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The global Machine Learning market is carefully researched in the report while largely concentrating on top players and their business tactics, geographical expansion, market segments, competitive landscape, manufacturing, and pricing and cost structures. Each section of the research study is specially prepared to explore key aspects of the global Machine Learning market. For instance, the market dynamics section digs deep into the drivers, restraints, trends, and opportunities of the global Machine Learning Market. With qualitative and quantitative analysis, we help you with thorough and comprehensive research on the global Machine Learning market. We have also focused on SWOT, PESTLE, and Porters Five Forces analyses of the global Machine Learning market.

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A perfect mix of quantitative & qualitative Machine Learning market information highlighting developments, industry challenges that competitors are facing along with gaps and opportunities available and would trend in Machine Learning market. The study bridges the historical data from 2014 to 2019 and estimated until 2025.

Leading players of the global Machine Learning market are analyzed taking into account their market share, recent developments, new product launches, partnerships, mergers or acquisitions, and markets served. We also provide an exhaustive analysis of their product portfolios to explore the products and applications they concentrate on when operating in the global Machine Learning market. Furthermore, the report offers two separate market forecasts one for the production side and another for the consumption side of the global Machine Learning market. It also provides useful recommendations for new as well as established players of the global Machine Learning market.

Quick Read Table of Contents of this Report @ https://www.adroitmarketresearch.com/industry-reports/machine-learning-market

A major chunk of this Global Machine Learning Market research report is talking about some significant approaches for enhancing the performance of the companies. Marketing strategies and different channels have been listed here. Collectively, it gives more focus on changing rules, regulations, and policies of governments. It will help to both established and new startups of the market.

In conclusion, the Machine Learning Market report is a reliable source for accessing the research data that is projected to exponentially accelerate your business. The report provides information such as economic scenarios, benefits, limits, trends, market growth rate, and figures. SWOT analysis is also incorporated in the report along with speculation attainability investigation and venture return investigation.

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Machine Learning Market 2019 Industry Analysis By Size, Share, Growth, Key-Companies, Trends, Demand, Future Prospects and Forecast Till 2025 - Cole...

We have heard of CPUs and TPUs, now, NVIDIA with the help of its recent acquisition Mellanox is bringing a new class of processors to power up deep learning applications DPUs or data processing units.

DPUs or Data Processing Units, originally popularised by Mellanox, now wear a new look with NVIDIA; Mellanox was acquired by NVIDIA earlier this year. DPUs are a new class of programmable processor that consists of flexible and programmable acceleration engines which improve applications performance for AI and machine learning, security, telecommunications, storage, among others.

The team at Mellanox has already deployed the first generation of BlueField DPUs in leading high-performance computing, deep learning, and cloud data centres to provide new levels of performance, scale, and efficiency with improved operational agility.

The improvement in performance is due to the presence of high-performance, software programmable, multi-core CPU and a network interface capable of parsing, processing, and efficiently transferring data at line rate to GPUs and CPUs.

According to NVIDIA, a DPU can be used as a stand-alone embedded processor. DPUs are usually incorporated into a SmartNIC, a network interface controller. SmartNICs are ideally suited for high-traffic web servers.

A DPU based SmartNIC is a network interface card that offloads processing tasks that the system CPU would normally handle. Using its own on-board processor, the DPU based SmartNIC may be able to perform any combination of encryption/decryption, firewall, TCP/IP and HTTP processing.

The CPU is for general-purpose computing, the GPU is for accelerated computing and the DPU, which moves data around the data centre, does data processing.

These DPUs are known by the name of BlueField that have a unique design that can enable programmability to run at speeds of up to 200Gb/s. The BlueField DPU integrates the NVIDIA Mellanox Connect best-in-class network adapter, encompassing hardware accelerators with advanced software programmability to deliver diverse software-defined solutions.

Organisations that rely on cloud-based solutions, especially can benefit immensely from DPUs. Here are few such instances, where DPUs flourish:

Bare metal environment is a network where a virtual machine is installed

The shift towards microservices architecture has completely transformed the way enterprises ship applications at scale. Applications that are based on the cloud have a lot of activity or data generation, even for processing a single application request. According to Mellanox, one key application of DPU is securing the cloud-native workloads.

For instance, Kubernetes security is an immense challenge comprising many highly interrelated parts. The data intensity makes it hard to implement zero-trust security solutions, and this creates challenges for the security team to protect customers data and privacy.

As of late last year, the team at Mellanox stated that they are actively researching into various platforms and integrating schemes to leverage the cutting-edge acceleration engines in the DPU-based SmartNICs for securing cloud-native workloads at 100Gb/s.

According to NVIDIA, a DPU comes with the following features:

Know more about DPUs here.

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What Are DPUs And Why Do We Need Them - Analytics India Magazine

Historians interested in the way events and people were chronicled in the old days once had to sort through card catalogs for old papers, then microfiche scans, then digital listings but modern advances can index them down to each individual word and photo. A new effort from the Library of Congress has digitized and organized photos and illustrations from centuries of news using state of the art machine learning.

Led by Ben Lee, a researcher from the University of Washington occupying the Librarys Innovator in Residence position, the Newspaper Navigator collects and surfaces data from images from some 16 million pages of newspapers throughout American history.

Lee and his colleagues were inspired by work already being done in Chronicling America, an ongoing digitization effort for old newspapers and other such print materials. While that work used optical character recognition to scan the contents of all the papers, there was also a crowdsourced project in which people identified and outlined images for further analysis. Volunteers drew boxes around images relating to World War I, then transcribed the captions and categorized the picture.

This limited effort set the team thinking.

I loved it because it emphasized the visual nature of the pages seeing the visual diversity of the content coming out of the project, I just thought it was so cool, and I wondered what it would be like to chronicle content like this from all over America, Lee told TechCrunch.

He also realized that what the volunteers had created was in fact an ideal set of training data for a machine learning system. The question was, could we use this stuff to create an object detection model to go through every newspaper, to throw open the treasure chest?

The answer, happily, was yes. Using the initial human-powered work of outlining images and captions as training data, they built an AI agent that could do so on its own. After the usual tweaking and optimizing, they set it loose on the full Chronicling America database of newspaper scans.

It ran for 19 days nonstop definitely the largest computing job Ive ever run, said Lee. But the results are remarkable: millions of images spanning three centuries (from 1789 to 1963) and organized with metadata pulled from their own captions. The team describes their work in a paper you can read here.

Assuming the captions are at all accurate, these images until recently only accessible by trudging through the archives date by date and document by document can be searched for by their contents, like any other corpus.

Looking for pictures of the president in 1870? No need to browse dozens of papers looking for potential hits and double-checking the contents in the caption just search Newspaper Navigator for president 1870. Or if you want editorial cartoons from the World War II era, you can just get all illustrations from a date range. (The team has already zipped up the photos into yearly packages and plans other collections.)

Here are a few examples of newspaper pages with the machine learning systems determinations overlaid on them (warning: plenty of hat ads and racism):

Thats fun for a few minutes for casual browsers, but the key thing is what it opens up for researchers and other sets of documents. The team is throwing a data jam today to celebrate the release of the data set and tools, during which they hope to both discover and enable new applications.

Hopefully it will be a great way to get people together to think of creative ways the data set can be used, said Lee. The idea Im really excited by from a machine learning perspective is trying to build out a user interface where people can build their own data set. Political cartoons or fashion ads, just let users define theyre interested in and train a classifier based on that.

A sample of what you might get if you asked for maps from the Civil War era.

In other words, Newspaper Navigators AI agent could be the parent for a whole brood of more specific ones that could be used to scan and digitize other collections. Thats actually the plan within the Library of Congress, where the digital collections team has been delighted by the possibilities brought up by Newspaper Navigator, and machine learning in general.

One of the things were interested in is how computation can expand the way were enabling search and discovery, said Kate Zwaard. Because we have OCR, you can find things it would have taken months or weeks to find. The Librarys book collection has all these beautiful plates and illustrations. But if you want to know like, what pictures are there of the Madonna and child, some are categorized, but others are inside books that arent catalogued.

That could change in a hurry with an image-and-caption AI systematically poring over them.

Newspaper Navigator, the code behind it and all the images and results from it are completely public domain, free to use or modify for any purpose. You can dive into the code at the projects GitHub.

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Millions of historic newspaper images get the machine learning treatment at the Library of Congress - TechCrunch

Sundar Pichai, CEO of Alphabet with one of Googles quantum computers. Source: AFP PHOTO / GOOGLE/HANDOUT

Scientific researchers are hard at work around the planet, feverishly crunching data using the worlds most powerful supercomputers in the hopes of a speedier breakthrough in finding a vaccine for the novel coronavirus.

Researchers at Penn State University think that they have hit upon a solution that could greatly accelerate the process of discovering a COVID-19 treatment, employing an innovative hybrid branch of research known as quantum machine learning.

When it comes to a computer science-driven approach to identifying a cure, most methodologies harness machine learning to screen different compounds one at a time to see if they might bond with the virus main protease, or protein.

This process is arduous and time-consuming, despite the fact that the most powerful computers were actually condensing years (maybe decades) of drug testing into less than two years time. Discovering any new drug that can cure a disease is like finding a needle in a haystack, said lead researcher Swaroop Ghosh, the Joseph R. and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering and Computer Science and Engineering at Penn State.

It is also incredibly expensive. Ghosh says the current pipeline for discovering new drugs can take between five and ten years from the concept stage to being released to the market, and could cost billions in the process.

High-performance computing such as supercomputers and artificial intelligence (AI) canhelp accelerate this process by screeningbillions of chemical compounds quicklyto findrelevant drugcandidates, he elaborated.

This approach works when enough chemical compounds are available in the pipeline, but unfortunately this is not true for COVID-19. This project will explorequantum machine learning to unlock new capabilities in drug discovery by generating complex compounds quickly.

Quantum machine learning is an emerging field that combines elements of machine learning with quantum physics. Ghosh and his doctoral students had in the past developed a toolset for solving a specific set of problems known as combinatorial optimization problems, using quantum computing.

Drug discovery computation aligns with combinatorial optimization problems, allowing the researchers to tap the same toolset in the hopes of speeding up the process of discovering a cure, in a more cost-effective fashion.

Artificial intelligence for drug discovery is a very new area, Ghosh said. The biggest challenge is finding an unknown solution to the problem by using technologies that are still evolving that is, quantum computing and quantum machine learning. We are excited about the prospects of quantum computing in addressing a current critical issue and contributing our bit in resolving this grave challenge.

Joe Devanesan | @thecrystalcrown

Joe's interest in tech began when, as a child, he first saw footage of the Apollo space missions. He still holds out hope to either see the first man on the moon, or Jetsons-style flying cars in his lifetime.

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Could quantum machine learning hold the key to treating COVID-19? - Tech Wire Asia

The global healthcare industry is booming. As per recent research, it is expected to cross the $2 trillion mark this year, despite the sluggish economic outlook and global trade tensions. Human beings, in general, are living longer and healthier lives.

There is increased awareness about living organ donation. Robots are being used for gallbladder removals, hip replacements, and kidney transplants. Early diagnosis of skin cancers with minimum human error is a reality. Breast reconstructive surgeries have enabled breast cancer survivors to partake in rebuilding their glands.

All these jobs were unthinkable sixty years ago. Now is an exciting time for the global health care sector as it progresses along its journey for the future.

However, as the worldwide population of 7.7 billion is likely to reach 8.5 billion by 2030, meeting health needs could be a challenge. That is where significant advancements in machine learning (ML) can help identify infection risks, improve the accuracy of diagnostics, and design personalized treatment plans.

source: Deloitte Insights 2020 global health care outlook

In many cases, this technology can even enhance workflow efficiency in hospitals. The possibilities are endless and exciting, which brings us to an essential segment of the article:

Do you understand the concept of the LACE index?

Designed in Ontario in 2004, it identifies patients who are at risk of readmission or death within 30 days of being discharged from the hospital. The calculation is based on four factors length of stay of the patient in the hospital, acuity of admission, concurring diseases, and emergency room visits.

The LACE index is widely accepted as a quality of care barometer and is famously based on the theory of machine learning. Using the past health records of the patients, the concept helps to predict their future state of health. It enables medical professionals to allocate resources on time to reduce the mortality rate.

This technological advancement has started to lay the foundation for closer collaboration among industry stakeholders, affordable and less invasive surgery options, holistic therapies, and new care delivery models. Here are five examples of current and emerging ML innovations:

From the initial screening of drug compounds to calculating the success rates of a specific medicine based on physiological factors of the patients the Knight Cancer Institute in Oregon and Microsofts Project Hanover are currently applying this technology to personalize drug combinations to cure blood cancer.

Machine learning has also given birth to new methodologies such as precision medicine and next-generation sequencing that can ensure a drug has the right effect on the patients. For example, today, medical professionals can develop algorithms to understand disease processes and innovative design treatments for ailments like Type 2 diabetes.

Signing up volunteers for clinical trials is not easy. Many filters have to be applied to see who is fit for the study. With machine learning, collecting patient data such as past medical records, psychological behavior, family health history, and more is easy.

In addition, the technology is also used to monitor biological metrics of the volunteers and the possible harm of the clinical trials in the long-run. With such compelling data in hand, medical professionals can reduce the trial period, thereby reducing overall costs and increasing experiment effectiveness.

Every human body functions differently. Reactions to a food item, medicine, or season differ. That is why we have allergies. When such is the case, why is customizing the treatment options based on the patients medical data still such an odd thought?

Machine learning helps medical professionals determine the risk for each patient, depending on their symptoms, past medical records, and family history using micro-bio sensors. These minute gadgets monitor patient health and flag abnormalities without bias, thus enabling more sophisticated capabilities of measuring health.

Cisco reports that machine-to-machine connection in global healthcare is growing at a rate of 30% CAGR which is the highest compared to any other industry!

Machine learning is mainly used to mine and analyze patient data to find out patterns and carry out the diagnosis of so many medical conditions, one of them being skin cancer.

Over 5.4mn people in the US are diagnosed with this disease annually. Unfortunately, the diagnosis is a virtual and time-taking process. It relies on long clinical screenings, comprising a biopsy, dermoscopy, and histopathological examination.

But machine learning changes all that. Moleanalyzer, an Australia-based AI software application, calculates and compares the size, diameter, and structure of the moles. It enables the user to take pictures at predefined intervals to help differentiate between benign and malignant lesions on the skin.

The analysis lets oncologists confirm their skin cancer diagnosis using evaluation techniques combined with ML, and they can start the treatment faster than usual. Where experts could identify malignant skin tumors, only 86.6% correctly, Moleanalyzer successfully detected 95%.

Healthcare providers have to ideally submit reports to the government with necessary patient records that are treated at their hospitals.

Compliance policies are continually evolving, which is why it is even more critical to ensure the hospital sites to check if they are being compliant and functioning within the legal boundaries. With machine learning, it is easy to collect data from different sources, using different methods and formatting them correctly.

For data managers, comparing patient data from various clinics to ensure they are compliant could be an overwhelming process. Machine learning helps gather, compare, and maintain that data as per the standards laid down by the government, informs Dr. Nick Oberheiden, Founder and Attorney, Oberheiden P.C.

The healthcare industry is steadily transforming through innovative technologies like AI and ML. The latter will soon get integrated into practice as a diagnostic aid, particularly in primary care. It plays a crucial role in shaping a predictive, personalized, and preventive future, making treating people a breeze. What are your thoughts?

Image: Depositphotos.com

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How Machine Learning Is Redefining The Healthcare Industry - Small Business Trends