining and Others.

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BenefitsThe study covers and analyses the Global Deep Learning Market. Bringing out the complete key insights of the industry the report aims to provide an opportunity for players to understand the latest trends current market scenario government initiatives and technologies related to the market. In addition it helps the venture capitalists in understanding the companies better and take informed decisions.> The report covers drivers restraints and opportunities (DRO) affecting the market growth during the forecast period (2017-2023).> It also contains an analysis of vendor profiles which include financial health business units key business priorities SWOT strategies and views.> The report covers competitive landscape which includes M&A joint ventures and collaborations and competitor comparison analysis.> In the vendor profile section for the companies that are privately held financial information and revenue of segments will be limited.

Region/Country Cover in the Report

North America EMEA Latin America and APAC

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Key Players Covered in the Report

Microsoft CorporationIBM CorporationAmazon Web ServicesNvidia CorporationDeepmind Technologies Ltd

This report covers aspects of the regional analysis market.The report includes data about North America, Europe, Asia Pacific, Latin America, the Middle East, and Africa.This report analyzes current and future market trends by region, providing information on product usage and consumption.Reports on the market include the growth rate of every region, based on their countries over the forecast period.

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What factors are taken into consideration when assessing the key market players?

The report analyzes companies across the globe in detail.The report provides an overview of major vendors in the market, including key players.Reports include information about each manufacturer, such as profiles, revenue, product pricing, and other pertinent information about the manufactured products.This report includes a comparison of market competitors and a discussion of the standpoints of the major players.Market reports provide information regarding recent developments, mergers, and acquisitions involving key players.

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What are the key findings of the report?This report provides comprehensive information on factors expected to influence the market growth and market share in the future.The report offers the current state of the market and future prospects for various geographical regions.This report provides both qualitative and quantitative information about the competitive landscape of the market.Combined with Porters Five Forces analysis, it serves as SWOT analysis and competitive landscape analysis.It provides an in-depth analysis of the market, highlighting its growth rates and opportunities for growth.

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Deep Learning market 2022 Industry Growth Analysis by Key Players, Segments, Competitive Landscape and Forecast to 2030 - Taiwan News

AI projects benefit children immensely since they help them to increase their knowledge and talents for their bright future

Traditional education has undoubtedly suffered, as online learning has grown in popularity, and some parents have even turned to home schools. Learning, on the other hand, is not restricted to the classroom. At home, students and beginners may do a variety of enjoyable and simple artificial intelligence (AI) and machine learning (ML) projects. The majority of these projects require a computer or laptop, as well as internet connectivity. These projects provide your child with an enjoyable learning experience, that introduces them to artificial intelligence and machine learning at a young age.

In an age, where changing your face to appear like your favorite dog or trying to seem royal by wearing a crown, is all the rage on the internet. This project allows you to do just that design your face app filters, and apply them to your face. This is a fun AI and machine learning project for kids, that teaches them how to use AI to recognise their face with a computer camera, then appropriately fit the filter to their face and even tilt it in line with how their face tilts.

In this project, you will have to anticipate the selling price of a new home in Boston, for this assignment. The prices of properties in various locations of the city are included in the projects dataset. The datasets for this experiment, are available at the UCI Machine Learning Repository. Aside from the costs of various properties, youll also obtain statistics on the peoples ages, the citys crime rate, and the locations of non-retail companies. Its a terrific assignment for youngsters, to put their knowledge to the test.

Developing a chatbot, is one of the top AI-based initiatives. Create a rudimentary customer service chatbot first. The chatbots, which may be found on many websites, can be used as inspiration. After youve made a basic chatbot, you may improve it and make a more complex version. The chatbots specialty may then be changed and its functions can be enhanced. AI allows you to construct a variety of innovative chatbots.

This is a popular artificial intelligence project concept. This research aims to build on a ground-breaking current deep Learning application: face emotion identification. The deep Learning face emotion detection and identification system are used to recognise and understand human facial emotions. It can identify happy, sad, angry, terrified, surprised, disgusted, and neutral human emotions in real-time.

This is one of the greatest beginners AI project ideas. The stock market is a favourite of machine learning scientists. Because it is tightly packed with information, this is the case. You may get a variety of data sets and start working on a project right away. This project would be perfect for students interested in working in the financial business, since it may give them significant insight into a variety of elements of the industry.

This is a simple AI-based space combat game, where you just use hand gestures to operate your vessel. PictoBlox, a computer or laptop with a camera or webcam, and an internet connection are all necessary. The foundations of the game are simple: you move your finger around, to control the movement of the spaceship. The game uses artificial intelligence to analyse your hand, and its movements via the camera, which then drives the games spaceship forward.

The next AI and machine learning project for students, are to control a 2-wheel drive robot with hand gestures rather than a computer, smartphone, or joystick. If you havent made the robot yet or are unsure how to do so, go here. A two-wheeled robot, a camera-equipped laptop or PC, PictoBlox, and an internet connection, are all necessary. Youll use machine learning to teach the model to recognise hand gestures, so it can move forward, turn left and right and stop in this project.

Students may use the speech recognition capability of PictoBloxsartificial intelligence extension, to operate household appliances in this project. The students will use PictoBlox, to create a room with appliances like lights, a fan, and a radio, then write a script in PictoBlox to operate them using voice commands. After youve completed the project, you may turn it into a real-life room with lights, fans, and radios.

Because of the present global scenario and the rise of online classrooms, the conventional attendance method may be difficult or cumbersome. This cutting-edge machine learning project enables your teacher to take class attendance, using facial recognition. It collects sample photographs of your face using machine learning, and the next time the computer scans a face, it uses AI to compare it to the stored samples, marking you as present if there is a match.

You will create a plagiarism detector for a project, that can detect similarities in text copies, and calculate the proportion of plagiarism. Users can register for this software, by generating a valid login id and password. The file will be separated into content and reference links, after the uploads are complete. The checker will next go through the whole document, checking for grammatical problems, visiting each reference link, and scanning the content of all of the links for matches, to your material.

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Top 10 Low-Cost AI Projects for Your Kids to Work on - Analytics Insight

A key requirement of machine learning is to label the data correctly to ensure the best results, but the process is long and time-consuming. This also brings about an issue when dealing with extremely large data sets in unsupervised or semi-supervised learning. The saviour here is active learning with strategies that assist developers in prioritising the data and selecting the most useful samples to label to have the highest training impact. Furthermore, it promises to reduce the samples needed by choosing the right examples.

Various strategies can be used depending on the applications and needs of the model. However, when it comes to learning active learning, the practice is generally a part of bigger machine learning modules, which is why we have created a one-stop guide to mastering active learning online through resources varying from online video tutorials to blog posts and academic papers.

YouTube

Computerphile is a popular YouTube channel that discusses computer science-related topics. Their tutorial on active learning is taught by Dr Michel Valstar, who holds a PhD in Computing and is currently a professor at the University of Nottingham. The tutorial is a foundational element for the basics of active learning, taught through diagrams and illustrations of the concepts.

ICML, the International Conference on Machine Learning, is one of the fastest-growing AI conferences that discuss the latest academic papers. During their 2019 conference, Robert Nowak and Steve Hanneke taught the basics of active learning theory and the popular algorithms to apply (the video is now available online). In addition, the tutorial focuses on sound active learning algorithms and how they can be used to reduce the labels on training data. Robert Nowak holds the Nosbusch Professorship in Engineering at the University of Wisconsin-Madison. Steve Hanneke is a Research Assistant Professor at the Toyota Technological Institute in Chicago, specialising in AI and ML.

Applied AI is a great resource for learning AI/ML online through core concepts and real-life applications. The channels collective views cross 12 million and are popular for the basic concepts thorough teachings. Their tutorial on active learning in ML breaks down the principles of the concept along with real-life examples and mathematical explanations.

PyData is an educational program of NumFOCUS, a US-based not for profit organisation that provides a forum for the international community of data science to share their ideas through conferences. Speaking at one of their events is Jan Freyberg, a machine learning software engineer at Google Health. In a detailed talk, Freyberg discusses active learning in the interactive Python environment, given the ease and comfort in the ecosystem.

Devansh is a Computer Science and Computational Math Double Major at the Rochester Institute of Technology. Through this YouTube tutorial, he comprehensively discusses the basics of active learning, its works and compares it to SSL and GANs. He further explains the concept in detail regarding its use and active learnings acquisition function.

Ranji Raj, holding a masters degree in data science, takes on Youtube to publish tutorials and classwork related to machine learning. His video on active learning gives an in-depth introduction to the subject while discussing important concepts through diagrams and demonstrations. Raj also has consequent coursework on his GitHub page for data scientists interested in learning further.

Scaleway is a French cloud computing company that creates Youtube videos consisting of short machine learning tutorials and real-world applications. In their webinar on active learning, the company collaborated with Kairntech, an AI modelling and dataset creation platform, to discuss the various applications of active learning. The video discusses training datasets and how active learning can be applied for classification. It also glossed over common issues and how to overcome them.

Blog tutorials

Ori Cohen is a PhD holder in CS, currently working as a senior director of data science at New Relic. His Towards Data Science blog post on active learning is an extensive tutorial that discusses the various scenarios possible while using active learning, the algorithms that can be used, the sample selection methods and the codings used for all.

A blog post on Data Camp, an online interactive learning platform, explains in depth the A-Zs of active learning in a moderate level of difficulty. The tutorial discusses the concept in detail with definitions, examples and visuals, and teaches how one can apply active learning on their datasets through a particular example.

Written by a CS and EE student at IIT, India, this post is an in-depth tutorial on using active learning with Python. The tutorial is technical, explaining the code and its concepts through codes and steps. In addition, the post discusses various inputs, outputs, and the Python codes needed to apply active learning correctly.

Alexandre Abraham, a senior research scientist at Dataiku and a Ph D holder in computer science, has written an extensive tutorial on active learning packages on his Medium blog post. The blog post analyses the active learning packages available through a feature comparison, their covered approaches, and their coding aspects. There are three main packages and different methods that data scientists can leverage.

Papers

The paper in discussion is written by Kai Wei, an assistant professor at UCLA, Rishabh Iyer, an assistant professor at the University of Texas, and Jeff Bilmes, a professor at the University of Washington. Their paper studies the problem of selecting a subset of data to train a classifier and how individuals can apply the active learning framework to mitigate the issue.

Online courses

The DeepLearning.AI course in ML data lifecycle has a fourth module, tagged Advanced Labeling, Augmentation and Data Preprocessing, that focuses on semi-supervised learning, dataset labelling, and the role played by active learning within. The instructor, Robert Crowe, works at TensorFlow by Google and has multiple degrees in AI, ML and data science.

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Top online resources to learn Active Learning - Analytics India Magazine

- 3rd generation NeuPro AI/ML architecture offers scalable performance of 20 to 1,200 TOPS at SoC and Chiplet levels, lowers memory bandwidth by 6X

- Targets broad use of AI/ML in automotive, industrial, 5G networks and handsets, surveillance cameras, and Edge Compute

LAS VEGAS, Jan. 6, 2022 /PRNewswire/ -- Consumer Electronics Show CEVA, Inc. (NASDAQ: CEVA), the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions, today announced NeuPro-M, its latest generation processor architecture for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor. An industry first, NeuPro-M supports both system-on-chip (SoC) as well as Heterogeneous SoC (HSoC) scalability to achieve up to 1,200 TOPS and offers optional robust secure boot and end-to-end data privacy.

NeuPro-M is the latest generation processor architecture from CEVA for artificial intelligence and machine learning (AI/ML) inference workloads. Targeting the broad markets of Edge AI and Edge Compute, NeuPro-M is a self-contained heterogeneous architecture that is composed of multiple specialized co-processors and configurable hardware accelerators that seamlessly and simultaneously process diverse workloads of Deep Neural Networks, boosting performance by 5-15X compared to its predecessor.

NeuProM compliant processors initially include the following pre-configured cores:

NPM11 single NeuPro-M engine, up to 20 TOPS at 1.25GHz

NPM18 eight NeuPro-M engines, up to 160 TOPS at 1.25GHz

Illustrating its leading-edge performance, a single NPM11 core, when processing a ResNet50 convolutional neural network, achieves a 5X performance increase and 6X memory bandwidth reduction versus its predecessor, which results in exceptional power efficiency of up to 24 TOPS per watt.

Built on the success of its' predecessors, NeuPro-M is capable of processing all known neural network architectures, as well as integrated native support for next-generation networks like transformers, 3D convolution, self-attention and all types of recurrent neural networks. NeuPro-M has been optimized to process more than 250 neural networks, more than 450 AI kernels and more than 50 algorithms. The embedded vector processing unit (VPU) ensures future proof software-based support of new neural network topologies and new advances in AI workloads. Furthermore, the CDNN offline compression tool can increase the FPS/Watt of the NeuPro-M by a factor of 5-10X for common benchmarks, with very minimal impact on accuracy.

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Ran Snir, Vice President and General Manager of the Vision Business Unit at CEVA, commented: "The artificial intelligence and machine learning processing requirements of edge AI and edge compute are growing at an incredible rate, as more and more data is generated and sensor-related software workloads continue to migrate to neural networks for better performance and efficiencies. With the power budget remaining the same for these devices, we need to find new and innovative methods of utilizing AI at the edge in these increasingly sophisticated systems. NeuPro-M is designed on the back of our extensive experience deploying AI processors and accelerators in millions of devices, from drones to security cameras, smartphones and automotive systems. Its innovative, distributed architecture and shared memory system controllers reduces bandwidth and latency to an absolute minimum and provides superb overall utilization and power efficiency. With the ability to connect multiple NeuPro-M compliant cores in a SoC or Chiplet to address the most demanding AI workloads, our customers can take their smart edge processor designs to the next level."

The NeuPro-M heterogenic architecture is composed of function-specific co-processors and load balancing mechanisms that are the main contributors to the huge leap in performance and efficiency compared to its predecessor. By distributing control functions to local controllers and implementing local memory resources in a hierarchical manner, the NeuPro-M achieves data flow flexibility that result in more than 90% utilization and protects against data starvation of the different co-processors and accelerators at any given time. The optimal load balancing is obtained by practicing various data flow schemes that are adopted to the specific network, the desired bandwidth, the available memory and the target performance, by the CDNN framework.

NeuPro-M architecture highlights include:

Main grid array consisting of 4K MACs (Multiply And Accumulates), with mixed precision of 2-16 bits

Winograd transform engine for weights and activations, reducing convolution time by 2X and allowing 8-bit convolution processing with <0.5% precision degradation

Sparsity engine to avoid operations with zero-value weights or activations per layer, for up to 4X performance gain, while reducing memory bandwidth and power consumption

Fully programmable Vector Processing Unit, for handling new unsupported neural network architectures with all data types, from 32-bit Floating Point down to 2-bit Binary Neural Networks (BNN)

Configurable Weight and Data compression down to 2-bits while storing to memory, and real-time decompression upon reading, for reduced memory bandwidth

Dynamically configured two level memory architecture to minimize power consumption attributed to data transfers to and from an external SDRAM

To illustrate the benefit of these innovative features in the NeuPro-M architecture, concurrent use of the orthogonal mechanisms of Winograd transform, Sparsity engine, and low-resolution 4x4-bit activations, delivers more than a 3X reduction in cycle count of networks such as Resnet50 and Yolo V3.

As neural network Weights and Biases and the data set and network topology become key Intellectual Property of the owner, there is a strong need to protect these from unauthorized use. The NeuPro-M architecture supports secure access in the form of optional root of trust, authentication, and cryptographic accelerators.

For the automotive market, NeuPro-M cores and its CEVA Deep Neural Network (CDNN) deep learning compiler and software toolkit comply with Automotive ISO26262 ASIL-B functional safety standard and meets the stringent quality assurance standards IATF16949 and A-Spice.

Together with CEVA's multi award-winning neural network compiler CDNN and its robust software development environment, NeuPro-M provides a fully programmable hardware/software AI development environment for customers to maximize their AI performance. CDNN includes innovative software that can fully utilize the customers' NeuPro-M customized hardware to optimize power, performance & bandwidth. The CDNN software also includes a memory manager for memory reduction and optimal load balancing algorithms, and wide support of various network formats including ONNX, Caffe, TensorFlow, TensorFlow Lite, Pytorch and more. CDNN is compatible with common open-source frameworks, including Glow, tvm, Halide and TensorFlow and includes model optimization features like 'layer fusion' and 'post training quantization' all while using precision conservation methods.

NeuPro-M is available for licensing to lead customers today and for general licensing in Q2 this year. NeuPro-M customers can also benefit from Heterogenous SoC design services from CEVA to help integrate and support system design and chiplet development. For further information, visit https://www.ceva-dsp.com/product/ceva-neupro-m/.

About CEVA, Inc.CEVA is the leading licensor of wireless connectivity and smart sensing technologies and integrated IP solutions for a smarter, safer, connected world. We provide Digital Signal Processors, AI engines, wireless platforms, cryptography cores and complementary software for sensor fusion, image enhancement, computer vision, voice input and artificial intelligence. These technologies are offered in combination with our Intrinsix IP integration services, helping our customers address their most complex and time-critical integrated circuit design projects. Leveraging our technologies and chip design skills, many of the world's leading semiconductors, system companies and OEMs create power-efficient, intelligent, secure and connected devices for a range of end markets, including mobile, consumer, automotive, robotics, industrial, aerospace & defense and IoT.

Our DSP-based solutions include platforms for 5G baseband processing in mobile, IoT and infrastructure, advanced imaging and computer vision for any camera-enabled device, audio/voice/speech and ultra-low-power always-on/sensing applications for multiple IoT markets. For sensor fusion, our Hillcrest Labs sensor processing technologies provide a broad range of sensor fusion software and inertial measurement unit ("IMU") solutions for markets including hearables, wearables, AR/VR, PC, robotics, remote controls and IoT. For wireless IoT, our platforms for Bluetooth (low energy and dual mode), Wi-Fi 4/5/6/6e (802.11n/ac/ax), Ultra-wideband (UWB), NB-IoT and GNSS are the most broadly licensed connectivity platforms in the industry.

Visit us at http://www.ceva-dsp.com and follow us on Twitter, YouTube, Facebook, LinkedIn and Instagram.

CEVA - a global leader in signal processing IP for everything smart and connected. (PRNewsFoto/CEVA, Inc.)

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CEVA Redefines High Performance AI/ML Processing for Edge AI and Edge Compute Devices with its NeuPro-M Heterogeneous and Secure Processor...

SEATTLE--(BUSINESS WIRE)--The Cloud Security Alliance (CSA), the worlds leading organization dedicated to defining standards, certifications, and best practices to help ensure a secure cloud computing environment, today released Artificial Intelligence (AI) in Healthcare. Drafted by the Health Information Management Working Group, the report provides an overview of the ways in which AI and machine learning (ML) can be used to bring about major transformations in healthcare while addressing the challenges their use presents, and offering guidance for how to best incorporate them into healthcare systems now and in the future.

The document shares examples, use cases, and treatment methods for how AI, machine learning, and data mining can be effectively utilized throughout a healthcare system, including in research, diagnosis, and treatment. It also addresses ethical and legal challenges, bias in AI, and how it relates to telehealth, big data, and cloud computing in healthcare.

This is the time when healthcare leaders should be accelerating their use of AI, which when used with cloud computing has the potential for drastically improving patient outcomes. But, as with any new technology entering the healthcare arena, there are several challenges, among them a lack of data exchange, regulatory compliance requirements, and patient and provider adoption. This paper offers a summary of the areas in which healthcare can benefit, while providing healthcare delivery organizations guidance on how to best address the challenges their use brings, said Dr. James Angle, the papers lead author and co-chair of the Health Information Management Working Group.

The emergence of AI as a tool for better healthcare offers opportunities to improve patient and clinical outcomes and reduce costs. The ever-increasing volume and complexity of healthcare data provide an ideal environment for the application of both AI and ML, and there are several applications where these technologies can deliver an incredible value. Even so, healthcare delivery organizations must evaluate each to determine if and how they can be adopted, said Michael Roza, a contributor to the paper.

The CSA Health Information Management Working Group aims to provide a direct influence on how health information service providers deliver secure cloud solutions (services, transport, applications, and storage) to their clients, and to foster cloud awareness within all aspects of healthcare and related industries. Individuals interested in becoming involved in Health Information Management future research and initiatives are invited to join the working group.

Download Artificial Intelligence in Healthcare.

About Cloud Security Alliance

The Cloud Security Alliance (CSA) is the worlds leading organization dedicated to defining and raising awareness of best practices to help ensure a secure cloud computing environment. CSA harnesses the subject matter expertise of industry practitioners, associations, governments, and its corporate and individual members to offer cloud security-specific research, education, training, certification, events, and products. CSA's activities, knowledge, and extensive network benefit the entire community impacted by cloud from providers and customers to governments, entrepreneurs, and the assurance industry and provide a forum through which different parties can work together to create and maintain a trusted cloud ecosystem. For further information, visit us at http://www.cloudsecurityalliance.org, and follow us on Twitter @cloudsa.

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Cloud Security Alliance Releases Guidance on Use of Artificial Intelligence (AI) in Healthcare - Business Wire