London, March 11, 2020 (GLOBE NEWSWIRE) -- TheArtificial Intelligence (AI) in supply chain market is expected to grow at a CAGR of 45.3% from 2019 to 2027 to reach $21.8 billion by 2027.

Artificial intelligence has emerged as the most potent technologies over the past few years, that is transitioning the landscape of almost all industry verticals. Although enterprise applications based on AI and Machine Learning (ML) are still in the nascent stages of development, they are gradually beginning to drive innovation strategies of the business.

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In the supply chain and logistics industry, artificial intelligence is gaining rapid traction among industry stakeholders. Players operating in the supply chain and logistics industry are increasingly realizing the potential of AI to solve the complexities of running a global logistics network. Adoption of artificial intelligence in the supply chain is routing a new era or industrial transformation, allowing the companies to track their operations, enhance supply chain management productivity, augment business strategies, and engage with customers in digital world.

The growth in the AI in supply chain market is mainly driven by rising awareness of artificial intelligence and big data & analytics and widening implementation of computer vision in both autonomous & semiautonomous applications. In addition, consistent technological advancements in the supply chain industry, rising demand for AI-based business automation solutions, and evolving supply chain complementing growing industrial automation are further offering opportunities for vendors providing AI solutions in the supply chain industry. However, high deployment and operating costs and lack of infrastructure hinder the growth of the artificial intelligence in supply chain market.

In this study, the global artificial intelligence(AI) in supply chain market is segmented on the basis of component, application, technology, end user, and geography.

Based on component, AI in supply chain market is broadly segmented into hardware, software, and services. The software segment commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increasing demand for AI-based platforms and solutions, as they offer supply chain visibility through software, which include inventory control, warehouse management, order procurement, and reverse logistics & tracking.

Based on technology, AI in supply chain market is broadly segmented into machine learning, computer vision, natural language processing, and context-aware computing. In 2019, the machine learning segment commanded the largest share of the overall AI in supply chain market. The growth in this market can be attributed to the growing demand for AI based intelligent solutions; increasing government initiatives; and the ability of AI solutions to efficiently handle and analyze big data and quickly scan, parse, and react to anomalies.

Based on application, AI in supply chain market is broadly segmented into supply chain planning, warehouse management, fleet management, virtual assistant, risk management, inventory management, and planning & logistics. In 2019, the supply chain planning segment commanded the largest share of the overall AI in supply chain market. The growth of this segment can be attributed to the increasing demand for enhancing factory scheduling & production planning and the evolving agility and optimization of supply chain decision-making. In addition, digitizing existing processes and workflows to reinvent the supply chain planning model is also contributing to the growth of this segment.

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Based on end-user, artificial intelligence(AI) in supply chain market is broadly segmented into manufacturing, food & beverage, healthcare, automotive, aerospace, retail, and consumer packaged goods sectors. The retail sector commanded the largest share of the overall AI in supply chain market in 2019. This can be attributed to the increase in demand for consumer retail products.

Based on geography, the global artificial intelligence (AI) in supply chain market is categorized into five major geographies, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa. In 2019, North America commanded for the largest share of the globalAI in supply chain market, followed by Europe, Asia-Pacific, Latin America, and the Middle East & Africa. The large share of the North American region is attributed to the presence of developed economies focusing on enhancing the existing solutions in the supply chain space, and the existence of major players in this market along with a high willingness to adopt advanced technologies.

On the other hand, the Asia-Pacific region is projected to grow at the fastest CAGR during the forecast period. The high growth rate is attributed to rapidly developing economies in the region; presence of young and tech-savvy population in this region; growing proliferation of Internet of Things (IoT); rising disposable income; increasing acceptance of modern technologies across several industries including automotive, manufacturing, and retail; and broadening implementation of computer vision technology in numerous applications. Furthermore, the growing adoption of AI-based solutions and services among supply chain operations, increasing digitalization in the region, and improving connectivity infrastructure are also playing a significant role in the growth of this AIin supply chain market in the region.

The global artificial intelligence in supply chain market is fragmented in nature and is characterized by the presence of several companies competing for the market share. Some of the leading companies in the AIin supply chain market are from the core technology background. These include IBM Corporation (U.S.), Microsoft Corporation (U.S.), Google LLC (U.S.), and Amazon.com, Inc. (U.S.). These companies are leading the market owing to their strong brand recognition, diverse product portfolio, strong distribution & sales network, and strong organic & inorganic growth strategies.

The other key players operating in the globalAI in supply chain market are Intel Corporation (U.S.), Nvidia Corporation (U.S.), Oracle Corporation (U.S.), Samsung (South Korea), LLamasoft, Inc. (U.S.), SAP SE (Germany), General Electric (U.S.), Deutsche Post DHL Group (Germany), Xilinx, Inc. (U.S.), Micron Technology, Inc. (U.S.), FedEx Corporation (U.S.), ClearMetal, Inc. (U.S.), Dassault Systmes (France), and JDA Software Group, Inc. (U.S.), among others.

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Artificial Intelligence (AI) in Supply Chain Market to Grow at a CAGR of 45.3% to Reach $21.8 billion by 2027, Largely Driven by the Consistent...

More than half of organizations have adopted AI for security efforts, but a majority are more confident in results verified by humans, according to WhiteHat Security.

Security professionals need a varied bag of tricks to keep up with savvy and sophisticated cybercriminals. Artificial intelligence is one valuable weapon in the arsenal as it can handle certain tasks faster and more efficiently than can human beings. But AI being AI, it's far from perfect. That's why many security pros still want the human element to play a significant role in their security defense, according to a survey from WhiteHat Security.

SEE:The 10 most important cyberattacks of the decade (free PDF)(TechRepublic)

Based on a survey of 102 industry professionals conducted at the RSA Conference 2020, WhiteHat's "AI and Human Element Security Sentiment Study" found that more than half of the respondents are using AI or machine learning (ML) in their security efforts. More than 20% said that AI-based tools have made their cybersecurity teams more efficient by eliminating a huge number of more mundane tasks.

Image: WhiteHat Security

Further, almost 40% of respondents said they feel their stress levels have dropped since adding AI tools to their security process. And among those, 65% said that AI tools let them focus more on migitating and preventing cyberattacks than they could previously.

However, incorporating AI doesn't take human beings out of the security equation; just the opposite. A majority of those polled agreed that the human element offers skills that AI and ML can't match.

Almost 60% of the respondents said they remain more confident in cyberthreat findings that are verified by human over AI. When asked why they prefer the human touch, 30% pointed to intuition as the most important human element, 21% mentioned the role of creativity, and almost 20% cited previous experience and frame of reference as the most critical advantage of humans over AI.

On its end, WhitePoint described three reasons it supplements its own AI and ML learning systems with human verification:

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Cybersecurity pros are using artificial intelligence but still prefer the human touch - TechRepublic

Wednesday, March 11, 2020

Stephanie Colombini/American Homefront

Credit: Stephanie Colombini/American Homefront

Above: Drs. Andrew Borkowski (left) and Stephen Mastorides analyze slides under a microscope to spot cancer in tissue samples in this undated photo.

Aired 3/11/20 on KPBS News

Listen to this story by Stephanie Colombini.

Inside a laboratory at the James A. Haley Veterans' Hospital in Tampa, Fla., machines are rapidly processing tubes of patients' body fluids and tissue samples. Pathologists examine those samples under microscopes to spot signs of cancer and other diseases.

But distinguishing certain features about a cancer cell can be difficult, so Drs. Stephen Mastorides and Andrew Borkowski, decided to get a computer involved.

In a series of experiments, they uploaded hundreds of images of slides containing lung and colon tissues into artificial intelligence software. Some of the tissues were healthy, while others had different types of cancer, including squamous cell and adenocarcinoma.

Then they tested software with more images the computer had never seen before.

"The module was able to put it together, and it was able to differentiate, 'Is it a cancer or is it not a cancer?'" Borkowski said. "And not only that, but it was also able to say what kind of cancer is it."

The doctors were harnessing the power of what's known as machine learning. Software pre-trained with millions of images, like dogs and trees, can learn to distinguish new ones. Mastorides, chief of pathology and laboratory medicine services at the Tampa VA, said it took only minutes to teach the computer what cancerous tissue looks like.

The two VA doctors recently published a study comparing how different AI programs performed when training computers to diagnose cancer.

"Our earliest studies showed accuracies over 95 percent," Mastorides said.

Enhance, not replace

The doctors said the technology could be especially useful in rural veterans clinics, where pathologists and other specialists aren't easily accessible, or in crowded VA emergency rooms, where being able to spot something like a brain hemorrhage faster could save more lives.

Borkowski. the chief of the hospital's molecular diagnostics section, said he sees AI as a tool to help doctors work more efficiently, not to put them out of a job.

"It won't replace the doctors, but the doctors who use AI will replace the doctors that don't," he said.

The Tampa pathologists aren't the first to experiment with machine learning in this way. The U.S. Food and Drug Administration has approved about 40 algorithms for medicine, including apps that predict blood sugar changes and help detect strokes in CT scans.

The VA already uses AI in several ways, such as scanning medical records for signs of suicide risks. Now the agency is looking to expand research into the technology.

The department announced the hiring of Gil Alterovitz as its first-ever Artificial Intelligence Director in July 2019 and launched The National Artificial Intelligence Institute in November. Alterovitz is a Harvard Medical School professor who co-wrote an artificial intelligence plan for the White House last year.

He said the VA has a "unique opportunity to help veterans" with artificial intelligence.

As the largest integrated health care system in the country, the VA has vast amounts of patient data, which is helpful when training AI software to recognize patterns and trends. Alterovitz said the health system generates about a billion medical images a year.

He described a potential future where AI could help combine the efforts of various specialists to improve diagnoses.

"So you might have one site where a pathologist is looking at slides, and then a radiologist is analyzing MRI and other scans that look at a different level of the body," he said. "You could have an AI orchestrator putting together different pieces and making potential recommendations that teams of doctors can look at."

Alterovitz is also looking for other uses to help VA staff members make better use of their time and help patients in areas where resources are limited.

"Being able to cut the (clinician) workload down is one way to do that," he said. "Other ways are working on processes, so reducing patient wait times, analyzing paperwork, etc."

Barriers to AI

But Alterovitz notes there are challenges to implementing AI, including privacy concerns and trying to understand how and why AI systems make decisions.

Last year, DeepMind Technologies, an AI firm owned by Google, used VA data to test a system to predict deadly kidney disease. But for every correct prediction, there were two false positives.

Those false results may cause doctors to recommend inappropriate treatments, run unnecessary tests, or do other things that could harm patients, waste time, and reduce confidence in the technology.

"It's important for AI systems to be tested in real-world environments with real-world patients and clinicians, because there can be unintended consequences," said Mildred Cho, the Associate Director of the Stanford Center for Biomedical Ethics.

Cho also said it's important to test AI systems with a variety of demographics, because what may work for one population may not for another. The DeepMind study acknowledged that more than 90 percent of the patients in the dataset it used to test the system were male veterans, and that performance was lower for females.

Alterovitz said the VA is taking those concerns into account as the agency experiments with AI and tries to improve upon the technology to ensure it is reliable and effective.

This story is part of our American Homefront Project, a public media collaboration on in-depth military coverage with funding from the Corporation for Public Broadcasting and The Patriots Connection.

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The VA Has Embraced Artificial Intelligence To Improve Veterans' Health Care - KPBS

The past 20 years have seen remarkable advances in the mining industry, particularly in mineral exploration technologies with vast volumes of data generated from geologic, geophysical, geochemical, satellite and other surveying techniques. However, the abundance of data has not necessarily translated into the discovery of new deposits, according to Colin Barnett, co-founder of BW Mining, a Boulder, Colorado-based data mining and mineral exploration company.

One of the problems were facing in exploration is the huge increase in the amounts of data we have to look at, said Barnett, in his presentation at theManaging and exploring big data through artificial intelligence and machine learning session at the recent PDAC 2020 convention in Toronto. And although its high-quality data, the sheer volume is becoming almost overwhelming for human interpreters, and so we need help in getting to the bottom of it.

By integrating hundreds or even thousands of interdependent layers of data, with each layer making its own statistically determined contribution, machine learning offers a solution to the problem of tackling the massive amounts of data generated, and a powerful new tool in the search for mineral deposits.

But, in an interview with The Northern Miner, he cautioned that to fully exploit the potential of machine learning in mineral exploration, prospectors will still need to devote considerable time and effort to the preparation of data before machine learning techniques can add value for companies.

To illustrate his point, Barnett demonstrated how he and his partner at BW Mining, Peter Williams, are using machine learning to analyze data from geological, geochemical and geophysical surveys of the Yukon in northwestern Canada to locate new deposits.

The Yukon became famous for the Klondike gold rush during the late 1890s, which petered out after a few years as prospectors moved onto Alaska. Today the area is experiencing a renewed interest in what has become known as the Tintina Gold Belt, with significant lode deposits being found over the past two decades and, according to Barnett, more waiting to be discovered.

We used the Yukon bedrock geology map published by the Yukon Geological Survey, which is very detailed and shows over 200 different geological formations, explained Barnett. However, you cant simply put 200 formations into a machine learning process. First, the data requires special treatment.

By representing each of the formations with a separate grid and by continuing the grids upward, they were able to see overlaps between formations, allowing them to consolidate the data by grouping the formations by rock type and age, and thereby reducing the data set down to around 50 discrete and different formations. They then used the same process to represent structural data provided by the map.

The structural data is important because it represents the pathways that the mineralization generally took to reach the surface, said Barnett. We then used geophysical maps of the area provided by Natural Resources Canada, which contain enormous amounts of information that can be extracted and subjected to the same statistical treatment, explained Barnett.

Applying the same approach to geochemical, gravity, topographical and satellite data, they were able to generate detailed data sets covering over 300,000 400,000 square kilometers of the study area.

The most critical layer of data for our machine learning process is the known deposits because this is used to train our artificial neural network against all the other layers to identify deposit formations, said Barnett.

Artificial neural networks operate much like the human brain. They can recognize patterns in the different layers of data and cluster or classify them into groups according to similarities in the input data. They are then capable of discriminating between zones of high and low mineral potential.

After scouring through geologic publications, company websites and NI 43-101 technical reports, Barnett and Williams were able to develop accurate mineral footprints for more than 30 deposits using their model, which, according to Barnett, reportedly contain over 46 million oz. of gold.

They then used an artificial neural network to establish the statistical favourability of a location containing an economically viable deposit across the entire region of interest. This approach is essentially an inversion process that uses exploration data relating to a given location as inputs to the network, which then produces the corresponding favourability as the output.

Image of a typical target. Red areas are highly favourable, while purple areas are shown as unfavourable for gold. Credit: BW Mining.

This requires very sophisticated software to analyze and interpret the data, so you cant just use off-the-shelf software, explained Barnett. We first started analyzing the data on a parallel-processor in the basement of the University of Sussex [in England] back in 1992, where my partner was a professor. But it would take five days to get an answer by which time wed forgotten what the question was.

However, with improvements to computer software and hardware, they are now able to generate an answer in a matter of hours using a common laptop.

Barnetts and Williams use of artificial intelligence and machine learning has led to a highly-focused target map that assigns numerical probabilities of making an economic discovery anywhere in the region of interest. And can be used to systematically rank and rate targets and plan cost-effective follow-up programs that take into account the expected return on investment for any given target.

Although Barnett believes there is currently a lack of understanding of artificial intelligence and machine learning in the industry, he is convinced that as these techniques become more widely used and available, machine learning and artificial intelligence will lead to a wave of discoveries. And within ten years, they will be commonly used tools in the mineral exploration industry.

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Artificial intelligence, machine learning primed to deliver 'a wave of discoveries' - The Northern Miner

ATLANTA, March 11, 2020 (GLOBE NEWSWIRE) -- Jvion, a leader in Clinical Artificial Intelligence (AI), today announced the release of its innovative Social Determinants of Health (SDOH) solution that identifies socioeconomic barriers driving an individuals health risk and opportunities for investment in community benefit programs to address gaps in care. Leveraging Jvions peer-reviewed analytics layer and Microsoft Azure Maps, the solution empowers providers and health systems to address underserved populations and inequalities in existing healthcare delivery. Jvion goes beyond helping providers better understand the impact of SDOH by offering individualized interventions that aid in aligning community benefits more effectively.

Providers and healthcare executives recognize the growing role of socioeconomic insights in healthcare, especially in meeting the needs of underserved populations. To date, capturing that data and turning it into meaningful and actionable intelligence has proved elusive for many, said Shantanu Nigam, CEO of Jvion. Our unique approach turns socioeconomic, environmental, and behavioral data into real clinical value that drives higher engagement, more tailored interventions, and greater alignment between need and risk, resulting in better outcomes for individuals and the community as a whole.

As alignment and access to community benefit programs continue to be the cornerstone of building healthier communities, providers need appropriate insight into their populations and individual healthcare needs. Hospitals spent $95 billion on community benefits in the most recent year data is available (American Hospital Association), and increasingly both federal and state regulators are seeking clarity on what benefits are being provided to communities with this spend and their impact. Jvions SDOH solution not only fulfills the federal and state assessment needs for healthcare organizations, but also strategically informs providers where to allocate their community benefit spend to have the greatest level of impact.

Jvions SDOH solution requires limited input from providers and none from patients, largely relying on its high-performing AI approach, which leverages a global instance of de-identified patients to power the inferential outputs of the solution. Through this approach, the community inherits the attributes of the individual versus traditional methods, which apply community qualities to the individual. The SDOH solution features an interactive map interface built using Microsoft Azure maps and a web-based portal.

Were pleased that this technology collaboration is helping healthcare organizations in transforming patient care and their businesses. The Microsoft platform helps responsibly unify people, devices, apps and information by prioritizing compliance, security and trust, said Gareth Hall, director of business strategy for Worldwide Healthcare at Microsoft. Our partners are critical in helping healthcare organizations use technology to address industry challenges and seize opportunities to impact peoples lives in a positive way. The combination of the Microsoft platform and partner innovation is key to helping our industry transform.

The Jvion SDOH solution is now available. Register here to schedule a virtual demo. Additional information is available at https://jvion.com/jvionclinicalai/.

The latest KLAS report, Healthcare AI 2019 - Actualizing the Potential of AI, recognized Jvion as having by far the largest client base in the healthcare AI market," and "the largest offering of pre-built healthcare content for machine learning models/vectors." Additionally, Jvion was featured in the CB Insights Digital Health 150, showcasing the most promising private digital healthcare companies in the world.

About JvionJvion enables healthcare organizations to prevent avoidable patient harm and lower costs through its AI-enabled prescriptive analytics solution. An industry first, the Jvion Machine goes beyond simple predictive analytics and machine learning to identify patients on a trajectory to becoming high risk and for whom intervention will likely be successful. Jvion determines the interventions that will more effectively reduce risk and enable clinical action. And it accelerates time to value by leveraging established patient-level intelligence to drive engagement across hospitals, populations, and patients. To date, the Jvion Machine has been deployed across about 50 hospital systems and 300 hospitals, who report average reductions of 30% for preventable harm incidents and annual cost savings of $6.3 million. For more information, visit http://www.jvion.com.

Jvion PR Contact:Lexi Herosianlexi@scratchmm.com

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Jvion Tackles Socioeconomic Barriers to Care with Industry-Leading Artificial Intelligence - GlobeNewswire