Page 2,668«..1020..2,6672,6682,6692,670..2,6802,690..»

Windows 10 Gets 64-Bit Version of OneDrive – WinBuzzer

Microsoft has announced Windows 10 users can now access the 64-bit version of OneDrive. The company is now rolling out the new version of its file sharing and cloud storage app.

Having a 64-bit version of OneDrive is important, not least because it means the app can support larger files more efficiently on Windows. Of course, this will depend on whether your version of Windows 10 supports 64-bit.

The 64-bit version is the right choice if you plan to use large files, if you have a lot of files, and if you have a computer thats running a 64-bit version of Windows, Microsofts Ankita Kirti said in a blog post at the time.

Advertisement

Computers running 64-bit versions of Windows generally have more resources such as processing power and memory than their 32-bit predecessors. Also, 64-bit applications can access more memory than 32-bit applications (up to 18.4 million Petabytes).

Back in April, Microsoft confirmed Windows 10 would finally get a 64-bit OneDrive application. Windows 10 is a little late to the party considering the macOS version of OneDrive has been 64-bit for three years. Apple moved to 64-bit with macOS High Sierra 10.13.4 in 2018.

Still, better late than never and it is good to see Microsoft bring Windows 10 up to speed with Apples platform.

We know this has been a long awaited and highly requested feature, and were thrilled to make it available for early access, added Kirti.

In recent OneDrive News, Microsoft brought its Universal Print feature to the app, via Microsoft 365. users of the cloud storage and file sharing platform will be able to tap into the printing tool directly within the OneDrive app.

Universal Print works through cloud servers provided by Microsoft Azure. It is designed to remove the complexity of organizations handling their own print servers.

Tip of the day:

Do you sometimes face issues with Windows 10 search where it doesnt find files or return results? Check our tutorial to see how to fix Windows 10 search via various methods.

Advertisement

See the rest here:
Windows 10 Gets 64-Bit Version of OneDrive - WinBuzzer

Read More..

Mayo Clinic, Alphabet’s Verily partner to build clinical decision support tools – Healthcare Dive

Dive Brief:

Verily and Mayo cited the exponential growth in medical knowledge that makes it difficult for doctors to keep up with the latest advances in care recommendations and protocol as the reason for co-developing new decision support products.

The tool, available for clinicians at the point of care, will provide evidence-based knowledge on disease management, care guidelines and treatment options to help doctors make decisions, the companies said.

The hope is that it will be used as a sort of "GPS for patient care,"Bradley Leibovich, medical director of Mayo Clinic's Center for Digital Health, said in a Thursday statement.

The tool will be developed based on a wide variety of relevant data sources, including deidentified health record data, and will use open standards to enable integration with multiple commercial electronic health records, according to the companies.

It will first be deployed at Mayo Clinic facilities before becoming available to Verily's health system partners and customers.

The new partnership comes shortly after news that Google is dissolving its health divisionafter three years as its chief, David Feinberg, departs to become CEO of EHR vendor Cerner. Google will split Google Health's projects and teams across several other segments of the company in a bid to improve execution, according to executives.

Mayo has said that the reorganization will not affect the decade-long cloud storage and development agreement with Google launched in 2019.

Health systems are increasingly partnering with resource-heavy tech companies like Google, Amazon and Microsoft as they move trillions of data points to the cloud and look for ways to leverage that data to improve care delivery and cut costs. Such R&D agreements have come under fire in the past (notably, the data-sharing project between Google and Ascension in 2019), though using medical data to develop new product lines without patient consent is legal under current law.

However, demand for such tools, including in the area of clinical decision support, is rising as doctors look to keep up with rapidly shifting medical knowledge and recommendations.

Startups selling clinical decision support software are reporting booming funding, raising $1.1 billion in the first half of 2021, according to Mercom Capital Group. That's more than double the amount raised in the first half of 2020.

More:
Mayo Clinic, Alphabet's Verily partner to build clinical decision support tools - Healthcare Dive

Read More..

Artificial Intelligence – an overview | ScienceDirect Topics

12.10 Conclusion and Future Research

AI blockchain enabled distributed autonomous energy organizations may help to increase the energy efficiency, cyber security, and resilience of the electricity infrastructure. These are timely goals as we modernize the US power grida complex system of systems that requires secure and reliable communications and a more trustworthy global supply chain. While blockchain, AI, and IoT are creating a buzz right now, many challenges remain to be overcome to realize the full potential of these innovative technological solutions. A lot of news and media coverage of blockchain today falsely suggests that it is a panacea for all that ails usclimate change, cyber security, and volatile financial systems. There is similar hysteria around AI, with articles suggesting that the robots are coming, and that AI will take all of our jobs. While these new technologies are disruptive in their own way and create some exciting new opportunities, many challenges remain. Several fundamental policy, regulatory, and scientific challenges exist before blockchain realizes its full disruptive potential.

Future research should continue to explore the challenges related to blockchain and distributed ledger technology. Applying AI blockchain to modernizing the electricity infrastructure also requires speed, agility, and affordable technology. AI-enhanced algorithms are expensive and often require prodigious data sets that must be broken down into a code that makes sense. However, a lot of noise (distracting data) is being collected and exchanged in the electricity infrastructure, making it difficult to identify cyber anomalies. When there is a lot of disparate data being exchanged at subzero-second speeds, it is difficult to determine the cause of the anomaly, such as a software glitch, cyber-attack, weather event, or hybrid cyber-physical event. It can be very difficult to determine what normal looks like and set the accurate baseline that is needed to detect anomalies. Developing an AI blockchainenhanced grid requires that the data be broken into observable patterns, which is very challenging from a cyber perspective when threats are complex, nonlinear, and evolving.

Applying blockchain to modernizing and securing the electricity infrastructure presents several cyber-security challenges that should be further examined in future research. For example, Ethereum-based smart contracts provide the ability for anyone to write electronic code that can be executed in a blockchain. If an energy producer or consumer agrees to buy or sell renewable energy from a neighbor for an agreed-upon price, it can be captured in a blockchain-based smart contract. AI could help to increase efficiency by automating the auction to include other bidders and sellers in a more efficient and dynamic waythis would require a lot more data and analysis to recognize the discernable patterns that inform the AI algorithm of the smart contracts performance. Increased automation, however, will also require that the code of the blockchain is more resilient to cyber-attacks. Previously, Ethereum was shown to have several vulnerabilities that may undermine the trustworthiness of this transaction mechanism. Vulnerabilities in the code have been exploited in at least three multimillion dollar cyber incidents. In June 2016 DAO was hackedits smart contract code was exploited, and approximately $50 million dollars were extracted. In July 2017 code in an Ethereum wallet was exploited to extract $30 million dollars of cryptocurrency. In January 2018 hackers stole roughly 58 billion yen ($532.6 million) from a Tokyo-based cryptocurrency exchange, Coincheck, Inc. The latter incident highlighted the need for increased security and regulatory protection for cryptocurrencies and other blockchain applications. The Coincheck hack appears to have exploited vulnerabilities in a hot wallet, which is a cryptocurrency wallet that is connected to the internet. In contrast, cold wallets, such as Trezor and Ledger Nano S, are cryptocurrency wallets that are stored offline.

Despite being a centralized currency, Coincheck was a cryptocurrency exchange with a single point of failure. However, the blockchain shared ledger of the account may potentially be able to tag and follow the stolen coins and identify any account that receives them (Fadilpai & Garlick, 2017). Storing prodigious data sets that are constantly growing in a blockchain can also create potential latency or bloat in the chain, requiring large amounts of memory. Requirements for Ethereum-based smart contracts have grown over time and the block takes a longer time to process. For time-sensitive energy transactions, this situation may create speed, scale, and cost issues if the smart contract is not designed properly. Certainly, future research is needed to develop, validate, and verify a more secure approach.

Finally, future research should examine the functional requirements and potential barriers for applying blockchain to make energy organizations more distributed, autonomous, and secure. For example, even if some intermediaries are replaced in the energy sector, a schedule and forecast still need to be submitted to the transmission system operator for the electricity infrastructure to be reliable. Another challenge is incorporating individual blockchain consumers into a balancing group and having them comply with market reliability and requirements as well as submit accurate demand forecasts to the network operator. Managing a balancing group is not a trivial task and this approach could potentially increase the costs of managing the blockchain. To avoid costly disruptions, blockchain autonomous data exchanges, such as demand forecasts from the consumer to the network operator, will need to be stress tested for security and reliability before being deployed at scale. In considering all of these innovative applications, as well as the many associated challenges, future research is needed to develop, validate, and verify AI blockchain enabled DAEOs.

Read more:
Artificial Intelligence - an overview | ScienceDirect Topics

Read More..

What is Artificial Intelligence? How Does AI Work …

The intelligence demonstrated by machines is known as Artificial Intelligence. Artificial Intelligence has grown to be very popular in todays world. It is the simulation of natural intelligence in machines that are programmed to learn and mimic the actions of humans. These machines are able to learn with experience and perform human-like tasks. As technologies such as AI continue to grow, they will have a great impact on our quality of life. Its but natural that everyone today wants to connect with AI technology somehow, may it be as an end-user or pursuing a career in Artificial Intelligence.

To learn more about this domain, check out Great Learnings PG Program in Artificial Intelligence and Machine Learning to upskill. This Artificial Intelligence course will help you learn comprehensive curriculum from a top-ranking global schools and to build job-ready artificial intelligence skills. The program offers a hands-on learning experience with top faculty and dedicated mentor support. On completion, you will receive a Certificate from The University of Texas at Austin. Great Learning Academy also offers Free Online Courses that can help you learn the foundations or the basics of the subject and give you a kick-start in your AI journey.

The short answer to What is Artificial Intelligence is that it depends on who you ask. A layman with a fleeting understanding of technology would link it to robots. Theyd say Artificial Intelligence is a terminator like-figure that can act and think on its own. If you ask about artificial intelligence to an AI researcher, (s)he would say that its a set of algorithms that can produce results without having to be explicitly instructed to do so. And they would all be right. So to summarise, Artificial Intelligence meaning is:

Even if we reach that state where an AI can behave as a human does, how can we be sure it can continue to behave that way? We can base the human-likeness of an AI entity with the:

Lets take a detailed look at how these approaches perform:

The basis of the Turing Test is that the Artificial Intelligence entity should be able to hold a conversation with a human agent. The human agent ideally should not able to conclude that they are talking to an Artificial Intelligence. To achieve these ends, the AI needs to possess these qualities:

As the name suggests, this approach tries to build an Artificial Intelligence model-based on Human Cognition. To distil the essence of the human mind, there are 3 approaches:

The Laws of Thought are a large list of logical statements that govern the operation of our mind. The same laws can be codified and applied to artificial intelligence algorithms. The issues with this approach, because solving a problem in principle (strictly according to the laws of thought) and solving them in practice can be quite different, requiring contextual nuances to apply. Also, there are some actions that we take without being 100% certain of an outcome that an algorithm might not be able to replicate if there are too many parameters.

A rational agent acts to achieve the best possible outcome in its present circumstances.According to the Laws of Thought approach, an entity must behave according to the logical statements. But there are some instances, where there is no logical right thing to do, with multiple outcomes involving different outcomes and corresponding compromises. The rational agent approach tries to make the best possible choice in the current circumstances. It means that its a much more dynamic and adaptable agent.Now that we understand how Artificial Intelligence can be designed to act like a human, lets take a look at how these systems are built.

Building an AI system is a careful process of reverse-engineering human traits and capabilities in a machine, and using its computational prowess to surpass what we are capable of. To understand How Aritificial Intelligence actually works, one needs to deep dive into the various sub domains of Artificial Intelligence and and understand how those domains could be applied into the various fields of the industry. You can also take up an artificial intelligence course that will help you gain a comprehensive understanding.

Not all types of AI all the above fields simultaneously. Different Artificial Intelligence entities are built for different purposes, and thats how they vary. AI can be classified based on Type 1 and Type 2 (Based on functionalities). Heres a brief introduction the first type.

Lets take a detailed look.

This is the most common form of AI that youd find in the market now. These Artificial Intelligence systems are designed to solve one single problem and would be able to execute a single task really well. By definition, they have narrow capabilities, like recommending a product for an e-commerce user or predicting the weather. This is the only kind of Artificial Intelligence that exists today. Theyre able to come close to human functioning in very specific contexts, and even surpass them in many instances, but only excelling in very controlled environments with a limited set of parameters.

AGI is still a theoretical concept. Its defined as AI which has a human-level of cognitive function, across a wide variety of domains such as language processing, image processing, computational functioning and reasoning and so on.Were still a long way away from building an AGI system. An AGI system would need to comprise of thousands of Artificial Narrow Intelligence systems working in tandem, communicating with each other to mimic human reasoning. Even with the most advanced computing systems and infrastructures, such as Fujitsus K or IBMs Watson, it has taken them 40 minutes to simulate a single second of neuronal activity. This speaks to both the immense complexity and interconnectedness of the human brain, and to the magnitude of the challenge of building an AGI with our current resources.

Were almost entering into science-fiction territory here, but ASI is seen as the logical progression from AGI. An Artificial Super Intelligence (ASI) system would be able to surpass all human capabilities. This would include decision making, taking rational decisions, and even includes things like making better art and building emotional relationships.Once we achieve Artificial General Intelligence, AI systems would rapidly be able to improve their capabilities and advance into realms that we might not even have dreamed of. While the gap between AGI and ASI would be relatively narrow (some say as little as a nanosecond, because thats how fast Artificial Intelligence would learn) the long journey ahead of us towards AGI itself makes this seem like a concept that lays far into the future.

Extensive research in Artificial Intelligence also divides it into two more categories, namely Strong Artificial Intelligence and Weak Artificial Intelligence. The terms were coined byJohn Searle in orderto differentiate the performance levels in different kinds of AI machines. Here are some of the core differences between them.

The purpose of Artificial Intelligence is to aid human capabilities and help us make advanced decisions with far-reaching consequences. Thats the answer from a technical standpoint. From a philosophical perspective, Artificial Intelligence has the potential to help humans live more meaningful lives devoid of hard labour, and help manage the complex web of interconnected individuals, companies, states and nations to function in a manner thats beneficial to all of humanity.Currently, the purpose of Artificial Intelligence is shared by all the different tools and techniques that weve invented over the past thousand years to simplify human effort, and to help us make better decisions. Artificial Intelligence has also been touted as our Final Invention, a creation that would invent ground-breaking tools and services that would exponentially change how we lead our lives, by hopefully removing strife, inequality and human suffering.Thats all in the far future though were still a long way from those kinds of outcomes. Currently, Artificial Intelligence is being used mostly by companies to improve their process efficiencies, automate resource-heavy tasks, and to make business predictions based on hard data rather than gut feelings. As all technology that has come before this, the research and development costs need to be subsidised by corporations and government agencies before it becomes accessible to everyday laymen. To learn more about the purpose of artificial intelligence and where it is used, you can take up an AI course and understand the artificial intelligence course details and upskill today.

AI is used in different domains to give insights into user behaviour and give recommendations based on the data. For example, Googles predictive search algorithm used past user data to predict what a user would type next in the search bar. Netflix uses past user data to recommend what movie a user might want to see next, making the user hooked onto the platform and increase watch time. Facebook uses past data of the users to automatically give suggestions to tag your friends, based on their facial features in their images. AI is used everywhere by large organisations to make an end users life simpler. The uses of Artificial Intelligence would broadly fall under the data processing category, which would include the following:

Theres no doubt in the fact that technology has made our life better. From music recommendations, map directions, mobile banking to fraud prevention, AI and other technologies have taken over. Theres a fine line between advancement and destruction. Theres always two sides to a coin, and that is the case with AI as well. Let us take a look at some advantages of Artificial Intelligence-

Lets take a closer look

As a beginner, here are some of the basic prerequisites that will help get started with the subject.

AI truly has the potential to transform many industries, with a wide range of possible use cases. What all these different industries and use cases have in common, is that they are all data-driven. Since Artificial Intelligence is an efficient data processing system at its core, theres a lot of potential for optimisation everywhere.

Lets take a look at the industries where AI is currently shining.

The field of robotics has been advancing even before AI became a reality. At this stage, artificial intelligence is helping robotics to innovate faster with efficient robots. Robots in AI have found applications across verticals and industries especially in the manufacturing and packaging industries. Here are a few applications of robots in AI:

Jobs in AI have been steadily increasing over the past few years and will continue growing at an accelerating rate. 57% of Indian companies are looking forward to hiring the right talent to match up the Market Sentiment. On average, there has been a 60-70% hike in salaries of aspirants who have successfully transitioned into AI roles. Mumbai stays tall in the competition followed by Bangalore and Chennai. As per research, the demand for AI Jobs have increased but efficient workforce has not been keeping pace with it. As per WEF, 133 million jobs would be created in Artificial Intelligence by the year 2020.

Machine learning is a subset of artificial intelligence (AI) which defines one of the core tenets of Artificial Intelligence the ability to learn from experience, rather than just instructions.Machine Learning algorithms automatically learn and improve by learning from their output. They do not need explicit instructions to produce the desired output. They learn by observing their accessible data sets and compares it with examples of the final output. The examine the final output for any recognisable patterns and would try to reverse-engineer the facets to produce an output.

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Deep Learning concepts are used to teach machines what comes naturally to us humans. Using Deep Learning, a computer model can be taught to run classification acts taking image, text, or sound as an input.Deep Learning is becoming popular as the models are capable of achieving state of the art accuracy. Large labelled data sets are used to train these models along with the neural network architectures.Simply put, Deep Learning is using brain simulations hoping to make learning algorithms efficient and simpler to use. Let us now see what is the difference between Deep Learning and Machine Learning.

As the above image portrays, the three concentric ovals describe DL as a subset of ML, which is also another subset of AI. Therefore, AI is the all-encompassing concept that initially erupted. It was then followed by ML that thrived later, and lastly DL that is now promising to escalate the advances of AI to another level.

A component of Artificial Intelligence, Natural Language Processing is the ability of a machine to understand the human language as it is spoken. The objective of NLP is to understand and decipher the human language to ultimately present with a result. Most of the NLP techniques use machine learning to draw insights from human language.Also Read:Most Promising Roles for Artificial Intelligence in India

Computer Vision is a field of study where techniques are developed enabling computers to see and understand the digital images and videos. The goal of computer vision is to draw inferences from visual sources and apply it towards solving a real-world problem.

There are many applications of Computer Vision today, and the future holds an immense scope.

Neural Network is a series of algorithms that mimic the functioning of the human brain to determine the underlying relationships and patterns in a set of data.Also Read:A Peek into Global Artificial Intelligence Strategies

The concept of Neural Networks has found application in developing trading systems for the finance sector. They also assist in the development of processes such as time-series forecasting, security classification, and credit risk modelling.

As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change, allowing us to make a difference before its too late. And all of these advancements are only the beginning, theres so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.

Ques. Where is AI used?

Ans. Artificial Intelligence is used across industries globally. Some of the industries which have delved deep in the field of AI to find new applications are E-commerce, Retail, Security and Surveillance. Sports Analytics, Manufacturing and Production, Automotive among others.

Ques. How is AI helping in our life?

Ans. The virtual digital assistants have changed the way w do our daily tasks. Alexa and Siri have become like real humans we interact with each day for our every small and big need. The natural language abilities and the ability to learn themselves without human interference are the reasons they are developing so fast and becoming just like humans in their interaction only more intelligent and faster.

Ques. Is Alexa an AI?

Ans. Yes, Alexa is an Artificial Intelligence that lives among us.

Ques. Is Siri an AI?

Ans. Yes, just like Alexa Siri is also an artificial intelligence that uses advanced machine learning technologies to function.

Ques. Why is AI needed?

Ans. AI makes every process better, faster, and more accurate. It has some very crucial applications too such as identifying and predicting fraudulent transactions, faster and accurate credit scoring, and automating manually intense data management practices. Artificial Intelligence improves the existing process across industries and applications and also helps in developing new solutions to problems that are overwhelming to deal with manually.

Ques. What is artificial intelligence with examples?

Ans. Artificial Intelligence is an intelligent entity that is created by humans. It is capable of performing tasks intelligently without being explicitly instructed to do so. We make use of AI in our daily lives without even realizing it. Spotify, Siri, Google Maps, YouTube, all of these applications make use of AI for their functioning.

Ques. Is AI dangerous?

Ans. Although there are several speculations on AI being dangerous, at the moment, we cannot say that AI is dangerous. It has benefited our lives in several ways.

Ques. What is the goal of AI?

Ans. The basic goal of AI is to enable computers and machines to perform intellectual tasks such as problem solving, decision making, perception, and understanding human communication.

Ques. What are the advantages of AI?

Ans. There are several advantages of artificial intelligence. They are listed below:

Ques. Who invented AI?

Ans. The term Artificial Intelligence was coined John McCarthy. He is considered as the father of AI.

Ques. Is artificial intelligence the future?

Ans. We are currently living in the greatest advancements of Artificial Intelligence in history. It has emerged to be the next best thing in technology and has impacted the future of almost every industry. There is a greater need for professionals in the field of AI due to the increase in demand. According to WEF, 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022. Yes, AI is the future.

Ques. What is AI and its application?

Ans. AI has paved its way into various industries today. Be it gaming, or healthcare. AI is everywhere. Did you now that the facial recognition feature on our phones uses AI? Google Maps also makes use of AI in its application, and it is part of our daily life more than we know it. Spam filters on Emails, Voice-to-text features, Search recommendations, Fraud protection and prevention, Ride-sharing applicationsare some of the examples of AI and its application.

Whats your view about the future of Artificial Intelligence? Leave your comments below.

Curious to dig deeper into AI, read our blog on some of the top Artificial Intelligence books.

KickStart your Artificial Intelligence Journey with Great Learning which offers high-rated Artificial Intelligence training with world-class training by industry leaders. Whether youre interested in machine learning, data mining, or data analysis, Great Learning has a course for you!

See the article here:
What is Artificial Intelligence? How Does AI Work ...

Read More..

Frontier Development Lab Transforms Space and Earth Science for NASA with Google Cloud Artificial Intelligence and Machine Learning Technology – SETI…

August 26, 2021, Mountain View, Calif., Frontier Development Lab (FDL), in partnership with the SETI Institute, NASA and private sector partners including Google Cloud, are transforming space and Earth science through the application of industry-leading artificial intelligence (AI) and machine learning (ML) tools.

FDL tackles knowledge gaps in space science by pairing ML experts with researchers in physics, astronomy, astrobiology, planetary science, space medicine and Earth science.These researchers have utilized Google Cloud compute resources and expertise since 2018, specifically AI / ML technology, to address research challenges in areas like astronaut health, lunar exploration, exoplanets, heliophysics, climate change and disaster response.

With access to compute resources provided by Google Cloud, FDL has been able to increase the typical ML pipeline by more than 700 times in the last five years, facilitating new discoveries and improved understanding of our planet, solar system and the universe. Throughout this period, Google Clouds Office of the CTO (OCTO) has provided ongoing strategic guidance to FDL researchers on how to optimize AI / ML , and how to use compute resources most efficiently.

With Google Clouds investment, recent FDL achievements include:

"Unfettered on-demand access to massive super-compute resources has transformed the FDL program, enabling researchers to address highly complex challenges across a wide range of science domains, advancing new knowledge, new discoveries and improved understandings in previously unimaginable timeframes, said Bill Diamond, president and CEO, SETI Institute.This program, and the extraordinary results it achieves, would not be possible without the resources generously provided by Google Cloud.

When I first met Bill Diamond and James Parr in 2017, they asked me a simple question: What could happen if we marry the best of Silicon Valley and the minds of NASA? said Scott Penberthy, director of Applied AI at Google Cloud. That was an irresistible challenge. We at Google Cloud simply shared some of our AI tricks and tools, one engineer to another, and they ran with it. Im delighted to see what weve been able to accomplish together - and I am inspired for what we can achieve in the future. The possibilities are endless.

FDL leverages AI technologies to push the frontiers of science research and develop new tools to help solve some of humanity's biggest challenges. FDL teams are comprised of doctoral and post-doctoral researchers who use AI / ML to tackle ground-breaking challenges. Cloud-based super-computer resources mean that FDL teams achieve results in eight-week research sprints that would not be possible in even year-long programs with conventional compute capabilities.

High-performance computing is normally constrained due to the large amount of time, limited availability and cost of running AI experiments, said James Parr, director of FDL. Youre always in a queue. Having a common platform to integrate unstructured data and train neural networks in the cloud allows our FDL researchers from different backgrounds to work together on hugely complex problems with enormous data requirements - no matter where they are located.

Better integrating science and ML is the founding rationale and future north star of FDLs partnership with Google Cloud. ML is particularly powerful for space science when paired with a physical understanding of a problem space. The gap between what we know so far and what we collect as data is an exciting frontier for discovery and something AI / ML and cloud technology is poised to transform.

You can learn more about FDLs 2021 program here.

The FDL 2021 showcase presentations can be watched as follows:

In addition to Google Cloud, FDL is supported by partners including Lockheed Martin, Intel, Luxembourg Space Agency, MIT Portugal, Lawrence Berkeley National Lab, USGS, Microsoft, NVIDIA, Mayo Clinic, Planet and IBM.

About the SETI InstituteFounded in 1984, the SETI Institute is a non-profit, multidisciplinary research and education organization whose mission is to lead humanity's quest to understand the origins and prevalence of life and intelligence in the universe and share that knowledge with the world. Our research encompasses the physical and biological sciences and leverages expertise in data analytics, machine learning and advanced signal detection technologies. The SETI Institute is a distinguished research partner for industry, academia and government agencies, including NASA and NSF.

Contact Information:Rebecca McDonaldDirector of CommunicationsSETI Institutermcdonald@SETI.org

DOWNLOAD FULL PRESS RELEASE HERE.

More here:
Frontier Development Lab Transforms Space and Earth Science for NASA with Google Cloud Artificial Intelligence and Machine Learning Technology - SETI...

Read More..

Embedding Gender in International Humanitarian Law: Is Artificial Intelligence Up to the Task? – Just Security

During armed conflict, unequal power relations and structural disadvantages derived from gender dynamics are exacerbated. There has been increased recognition of these dynamics during the last several decades, particularly in the context of sexual and gender-based violence in conflict, as exemplified for example in United Nations Security Council Resolution 1325 on Women, Peace, and Security. Though initiatives like this resolution are a positive advancement towards the recognition of discrimination against women and structural disadvantages that they suffer from during armed conflict, other aspects of armed conflict, including, notably, the use of artificial intelligence (AI) for targeting purposes, have remained resistant to insights related to gender. This is particularly problematic in the operational aspect of international humanitarian law (IHL), which contains rules on targeting in armed conflict.

The Gender Dimensions of Distinction and Proportionality

Some gendered dimensions of the application of IHL have long been recognized, especially in the context of rape and other categories of sexual violence against women occurring during armed conflict. Therefore, a great deal of attention has been paid in relation to ensuring accountability for crimes of sexual violence during times of armed conflict, while other aspects of conflict, such as the operational aspect of IHL, have remained overlooked.

In applying the principle of distinction, which requires distinguishing civilians from combatants (only the latter of which may be the target of a lawful attack), gendered assumptions of who is a threat have often played an important role. In modern warfare, often characterized by asymmetry and urban conflict and where combatants can blend in with the civilian population, some militaries and armed groups have struggled to reliably distinguish civilians. Due to gendered stereotypes of expected behavior of women and men, gender has operated as a de facto qualified identity that supplements the category of civilian. In practice this can mean that, for women to be targeted, IHL requirements are rigorously applied. Yet, in the case of young civilian males, the bar seems to be lower gender considerations, coupled with other factors such as geographical location, expose them to a greater risk of being targeted.

An illustrative example of this application of the principle of distinction is in so-called signature strikes, a subset of drone strikes adopted by the United States outside what it considers to be areas of active hostilities. Signature strikes target persons who are not on traditional battlefields without individually identifying them, but rather based only on patterns of life. According to reports on these strikes, it is sufficient that the persons targeted fit into the category military-aged males, who live in regions where terrorists operate, and whose behavior is assessed to be similar enough to those of terrorists to mark them for death. However, as the organization Article 36 notes, due to the lack of transparency around the use of armed drones in signature strikes, it is difficult to determine in more detail what standards are used by the U.S. government to classify certain individuals as legal targets. According to a New York Times report from May 2012, in counting casualties from armed drone strikes, the U.S. government reportedly recorded all military-age males in a strike zone as combatants [] unless there is explicit intelligence posthumously proving them innocent.

However, once a target is assessed as a valid military objective, the impact of gender is reversed in conducting a proportionality assessment. The principle of proportionality requires ensuring the anticipated harm to civilians and civilian objects is not excessive compared to the anticipated military advantage of an attack. But in assessing the anticipated advantage and anticipated civilian harms, the calculated military advantage can include the expected reduction of the commanders own combatant casualties as an advantage in other words, the actual loss of civilian lives can be offset by the avoidance of prospective military casualties. This creates the de facto result that the lives of combatants, the vast majority of whom are men, are weighed as more important than those of civilians who in a battlefield context, are often disproportionately women. Taking these applications of IHL into account, we can conclude that a gendered dimension is present in the operational aspect of this branch of law.

AI Application of IHL Principles

New technologies, particularly AI, have been increasingly deployed to assist commanders in their targeting decisions. Specifically, machine-learning algorithms are being used to process massive amounts of data to identify rules or patterns, drawing conclusions about individual pieces of information based on these patterns. In warfare, AI already supports targeting decisions in various forms. For instance, AI algorithms can estimate collateral damage, thereby helping commanders undertake the proportionality analysis. Likewise, some drones have been outfitted with AI to conduct image-recognition and are currently being trained to scan urban environments to find hidden attackers in other words, to distinguish between civilians and combatants as required by the principle of distinction.

Indeed, in modern warfare, the use of AI is expanding. For example, in March 2021 the National Security Commission on AI, a U.S. congressionally-mandated commission, released a report highlighting how, in the future, AI-enabled technologies are going to permeate every facet of warfighting. It also urged the Department of Defense to integrate AI into critical functions and existing systems in order to become an AI-ready force by 2025. As Neil Davison and Jonathan Horowitz note, as the use of AI grows, it is crucial to ensure that its development and deployment (especially when coupled with the use of autonomous weapons) complies with civilian protection.

Yet even if IHL principles can be translated faithfully into the programming of AI-assisted military technologies (a big and doubtful if), such translation will reproduce or even magnify the disparate, gendered impacts of IHL application identified previously. As the case of drones used to undertake signature strikes demonstrates, the integration of new technologies in warfare risks importing, and in the case of AI tech, potentially magnifying and cementing, the gendered injustices already embodied in the application of existing law.

Gendering Artificial Intelligence-Assisted Warfare

There are several reasons that AI may end up reifying and magnifying gender inequities. First, the algorithms are only as good as their inputs and those underlying data are problematic. To properly work, AI needs massive amounts of data. However, neither the collection nor selection of these data are neutral. In less deadly application domains, such as in mortgage loan decisions or predictive policing, there have been demonstrated instances of gender (and other) biases of both the programmers and the individuals tasked with classifying data samples, or even the data sets themselves (which often contain more data on white, male subjects).

Perhaps even more difficult to identify and correct than individuals biases are instances of machine learning that replicate and reinforce historical patterns of injustice merely because those patterns appear, to the AI, to provide useful information rather than undesirable noise. As Noel Sharkey notes, the societal push towards greater fairness and justice is being held back by historical values about poverty, gender and ethnicity that are ossified in big data. There is no reason to believe that bias in targeting data would be any different or any easier to find.

This means that historical human biases can and do lead to incomplete or unrepresentative training data. For example, a predictive algorithm used to apply the principle of distinction on the basis of target profiles, together with other intelligence, surveillance, and reconnaissance tools, will be gender biased if the data inserted equate military-aged men with combatants and disregard other factors. As the practice of signature drone strikes has demonstrated, automatically classifying men as combatants and women as vulnerable has led to mistakes in targeting. As the use of machine learning in targeting expands, these biases will be amplified if not corrected for with each strike providing increasingly biased data.

To mitigate this result, it is critical to ensure that the data collected are diverse, accurate, and disaggregated, and that algorithm designers reflect on how the principles of distinction and proportionality can be applied in gender-biased ways. High quality data collection means, among other things, ensuring that the data are disaggregated by gender otherwise it will be impossible to learn what biases are operating behind the assumptions used, what works to counter those biases, and what does not.

Ensuring high quality data also requires collecting more and different types of data, including data on women. In addition, because AI tools reflect the biases of those who build them, ensuring that female employees hold technical roles and that male employees are fully trained to understand gender and other biases is also crucial to mitigate data biases. Incorporating gender advisors would also be a positive step to ensure that the design of the algorithm, and the interpretation of what the algorithm recommends or suggests, considers gender biases and dynamics.

However, issues of data quality are subsidiary to larger questions about the possibility of translating IHL into code and, even if this translation is possible, the further difficulty of incorporating gender considerations into IHL code. Encoding gender considerations into AI is challenging to say the least, because gender is both a societal and individual construction. Likewise, the process of developing AI is not neutral, as it has both politics and ethics embedded, as demonstrated by documented incidents of AI encoding biases. Finally, the very rules and principles of modern IHL were drafted when structural discrimination against women was not acknowledged or was viewed as natural or beneficial. As a result, when considering how to translate IHL into code, it is essential to incorporate critical gender perspectives into the interpretation of the norms and laws related to armed conflict.

Gendering IHL: An Early Attempt and Work to be Done

An example of the kind of critical engagement with IHL that will be required is provided by the updated International Committee of the Red Cross (ICRC) Commentary on the Third Geneva Convention. Through the incorporation of particular considerations of gender-specific risks and needs (para. 1747), the updated commentary has reconsidered outdated baseline gender assumptions, such as the idea that women have non-combatant status by default, or that women must receive special consideration because they have less resilience, agency or capacity (para. 1682). This shift has demonstrated that it is not only desirable, but also possible to include a gender perspective in the interpretation of the rules of warfare. This shift also underscores the urgent need to revisit IHL targeting principles of distinction and proportionality to assess how their application impacts genders differently, so that any algorithms developed to execute IHL principles incorporate these insights from the start.

As a first cut at this reexamination, it is essential to reassert that principles of non-discrimination also apply to IHL, and must be incorporated into any algorithmic version of these rules. In particular, the principle of distinction allows commanders to lawfully target only those identified as combatants or those who directly participate in hostilities. Article 50 of Additional Protocol I to the Geneva Conventions defines civilians in a negative way, meaning that civilians are those who do not belong to the category of combatants and IHL makes no reference to gender as a signifier of identity for the purpose of assessing whether a given individual is a combatant. In this regard, being a military-aged male cannot be a shortcut to the identification of combatants. Men make up the category of civilians as well. As Maya Brehm notes, there is scope for categorical targeting within a conduct of hostilities framework, but the principle of non-discrimination continues to apply in armed conflict. Adverse distinction based on race, sex, religion, national origin or similar criteria is prohibited.

Likewise, in any attempt to translate the principle of proportionality into code, there must be recognition of and correction for the gendered impacts of current proportionality calculations. For example, across Syria between 2011 and 2016, 75 percent of the civilian women killed in conflict-related violence were killed by shelling or aerial bombardment. In contrast, 49 percent of civilian men killed in war-related violence were killed by shelling or aerial bombardment; men were killed more often by shooting. This suggests that particular tactics and weapons have disparate impacts on civilian populations that break down along gendered lines. The studys authors note that the evolving tactics used by Syrian, opposition, and international forces in the conflict contributed to a decrease in the proportion of casualties who were combatants, as the use of shelling and bombardment two weapons that were shown to have high rates of civilian casualties, especially women and children civilian casualties increased over time. Study authors also note, however, that changing patterns of civilian and combatant behavior may partially explain the increasing rates of women compared to men in civilian casualties: A possible contributor to increasing proportions of women and children among civilian deaths could be that numbers of civilian men in the population decreased over time as some took up arms to become combatants.

As currently understood, IHL does not require an analysis of the gendered impacts of, for example, the choice of aerial bombardment versus shooting. Yet this research suggests that selecting aerial bombardment as a tactic will result in more civilian women than men being killed (nearly 37 percent of women killed in the conflict versus 23 percent of men). Selecting shooting as a tactic produces opposite results, with 23 percent of civilian men killed by shooting compared to 13 percent of women. There is no right proportion of civilian men and women killed by a given tactic, but these disparities have profound, real-world consequences for civilian populations during and after conflict that are simply not considered under current rules of proportionality and distinction.

In this regard, although using force protection to limit ones own forces casualties is not forbidden, such strategy ought to consider the effect that this policy will have on the civilian population of the opposing side including gendered impacts. The compilation of data on how a certain means or method of warfare may impact the civilian population would enable commanders to take a more informed decision. Acknowledging that the effects of weapons in warfare are gendered is the first key step to be taken. In some cases, there has been progress in incorporating a gendered lens into positive IHL, as in the case of cluster munitions, where Article 5 of the convention banning these weapons notes that States shall provide gender-sensitive assistance to victims. But most of this analysis remains rudimentary and not clearly required. In the context of developing AI-assisted technologies, reflecting on the gendered impact of the algorithm is essential during AI development, acquisition, and application.

The process of encoding IHL principles of distinction and proportionality into AI systems provides a useful opportunity to revisit application of these principles with an eye toward interpretations that take into account modern gender perspectives both in terms of how such IHL principles are interpreted and how their application impacts men and women differently. As the recent update of the ICRC Commentary on the Third Geneva Convention illustrates, acknowledging and incorporating gender-specific needs in the interpretation and suggested application of the existing rules of warfare is not only possible, but also desirable.

Disclaimer:This post has been prepared as part of a research internship at theErasmus University Rotterdam, funded by the European Union (EU) Non-Proliferation and Disarmament Consortium as part of a larger EU educationalinitiative aimed at building capacity in the next generation of scholars and practitioners innon-proliferation policy and programming. The views expressed in this post are those of theauthor and do not necessarily reflect those of the Erasmus University Rotterdam, the EU Non-Proliferation andDisarmament Consortium or other members of the network.

See the original post here:
Embedding Gender in International Humanitarian Law: Is Artificial Intelligence Up to the Task? - Just Security

Read More..

heliosDX Adds Artificial Intelligence to its Suite of Diagnostics Services and Solutions – Yahoo Finance

ALPHARETTA, GA / ACCESSWIRE / August 26, 2021 /RushNet, Inc (OTC PINK:RSHN), (the "Company" or "heliosDX") is pleased to announce through its subsidiary heliosDX the investment and adoption of Artificial Intelligence ("AI") into the diagnostic laboratory. heliosDX signed an agreement to utilize Arkstone OneChoice technology and reporting. The companies have been working to integrate the OneChoice technology into the heliosDX systems the last month. The project is nearing completion with test results already meeting expectations.

This is a major upgrade to heliosDX and our infectious disease platform. Through Arkstone, we expect to be able to utilize machine-learning artificial intelligence to better guide physicians with multiple treatment plans based on many patient factors. Once analysis has been completed, the patient's report will be assigned an ArkScore, which essentially identifies the severity of infection should one be detected. The report will also offer recommended treatment and secondary treatment options should one be available. heliosDX believes this is a tremendous advantage to the many physicians using our infectious disease platform. Ultimately, the physician will make the final determination for patient treatment, but, we believe, the Arkstone technology is a state-of-the-art guide for the physicians and staff.

We share this view of the technology from the Arkstone website: "The OneChoice decision engine combines machine-learning artificial intelligence with decades of deep infectious disease expertise to guide physicians to a singular treatment regimen that targets the most relevant infection, with the lowest risk to the patient"

"When faced with multiple detected organisms, physicians often over-prescribe antibiotics, instead of finding a singular treatment regimen. OneChoice weighs dozens of variables, including the source of infection, the organisms and resistance genes detected, patient allergies, age, and sex, to arrive at a focused treatment recommendation" To view a sample Arkstone report Click Here. The final report will be branded heliosDX upon launch to our clients. To learn more about Arkstone OneChoice, visit https://arkstonemedical.com/onechoice_report#onechoice

Story continues

Without artificial intelligence the number of permutations with regard to the potential for disease identification and disease potential identification is endless. Our approach, while costly, we believe significantly enhances medical care while significantly reducing the prospect of undiagnosed disease states. We at heliosDX have determined that we owe this extra effort and cost to our patients and their medical physicians.

heliosDX has also adopted artificial intelligence in other facets of its business. We will use AI internally for training and, externally, for sales/marketing, training and better prepare, in our view, our clients with today's state of the art testing methodologies. We intend to further incorporate Artificial Intelligence into our reporting to patients and physicians above what we are doing with Arkstone. This is still in the implementation stages but, if successful, this is expected to be a first in class solution, and a one of a kind for the diagnostic space. Ashley Sweat, CEO of heliosDX says, "We plan to utilize Artificial Intelligence in the diagnostic space like never seen before. We are excited about the possibilities" The video below was created using Artificial Intelligence, and demonstrates one of many ways we will use it externally.

About HeliosDx:

heliosDX is a National Clinical Reference Laboratory offering High-Complexity Urine Drug Testing ("UDT"), Behavioral Drug Testing, Allergy Droplet Cards, Oral Fluids, Infectious Disease ("PCR"), and NGS Genetic Testing. [Ashley, would suggest a format in this series consistent throughout.] We [have contracts?] in 44 of the lower 48 states and are looking to expand our reach and capabilities. We intend to always stay ahead of the curve by continually investing in our infrastructure with the most efficient scientifically proven instruments and latest cutting-edge software for patient and physician satisfaction. In management's opinion, following such best practices are intended to allow heliosDX to provide physicians fast and accurate reporting, at least meeting if not exceeding industry benchmarks. It is our goal to excel in patient and client care through physician designed panels that aid in testing compliance and reporting education.

Contact:

Ashley Sweatasweat@heliosdx.comwww.heliosdx.comTwitter Handle: @dx_helios

Safe Harbor Notice

Certain statements contained herein are "forward-looking statements" (as defined in the Private Securities Litigation Reform Act of 1995). The Company cautions that statements, and assumptions made in this news release constitute forward-looking statements and make no guarantee of future performance. Forward-looking statements are based on estimates and opinions of management at the time statements are made. These statements may address issues that involve significant risks, uncertainties, estimates made by management. Actual results could differ materially from current projections or implied results. The Company undertakes no obligation to revise these statements following the date of this news release.

Investor caution/added risk for investors in companies claiming involvement in COVID-19 initiatives -

On April 8, 2020, SEC Chairman Jay Clayton and William Hinman, the Director of the Division of Corporation Finance, issued a joint public statement on the importance of disclosure during the COVID-19 crisis.

The SEC and Self-Regulatory Organizations are targeting public companies that claim to have products, treatment or other strategies with regard to COVID-19.

The ultimate impact of theCOVID-19 pandemic on the Company's operations is unknown and will depend on future developments, which are highly uncertain and cannot be predicted with confidence, including the duration of theCOVID-19 outbreak. Additionally, new information may emerge concerning the severity of the COVID-19 pandemic, and any additional preventative and protective actions that governments, or the Company, may direct, which may result in an extended period of continued business disruption, reduced customer traffic and reduced operations. Any resulting financial impact cannot be reasonably estimated at this time.

We further caution investors that our primary objective and goal is to battle this pandemic for the good of the world. As such, it is possible that we may find it necessary to make disclosures which are consistent with that goal, but which may be adverse to the pecuniary interests of the Company and of its shareholders.

SOURCE: RushNet, Inc.

View source version on accesswire.com: https://www.accesswire.com/661576/heliosDX-Adds-Artificial-Intelligence-to-its-Suite-of-Diagnostics-Services-and-Solutions

More:
heliosDX Adds Artificial Intelligence to its Suite of Diagnostics Services and Solutions - Yahoo Finance

Read More..

Valued to be $4.9 Billion by 2026, Artificial Intelligence (AI) in Oil & Gas Slated for Robust Growth Worldwide – thepress.net

Country

United States of AmericaUS Virgin IslandsUnited States Minor Outlying IslandsCanadaMexico, United Mexican StatesBahamas, Commonwealth of theCuba, Republic ofDominican RepublicHaiti, Republic ofJamaicaAfghanistanAlbania, People's Socialist Republic ofAlgeria, People's Democratic Republic ofAmerican SamoaAndorra, Principality ofAngola, Republic ofAnguillaAntarctica (the territory South of 60 deg S)Antigua and BarbudaArgentina, Argentine RepublicArmeniaArubaAustralia, Commonwealth ofAustria, Republic ofAzerbaijan, Republic ofBahrain, Kingdom ofBangladesh, People's Republic ofBarbadosBelarusBelgium, Kingdom ofBelizeBenin, People's Republic ofBermudaBhutan, Kingdom ofBolivia, Republic ofBosnia and HerzegovinaBotswana, Republic ofBouvet Island (Bouvetoya)Brazil, Federative Republic ofBritish Indian Ocean Territory (Chagos Archipelago)British Virgin IslandsBrunei DarussalamBulgaria, People's Republic ofBurkina FasoBurundi, Republic ofCambodia, Kingdom ofCameroon, United Republic ofCape Verde, Republic ofCayman IslandsCentral African RepublicChad, Republic ofChile, Republic ofChina, People's Republic ofChristmas IslandCocos (Keeling) IslandsColombia, Republic ofComoros, Union of theCongo, Democratic Republic ofCongo, People's Republic ofCook IslandsCosta Rica, Republic ofCote D'Ivoire, Ivory Coast, Republic of theCyprus, Republic ofCzech RepublicDenmark, Kingdom ofDjibouti, Republic ofDominica, Commonwealth ofEcuador, Republic ofEgypt, Arab Republic ofEl Salvador, Republic ofEquatorial Guinea, Republic ofEritreaEstoniaEthiopiaFaeroe IslandsFalkland Islands (Malvinas)Fiji, Republic of the Fiji IslandsFinland, Republic ofFrance, French RepublicFrench GuianaFrench PolynesiaFrench Southern TerritoriesGabon, Gabonese RepublicGambia, Republic of theGeorgiaGermanyGhana, Republic ofGibraltarGreece, Hellenic RepublicGreenlandGrenadaGuadaloupeGuamGuatemala, Republic ofGuinea, RevolutionaryPeople's Rep'c ofGuinea-Bissau, Republic ofGuyana, Republic ofHeard and McDonald IslandsHoly See (Vatican City State)Honduras, Republic ofHong Kong, Special Administrative Region of ChinaHrvatska (Croatia)Hungary, Hungarian People's RepublicIceland, Republic ofIndia, Republic ofIndonesia, Republic ofIran, Islamic Republic ofIraq, Republic ofIrelandIsrael, State ofItaly, Italian RepublicJapanJordan, Hashemite Kingdom ofKazakhstan, Republic ofKenya, Republic ofKiribati, Republic ofKorea, Democratic People's Republic ofKorea, Republic ofKuwait, State ofKyrgyz RepublicLao People's Democratic RepublicLatviaLebanon, Lebanese RepublicLesotho, Kingdom ofLiberia, Republic ofLibyan Arab JamahiriyaLiechtenstein, Principality ofLithuaniaLuxembourg, Grand Duchy ofMacao, Special Administrative Region of ChinaMacedonia, the former Yugoslav Republic ofMadagascar, Republic ofMalawi, Republic ofMalaysiaMaldives, Republic ofMali, Republic ofMalta, Republic ofMarshall IslandsMartiniqueMauritania, Islamic Republic ofMauritiusMayotteMicronesia, Federated States ofMoldova, Republic ofMonaco, Principality ofMongolia, Mongolian People's RepublicMontserratMorocco, Kingdom ofMozambique, People's Republic ofMyanmarNamibiaNauru, Republic ofNepal, Kingdom ofNetherlands AntillesNetherlands, Kingdom of theNew CaledoniaNew ZealandNicaragua, Republic ofNiger, Republic of theNigeria, Federal Republic ofNiue, Republic ofNorfolk IslandNorthern Mariana IslandsNorway, Kingdom ofOman, Sultanate ofPakistan, Islamic Republic ofPalauPalestinian Territory, OccupiedPanama, Republic ofPapua New GuineaParaguay, Republic ofPeru, Republic ofPhilippines, Republic of thePitcairn IslandPoland, Polish People's RepublicPortugal, Portuguese RepublicPuerto RicoQatar, State ofReunionRomania, Socialist Republic ofRussian FederationRwanda, Rwandese RepublicSamoa, Independent State ofSan Marino, Republic ofSao Tome and Principe, Democratic Republic ofSaudi Arabia, Kingdom ofSenegal, Republic ofSerbia and MontenegroSeychelles, Republic ofSierra Leone, Republic ofSingapore, Republic ofSlovakia (Slovak Republic)SloveniaSolomon IslandsSomalia, Somali RepublicSouth Africa, Republic ofSouth Georgia and the South Sandwich IslandsSpain, Spanish StateSri Lanka, Democratic Socialist Republic ofSt. HelenaSt. Kitts and NevisSt. LuciaSt. Pierre and MiquelonSt. Vincent and the GrenadinesSudan, Democratic Republic of theSuriname, Republic ofSvalbard & Jan Mayen IslandsSwaziland, Kingdom ofSweden, Kingdom ofSwitzerland, Swiss ConfederationSyrian Arab RepublicTaiwan, Province of ChinaTajikistanTanzania, United Republic ofThailand, Kingdom ofTimor-Leste, Democratic Republic ofTogo, Togolese RepublicTokelau (Tokelau Islands)Tonga, Kingdom ofTrinidad and Tobago, Republic ofTunisia, Republic ofTurkey, Republic ofTurkmenistanTurks and Caicos IslandsTuvaluUganda, Republic ofUkraineUnited Arab EmiratesUnited Kingdom of Great Britain & N. IrelandUruguay, Eastern Republic ofUzbekistanVanuatuVenezuela, Bolivarian Republic ofViet Nam, Socialist Republic ofWallis and Futuna IslandsWestern SaharaYemenZambia, Republic ofZimbabwe

Read the original here:
Valued to be $4.9 Billion by 2026, Artificial Intelligence (AI) in Oil & Gas Slated for Robust Growth Worldwide - thepress.net

Read More..

Artificial Intelligence And Subject Matter Eligibility In US Patent Office Appeals Part One Of Three – Intellectual Property – United States – Mondaq…

To print this article, all you need is to be registered or login on Mondaq.com.

Note: First published inThe Intellectual PropertyStrategistandLaw.com.

This article is Part One of a Three-Part Article Series

Artificial intelligence is changing industry and society, andmetrics at the US Patent and Trademark Office (USPTO) reflect itsimpact. In a recent publication, the USPTO indicated that from 2002to 2018 the share of all patent applications relating to artificialintelligence grew from 9% to approximately16%.SeeInventing AI, Tracing thediffusion of artificial intelligence with U.S. patents,Office of the Chief Economist, IP Data Highlights (October 2020).For the foreseeable future, patent applications involvingartificial intelligence technologies, including machine learning,will increase with the continued proliferation of suchtechnologies. However, subject matter eligibility can be asignificant challenge in securing patents on artificialintelligence and machine learning.

This three-part article series explores USPTO handlingofAliceissues involving artificialintelligence and machine learning through a sampling of recentPatent Trial and Appeal Board (PTAB) decisions.See AliceCorp. v. CLS Bank Int'l, 134 S. Ct. 2347 (2014). Somedecisions dutifully applied USPTO guidelines on subject mattereligibility, including Example 39 thereof, to resolve appeal issuesbrought to the PTAB. In one case, the PTABsuasponteoffered eligibility guidance even withnoAliceappeal issue before it. These decisionsinform strategies to optimize patent drafting and prosecution forartificial intelligence and machine learning relatedinventions.

Generic Machine LearningAlgorithm

InEx parte Hussain, Appeal No. 2020-005406 (PTABFeb. 18, 2021), the PTAB considered the subject matter eligibilityof claims reciting a machine learning algorithm inrelation to mitigation of risk of consumer default on an onlinetransaction. Representative claim 1 recited as follows:

Id.at 2-3 (emphasis added). To assess subjectmatter eligibility of the representative claim, the PTAB appliedUSPTO guidelines mandating the familiar two step analyticalframework.SeeUSPTO, 2019 Revised PatentSubject Matter Eligibility Guidance, 84 Fed. Reg. 50 (Jan. 7,2019); USPTO, October 2019 Update: Subject Matter Eligibility, 84Fed. Reg. 55942 (Oct. 17, 2019).

As to the first prong of Step 2A in the analytical framework,the PTAB indicated that the representative claim used only ageneric machine learning algorithm to output afidelity score in some unspecified manner. The PTABalso addressed Example 39 of the USPTO guidelines, the examplereciting machine learning in a hypothetical claim deemed eligible.In particular, the PTAB contrasted relevant detail in the claim ofExample 39 versus the relative absence of such detail in therepresentative claim. The PTAB acknowledged that the representativeclaim expressly recited that the machine learning algorithm wastrained to infer characteristics about a user from variable valuesgenerated from specific types of data. Nonetheless, the PTABreiterated that the machine learning algorithm as claimed wastrained to make inferences in an unspecified way without anytechnical details. The PTAB gave little consideration to therecited transformation of the specific types of data into thevariable values, which were specifically claimed as inputs to themachine learning algorithm. Depending on the facts, the claimedinputs to the machine learning algorithm could have been deemedsuggestive of data to train the machine learning algorithm. Forthat reason, the claimed inputs might have been argued topotentially resemble or parallel the recitation of training datadetails supporting eligibility in Example 39. However, no sucharguments were raised.

The PTAB found that a machine learning algorithm assuch was not described in the specification despitethe acknowledged references in the specification to a logisticregression, random forest, supervised learning algorithm, neuralnetwork, vector machine, and other classification algorithm.According to the PTAB, the description of these otherconcepts without technical details confirmed the abstractnature of the claimed machine learning algorithm. In particular,the PTAB noted that the specification described algorithms togenerate fidelity scores without details of trainingthem to infer characteristics about users. Refusing to alsoconsider the machine learning claim limitations under the secondprong because they recited the abstract idea under the first prong,the PTAB ultimately determined that the representative claim wasineligible after finding no inventive concept.

Accordingly, not just any claimed specifics about an artificialintelligence related invention will satisfy the PTAB abouteligibility. Although the representative claiminHussainrecited the inference specificallygenerated by the machine learning algorithm, the PTAB indicatedthat the claim still did not specify enough. In view of thePTAB's observation that both the specification andrepresentative claim lacked technical detail, expressly claimingtraining data and identifying it as such and of coursebeforehand drafting the patent application in support thereof might have secured a different outcome.

Part Two of this article series will further analyze recent PTABdecision making regarding artificial intelligence and subjectmatter eligibility.

The content of this article is intended to provide a generalguide to the subject matter. Specialist advice should be soughtabout your specific circumstances.

POPULAR ARTICLES ON: Intellectual Property from United States

Frankfurt Kurnit Klein & Selz

Another bombshell has dropped. Is change coming? For over a decade, it was generally considered safe to include an image or a video on your website that was linked through to the social media...

Obhan & Associates

Trademarks Comparative Guide for the jurisdiction of India, check out our comparative guides section to compare across multiple countries

Finnegan, Henderson, Farabow, Garrett & Dunner, LLP

In Omni MedSci, Inc., v. Apple Inc., Nos. 2020-1715, -1716 (Fed. Cir. Aug. 2, 2021), a divided Federal Circuit panel affirmed the district court's denial of Apple's motion to dismiss.

Husch Blackwell LLP

Aside from the regulatory requirements imposed on beer labels, as discussed in the Anatomy of a Beer Label: Part I post on COLAs, brewers should consider protecting the trademarks featured on their beer labels.

See the article here:
Artificial Intelligence And Subject Matter Eligibility In US Patent Office Appeals Part One Of Three - Intellectual Property - United States - Mondaq...

Read More..

COVID-19: quality of life and artificial intelligence | JMDH – Dove Medical Press

Introduction

History has a way of reminding us that while the good times are great, a business as usual comes with many unforeseen risks and challenges. On a positive note, stress, anxiety, and other mental health issues have turned around many mindsets in certain groups. There are now significant and unprecedented levels of compassion, empathy, and more, originating from many populations. One such instance, wherein significant challenges were posed to the community is at the time of the First World War. Besides, there was the Spanish plague, there was the second world war and for the last 60 plus years, we have had to live in a world of misgivings; ranging from populism to political unrests and instability in several parts of the world, primarily the Middle East and some parts of Asia.

When the current Coronavirus disease 2019 (COVID-19) started in December 2019, many assumed that like its predecessors H1N1, SARS, different plagues, and viruses, etc., it was going to pass with a thud (Chatterjee et al 2020: para 9).1 Five months into the pandemic and countries continue to live in fear, driven by many unknowns and limited scientific evidence. In the meantime, this aggressive, stealth, and brutal virus continues to spiral unabated. There is at least some consensus that once the peak of the pandemic has been achieved, there will be a reason for optimism. This is based on the assumption that everything being equal (continuous self-exclusion, personal hygiene, social distancing, etc.), the worst would then be behind us. For the most part, this assumption is correct if the processes are effectively and comprehensively implemented. The reality is that the potential for a subsequent wave is real and compelling. To be specific, as per the study findings of Salyer et al,2 the second wave of Covid-19, which was evident by December 2020, was more aggressive than the first one in several cases. In this regard, the Spanish flu, also known as the 1918 flu pandemic, serves as a classical example. Its second wave of infection proved to be even deadlier than the first after non-medical intervention measures put in place at the time had been relaxed.3

It must also be noted that during the outburst of COVID-19 also known as SARS-CoV-2 disease, healthcare workers are found to play a pivotal role. According to the report published by World Health Organization (WHO),4 healthcare workers have been providing frontline services in the pandemic. They are also found to undertake several responsibilities in maintaining health and wellbeing during the outbreak of the coronavirus such as implementing effective health measures, which, in turn, can protect the occupational health and safety aspects of the healthcare organizations. Their significant roles, as well as responsibilities in all the Covid-19 pandemic stages, are found to expose them to risks. The hazards that these healthcare workers have been immensely exposed to during this pandemic include psychological distress, pathogen exposure, fatigue, psychological violence, physical impacts, occupational burnout, extensive working hours, and stigma, among others (World Health Organization (WHO) 2021: 1).4 Even community health workers are found to be playing a vital role in facilitating successful COVID-19 vaccination programs. Health workers are found to plan, as well as coordinate the vaccine rollouts. They are also responsible for identifying the target groups for vaccination along with engaging communities, service delivery, facilitating mobilization, tracking progress, and conduct follow-ups (World Health Organization and the United Nations Childrens Fund (UNICEF) 2021: 8).5

Additionally, Al Thobaitya and Alshammari6 asserted that healthcare workers and nurses have played a significant role in disasters and daily routine, especially during the COVID-19 pandemic. They are engaged in providing holistic care to all patients. Since nurses constitute many of the healthcare professionals, they have an important role within healthcare systems. Specifically,

Their roles in treating patients with COVID-19 involve triaging patients and detecting suspected cases with infections; providing essential treatment in an emergency and dealing with suspected patients with precautions; helping in decontamination and coordination with other healthcare providers; supplying holistic nursing practices in managing multiple infections simultaneously; playing critical roles in expanding care services; and dealing with relatives.6

However, Lahner et al7 stated that due to their pivotal role in maintaining the health and wellbeing of the patients even during Covid-19, health workers are found to be at a high risk of getting infected. In the context of Covid-19, it has highly influenced the dynamics of quality of life along with incorporating AI. This has been particularly highlighted in the scientific research conducted by Laudanski et al.8 In this study, it has been understood that technological advancements of AI have significant scope to improve the pandemic response at every stage. Appendix 1 illustrates the pandemic phases propounded by the WHO, wherein distinctive AI applications have been visible considering hypothetical cases. It shows that in the majority of all the stages, AI can be applied in one way or the other. It is during this pandemic that AI engines have been prominently performing with a higher level of sensitivity. This has helped to track cases along with the performance of response programs. Even in cases, wherei limited data are available, AI can be developed and deployed. However, pre-training of AI is found to be highly necessary so that appropriate outcomes can be attained.9

Hence, with the consideration of the COVID-19 Pandemic, the transformation, which has been evident across the world concerning the quality of life as well as AI technological advancements, will be explored in this research paper. The key objective of this research paper is to perform an exploratory review of the varied dynamics of the COVID-19 pandemic, in addition to emphasizing the theme of pandemic morbidity and mortality. AI and its contributing role will also be reviewed. The reason for conducting this exploratory review on the concerned topic is to explore the pandemic dynamics, and its contribution in addressing such issues in the future. The present study indicates that it has a high contribution to the existing literature. This is because this topic can be relevant to other health and social issues. For understanding the literature gap, a literature review has been conducted. Thus, it must be noted that limited literature is present, which examines the dynamics of AI and QOL concerning the recent outbreak of the COVID-19 pandemic. Therefore, it can be evident that this study can provide important information concerning the QOL and AI dynamics during the pandemic.

The method, which has been incorporated in this research study, is a review of the literature. Besides, anecdotal evidence along with exploratory reviews and reports on the morbidity of COVID-19 have also been taken into due consideration for understanding the dynamics of QOL and AI. This research paper also provides the scope of the devastating effects of the pandemic in select countries: a challenge that should awake all policymakers and create scope for more innovative, cost-effective, and pragmatic interventions. In that regard, the importance of supply chain management systems cannot be adequately emphasized. For the study, a literature review has been conducted by collecting reports and anecdotal evidence. Only recent sources have been selected or included for exploring the review. This is because the issue of the pandemic is recent. Hence, only recent sources are valid for the study. The sources before 2019 have been excluded from the study.

A troublesome pre-occupation in many affected regions is vulnerability. The notion that we are all equal in the fight against this virus has been quickly dispelled with early findings, revealing health inequalities amongst populations ranging from front-line service providers to marginalized communities to racial minority groups (Centers for Disease Control and Prevention (CDC) 2020).10

Specifically, in the United States (US), preliminary nationwide data released by the Centers for Disease Control and Prevention (CDC)11 revealed that although African Americans represent approximately 13% of the US population, they accounted for 30% of all COVID-19 patients. Although far from complete, these data are consistent with the findings from other data collected on race and COVID-19 so far. A disproportionate toll is also being seen in the UK after the Guardian did an analysis of 12, 593 patients who died of COVID-19 as of April 19, 2020. It showed that 19% were Black, Asian, and minority ethnic (BAME) even though they make up 15% of the population.12

In many cases, keeping food on the table means foregoing safe working conditions and a greater risk of exposure to COVID-19. Hence, it can be stated that this issue closely aligns with pandemic morbidity: the focus of the present paper. Besides, a lack of economic resources often translates to food insecurity, amongst other things, which in turn often leads to poorer health outcomes that include a higher risk of underlying health conditions. In India, millions of people, including migrant laborers and daily wage earners, are facing hunger since the countrys shut down in late March 2020 left them with no means to earn a living. A similar dire outlook is also threatening First Nations communities in Canada and black communities in the US. Canada does not report Coronavirus morbidity by race or ethnicity; making it difficult to address disparities. The study conducted by Nguyen further suggested that to eliminate such economic issues, AI technology can be implemented. In this context, it has been recommended that economic recovery can be predicted, as well as tracked with the help of AI applications by detecting cars and solar panel installations in parking lots.13

Many front-line workers like transport employees, sanitary workers, delivery personnel, etc., are often made up of BAME groups.14 In New York City, for example, Blacks and Latinos make up more than 60% of the hard-hit Metropolitan Transportation Authority (MTA). As of April 22, 2020, eighty-three MTA workers have died.15 Apart from them, healthcare workers are also found to be adversely affected due to COVID-19 economically. According to Shukla, Pradhan, and Malik,16 the outbreak of COVID-19 has posed an economic impact on the healthcare sector of India. As a result of which

A stimulus package at 0.8% of GDP was announced on 26 March 2020 and included in-kind and cash transfer to lower income households, insurance coverage of healthcare workers and financial support to low wage workers and others seeking jobs.16

Even the most basic health recommendations to avoid contracting or spreading infection like hand washing and social distancing are major challenges in marginalized communities without sufficient access to water or housing. The number of people who do not have regular access to water is mind-boggling: 36 million people in Mexico, over 2 million in the US, more than 100 in First Nations communities in Canada, 63.4 million in India, etc. In all, 40% of the worlds population lack access to basic hand-washing facilities in their homes.1720

The inability to self-isolate, when faced with a virulent virus, places additional stress on people within communities, who are affected by overcrowding and housing shortages. In many Indigenous communities in Canada often living in remote areas with limited medical services there are sometimes two or three families living in the same house.21 Indigenous Australians face the same troubling dilemma, compounded by a higher prevalence of underlying health conditions in Indigenous communities compared to general populations.21 There is compelling evidence that unprecedented measures such as national lockdowns were incorporated in Italy due to the pandemic. The main reason for undertaking such measures was that Italian people were facing several health issues, including psychological issues. Even post-traumatic symptoms were evident and hence, psychological interventions were suggested in the study present by Roma et al.22

For Brazils Indigenous groups, where some have little or no contact with non-Indigenous society leaves them particularly vulnerable to disease. Fears grow that the entire community could be wiped out amidst a rising number of illegal land invasions from loggers, miners, etc. As of April 17, 2020, Brazils Socio-Environmental Institute (ISA) has recorded at least 27 confirmed COVID-19 cases and 3 deaths, including a 15-year-old from a village on the Uraricoera River - an access route for gold rush miners.23 Besides, in South-East Asia, it has been reported that Covid-19 was evident earlier than in other parts of the world. The concerned states took 17 days to declare an emergency ie, after 50 positive cases of the contamination of the virus.24 Similarly, several African nations have recorded lesser than 1000 cases. Specifically,

WHO has warned that the pandemic could kill between 83,000 and 190,000 people in 47 African countries in the first year, mostly depending on governments responses; and the virus could smolder for several years.25

Based on the understanding derived from the preliminary research, it has been found that due to the significant roles and responsibilities undertaken by the healthcare workers, they become prone to being infected by the virus. This is the reason why Lahner et al. affirmed that there is a high prevalence of COVID-19 infection among healthcare workers. This was prominently evident from the cross-sectional study, which was done considering the retrospective data of healthcare workers. The results of this study showed that

A total of 2057 HWs (median age 46, 1969 years, females 60.2%) were assessed by the RNA RT-PCR assay and 58 (2.7%) tested positive for SARS-CoV-2 infection. Compared with negative HWs, SARS-CoV-2-positives were younger (mean age 41.7 versus 45.2, p < 0.01; 50% versus 31% under or equal to 40 years old, p < 0.002) and had a shorter duration of employment (64 versus 125 months, p = 0.02). Exposure to SARS-CoV-2 was more frequent in positive HWs than in negatives (55.2% versus 27.5%, p < 0.0001).7

It was further observed that nearly half of the healthcare workers considered for this study were not exposed to any COVID-19 infected subjects. This helps in assesing the vulnerability of the healthcare workers while dealing and responding to the pandemic because they are playing the essential role of the frontline workers.7 This study is found to significantly contribute to the literature review. The main reason being that in conducting vaccination drives, the healthcare professionals have important roles. However, if they are affected, the healthcare programs may not lead to positive outcomes. This study can be used in the future for exploring the situations and understanding the risks that are associated with frontline workers so that the third wave of COVID-19 can be managed appropriately along with responding to future healthcare issues.

While lockdowns continue to serve as a geopolitical prevention strategy against COVID-19, the financial and economic outcomes on the poor populations undoubtedly are remarkably onerous. In Asia, for example, and according to the Economic and Social Commission for Asia and the Pacific (ESCAP), 70% of workers belong to the informal economy (no benefits or safety net).26 Many countries in this region have introduced support mechanisms financial and economic (rice, sugar, etc.). These strategies are necessary but not sufficient! As demonstrated by the lockdown insubordination in countries like Bangladesh, the poor in these economies remain vulnerable with limited options and an extremely unenviable way of life: contract the virus by risking going out or follow the lockdown and starve.

The biggest concern for the World Health Organization (WHO) is COVID-19s potential to spread in countries with weak health systems. While the 2019 Global Health Security Index, a health security assessment listing of 195 countries, highlighted fundamental weaknesses of healthcare systems around the world, its not surprising that many countries found to be the least prepared were in Africa.27 Less than 50% of the continents population has access to modern health facilities and countries are plagued with shortages ranging from low numbers of healthcare workers in ratio to the population to medical equipment, medications, and capacity (AFRIC 2019).28

Densely populated cities, slums, and displacement camps; struggles with other simultaneous communicable diseases, ongoing conflicts in some regions, and myriads of other dangerous conditions, make it inevitable that the continent will experience a substantial epidemic.

The one silver lining in terms of mortality rates is that Africa has the youngest population in the world 60% of its 1.25 billion population is under the age of twenty-five, an age group likely to recover from COVID-19 infection.

Besides, data collected from the Chinese Center for Disease Control and Prevention (China CDC) in January and February 2020, identified people aged 60 and over as the most vulnerable to COVID-19. Mortality rates based on these findings were determined by University of Bern researchers as 4.6% for ages 6069, 9.8% for ages 7079, and 18% for ages 80 and over.

Unsurprisingly, with 23.1% of Italys population being 65 and over, it has one of the highest mortality rates in the world (28,236 as of May 1, 2020). In Canada, 79% of all deaths in the country have been linked to seniors homes and long-term care facilities as of April 13, 2020, according to chief public health officer Theresa Tam. Similarly, as per the study conducted by Bhapkar et al, the mortality rate during the pandemic is constantly altering with time and hence, it has been termed Progressive Mortality Rate (PMR). In this study, it was observed that the PMR rates of Russia, India, Japan, the US, Brazil, Germany, China, Mexico, Singapore, New Zealand, and Canada were 1.83, 2.82, 2.75, 3.61, 3.92, 4.35, 5.34, 12.79, 0.05, 1.4, and 7.63 respectively. On the other hand, Progressive Recovery Rates (PRR) of the same countries were recorded to be 85.58, 106.44, 101.15, 38.89, 96.53, 94.85, 94.44, 95.6, 97.93, 97.7, and 92.63 accordingly.29

Furthermore, the study of Samlani et al, suggested that in Morocco, the quality of life of the people was moderately affected by the pandemic. This was because the Mental Health Score (MCS) of all the participants was 34.49. On the other hand, their Physical Health Score (PCS) accounted for 36.10. It was also found that the impact of the concerned pandemic was evident in those people with chronic illnesses, which significantly deteriorated their wellbeing and quality of life. The main reason for such results is that people with or without chronic illness were found to suffer from mental health and panic issues. Besides, the isolation and quarantine made people face psychological health problems.30 It has also been observed that Covid-19 has led to the death of several people, which has further affected the food systems and presented unprecedented challenges to work-life and public health.31

On the other hand, as of March 2021, a total of 1,521,068 people have been infected by the pandemic in South Africa and the most affected region was Gauteng (Johannesburg), which reported about 406,729 Covid-19 cases. It was also found that the highest increase in the daily cases of Coronavirus was evident on 8th January 2021 with 21, 980 new cases. Besides, viewing from a different perspective, it has been found that the pandemic significantly hampered the businesses across the nation, thereby adversely affecting their survivability at large.32,33 This indicates that South Africa has been largely affected by the pandemic, which is bound to change the quality of life of people living and working therein. Concerning South Africa, Covid-19 largely influenced the deaths and mortality rates of the nation due to the presence of underlying causes. It has made a significant impact on the quality of life. Contextually, the mental health of people was negatively affected by the pandemic due to the uncertainty that it created. Besides, restrictions, quarantine, financial losses, high infectivity, continuous lockdowns, fatality, and unemployment rates have altered the daily lives, as well as activities of people. This has led problems associated with mental health along with substance abuse. Even educational institutions have remained closed, which negatively affected the learning and teaching activities of people. Even teenage marriages were observed to increase along with gender-based violence, demonstrations, and social unrest. This implies that there are less human capital and economic opportunities in the future of the nation.34

Another study conducted by Guo et al portrayed those lockdowns, which have been implemented as a precautionary measure during the Covid-19 pandemic have significantly influenced the quality of life of people with Parkinson's disease (PD). In this regard, it was found that the concerned patients were unable to seek medical advice or guidance from their respective doctors. As a result, most of the patients had to alter their routine medicines, which made their quality of life or health conditions even worse. In such situations, telemedicine is found to be significantly effective and efficient for the patients during the lockdown. The challenges concerning adequate treatment caused the symptoms of patients to get aggravated, which further exacerbated their quality of life. On the other hand, healthcare professionals are also finding it difficult to maintain healthcare quality.35,36 Zhang and Ma further affirmed from their study that the quality of life, as well as mental health of local people, especially that of China has deteriorated significantly. Specifically, a mild level of stress was evident among most of the survey participants irrespective of the devastating pandemic outbreak. The mean Impact of Event Scale (IES) score was found to be 13.6 7.7.37 Even social and economic developments have been adversely affected, thereby increasing poverty along with inequality.38 All these aspects indicate that Covid-19 has largely affected people throughout the world, thereby transforming the way they live or their quality of life.

On a similar note, Dey et al highlighted that because of COVID-19, there are several psychosocial and psychological impacts: especially fear among the public. In this review, it was particularly found that the psychological effect was more taxing. Hence, long-term quarantine was implemented by the governmental bodies of various countries. This is the reason why boredom, fear, and frustration have been observed to be highly evident among the citizens. This has increased the difficulties in the trying times of the Covid-19 outbreak. The latter stages of the pandemic were observed to pose more significant impacts such as psychological disorder and stress along with mental stigma and financial losses. This study found that 22% of adults (a survey among 1000 people) have been experiencing worse sleep patterns during this pandemic, which may increase the risk of cardiovascular events [9]. In this situation of adversity, yoga, meditation, and video chat with relatives and friends induce mental relaxation, to some extent. In contrast, self-isolation gives us opportunities to connect with our passions and inner identity.39

Additionally, AI along with augmented intelligence plays a significant role in understanding the collected data through data analytics, pattern recognition, anomaly detection, and machine learning.40 Similarly, Mukherjee et al stated that AI-driven tools have been used to track as well as observe the developments of positive cases during the outbreak of the pandemic. However, it was argued that differences in data can influence the critical decision-making concerning the preparedness and responses of the pandemic. With the advancement in the pandemic stages, technical innovations concerning AI have also been evident, especially for detecting and predicting purposes.41

Currently, there are several achievements, which have been evident during the outbreak of a pandemic. According to report findings of United Nations, telephone-enabled services such as teleconferencing along with social media and other smartphone applications as well as online shopping have been increasing. These services are used to resolve the problems due to Covid-19 in most of the nations, including the US and China. These improvements have increased e-commerce business activities and forced traditional businesses to undergo digitalization.42 In the social context, one of the positive aspects, which have been highlighted by the pandemic, is the role and contribution of women in society. Cities and communities have facilitated innovation for achieving sustainable developments even in this crisis. Besides, marine, as well as land ecosystems are also improving during this pandemic due to reduced exploitation of resources. Also, due to lockdowns and isolations, the flora and fauna are being restored in their natural habitat, as they are not disturbed by humans. Another positive aspect of this pandemic is the unity with which people have been fighting against Coronavirus.43,44 Furthermore, Covid-19 has facilitated the importance of distance learning. However, there are students, who are facing problems in switching to the online mode of learning due to the lack of adequate resources and support from their parents IESALC 2020: 45; UNESCO 2020).4547 On the contrary, Gonzalez et al48 affirmed that the confinement evident due to Covid-19 had a positive impact on the performance of the students in Spain. Similarly, it was found in the study conducted by Chaudhary, Gupta, Jain, and Santosh that the air quality was considerably improved during the lockdown phase of the COVID-19 pandemic in most nations. Hence, it can be stated that due to COVID-19, isolation practices were implemented, which proved to be climate favorable. In many regions of the US, Brazil, China, and India, air quality indices improved due to restrictions in air pollution activities.49 Besides, currently, big data and AI incorporation have been evidenced to enhance the pandemic situation and reduce the adverse impacts of COVID-19. In this context, it was found that By training on an open-source dataset with 13, 975 images of 13, 870 patients, the proposed CNN model can achieve an accuracy of 93.3%. (p. 5).50 Herein, CNN model refers to the convolution neural network (CNN), which incorporates AI techniques.50

Ethical issues are being faced in several areas during the pandemic, especially in terms of physical distancing, conducting clinical trials, rights of healthcare workers, priority-setting, public health surveillance, and resource allocation. The ethical issues are mainly at the time of conducting healthcare research, policy-making, and decision-making process.51 Hence, ethical aspects must be closely considered while responding to the issue at the post-pandemic stage. Specifically, ethical concerns have emerged with the increase in the influx of patients requiring ICUs. Healthcare professionals have been facing ethical dilemmas along with life-support withdrawal decisions. Similar issues have also been faced concerning the quality of end-of-life support and family visits. Hence, effective triage policies are to be formulated so that these issues may not be faced in the post-pandemic phase.52 Similar aspects have been highlighted by McGuire et al wherein it has been affirmed that ethical issues emerged not only within the healthcare system but also in society. Particularly, ethical issues can be evident while defining the benefits, handling informed consent, understanding the special needs of other patients, mitigating discrimination, identifying structural inequalities, and engaging communities.53 Ethical issues have also been found to emerge at the time of resetting healthcare services after the outbreak ie, post-pandemic.8 Contextually, it has been affirmed in the study of Laudanski et al that

Numerous predictive models of COVID-19 prognosis in various individuals based on AI-driven algorithms have been designed and published [7580]. Their ability to distinguish between favorable outcomes and demise is significantly accurate. A few of them were implemented to test their suggestions in real life, a fact that leaves unaddressed concerns about dataset impartiality and concomitant ethical concerns about the implication of AIdriven decisions.

This indicates that in the post-pandemic era, ethical concerns have been prominent, especially at the time of implementing AI-driven decisions.54

The latest technology has been of utmost importance during the pandemic. This is because AI is found to be effective not only in detecting pathogens but also in responding and recovering from Covid-19. According to a report presented by OECD, AI systems had predicted an outbreak of pneumonia in China before coronavirus became the worldwide threat. Hence, understanding the effectiveness of this technology, it is clear that AI technologies and tools can be incorporated for supporting the efforts of medical communities, policymakers, and societies. This can enable the concerned authorities to manage activities at all stages of the pandemic, including the acceleration of research, detection, response, prevention, and recovery. AI can be effective in enhancing research for the discovery of proper solutions such as vaccines and drugs through distributed computing and open data projects.55 Similar opinions have been provided by Arora, Banerjee, and Narasu, wherein AI largely contributed to developing several types of vaccines to date. It seemed that there is a race between the virus and vaccine developers. Hence, for the betterment of mankind and to improve the situation created by the pandemic, AIs ability continues to be vital. This was because The pace of the discovery can be accelerated manifold by harnessing the power of AI.56

To win in the race, several biotechnology companies are depending on AI such as Blue Dot for pacing up the ways to find a cure for the virus. This technology has the potential to identify changes and spot patterns so that the process of vaccine development does not get hampered. In this context, several successful trials have been made. For instance, the Deep Learning-Based Drug Screening method was created using DenseNet for predicting the interactions between ligands and proteins, which further helped in determining the drug combination that worked well while responding to Covid-19. Besides, DeepMind has used the AlphaFold library for understanding the protein structure of the virus. Furthermore, Machine learning (ML) models were developed by the AI scientists of Wuhan for identifying the infection intensity with the help of factors such as gender and age.57 As a result of such initiatives, an AI-based flu vaccine has been developed in the US for which the clinical trials are being sponsored by the National Institute of Allergy and Infectious Diseases. The scientists of Flinders University used synthetic chemist, which is an AI program that generated numerous synthetic compounds. They also used the Search Algorithm for Ligands (SAM), which is an AI program that assisted the scientists to determine good candidates for vaccine trials. This program has shortened the development process of vaccines. This indicates that AI can contribute not only to examine the drugs that are currently available but also helps in accelerating the antivirus development procedure.58 Additionally, the Human Vaccines Project, as well as the Harvard T.H. Chan School of Public Health, has started the Human Immunomics Initiative.This initiative made use of AI models to speed up the process of vaccine and therapeutic development, thereby understanding effective immunity concerning old-aged populations.59

The pandemic has also illustrated that with cooperation at the local, national, and global levels communities can thrive in the wake of the crisis.60 It has also been understood that at the time of pandemic without effective control and prevention measures, the healthcare systems become restricted when considering general measures such as limited travel, social contact, hygiene and sanitary measures, usage of PPE, isolation, and quarantine.61,62 The ongoing carnage experienced by this population is not only despicable but also confirms the degree of incompetence and lackadaisical efforts of some institutions both government and private.

As counterfactuals, there are compelling needs to know if these gruesome and unacceptable mortality rates could have been avoided if:

The memories of this pandemic in these vulnerable communities will be long-lasting and tenuous, especially between the affected families and these institutions.

Additionally, it must also be noted that communities need to prioritize and appreciate essential values along with their needs so that the true importance of healthcare professionals and frontline workers in maintaining the wellbeing of people can be understood. Even the businesses require focusing on values and fulfill the needs of the people. Piccialli et al affirmed that AI technologies have the potential to be successfully used in healthcare systems so that society can be benefitted in future pandemic situations.63

Irrespective of several positive achievements evident during the pandemic due to lockdowns and less human intentions on social, business, and environmental aspects, it has posed significant adverse impacts. To minimize or mitigate the negative consequences of the Covid-19 pandemic, certain strategic decisions need to be undertaken by the nations at the post-pandemic stage. Innovation has been one of the widely used strategic initiatives to be undertaken by several countries, especially to revive the healthcare systems along with gain economic stability.64 On the other hand, a recent Organisation for Economic Co-operation and Development (OECD) report highlighted that the social economy has been playing an essential role in addressing or minimizing the impacts of the pandemic. This indicates that nations must focus on strengthening their social economies so that both long-term and short-term impacts can be eliminated during the post-pandemic phase. This is because social economy firms have the potential to reshape the national economy, thereby encouraging sustainable economic along with inclusive models. This, in turn, can facilitate social innovation, which will help the economy to improve in the future.65 It has also been suggested by Piccialli et al that in the post-pandemic era, careful application of AI technologies must be enabled for managing complex situations similar to COVID-19 in the future, thereby involving research, healthcare, and society.63

As we go through these trying times, there is a need to regularly remind ourselves that while the vulnerable groups on the front lines specifically continue to subject themselves to this devastating virus, their motivation and dedication to respond to this professional call of duty requires special recognition, empathy, and compassion at all levels. This applies specifically to health professionals who continue to expose themselves daily to alleviate the suffering of victims of the pandemic. Institutional support remains relatively inadequate and yet its involvement is a sine qua non that cannot be adequately emphasized. Institutional support needs to be strengthened, especially concerning individual risks and supply chain coordination.

In the future, it will be important to take effective public interventions so that new cases of Covid-19 can be prevented along with mitigating community transmission. Besides, innovation must be taken into consideration for tracing cases along with online learning and telemedicine for managing the second wave of the Covid-19 outbreak more effectively than the earlier one.66 Since the second wave has been phased out; recommendations for the third wave must be taken into consideration. Vigilant monitoring of the cases must be maintained for tracking the new variants to control the cases at the earliest.67 Disparities evident during the pandemic must also be eliminated cooperatively in order to ensure that future pandemics and similar issues can be averted effectively. Additionally, the health issues such as anxiety and stress must be evaluated, as well as addressed immediately among the healthcare staff.68 It has also been understood that elderly people have higher risks of transmission, which suggests that in the future, the healthcare requirements of the older citizens must be taken into high consideration so that their safety and wellbeing can be ensured.62 Besides, the importance of AI technology has also been found to be immensely imperative, as it has been estimated to play a vital role in tackling COVID-19. AI can contribute not only to pacing up the vaccine development procedure but also in identifying future threats posed by viruses beforehand. It also helps in diagnosing, predicting infections, surveillance, gathering information, delivering materials, deploying services, and tracking the recovery process, thereby expanding strategies. Evidence of these implications has not been evident to date. This can be highly effective in tackling Covid-19 in the future.65 Additionally, AL-Hashimi and Hamdan asserted that AI has been showing positive results in detecting conditions such as diseases. It must also be effectively used in the healthcare sector. With its implentation, healthcare organizations can track the progress of any situation at a quick pace. With more advancement in AI-driven technologies, higher-quality healthcare services can be delivered for the betterment of society.69

Based on the findings gathered in the above sections, it has been understood that Covid-19 has significantly affected the world both positively and negatively. It can be concluded that the pandemic has facilitated global transformations, especially by deteriorating the quality of life of millions of people. Additionally, the public along with healthcare workers was also found to be adversely affected due to COVID-19. It became highly important on the part of the healthcare workers that their health and safety were maintained in order to perform their duties effectively. On a positive note, COVID-19 has made the best use of AI-driven technologies for aiding or responding to the pandemic. Hence, it has been suggested that its full potential needs to be explored in the future. This can help in providing better quality healthcare services in pandemic situations in the future both efficiently and effectively. This will confirm that the objective of the research study has been met effectively. Finally, an exploratory review of COVID-19 has been conducted by emphasizing the theme of pandemic morbidity and considering the dynamics of AI and Quality of Life (QOL).

The pandemic also made us realize the importance of cooperation among people along with values. It is also understood that healthcare workers and other frontline workers are vital in responding to the pandemic. Additionally, innovative approaches and effective health interventions are found to be essential in addressing the adverse consequences of the crisis. This further indicates the lessons that must be learned from the pandemic so that new waves and future epidemics can be handled as effectively as possible. One of such future implications is to ensure the health and wellbeing of elderly people. Another important future implication is to optimally utilize AI capabilities to tackle the pandemic throughout its different stages.

Personal Funds.

The author reports no conflicts of interest for this work.

1. Chatterjee P, Nagi N, Agarwal A, et al. The 2019 novel coronavirus disease (COVID-19) pandemic: a review of the current evidence. Indian J Med Res. 2020;151(23):147159.

2. Salyer SJ, Maeda J, Sembuche S, et al. The first and second waves of the COVID-19 pandemic in Africa: a cross-sectional study. The Lancet. 2021;397(10281):12651275.

3. Martini M, Gazzaniga V, Bragazzi NL, Barberis I. The Spanish Influenza Pandemic: a lesson from history 100 years after 1918. J Prev Med Hyg. 2019;60(1):E64E67.

4. World Health Organization (WHO). Coronavirus Disease (Covid-19) Outbreak: Rights, Roles and Responsibilities of Health Workers, Including Key Considerations for Occupational Safety and Health. World Health Organization. 13. 2021.

5. World Health Organization and the United Nations Childrens Fund (UNICEF). The Role of Community Health Workers in COVID-19 Vaccination: Implement Support Guide. 2021;115.

6. Al Thobaitya A, Alshammari F. Nurses on the Frontline against the COVID-19 pandemic: an integrative review. Dubai Med J. 2020;3:8792.

7. Lahner E, Dilaghi E, Prestigiacomo C, et al. Prevalence of Sars-Cov-2 Infection in Health Workers (HWs) and diagnostic test performance: the experience of a teaching hospital in central Italy. Int J Environ Res Public Health. 2020;17(12):112.

8. Laudanski K, Shea G, DiMeglio M, Rastrepo M, Solomon C. What can covid-19 teach us about using AI in pandemics? Healthcare. 2020;8:114.

9. Laudanski K, Shea G, DiMeglio M, Rastrepo M, Solomon C. What can covid-19 teach us about using AI in pandemics? Healthcare. 2020;8:114.

10. Centers for Disease Control and Prevention (CDC). Cases in US; 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html.Accessed April 30, 2020.

11. Centers for Disease Control and Prevention (CDC). Cases in US; 2020. Available from: https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/cases-in-us.html. Accessed April 30, 2020.

12. The Guardian. Ethnic minorities dying of Covid-19 at higher rate, analysis shows; 2020. Available from: https://www.theguardian.com/world/2020/apr/22/racial-inequality-in-britain-found-a-risk-factor-for-covid-19. Accessed April 30, 2020.

13. Nguyen TT. Artificial Intelligence in the Battle Against Coronavirus (COVID-19): A Survey and Future Research Directions. Deakin University; 2020: 113.

14. The Metropolitan Transportation Authority. Diversity committee meeting. Available from: http://web.mta.info/mta/news/books/archive/170221_1415_Diversity.pdf. Accessed April 3, 2020.

15. Politico. With death toll hitting 83, the MTA contemplates a memorial for its Covid fallen. Available from: https://www.politico.com/states/new-york/albany/story/2020/04/22/with-death-toll-hitting-83-the-mta-contemplates-a-memorial-for-its-covid-fallen-1279032. Accessed April 30, 2020.

16. Shukla D, Pradhan A, Malik P. Economic impact of COVID-19 on the Indian healthcare sector: an overview. Int J Comm Med Public Health. 2021;8(1):489494.

17. The Council of Canadians. Fighting covid-19 starts with universal access to water and sanitation; 2020. Available from: https://canadians.org/analysis/fighting-covid-19-starts-universal-access-water-and-sanitation. Accessed April 30, 2020.

18. United Nations. UN Water; n.d.. Available from: https://www.unwater.org/water-facts/handhygiene/. Accessed April 30, 2020.

19. US Water Alliance. Closing the water access gap in the United States; 2020. Available from: http://uswateralliance.org/sites/uswateralliance.org/files/Closing%20the%20Water%20Access%20Gap%20in%20the%20United%20States_DIGITAL.pdf. Accessed April 30, 2020.

20. WaterAid. India ranked first in the world for most rural people without access to clean water; 2020. Available from: https://www.wateraidindia.in/media/india-ranked-first-in-the-world-for-most-rural-people-without-access-to-clean-water. Accessed April 30, 2020.

21. Statistics Canada. List of health indicators by aboriginal and non-aboriginal populations; 2020. Available from: https://www150.statcan.gc.ca/n1/pub/82-624-x/2013001/article/app/11763-01-app1-eng.htm. Accessed April 30, 2020.

22. Roma P, Monaro M, Colasanti M, et al. A 2-month follow-up study of psychological distress among Italian people during the COVID-19 lockdown. Int J Environ Res Public Health. 2020;17(21):8180. doi:10.3390/ijerph17218180

23. Cowie S.Brazil indigenous fear coronavirus decimatecommunities.Aljazeera; 2020. Available from: https://www.aljazeera.com/indepth/features/brazil-indigenous-fear-coronavirus-decimate-communities-200421130720967.html.AccessedMay1, 2020.https://www.aljazeera.com/indepth/features/brazil-indigenous-fear-coronavirus-decimate-communities-200421130720967.html

24. United Nations. Policy Brief: The Impact of Covid-19 on South-East Asia. UNESCAP; 2020a: 229.

25. United Nations. Policy Brief: Impact of Covid-19 in Africa. UN; 2020b: 228.

26. UNESCAP. The impact and policy responses for covid-19 in Asia and the Pacific. Available from: https://www.unescap.org/sites/default/files/COVID%20_Report_ESCAP.pdf. Accessed May 1, 2020.

27. Global Health Security Index. Welcome to the 2019 Global Health Security Index; 2020. Available from: https://www.ghsindex.org/. Accessed April 30, 2020.

28. AFRIC. Africas health care system in need of more financing. Available from: https://afric.online/10961-africas-health-care-system-in-need-of-more-financing/. Accessed April 30, 2020.

29. Bhapkar HR, Mahalle PN, Dey N, Santosh KC. Revisited COVID-19 mortality and recovery rates: are we missing recovery time period? J Med Syst. 2020;44(12):15. doi:10.1007/s10916-020-01668-6

30. Samlani Z, Lemfadli Y, Errami AA, Oubaha S, Krati K. The impact of the COVID-19 pandemic on quality of life and well-being in Morocco. Arch Commun Med Public Health. 2020;6(2):130134. doi:10.17352/2455-5479.000091

31. Chriscaden K Impact of COVID-19 on peoples livelihoods, their health and our food systems; 2020. Available from: https://www.who.int/news/item/13-10-2020-impact-of-covid-19-on-peoples-livelihoods-their-health-and-our-food-systems. Accessed March 11, 2021.

32. Galal S. Coronavirus (COVID-19) cases in South Africa as of March 7, 2021, by region; 2021. Available from: https://www.statista.com/statistics/1108127/coronavirus-cases-in-south-africa-by-region/#:~:text=Regionally%2C%20Gauteng%20(Johannesburg)%20was,and%20278%2C883%20coronavirus%20cases%2C%20respectively. Accessed March 11 2021.

33. Galal S Number of new daily coronavirus (COVID-19) cases in South Africa as of March 7, 2021; 2021a. Available from: https://www.statista.com/statistics/1107993/coronavirus-cases-in-south-africa/. Accessed March 11, 2021.

34. Mbunge E. Effects of COVID-19 in South African health system and society: an explanatory study. Diabetes Metabol Syndr. 2020;14(6):18091814. doi:10.1016/j.dsx.2020.09.016

35. Guo D, Han B, Lu Y, et al. Influence of the COVID-19 pandemic on quality of life of patients with Parkinsons disease. Parkinsons Dis. 2020;16.

36. Haleem A, Javaid M, Vaishya R. Effects of COVID 19 pandemic in daily life. Current Med Res Pract. 2020;10(2020):7879. doi:10.1016/j.cmrp.2020.03.011

37. Zhang Y, Ma ZF. Impact of the COVID-19 pandemic on mental health and quality of life among residents in Liaoning Province, China: a cross-sectional study. Int J Environ Res Public Health. 2020;17(7):112.

38. Millard J. Impacts of COVID-19 on Social Development and Implications for the Just Transition to Sustainable Development. United Nations; 2020: 110.

39. Dey N, Mishra R, Fong SJ, Santosh KC, Tan S, Crespo RG. COVID-19: psychological and psychosocial impact, fear, and passion. Dig Govt. 2020;2(1):14.

40. Santosh KC. COVID-19 prediction models and unexploited data. J Med Syst. 2020;44(9):14. doi:10.1007/s10916-020-01645-z

41. Mukherjee H, Dhar A, Obaidullah SM, Santosh KC, Roy K. COVID-19: a necessity for changes and innovations. In: COVID-19: Prediction, Decision-Making, and Its Impacts. Springer Nature Singapore Pte Ltd; 2021:99105.

42. United Nations. Impact of the Covid-19 Pandemic on trade and development. United Nations Conference on Trade and Development, 2020 4112. Available from: https://unctad.org/system/files/official-document/osg2020d1_en.pdf. Accessed August 24,2021.

43. Gulseven O, Al Harmoodi F, Al Falasi M, ALshomali I. How will the COVID-19 pandemic affect the UN sustainable development goals (SDGs)? SSRN Elec J. 2020;128. doi:10.2139/ssrn.3592933

44. Srivastava A, Sharma RK, Suresh A. Impact of Covid-19 on sustainable development goals. Int J Advan Sci Technol. 2020;29(9 Special Issue):49684972.

45. Di Pietro G, Biagi F, Costa P, Karpinski Z, Mazza J. The likely impact of COVID-19 on education: reflections based on the existing literature and recent international datasets. JT Rep. 2020;146. doi:10.2760/126686

46. IESALC. COVID-19 and Higher Education: Today and Tomorrow. The UNESCO International Institute for Higher Education in Latin America and the Caribbean 2020; 146.

47. UNESCO. UNESCOs key achievements in 2020 with the specific focus on COVID-19; 2020. Available from: https://en.unesco.org/news/unescos-key-achievements-2020-specific-focus-covid-19. Accessed March 11, 2021.

48. Gonzalez T, De La Rubia MA, Hincz KP, et al. Influence of COVID-19 confinement on students performance in higher education. PLoS One. 2020;15(10):123. doi:10.1371/journal.pone.0239490

49. Chaudhary A, Gupta V, Jain N, Santosh KC. COVID-19 on air quality index (AQI): a necessary evil? COVID-19. In: Prediction, Decision-Making, and Its Impacts. 2021;127137.

50. Pham QV, Nguyen DC, Hwang WJ, Pathirana PN. Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access. 2020;4:119.

51. WHO. Ethics and covid-19; 2021. Available from: https://www.who.int/teams/health-ethics-governance/diseases/covid-19. Accessed April 30, 2020.

Originally posted here:
COVID-19: quality of life and artificial intelligence | JMDH - Dove Medical Press

Read More..