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

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The Artificial Intelligence could carry out attacks without human supervision, a breakthrough technology and also a great risk

The project called CLAWS is directed by the Office of Naval Research, which is responsible for the science and technology programs of the United States Navy and Marine Corps.

Budget documents discovered by New Scientist describe CLAWS as an autonomous system of unmanned submarine weapons that could be installed in underwater robots such as the Orca submarine vehicle developed by Boeing. which can be armed with 12 torpedo tubes that could be controlled by CLAWS without any human intervention.

It will clandestinely extend the reach of large UUVs [ unmanned underwater vehicles] and increase mission areas for kinetic purposes, the documents read .

CLAWS was first revealed in 2018 as an attempt to improve the autonomy and survival of large and extra-large unmanned underwater vehicles, however, until now the capacity of the weapons had not been mentioned, according to New Scientist.

Lethal autonomous weapons

Budget documents reveal that CLAWS has been allocated USD 26 million this year and another 23 million for the next, and it is known that they will be used to develop the idea in a functional submarine prototype .

Stuart Russell, a professor of computer science at the University of California, who described CLAWS as a dangerous development, explained: Equipping a fully autonomous underwater vehicle with lethal weapons is a significant step, and one that runs the risk of accidental climbing in a way that does not apply to marine mines.

Chinese scientists hope to deploy unmanned military submarines in the worlds oceans in the early 2020s, although the final decision on whether to attack will still be taken by commanders, for now the developments suggest that a new front is opening in the career arms of IA at sea.

The rush to develop autonomous and lethal underwater weapons is becoming a growing concern for activists such as the Campaign to Stop Killer Robots, which has attracted the support of technological luminaries such as Elon Musk of Tesla and Mustafa Suleyman of Alphabet, are backed by German Foreign Minister Heiko Maasban, who urged states to ban totally autonomous weapons , before its too late!

Source: La Verdad Noticias

Marketing manager and co-Chief Editor of Maritime Herald.

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Artificial Intelligence Controlled Submarines Are Developed By The US Navy - Maritime Herald

MOUNTAIN VIEW, Calif., March 11, 2020 /PRNewswire/ --

Highlights:

Synopsys, Inc.(NASDAQ:SNPS) today announced a major breakthrough in electronic design technology with the introduction of DSO.ai (Design Space Optimization AI), the industry's first autonomous artificial intelligence application for chip design. Inspired by DeepMind's AlphaZero that mastered complex games like chess or Go, Synopsys' DSO.ai solution is an artificial intelligence and reasoning engine capable of searching for optimization targets in very large solution spaces of chip design. DSO.ai revolutionizes chip design by massively scaling exploration of options in design workflows while automating less consequential decisions, allowing SoC teams to operate at expert levels and significantly amplifying overall throughput.

"As new silicon technologies are testing the limits of physics, our customers are looking for manufacturing solutions that enable their innovative products," said Jaehong Park, executive vice president of Foundry Design Platform Development at Samsung Electronics. "In our design environment, Synopsys' DSO.ai systematically found optimal solutions that exceeded our previously achieved power-performance-area results. Furthermore, DSO.ai was able to achieve these results in as few as 3 days; a process that typically takes multiple experts over a month of experimentation. This AI-driven design methodology will enable Samsung Foundry customers to fully utilize the benefits of our cutting-edge silicon technologies for their SOC designs."

Developed from the ground up at Synopsys, DSO.aiis part of a multiyear, company-wide initiative and strategic investment in AI-based design technology.

Chip Design: A Vast Search Space

Today, AI can interact with humans through natural language, identify bank fraud and protect computer networks, drive cars around city streets, and play intelligent games like chess and Go. Chip design too is a very large space of potential solutions, trillions of times larger than, for example, the game of Go.

Searching this vast space is a very labor-intensive effort, typically requiring many weeks of experimentation, and often guided by past experiences and tribal knowledge. A chip design workflow typically consumes and generates terabytes of highly dimensional data compartmentalized and fragmented across many separately optimized silos. To create an optimal design recipe, engineers have to ingest volumes of high-velocity data and make complex decisions on the fly with incomplete analysis, often leading to decision fatigue and over-constraining of their design.

With today's hypercompetitive markets and stringent silicon manufacturing requirements, the difference between a good recipe and an optimal recipe can be 100s of MHz of performance, hours of battery life, and millions of dollars in design costs.

The EDA Industry's First Autonomous AI Application for Chip Design

Synopsys' DSO.ai solution revolutionizes the process of searching for optimal solutions by enabling autonomous optimization of broad design spaces. DSO.ai engines ingest large data streams generated by chip design tools and use them to explore search spaces, observing how a design evolves over time and adjusting design choices, technology parameters, and workflows to guide the exploration process towards multi-dimensional optimization objectives. DSO.ai uses cutting-edge machine-learning technology invented by Synopsys R&D to execute searches at massive scale, autonomously operating tens-to-thousands of exploration vectors and ingesting gigabytes of high-velocity design analysis data all in real-time.

At the same time, DSO.ai automates less consequential decisions,like tuning tool settings, relieving designers of menial tasks and allowing teams to operate at a near-expert level. Knowledge is shared and applied with high effectiveness across entire design teams. This level of productivity means that engineers are now available for more projects, apply more time on a given problem to achieve better results, handle larger parts of a project, and focus on creative and value-added tasks.

A Leap in Productivity

"Ever since the introduction of Design Compiler in the late '80s, Synopsys has been enabling silicon innovators with tools and technologies across the design spectrum," said Sassine Ghazi, general manager, Design Group at Synopsys. "With DSO.ai, once again, Synopsys is starting a new chapter in semiconductor design. More than two years ago we set out on a fascinating journey to bring AI to chip design, partnering with academic researchers, industry thought leaders, and AI technology pioneers. Today's announcement marks a very important milestone, and our journey in AI is only just beginning."

Synopsys' DSO.ai solution is currently in select deployments with industry-leading partners with broader availability planned for the second half of 2020.

About Synopsys

Synopsys, Inc. (NASDAQ:SNPS) is the Silicon to Software partner for innovative companies developing the electronic products and software applications we rely on every day. As the world's 15th largest software company, Synopsys has a long history of being a global leader in electronic design automation (EDA) and semiconductor IP and is also growing its leadership in software security and quality solutions. Whether you're a system-on-chip (SoC) designer creating advanced semiconductors, or a software developer writing applications that require the highest security and quality, Synopsys has the solutions needed to deliver innovative, high-quality, secure products. Learn more at http://www.synopsys.com.

Editorial Contact:Simone Souza Synopsys, Inc. 650-584-6454simone@synopsys.com

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Synopsys Advances State-of-the-Art in Electronic Design with Revolutionary Artificial Intelligence Technology - Benzinga

LOS ANGELES, March 11, 2020 /PRNewswire/ --Neil Sahota, a leading artificial intelligence (AI) expert, called today for creation of a new ecosystem to encourage partnerships that could provide a solution to the coronavirus and possibly future pandemic diseases.

Sahota, author of the book Own the A.I Revolution (McGraw-Hill) and anIBM Master Inventor who led the IBMWatson Group, called onfellow researchers, clinicians, doctors, and scientists to "collaborate, treat, and hopefully, cure this disease." He said some big tech companies are "already providing chat and video conferencing tools to support their work, but there is an opportunity for deeper collaboration, perhaps through an ecosystem dedicated to coronavirus and potential future pandemic diseases."

The United Nations AI for Goodadvisor noted that important new perspectives to the effects of the devastating 2010 Deepwater Horizon oil spill were developed through teamwork by environmental scientists, technologists, mechanical engineers and other experts.

"Why not find similar opportunities to create multi-disciplinary teams to combat the coronavirus?" Sahota asks. "Right now, we face two challenges. First, intense time pressure leads to a silo-mentality and we don't recognize there might be more efficient solutions. Second, we may hope for a 'magic bullet' solution with technology like artificial intelligence. But, AI needs lots of data and training, and teamwork is essential.

"That's why a new ecosystem might be effective. Consider the United Nations ITU AI for Good initiative that united government agencies, industry, academia and others creates new solutions for Sustainable Development Goals using AI. The combination of different perspectives, domain knowledge and approaches create a unique synergy that accelerates usable solutions. It could be a very powerful approach for us in fighting the corona virus and future, potential pandemic diseases."

AboutNeil Sahota:Neil Sahotais a futurist and leading expert on Artificial Intelligence (AI) and other next generation technologies. He is the author ofOwn the AI Revolution(McGraw Hill) and works with the United Nations on theAI for Goodinitiative. Sahota is also an IBM Master Inventor, former leader of the IBM Watson Group and professor at theUniversity of California/Irvine. His work spans multiple industries, including legal services, healthcare, life sciences, retail, travel, transportation, energy, utilities, automotive, telecommunications, media, and government.

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Artificial Intelligence Authority Neil Sahota Calls For Creation Of A New Ecosystem Of Experts To Find Solution To Fight Coronavirus - PRNewswire

Leading Artificial Intelligence Company machineVantage Deploys AI and Neuroscience to Identify Highly Effective Means For Public Communications Regarding Covid-19

BERKELEY, Calif., March 11, 2020 /PRNewswire/ --As the global Coronavirus pandemic spreads, one of the major challenges that governments, public health institutions, businesses, and the public face is how to communicate most effectively about actions to take regarding the disease.

MachineVantage (PRNewsfoto/MachineVantage)

Applying artificial intelligence and machine learning systems, combined with advanced neuroscience knowledge, leading AI company machineVantage (www.machinevantage.com) has identified a highly effective communication model to address the international health crisis. The firm specializes in extracting metaphors that connect deeply with the non-conscious mind, which is where over 95% of daily decisions are made.

"Neuroscience teaches us that metaphors are the 'language of the non-conscious mind', and they represent a very powerful method of communicating critical information," said Dr. A. K. Pradeep, founder and CEO of machineVantage. "They are essentially a form of 'shorthand' for the brain, which assigns a high priority to this form of information. By applying customized AI-powered algorithms, accessing a vast library of existing metaphors, and relying on neuroscientific learnings, we are able to extract the most meaningful and impactful new metaphor to use in addressing the Coronavirus crisis."

"That metaphor is 'Health connects to Gateways," Dr. Pradeep said. "We rank metaphors in four levels, and our AI systems isolated this Gateway metaphor as 'Emergent'meaning it is gaining importance in the non-conscious mind. We wish to make this finding universally available as a means of doing our part to help in the struggle against this disease by facilitating better communication to the public."

Dr. Pradeep explained that the non-conscious mind connects Health and Gateways in many ways.

Two primary ways are concepts embedded in the Gateway Metaphor:

A. Health is a Gateway to a better life and to things that matterB. Gateways to Health passageways that enable Health, and preserve being Healthy

Both are activated in the scenario that the destruction of our Health closes gateways to a better future, and Gateways need to be closed to help us be Healthy and remain Healthy in the presence of the Covid-19 virus.

Gateways are typically perceived as physical structures, such as doors, iron gates, or bridges. In the non-conscious mind, Gateways are conceptualized as metaphoric portals, allowing or preventing access. A virus such as Covid-19 as an enemy activates the Gateway Metaphor in the non-conscious mind.

Dr. Pradeep identified six key messaging concepts that the Gateway Metaphor prompts:

"Understanding how the non-conscious mind processes and responds to Coronavirus-related information through the lens of this Gateway Metaphor provides important direction on how to construct and convey messages to the public about this disease," said Dr. Pradeep.

Retail data regarding consumer buying patterns confirm the activation of the Gateway Metaphor in the non-conscious mind. The stockpiling of basic consumables such as toilet paper and canned soup shows the activation of "gates may be closed for awhile". The collection of entertainment items such as games, CDs, and DVDs indicates that "the wait inside may be stressful" is also activated in the non-conscious.

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Artificial Intelligence Unlocks "Gateway" Metaphor to Aid the Public, Policy Makers, and Companies in Addressing the Coronavirus Crisis -...

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In 1928, a Scottish scientist named Sir Alexander Fleming left his lab where he was studying the staphylococcus bacteria to go on a two-week vacation with his family. When he returned to his lab bench, he not only realized he hadn't tidied his work space very well, but that the dishes with the bacteria in them were growing mold. He also noticed that the bacteria seemed to be actively avoiding the moldy areas of the petri dish. Later he said "I certainly didn't plan to revolutionize all medicine by discovering the world's first antibiotic, or bacteria killer. But I suppose that was exactly what I did."

These days it doesn't take a slovenly scientist to discover important new antibiotics it just takes a computer. A group of researchers at the Massachusetts Institute of Technology (MIT) have used artificial intelligence (AI) to identify a new antibiotic that kills even some hitherto antibiotic-resistant strains.

But does this mean they staffed the lab with robots rather than people? Nope! The research team created a computer model that systematically screened more than a hundred million chemical compounds in just a few days a feat that would take lab technicians many years (and a lot of the same sort of scientific serendipity that visited Fleming) to accomplish.

Very few new antibiotics have been discovered in the past decade, during which time bacteria are getting tougher.

"We're facing a growing crisis around antibiotic resistance, and this situation is being generated by both an increasing number of pathogens becoming resistant to existing antibiotics, and an anemic pipeline in the biotech and pharmaceutical industries for new antibiotics," said James Collins, a professor in MIT's Institute for Medical Engineering and Science (IMES) and Department of Biological Engineering, in a press release.

The research team developed a machine-learning computer model that could identify about 2,500 molecular compounds that prohibited the growth of bacteria in this case, E. coli, specifically. They then introduced the program to 6,000 drugs that are currently being studied to see if any of them might be useful in curing known human diseases. Once the model selected the molecule with the strongest antibacterial potential that didn't look similar to any known antibiotics, the team used a different model to see if the molecule would be detrimental to people.

Et voila! The model narrowed the candidates down to one the researchers have dubbed it "halicin" which has been tested in the past as a drug to treat diabetes. Halicin has been tested on lab samples of several different antibiotic-resistant strains of bacteria and has been shown to kill almost all of them, with the exception of one very stubborn lung pathogen.

After discovering halicin, the research team used the model to identify 23 more candidates using another database of compounds and found two that were particularly powerful. The researchers are now working to find antibiotics that are more selective in the bacteria they kill, so they don't destroy all our beneficial gut flora while saving our lives. As for halicin, the researchers plan to work with a pharmaceutical company or nonprofit to develop the drug for use in humans, according to the press release.

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Artificial Intelligence Discovers Antibiotic in Record Time - HowStuffWorks