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DBMR Analyst have added a new research study on TitleGlobal Artificial Intelligence Robots Market Research Reportwith a detailed overview of market dynamics, segmentation, product portfolio, business plans and latest development in the industry. This report covers the growth opportunities and the limiting factors of the Artificial Intelligence Robots Market. Artificial Intelligence Robots Market size report contains growth rate, revenue, segmentation with product type, application, end-users, regions, manufacturers, and more. The Study also provides in-depth comprehensive analysis for regional segments that covers North America, Europe, Asia-Pacific, Middle East and Africa and Rest of World with Global Outlook and includes Clear Market definitions, classifications, manufacturing processes, cost structures, development policies and plans. The facts and data are well presented in the report using diagrams, graphs, pie charts, and other pictorial representations with respect to its Current Trends, Dynamics, and Business Scope & Key Statistics.

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Global Artificial Intelligence Robots Market Analysis:

Artificial intelligence robots market is expected to grow at a CAGR of 20.38% in the forecast period of 2020 to 2027. Data Bridge Market Research report on artificial intelligence robots market provides analysis and insights regarding the various factors expected to be prevalent throughout the forecasted period while providing their impacts on the markets growth.

Thinking One Step Ahead

IN TODAYScompetitive world you need to think one step ahead to chase your competitors, our research offers reviews about key players, major collaborations, merger & acquisitions along with trending innovation and business policies to present better insights to drive the business into right direction.

Key Market Competitors:

The major players covered in the artificial intelligence robots market report are Welltok, Inc., Intel Corporation, Nvidia Corporation, Google Inc., IBM, Microsoft Corporation, General Vision, Enlitic, Inc., Next IT Corporation, iCarbonX, Amazon Web Services Inc., Apple Inc., Facebook Inc., Siemens, General Electric, Micron Technology, Samsung, Xillinx, Iteris, Atomwise, Inc., Lifegraph, Sense.ly, Inc., Zebra Medical Vision, Inc., Baidu, Inc., H2O ai, Enlitic, Inc. and Raven Industries, among other domestic and global players.

Some of the Major Highlights of TOC covers: Global Artificial Intelligence Robots Market

Chapter 1: Methodology & Scope

Definition and forecast parameters

Methodology and forecast parameters

Data Sources

Chapter 2: Executive Summary

Business trends

Regional trends

Product trends

End-use trends

Chapter 3: Access Control Industry Insights

Industry segmentation

Industry landscape

Vendor matrix

Technological and innovation landscape

Chapter 4: Access Control Market, By Region

Chapter 5: Company Profile

Business Overview

Financial Data

Product Landscape

Strategic Outlook

SWOT Analysis

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The report provides an in-depth analysis of the global Artificial Intelligence Robots Market. It highlights the latest product launches and recent innovations in the market and states their impact on the growth of the market. The competitive landscape has been discussed in detail and predictions are made with respect to leading companies and products in the coming years. Forecast values have been provided for the market for the Defined Forecast period. The factual figures have been obtained through trusted sources. Moreover, these predictions are made on the basis of extensive research analysis methods, coupled with the opinions of experienced market research professionals.

The 2020 Annual Artificial Intelligence Robots Market offers:

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Artificial Intelligence Robots Market Business Growth Tactics, Future Strategies, competitive Outlook, Industrial Demand and Opportunities -...

According to the report the Zion Market Research Reports Global Artificial Intelligence in Diabetes Management Market Is Expected To Witness Tremendous Growth During The Forecast Period. The global Artificial Intelligence in Diabetes Management Market report offers an in-depth analysis of the Artificial Intelligence in Diabetes Management Market. It presents a succinct outline of theArtificial Intelligence in Diabetes Management Marketand explains the major key elements of the market. Additionally, the report highlights significant players in the global Artificial Intelligence in Diabetes Management Market along with their investment in the market to assess their growth during the estimated time. The foremost market players in the industry are also included in this report for a better understanding of business strategies, growth analysis, sales and revenue and growth factors. The report discusses the most recent expansions while predicting the development of the key players in the near future.

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Global Artificial Intelligence in Diabetes Management Market Is Expected To Witness Tremendous Growth During The Forecast Period - re:Jerusalem

We are laser-focused on continuous improvements to customers experience across our stores. By leveraging Everseens Visual AI and machine-learning technology, were not only able to remove friction for the customer, but we can also remove controllable costs from the business and redirect those resources to improving the customer experience even more. Mike Lamb, LPC, Krogers VP of Asset Protection

This post was inspired by a recent Kroger article announcing the deployment of visual artificial intelligence (AI) in 2,500 stores and new IHL Group edge computing research. Multiple technological trends have been converging for some time, and their combination is leading to transformative store operations improving solutions.

By 2021, one billion video cameras will be deployed around the world. Endless possibilities in creating immersive consumer experiences emerge when artificial intelligence and machine learning are coupled with these visual data gathering devices.

COVID-19 has become a disruptive accelerator of digital transformation trends that were already underway. It takes 66 days or approximately two months to form a new permanent habit. New shopping journey habits have emerged during the pandemic that will require intensified analysis of millions of data inputs to both protect transactions and remove negative experience friction.

What are some of the leading visual AI or computer vision applications today? In retail, whats the return on investment (ROI)? What makes these technologies critical to the future of retail?

Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. Using digital images from cameras and videos and deep learning models, machines can accurately identify and classify objectsand then react to what they see.

This visual AI technology delivers valuable insights that dramatically improves decision making capabilities. My latest edition of the continuously updated Disruptive Future of Retail presentation includes this chart summarizing selected innovative applications.

In a pre-pandemic research report published by Fortune Business Insights, the retail AI market size was valued at $2,306.8 million in 2018 and will grow to $23,426.3 million by 2026. Computer vision and machine learning are key innovation drivers for this segment.

Fully expect the market size and the value of visual AI applications to increase because of COVID-19.

Data is exploding in the retail industry. Walmart as one example generates more than 1 million customer transactions every hour, feeding databases estimated at more than 2.5 petabytesequivalent to 167 times the books in the US Library of Congress.

In all industries, Internet-of-Things (IoT) connected devices are adding substantial amounts of data to the mix. In 2020, machine-generated data will account for over 40 percent of internet data.

The major cloud providers are already coming to the conclusion to distribute workloads to the appropriate edge where they run best. As the IHL Group points out in their latest research, edge computing is critical to retails success in this decade. Example applications that they point to in their analysis are the very important new shopping journeys that have been accelerated by the pandemic. Note the margin challenges shown above when these solutions are not optimized.

Edge system architecture delivers substantial margin improvement benefits to these new retail services. Visual artificial intelligence in the cloud and at the edge plus deployment of instore sensors will dramatically improve analysis and decision-making capabilities throughout the physical store.

The bottom line is that the retailers that not only survive and thrive in the next decade will be those that are able to apply artificial and machine learning to operational data at the store level. Yes, e-commerce is a key part of retails growth, but the key advantage that retailers have over pure-play e-commerce competitors is the stores and proximity to the customer.

The last two years have been very rewarding in working with leading retailers and technology providers in driving the future of retail.

Having spent a substantial portion of my career in point-of-sale, it is still today one of the areas that I follow as it is often that last moment of truth in engaging the consumer for both positive and negative results.

Applying visual artificial intelligence at point-of-sale is already delivering substantial positive results. In deployments protecting over $400 billion in retailers revenue across 75,000-plus checkout lanes, the average sales uplift has been 0.5 percent and the margin increase a substantial 20 percent.

POS devices are only the beginning in whats possible in terms of measurable operational improvements. The future of retail includes digitally supported leadership branding coupled with hyper-personalized immersive consumer experiences across the entire store.

Visual AI and edge computing are critical technologies that will deliver frictionless commerce and optimize consumer journeys whose importance has dramatically increased because of COVID-19. We are on the edge of revolutionizing the future of retail.

For additional retail, technology, and leadership information, visit http://www.tonydonofrio.com.

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Visual Artificial Intelligence on the Edge of Revolutionizing Retail - Loss Prevention Magazine

In the operating theater of the future, computer-based assistance systems will make work processes simpler and safer and thereby play a much greater role than today. However, such support features are only possible if computers are able to anticipate important events in the operating room and provide the right information at the right time, explains Prof. Stefanie Speidel. She is head of the Department of Translational Surgical Oncology at the National Center for Tumor Diseases Dresden (NCT/UCC) in Germany.

Together with the Centre for Tactile Internet with Human-in-the-loop (CeTI) at TU Dresden, she has developed a method that uses artificial intelligence (AI) to enable computers to anticipate the usage of surgical instruments before they are used.

This kind of system does not just provide an important basis for the use of autonomous robotic systems that could take over simple minor tasks in the operating theater, such as blood aspiration. It could also issue early warnings of complications if these are inherent to the use of a particular instrument. Furthermore, it would increase efficiency where preparing instruments is concerned. However, our vision is not to replace the surgeon with a robot or other assistants. The intelligent systems should merely act as a helping hand and lighten the load for both the doctor and the entire surgical team, says Prof. Jrgen Weitz, Managing Director at NCT/UCC and Director of the Clinic for Visceral, Thoracic and Vascular Surgery at the University Hospital Carl Gustav Carus in Dresden.

In order to teach computers how to anticipate the use of surgical instruments on a situation-specific basis a few minutes before they are actually put to use, scientists at NCT/UCC and CeTI used an artificial neural network that mimics the human ability to learn by example. By using a continuous analysis of video images of a surgical procedure, the usage of certain instruments was shown a few minutes before they were actually used. They then trained the neural network with 60 videos of gall bladder removal surgery. These were recorded by default with a laparoscope in the abdomen. Five different instruments were highlighted in these videos.

Afterwards the neural network had to demonstrate its knowledge on 20 more videos without any markers. The scientists were able to verify that the system had made important advances in learning. Plus, in many cases it was able to correctly anticipate how the instruments would be used.

Compared to other methods, this neural network proved to be much more suitable for practical applications. Consequently, this also means that is capable of solving complex tasks. Other methods treat the timing of a specific situation as a matter of routine and the network just needs to decide between various possible situations. In contrast, we have been able to show that an artificial neural network with specific adaptations and a suitably formulated mathematical function is capable of making sensible assessments about the type of instrument that should be selected and the time frame of its application with a minimum of coding effort, says Dominik Rivoir from the Department of Translational Surgical Oncology at NCT/UCC, first author of the study presented at the International Conference on Medical Image Computing & Computer-Assisted Intervention (MICCAI).

Next, the scientists want to refine the method and add more data sets to the neural network. One focus in this is on surgical videos that show more severe bleeding. Using this image data, the network should be able to learn even better when hemorrhages need to be aspirated with a special instrument. In the presented study, the researchers were already able to show that the network interpreted, for example, the appearance of a clamp for clamping a blood vessel with a high degree of accuracy as a characteristic. This way, it was abe to anticipate the use of scissors soon afterward. In future, this could serve as a basis for timing the use of robot-guided aspiration instruments or for anticipating complications.

The National Center for Tumor Diseases Dresden (NCT/UCC) is a joint venture of the German Cancer Research Center (DKFZ), the University Hospital Carl Gustav Carus Dresden, the Medical Faculty Carl Gustav Carus of the TU Dresden, and the Helmholtz Center Dresden-Rossendorf (HZDR).

Title image: Autonomous robotic systems and other intelligent assistance systems will provide enhanced support for the surgical team in the future. NCT/UCC/Andr Wirsig

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Artificial intelligence anticipates how instruments are used during surgery - Innovation Origins

Researchers have discovered a spooky similarity between how human brains and artificial intelligence computers see three-dimensional objects.

The discovery is a significant step towards better understanding how to replicate human vision with AI, said scientists at Johns Hopkins University who made the breakthrough.

Natural and artificial neurons registered nearly identical responses when processing 3D shape fragments, despite the artificial neurons being trained using images on two-dimensional photographs.

The AlexNet AI network unexpectedly responded to the images in the same way as neurons that are found within an area of the human brain called V4, which is the first stage in the brains object vision pathway.

I was surprised to see strong, clear signals for 3D shape as early as V4, said Ed Connor, a neuroscience professor at the Zanvyl Krieger Mind/Brain Institute at Johns Hopkins University.

But I never would have guessed in a million years that you would see the same thing happening in AlexNet, which is only trained to translate 2D photographs into object labels.

Professor Connor described a spooky correspondence between image response patterns in natural and artificial neurons, especially given that one is a product of thousands of years of evolution and lifetime learning, and the other is designed by computer scientists.

Artificial networks are the most promising current models for understanding the brain, Professor Connor said.

Conversely, the brain is the best source of strategies for bringing artificial intelligence closer to natural intelligence.

A research paper detailing the discovery was published in the scientific journal Current Biology on Thursday.

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Humans and artificial intelligence see objects in same way, researchers discover - The Independent