Category Archives: Artificial Intelligence
Artificial intelligence to the rescue | Agriculture | lmtribune.com – Lewiston Morning Tribune
Washington State University will lead a consortium of institutions in the development of new artificial intelligence tools to help address problems in the agricultural field.
Whats being called the AgAID Institute is being funded with a five-year, $20 million grant from the National Science Foundation.
Ananth Kalyanaraman, who holds the Boeing Chair at WSUs School of Electrical Engineering and Computer Science, is the lead principal investigator for the institute.
Kalyanaraman said the agricultural industry has tremendous potential to benefit from AI applications. However, researchers still need to learn how best to marry the technology with the human ability to make complex decisions.
What AI is really good at is looking at past actions and consequences and learning from them, he said. But the reasoning part thats hard to do. Part of this initiative is to understand what humans are good at and what AI is good at, so we can create a partnership.
Other participants in the AgAID Institute include Oregon State University, the University of California-Merced, University of Virginia, Carnegie Mellon University, Heritage University, Wenatchee Valley College and Kansas State University. Private industry partners include IBM Research and innov8.ag.
During its first year of operation, the institute will emphasize stakeholder interaction, Kalyanaraman said. The goal is to create software applications and other AI tools that have a high likelihood of being adopted and used in the field.
To improve the chances of that happening, he said, researchers will work closely with growers to develop the new technology.
And rather than focus on commodity crops like wheat, the institute intends to look at specialty crops like apples, cherries, mint or almonds.
The reason for that, Kalyanaraman said, is that specialty crops involve a mix of complex decisions. They provide a broad range of problems and conditions, so tools developed for those environments should be flexible enough to help other agricultural producers.
AI needs a lot of data, he said. But for tools to be practical in the field, theyll need to be able to handle a variety of data sources.
Some data might be noisy, some might be unreliable, some might be incomplete, Kalyanaraman said. So a huge part of this research is about how to develop robust tools that take into account uncertainties.
Writing software programs and refining the tools will be the focus of the second and third years of the grant, he said. Pilot projects testing the new technology will begin in years three and four.
WSU will build a demonstration farm where we can test the technology and collect data in a field setting, Kalyanaraman said. Well also use it for education and training.
As an example of the type of problems AI tools might be able to address, he pointed to tree pruning.
It requires a lot of skill to decide which limbs to remove from a fruit tree, he said. However, it may be possible to develop a phone-based AI app that allows lower-skilled workers to take a photo of the tree and get a recommendation on where to trim. That not only enables them to get the job done, it helps them learn proper technique as they go along.
It would help close the skill level gap, Kalyanaraman said.
Researchers also plan to create learning circles with ag producers, possibly as early as this month. That will give growers an opportunity to offer their own ideas and suggestions for ways AI applications can help the industry.
It will help us find other places where AI can have a quick impact, Kalyanaraman said. Well be learning from growers about what matters most to them.
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Artificial intelligence to the rescue | Agriculture | lmtribune.com - Lewiston Morning Tribune
Artificial Intelligence Myth Vs Reality: Where Do Healthcare Experts Think We Stand? – Forbes
Artificial intelligence's applicability in healthcare settings may not have lived up to corporate ... [+] and investor hype yet, but AI experts believe we're still in the very early stages
The AI in healthcare: myth versus reality discussion has been happening for well over a decade. From AI bias and data quality issues to considerable market failures (e.g., the notorious missteps and downfall of IBMs Watson Health unit), the progress and efficacy of AI in healthcare continues to face extreme scrutiny.
John Halamka, M.D., M.S., is President of The Mayo Clinic Platform
As President of the Mayo Clinic Platform, John Halamka, M.D., M.S., is not disappointed in the least about AIs progress in healthcare. I think of it as a maturation process, he said. Youre asking why your three-year-old isnt doing calculus. But can your three-year-old add a column of numbers? Thats actually not so bad.
In an industry as complicated and high-stakes as healthcare, the implementation of artificial intelligence and machine learning comes with challenges that have created a credibility gap. Among the many challenges that Halamka and others acknowledge and are working to address include:
Its not all gloom and doom, though, especially when it comes to AI and machine learning for healthcare administration and process efficiency. For example, hospitals and health systems have successfully employed AI to improve physician workflows, optimize revenue cycle and supply chain management strategies, and improve the patient experience.
Iodine Software is one such company thats making an impact in hospital billing and administration through its AI engine, which is designed to help large health systems capture more mid-cycle revenue through clinical documentation improvement (CDI). The companys co-founder and CEO, William Chan, agrees that perceived shortcomings of AI are an overgeneralization.
"The impression that AI hasn't yet been successful is an assumption when you look primarily at the big headline applications of AI over the past 10 years. Big tech has, in many cases, thrown big money at broad and highly publicized efforts, many of which have never met their proclaimed and anticipated results," said Chan. "There are multiple examples of AI in healthcare that can be deemed successful. However, the definition of success is important, and each use case and AI application will have a different definition of success based on the problem that the 'AI' is trying to solve."
And when it comes to solving problems in clinical care delivery, AI-driven clinical decision support (CDS) solutions are another animal altogether. But for those deep in the field, who have been studying, testing and developing AI and machine learning solutions in healthcare for decades, the increase in real-world evidence (RWE) and heightened focus on responsible AI development are reason enough to be hopeful about its future.
Real World Evidence (RWE) and Clinical Effectiveness: An Exciting Time for Healthcare AI
Dr. Suchi Saria is Founder and CEO at Bayesian Health, and the John C. Malone Associate Professor at ... [+] Johns Hopkins University
Personally I think its a very exciting time for AI in healthcare, said Suchi Saria, Ph.D, CEO and CSO at Bayesian Health, an AI-based clinical decision support platform for health systems using electronic health record (EHR) systems. For those of us in the field, weve been seeing steady progress, including peer-reviewed studies, showing the efficacy of ideas in practice.
This spring, Bayesian Health published findings from a large, five-site study that analyzed the impact of its AI platforms sepsis model. The two-year study showed that Bayesian's sepsis module drove faster antibiotic treatment by nearly two hours. Of note, while most CDS tools historically have adoption rates in the low teens, this study, over a wide base of physicians (2000+), showed sustained adoption at 89%. Another separate, single-site study found a 14% reduction in ICU admissions and 12% reduction in ICU length of stay, which translated to a $2.5M annualized benefit for the 250 bed study site hospital.
A 2020 study from scientists at UCSF Radiology and Biomedical Imaging also showed AIs promise in improving care for those with Glioblastoma, the most common and difficult to treat form of brain cancer. Using an AI-driven "virtual biopsy" approach beyond the scope of human abilities UCSF is able to predict the presence of specific genetic alterations in individual patient's tumors using only an MRI. UCSF found that it was also able to accurately identify several clinically relevant genetic alterations, including potential treatment targets.
Most recently, Johns Hopkins Kimmel Cancer Center researchers found that a novel AI blood testing technology they developed could detect lung cancer in patients. Using the DELFI approach DNA evaluation of fragments for early interception on 796 blood samples, researchers found that, when combined with clinical risk factor analysis, a protein biomarker, and computer tomography imaging, the technology accurately detected 94% of patients with cancer across different stages and subtypes.
Abroad, AI is bringing precision care to cardiology with impressive results through HeartFlows AI-enabled software platform a non-invasive option to assist with the diagnosis, management and treatment of patients with heart disease. HeartFlows technology has proven to limit redundant non-invasive diagnostic testing, reduce patient time in hospital and face-to-face clinical contact, and streamline hospital visits, while demonstrating higher diagnostic accuracy compared to other noninvasive tests with an 83% reduction in unnecessary invasive angiograms and significant reduction in the total cost of care.
Data Quality, Availability, Labeling, and Transparency Challenges
In her dual role as director of machine learning and professor of engineering and public health at Johns Hopkins University, Saria lives and breathes AI research, analysis and development. She also deeply understands the benefits, challenges and possibilities of the marriage between AI and real world datasets, including those in EHRs. Bayesian makes the EHR proactive, dynamic and predictive, said Saria, by bringing together data from diverse sources including the EHR to provide a clinical decision support platform that catches life threatening disease complications early, with their sepsis module and results being just one example of a clinical impact area.
However, as anyone working with EHR data can attest to, issues with EHR data quality and usability remain an issue. As Saria notes, In order to draw safe, reliable inferences, you're going to need high-quality approaches that correct for the messiness that exists in the data.
AI is only as good as the curated training set that is used to develop it, said Halamka, noting that EHR data is, by its very nature, incomplete and highly-unfit for purpose. EHR data repositories may only have a small subset of data, for example, or limited API functionally, and thus might not have the richness to develop a comprehensive algorithm.
At Mayo, there is an AI model for breast cancer prediction that has 84 input variables; the EHR data is only a small portion of that. Additionally, in order to account for social determinants of health (SDoH) which drive 80% of an individuals health status and other information thats material to the model, Halamka noted that youre going to have to go beyond traditional EHR data extraction.
EHR vendor AI adoption tactics and results have also been scrutinized. Algorithms from industry EHR giant Epic were found to be delivering inaccurate or irrelevant information to hospitals about the care of seriously ill patients, a STAT News investigation found. Additionally, STAT found that Epic financially incentivizes hospitals and health systems to use its AI algorithms for sepsis. This is concerning for many reasons, chief among them being false predictions and other concerns voiced by health system leaders who have used the algorithm, as well as adding to AIs longstanding credibility problem. It also makes clear the industrys need for broader AI standards and oversight.
Fixing AIs Credibility Problem: Responsible AI Development
To develop a responsible AI model and help to fix AIs credibility problem Halamka notes that there are a number of data must-haves: a longitudinal data record, including structured and unstructured data, telemetry and images, omics, and even digital pathology. Importantly, AI developers also need to continually evaluate the purpose of the data over the course of its lifetime in order to account for and correct dataset shifts.
Left unchecked, a dataset shift can severely impact AI model development. Dataset shifts occur when the data used to train machine learning models differs from the data the model uses to provide diagnostic, prognostic, or treatment advice. Because data and populations can and will shift, AI developers need to continually monitor, detect, and correct for these shifts, which means continuous evaluation. Evaluation not just of performance and models, but of use, said Saria, adding that overreliance can lead to overtreatment.
On top of dataset quality and shifts, there are also financial obstacles to getting usable data. While one of the most exciting domains for AI is in medicine and healthcare, labeled data is an incredibly scarce resource. And its incredibly expensive to get it labeled, said Nishith (Nish) Khandwala, founder of BunkerHill, a startup and consortium connecting health systems to facilitate multi-institutional training, validation and deployment of experimental AI algorithms for medical imaging.
Born out of Stanford University's Artificial Intelligence in Medicine and Imaging (AIMI) Center, BunkerHill does not develop AI algorithms itself, but instead is building a platform and network of health systems to allow them to test algorithms against different data sets. This kind of validation and health-system partnership is aimed at addressing the legal and the technical roadblocks to collaboration across different health systems, which BunkerHill partner UKHC calls key to successful AI development and application in radiology.
Taking a step back, there are a number of other questions and problems that AI developers must consider when initially creating an algorithm, explained Khandwala. What does it even mean to make an algorithm for healthcare? What problem or subset of a problem do you start with? Another challenge is bringing AI to market, which is a moving/non-existent target at the moment.
For medical devices and novel drug development, there is a clear, established regulatory process: there are documented procedures and institutions to guide the way. That does not exist with AI, said Khandwala.
And this continues to be an issue for AI development: While there is an established methodical, research-first mindset and regulatory process when it comes to drug discovery, research, development and clinical validation as youd expect to see in any other scenario of invention for therapeutic benefit this is not the case when it comes to AI, where the healthcare industry is still learning how to evaluate these types of solutions.
Standards, Reimbursement and Regulatory Oversight
Dale C. Van Demark is a Partner at McDermott Will & Emery and co-chair of its Digital Health ... [+] practice
The industry is also still evaluating how to pay for AI solutions. Figuring out how a new delivery tool actually gets traction as a commercial product can be very difficult because the healthcare payment system and all the ways we regulate is a fairly unusual marketplace, said Dale Van Demark, Health Industry Advisory Practice partner at McDermott Will & Emery.
Healthcare also operates under a highly complex and regulated set of payment systems federal, quasi federal, private and employer plans with myriad experimentations happening in terms of new care models for better, quality care, said Van Demark. And within all of that, you have lots of regulatory and program integrity concerns especially in Medicare, for example.
And anything having to do with the delivery of care to an individual is ultimately where you get the most regulation. Thats where the rubber meets the road, Van Demark says, though he doesnt see the FDA regulatory process today to be particularly challenging when it comes to getting an AI product to market. The challenge is in figuring out the business of that technology in the market, and having a deep understanding of how that market works in the regulatory environment.
Jiayan Chen is a Partner at McDermott Will & Emery
Another challenging component? Getting real-world evidence. For AI to be paid for, you need data that shows your product is making a difference, says Jiayan Chen, also a partner in the Health Industry Advisory Practice Group of McDermott Will & Emery. To do that, you need massive quantities of data to develop the tool or algorithm, but you also have to show that it works in a real-world setting.
Chen also sees issues stemming from the constant blurring of lines in terms of the frequently changing roles of an AI developer. At what point are you engaging in product development and research, or acting as a service provider? The answer to that will determine the path forward from a regulatory standpoint.
So what should an AI development process look like, and who should be involved? In terms of developing an AI certification process, similar to the early days of Meaningful Use, EHR software certifications and implementation guides, Halamka notes that there will eventually be certifying entities for AI as well to ensure an algorithm is doing what its supposed to do.
AI oversight should not be limited to government bodies. Starting this year, Halamka predicts healthcare will see new public-private collaborations develop to tackle concerns about AI bias, equity and fairness, and wants to see more oversight and higher standards in terms of published studies. Medical journals shouldnt publish the results of an algorithm model unless it has a label that says it's been peer-reviewed and clinically validated.
At the moment, theres no governing body explaining the right way to do predictive tool evaluations. But the idea is to ultimately give the FDA better tools for avoiding common pitfalls when evaluating AI and predictive solutions, says Saria; for example, only considering workflow implications instead of looking deeper at the models themselves, or incorrectly measuring impact on health outcomes.
This is also what she is focused on in her role at Bayesian Health: evaluating the underlying technology, making it easy to use and actionable in nature, monitoring and adjusting models in real time, and making sure everything is studied and clinically validated.
Its not rocket science; were doing things that everyone should be doing.
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Artificial Intelligence Myth Vs Reality: Where Do Healthcare Experts Think We Stand? - Forbes
Bank of Italy initiatives on Artificial Intelligence in the banking, financial and payment sector – JD Supra
Bank of Italy launched initiatives regarding the use of Artificial Intelligence for innovating the banking, financial and payment sector and complaints handling.
Last week the Bank of Italy published a Call for Proposals to submit FinTech projects to the Milano Hub, i.e. the Bank of Italy hub established with the aim of supporting the development of innovative projects and fostering the digital evolution of the Italian banking and financial market.
The theme of the Call for Proposals is the contribution of Artificial Intelligence (AI) in improving the provision of banking, financial and payment services to businesses, households and public administrations, with a particular focus on financial inclusion, adequate consumer protection and data security.
The projects can be submitted to Milano Hub by three different categories of aspiring (Italian and foreign) participants. Indeed, within Milano Hub there is a dedicated area of operations for each type of participant:
Specific requirements must be met to participate to the Call for Proposals.
Moreover, in the context of the annual report on complaints of banks and financial intermediaries customers, published by the Bank of Italy on 28 September 2021, the regulator highlighted the opportunity to use AI for a more efficient complaints handling process.
Applications to the Milano Hub Call for Proposals may be submitted from 27 September to 29 October 2021.
Maximum of 10 projects will be eligible for support from the Milano Hub.
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The Recommendations Regarding Data Protection in the Field of Artificial Intelligence – JD Supra
The Recommendations on Data Protection in the Field of Artificial Intelligence (the "Recommendations") was published by the Turkish Personal Data Protection Authority (the "DPA")1 on its website on 15 September 2021.
The scope of the Recommendations address the Developers, Manufacturers, Service Providers and Decision Makers in accordance with the Law on the Protection of Personal Data numbered 6698 and its secondary legislation (the "Law"). This is the first time that DPA has published a document regarding data protection regarding AI-based applications.
The Recommendations consist of three parts, namely: (i) general recommendations; (ii) the recommendations for developers; manufacturers and service providers and (iii) recommendations for decision makers.
Under the Recommendations the term Artificial Intelligence (the "AI") is defined as the human-specific abilities to be analysed and passed to machines. The AI focuses on creating algorithms and computer software, which can think, interpret and make decisions as humans.
The Recommendations put forward the definitions of Developer, Manufacturer and Service Provider but do not define the Decision Maker. Considering the European Union documents on the issue, we believe that Decision Maker corresponds to the legislative organ and policy makers.
Further while the Developers are introduced as any real persons or legal entities developing content or application for the AI systems whereas the Manufacturers are real persons or legal entities who produce any products such as software and hardware systems that constitutes these systems.
Service Providers are defined as any real people or legal entities who offer a product and/or service using the AI based systems, data collection systems, software or devices under the Recommendations.
Under the General Recommendations section, the importance of protecting fundamental rights and freedoms of real persons whose data are being processed (the "Data Subject") in the process of developing and applying the AIs is emphasized.
In this context, the right to protection of human dignity should be respected and the principles of "compliance with the law, fairness, proportionality, transparency, accuracy and accuracy of personal data, specific and limited purpose of the use of personal data" should be a basis for the AI developments relying on the processing of personal data and data collection.
Considering the individual and social effects of the data processing activity conducted by the AI, the Data Subject should have the control over. The Recommendations include further guidelines on the issue for everyone working in the field.
Regarding AI developments relying on the processing of personal data;
While developing and applying artificial intelligence technologies, if reaching to the same result is possible without processing personal data, the data should be processed by anonymization3.
Pursuant to the Recommendations, an approach that complies with national and international regulations which respects data privacy should be adopted in AI-oriented designs. In addition, Data Subject's rights regarding their personal data arising from both national and international regulations should be preserved.
Together with these, the points below are stressed in the scope of the Recommendations:
This section includes advices for Decision Makers who are working in the field of personal data protection.
Pursuant to the Recommendations, the Decisions Makers should:
This is the first time that DPA has published recommendations regarding AI-based applications. Since EU has been focusing on AI based works heavily, we believe the Recommendations published by DPA is a result of getting under this influence. While considering the DPA's Recommendations, the principles of human agency and oversight; privacy and data governance; transparency; diversity, non-discrimination and fairness and accountability should also be taken into account.
Although the recommendations presented are general and each of them are different matters of debate, this document signals the AI ethics as a rising toping that we can expect to hear more.
Click here to download 'The Recommendations Regarding Data Protection in the Field of Artificial Intelligence' PDF in Turkish.
1 https://www.kvkk.gov.tr/Icerik/7048/Yapay-Zeka-Alaninda-Kisisel-Verilerin-Korunmasina-Dair-Tavsiyeler2 According to the Article 6 of the Law, "personal data relating to the race, ethnic origin, political opinion, philosophical belief, religion, religious sect or other belief, appearance, membership to associations, foundations or trade-unions, data concerning health, sexual life, criminal convictions and security measures, and the biometric and genetic data" are deemed to be special categories of personal data.3 According to the Article 3 of the Law, anonymization is defined as "rendering personal data impossible to link with an identified or identifiable natural person, even through matching them with other data".
Selin Kaledelen (Associate), Elif Engin (Legal Intern)and Deniz Alkan (Summer Intern) of GKC Partners authored this publication.
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The Recommendations Regarding Data Protection in the Field of Artificial Intelligence - JD Supra
UGA receives grant to study turfgrass water conservation using artificial intelligence – The Albany Herald
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UGA receives grant to study turfgrass water conservation using artificial intelligence - The Albany Herald
Artificial intelligence may help reveal the next animal-to-human virus threat – Study Finds
GLASGOW, Scotland Artificial intelligence may be the best hope humans have for finding the next virus jumping from animals to humans before it becomes a pandemic. Scientists from the University of Glasgow say a form of AI which analyzes viral genomes could predict and possibly stop the next pathogen which is ready to jump from other species into humans like COVID-19.
The exact origins of COVID-19 are still unclear. However, most scientists agree that at some point SARS-CoV-2 jumped from an animal (like bats) to humans. While COVIDs outbreak is bringing the threat of animal-to-human disease transmission to the forefront of the conversation, the reality is that many infectious diseases in recent years originated within an animal before crossing over. Researchers say this is why identifying new high-risk zoonotic viruses before they have a chance to spread is so important.
Its no easy feat identifying animal viruses potentially capable of infecting humans. Estimates show there are 1.67 million animal viruses out there, but only a small portion are capable of infecting humans. So, in order to create AI models capable of using viral genome sequences, researchers put together a dataset of 861 virus species from 36 families.
From that point, the team constructed machine learning models which assigned a human infection probability score for each virus based on patterns in their genomes. Researchers used the top performing AI model to analyze patterns in the predicted zoonotic potential of additional virus genomes from various species.
That process led researchers to conclude that viral genomes may have generalizable features that preadapt these viruses to infect humans. Study authors then created more machine learning models capable of identifying specific viruses likely to infect humans via viral genomes.
While this work is very promising, the team concedes that their models do have limitations. They add using AI is just the first step in terms of identifying animal-based viruses which can pass to humans. Researchers say any viruses the models red flag should be subject to further lab tests.
Moreover, just because an animal virus may be able to infect human beings, that doesnt necessarily mean the virus will actually prove especially harmful, or particularly contagious for that matter.
Our findings show that the zoonotic potential of viruses can be inferred to a surprisingly large extent from their genome sequence. By highlighting viruses with the greatest potential to become zoonotic, genome-based ranking allows further ecological and virological characterization to be targeted more effectively, the researchers write in a media release.
These findings add a crucial piece to the already surprising amount of information that we can extract from the genetic sequence of viruses using AI techniques, adds study co-author Simon Babayan.
A genomic sequence is typically the first, and often only, information we have on newly-discovered viruses, and the more information we can extract from it, the sooner we might identify the virus origins and the zoonotic risk it may pose. As more viruses are characterized, the more effective our machine learning models will become at identifying the rare viruses that ought to be closely monitored and prioritized for preemptive vaccine development.
The study appears in the journal PLoS Biology.
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Artificial intelligence may help reveal the next animal-to-human virus threat - Study Finds
Artificial Intelligence in Manufacturing Industry is Expected to Reach US$ 11.5 Bn by 2027 – GlobeNewswire
PLEASANTON CA, Sept. 30, 2021 (GLOBE NEWSWIRE) -- The latest study titled Global Artificial Intelligence in Manufacturing Market Ecosystem By Components; By Deployment; By Technology; By Application; By Device; By Region; By End Users (Logistics, Healthcare, Automotive, Retail, BFSI, Defence, Aerospace, Oil & Gas, Others) Forecast by 2027 published by AllTheResearch, features an analysis of the current and future scenario of the global Artificial Intelligence (AI) in Manufacturing Market.
The Global Artificial Intelligence (AI) in Manufacturing Market was valued at USD 2.1 Bn in 2020 and is expected to reach USD 11.5 Bn by 2027, with a growing CAGR of 27.2% during the forecast period.
The Artificial Intelligence in manufacturing market is forecasted to grow at a high rate owing to the accelerating innovations in industrial IoT and automation.
The manufacturing industry is expected to be among the market leader in the artificial intelligence market. Further, the manufacturing industry is also expected to display the fastest growth during the forecast period due to rapid digital transformation to promote smart solutions in factories, logistics and management. The manufacturing industry is expected to generate an excess of 2,000 Pb of data every year, which is far more than industries such as BFSI, retail, and aerospace & defense, among others.
Request for sample copy of the report including ToC, Tables, and Figures with detailed informationathttps://www.alltheresearch.com/sample-request/381
Artificial Intelligence (AI) in Manufacturing Market Report Overview:
The report overview includes studying the market scope, leading players like Google, Amazon, Microsoft Corporation, IBM Corporation, Intel, etc., market segments and sub-segments, market analysis by type, application, geography.The report covers Leading Countries and analyzes the potential of the global Artificial Intelligence in Manufacturing industry, providing statistical information about market dynamics, growth factors, major challenges, PEST analysis, and market entry strategy Analysis, opportunities, and forecasts. The biggest highlight of the report is to provide companies in the industry with a strategic analysis of the impact of COVID-19.
Key Findings:
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The key players operating in the AI in manufacturing market are: IBM Corporation, Google Inc., Amazon.com Inc., Microsoft Corporation, Intel, General Electric (GE) Company, Nvidia, Siemens, Cisco Systems, Oracle Corporation, Alphabet Inc., SparkCognition Inc., Mitsubishi Electric, Micron Technology, Rockwell Automation, Sight Machine, Aquant Inc., Progress Software Corporation, Aibrain, General Vision Inc., SAP, Vicarious, Ubtech Robotics, Rethink Robotics, Flutura Decision Sciences & Analytics, Bright Machines, and More
Global AI in manufacturing market is expected to propel at a significant rate during the forecast period owing to the extensive application of artificial intelligence technology in varied industries such as automobile, energy and power, pharmaceuticals, and food & beverages.
The Global Artificial Intelligence in Manufacturing Market Segmentation:
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Regional Analysis of Artificial Intelligence in Manufacturing Market:
In terms of geography, the Asia Pacific region was accounted to hold the largest market share in 2020 and is anticipated to grow at a significant pace throughout the forecast period. The dominance is majorly attributed to the growing manufacturing plants in developed and developing countries such as India, South Korea, China, and Japan. For instance, in May 2018, the South Korean Government announced its plan to invest 2.2 trillion South Korean Won for AI research till 2022. The Ministry of Information and Communication along with the Ministry of Education, Science and Technology established the artificial intelligence R&D strategy called National Strategy for Artificial Intelligence. The investment is expected to create various developments and advantages in the AI in manufacturing Market.
Moreover, the outbreak of covid-19 pandemic has disrupted the supply chain for various industries and also the manufacturing process in several countries including in APAC region. However, China was successful in preventing destructions to human health in Wuhan by implementing a lockdown. With decreasing risk to human health in Wuhan, the manufacturing activities are normalizing in China, thus boosting the AI in manufacturing Market.
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Artificial Intelligence in Manufacturing Industry is Expected to Reach US$ 11.5 Bn by 2027 - GlobeNewswire
Why automation, artificial intelligence and machine learning are becoming increasingly critical for SOC operations – Security Magazine
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Why automation, artificial intelligence and machine learning are becoming increasingly critical for SOC operations - Security Magazine
Researchers will create super-sophisticated artificial intelligence by ‘copying’ the human brain to a chip – ModularPhonesForum
AI is already an integral part of everyday technology, but there are still some barriers in the way of truly advanced AI. Samsung is now working on an exciting new approach that gives associations to science fiction stories.
The South Korean technology giant, in collaboration with researchers from Harvard University in the United States of America, He has a project in progress Which involves copying the human brain onto a computer chip thus bringing AI many steps forward.
The approach involves using so-called reverse engineering on the human brain to get to the bottom of how it is assembled, to simulate the architecture and operation of a computer chip.
The method is described in technical detail in a research document published by the journal Science temper nature (requires payment).
Admittedly, the idea itself isnt entirely new, and there are other AI concepts that draw inspiration from the way the brain works, but in this case its about more direct modeling of the brain.
This is a demanding job because the human brain is made up of an extremely wide network of about a hundred billion neurons and a thousand times more synapses, called synapses. In this regard, Samsung and the research team are planning to use what they themselves refer to as a revolutionary device the nanoelectrode array.
These nanoelectrodes should be able to detect electrical signals from brain cells with very high sensitivity, and use them to map where neurons connect to each other and how strong the connections are. In this way, an overview of the structure used to be recreated in a piece is configured.
Of course, this is not an ordinary computer chip, but a so-called neural chip that uses a different type of Samsungs 3D memory architecture. Neuroprocessing is also being worked on by many other actors, It was mentioned in previous articles here on digi.no (subscription required).
What the researchers envision is using this approach to develop a new type of memory chip that approximates the brains unique characteristics, characteristics such as low energy consumption, the ability to adapt to different environments, and learning without significant difficulties, not least of which is independence and cognitive abilities.
It remains to be seen whether the piece will become a reality, and the researchers admit that the vision is very ambitious. However, Samsung said it will continue to research the technology, so one fine day well likely see results in one form or another.
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Researchers will create super-sophisticated artificial intelligence by 'copying' the human brain to a chip - ModularPhonesForum
It will use artificial intelligence to predict which virus will be next to move from animal to human – ModularPhonesForum
It is estimated that there are about 1.7 million types of viruses that attack different types of animals. Some of these have or may develop the ability to attack humans. This applies to a few of them, but how does one find out in advance which of them?
This matter was further investigated by British researchers at the University of Glasgow. They hope that in the future, artificial intelligence and machine learning can be used to beat viruses.
What the researchers found is that, to a surprising degree, it appears that the genetics of a virus determine whether or not it can become zoonotic that is, moving from animals to humans.
This is good news, because genetic sequencing is often the first and only source of information when newly discovered viruses emerge. Thus, one has a better chance than before to quickly determine the origin of the virus and to assess the animal risks it may pose.
The more viruses that are tested and characterized in this way, the better the machine learning model will be.
It could be a huge help in identifying rare viruses that should be closely monitored and prioritized for developing a preventative vaccine, said Simon Papian, one of the researchers behind the project. phys.org.
The research team began by collecting a data set of 861 known viruses from a total of 36 different virus families. They then built various machine learning models that determined the likelihood of infecting humans, based on the classification and relationship of known viruses that can infect humans.
Then they used the model that gave the best result, to analyze how likely it is that several viruses in animals could infect humans.
This provides a good basis for further laboratory research, but is still only a step in the right direction. The method the research team is developing, for example, says nothing about how easily the virus is transmitted between humans, or how suitably viruses are to actually causing disease.
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It will use artificial intelligence to predict which virus will be next to move from animal to human - ModularPhonesForum