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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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Apple Inc., Diabnext, Glooko Inc., Google Inc., IBM Corporation, Tidepool, and Vodafone Group Plc.

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The report assesses the global Artificial Intelligence in Diabetes Management Market volume in the recent years. Additionally, the report also highlights key controllers and drivers determining the market expansions. It also uncovers the estimate of the market for the predicted time. The report emphasizes the emergent trends related to development possibilities of the global Artificial Intelligence in Diabetes Management Market. Moreover, the market report includes the main product category and industry key segments as well as the sub-segments of the global Artificial Intelligence in Diabetes Management Market.

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The global Artificial Intelligence in Diabetes Management Market research report evaluates the market expansion crosswise over major regional segments. It is sorted on the basis of topography such as Europe, Asia Pacific, Latin America, North America, and the Middle East & Africa.

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The Artificial Intelligence in Diabetes Management Market is growing at a rapid pace and with the rise in technological innovation, competition and M&A activities in the industry. Further, the research report is segmented on the basis of Application & Other with historical and projected market share and compounded annual growth rate (CAGR). In addition, the research provides key market features, revenue, capacity, capacity, price, growth rate, consumption, production, supply & demand, market share, and CAGR. The report offers a wide-ranging study of imperative market dynamics and their latest trends, coupled with pertinent market segments.

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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

Drone services company Helios Visions (https://www.heliosvisions.com) has joined forces with T2D2 (http://www.t2d2.ai), a software as a service (SaaS) platform that uses artificial intelligence (AI) to identify and assess damage and deterioration to building envelopes and structures to provide AI-powered drone facade inspection services.

Together, Helios Visions and T2D2 will provide a robust end-to-end solution for facade condition assessment. Using the latest in drone and AI technology, the program helps support critical inspections and significantly enhances visual inspections. It also makes it easier, faster, safer and less costly to inspect structures.

The use of drones for high-rise building faade inspections is faster and can be as much as 50% cheaper than traditional methods, which require expensive scaffolding, drops and lifts, Helios Visions Co-founderTed Parisotsaid. WithT2D2, wecan streamline thefacade inspection process, and greatly improve planning and decision-making for building owners and property managers. More frequent assessment of building conditions can increase safety and decrease repair costs by spotting problems before they require expensive and invasive solutions.

T2D2, developed withinThornton Tomasettis CORE studio incubatorand commercialized through the firmsTTWiiN accelerator, uses data from Thornton Tomasettis more than 50 years of building inspection and forensic investigation work as well as detailed drone imagery provided by Helios Visions.

The detailed drone images provided by Helios Visions allow T2D2s artificial intelligence programs to quickly and accurately identify any issues that may exist in a buildings facade. We are excited for the ongoing partnership between T2D2 and Helios Visions, which will enable the AI program to continuously learn as more drone photometry is fed into the system, said Thornton Tomasetti Director of CORE AI and T2D2 Founder and CEOBadri Hiriyur.

In late September, T2D2 was one of four winners in the New York City Department of Buildings first-everHack the Building Code Innovation Challenge, which was created to highlight ideas on how to improve building safety and modernize the development process inNew York City.

About Helios Visions

Helios Visions is a safety-oriented drone services company specializing indrone based facadeinspection,drone mapping,drone photos and video, and recently becamethe first drone servicescompany inChicagoto receive an FAA waiver to fly over people. Helios Visions is a member of the

CompTIA Drone Advisory Counciland is fully compliant withFAA drone regulationswith anextensive portfolioof successful client projects. Additional information: call +1 (312) 999-0071, visitHelios Visions

About T2D2

T2D2is a self-learning, AI-based software-as-a-service platform that automatically detects visible damage in a variety of building materials. It expeditescondition assessments, saving time and money, and allows for more frequent assessments to detect and repair damage before it escalates.T2D2s superpowered algorithms were trained onThornton Tomasettis massive multi-year forensics database. For more information, go toT2D2.ai,or call +1 917-661-7800

About Thornton Tomasetti

Thornton Tomasetti applies engineering and scientific principles to solve the worlds challenges starting with yours. An independent organization of creative thinkers and innovative doers collaborating from offices worldwide, our mission is to bring our clients ideas to life and, in the process, lay the groundwork for a better, more resilient future. For more information visitwww.ThorntonTomasetti.comor connect with us on LinkedIn, Twitter, Instagram, Facebook, Vimeo or YouTube.

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Helios Visions Partners with Thornton Tomasettis T2D2 to Provide Artificial Intelligence-Powered Drone Solution for Facade Inspection - sUAS News