Showing posts with label Security. Show all posts
Showing posts with label Security. Show all posts

Applied Artificial Intelligence for a Post COVID-19 Era

 


In the post-COVID-19 era, businesses will use artificial intelligence (AI) in a variety of ways, according to this article. We demonstrate how AI can be used to create an inclusive paradigm that can be applied to companies of all sizes.



Researchers may find the advice useful in identifying many approaches to address the challenges that businesses may face in the post-COVID-19 period. 


Here we examine a few key global challenges that policymakers can remember before designing a business model to help the international economy recover once the recession is over.

Overall, this article aims to improve business stakeholders' awareness of the value of AI application in companies in a competitive market in the post-COVID-19 timeframe.

The latest COVID-19 epidemic, which began in December in Wuhan, China, has had a devastating effect on the global economy. 



It is too early to propose a business model for businesses that would be useful until the planet is free of the COVID-19 pandemic during this unparalleled socioeconomic crisis for business. 

Researchers have begun forecasting the effects of COVID-19 on global capital markets and its direct or indirect impact on economic growth based on current literature on financial crises or related exogenous shocks.

Following the failure of Lehman Brothers in, a body of literature has emerged that focuses on the application of emerging technology such as artificial intelligence to the ‘Space Economy' (AI). Existing AI research demonstrates the AI's applicability and usefulness in restructuring and reorganizing economies and financial markets around the world.



The implementation of this technology is extremely important in academia and practice to kick-start economic growth and reduce inequalities in resource distribution for stakeholders' development. 


Based on the topic above, the aim of this article is to determine the extent of AI use by companies in the post-COVID-19 crisis era, as there are few comprehensive studies on the effect of using AI to resolve a pandemic shock like the one we are witnessing at the start of the year. 

To the best of our understanding, this is the first report to demonstrate the potential for AI use by businesses in the COVID-19 recovery process.



Advantages in Using AI Until COVID-19 Is Over.


Companies may increase the value of their businesses by lowering operational costs. According to Porter COVID-19, firms use their sustainability models to gain a comparative edge over their competitors. Dealing with big data generated by fast knowledge traffic across the Internet has been one of the biggest problems faced by businesses over the last decade.



To fix this problem, businesses have begun to use artificial intelligence (AI) to boost the global economy COVID-19. 


Small to medium-sized businesses, including large corporations, benefit from government interventions that force them to think creatively. 

Furthermore, when implementing AI, these firms make some disruptive improvements to their operations.

The construction of such infrastructure by large, medium, and small businesses has a positive effect on many countries' jobs, GDP, and inflation rates, to name a few. 

Furthermore, the use of a super-intelligent device opens new possibilities for businesses of all sizes, allowing for the transfer of critical data in a matter of nanoseconds.

As a result, the economy's growth is noticeable because businesses of all sizes, especially in advanced countries, can use this sophisticated and effective business model built on advanced technology like AI. 



Big data processing enables businesses to reduce the percentage of error in their business models.


Furthermore, the deployment of these emerging technologies has expanded global collaboration and engagement as awareness and research and development (R&D) continue to spread globally from one country to another. 

Competition among rivals in the same market, as well as between large and small companies, influences competition in the search for a long-term business model. 



By incorporating user-friendly technology into everyday life, AI-based models allow businesses to enter rural or underdeveloped areas.


In the absence of a person, a digital-biological converter, for example, will render a variety of copies of flu vaccines remotely to benefit the local health system. 

As a result, different sectors such as health, transportation, manufacturing, and agriculture contribute to the growth of the country's economy, which has an impact on the global economy.

During the financial crisis of 2008, businesses' use of AI remained relatively constrained. Companies are now attempting to use a hybrid Monte Carlo decision-making method in the increasingly unstable post-coronavirus timeframe due to rapid technological advancements. 

Companies must understand the extraordinary harm inflicted by the novel coronavirus before adapting AI-based models to stabilize the economy from the current recession, which is not equivalent to past financial crises, such as the crash of Lehman Brothers.

 


AI for Global Development in the Post-COVID-19 Era


One of the main environmental problems of recent decades has been to limit global warming below 2 degrees Celsius in order to minimize the chance of biodiversity loss. The human and animal kingdoms' livelihoods are also at risk because of accelerated climate change.

According to several reports (https://www.eauc.org.uk/), failing to protect biodiversity can pose a challenge to humans. Furthermore, modern business practices affect the climate, and may cause a dangerous virus to take up residence in a human being. 

As a result, biodiversity conservationists must maintain a broad archive related to industry that is impossible to obtain manually.



Businesses must first find ecosystems to preserve before establishing wildlife corridors, which are extremely important biologically. 


Consider the states of Montana and Idaho in the United States. The AI-assisted device is being used by wild animal conservation scientists to monitor and document the movements of wild animals. As a result, the organization will use AI embedded technology to reduce biodiversity threats and continue to focus on sustaining climate change throughout the post-COVID-19 pandemic era.



The vast application of AI can be seen in the healthcare industry, which is a major problem for all countries. During the recent pandemic, we saw the relevance of active learning and cross-population test models, as well as the use of AI-driven methods. 


For example, robotics can clean hospitals to aid health workers, D printers can produce personal protective equipment (PPE) for health workers in hospitals and nursing homes, and a smartphone-enabled monitoring device can detect close contact between infected people, to name a few examples. 

We can see an introduction of AI among healthcare businesses in the past decades, like the COVID-19 pandemic.

For example, IBM Watson Health's AI scheme has been used in conjunction with Barrow Neurological Institute to coordinate the study of several trials to draw conclusions regarding the genes linked to Amyotrophic Lateral Sclerosis (ALS) disorder.

Furthermore, only modern equipment allows for remote treatment without endangering the health care provider's safety. As a result, after we've recovered from the recession, businesses will need to analyze a massive amount of data from any impacted country using their AI-based forecasting model.

This will help to reduce the chances of another pandemic occurring in the future. In recent days, we've seen a massive investment in renewable energy from both the public and private sectors in both developed and developing countries (Bloomberg NEF). 



With the assistance of AI-based technologies, businesses will start using their invested capital and produce more units of renewable energy (or green energy) in the post-COVID-19 period. 


Quantum computing, for example, will cause a plasma reaction in a nuclear fusion reactor, reducing the use of fossil fuels and producing renewable energy.

Companies may also rely on assisting major companies in finding a technology-enhanced way to manage the expense of the cooling system in the big data center. Deep mind is an example of cost-effective, smarter energy used by large corporations such as Google.

We may observe a dead subjectivity in metaphysical zombies (p-zombies) generated by non-self-improving AI. Companies can solve complex issues using biological or artificial neural networks COVID-19, or they can use AI that does not self-improve even when communicating with government systems, by integrating AI with current technologies. 

Industry should concentrate on a limited time span to develop an accurate early forecasting model with a specific dataset to test the suitability of an AI program.

If companies will learn how to reduce the cost of AI application, how to integrate AI with time COVID-19, and how to manage different parameters of global issues using AI COVID-19, they can be more effective. As a result, the global control mechanism would be able to implement a small superintelligence for the good of humanity. 



An Investigation into the Use of Artificial Intelligence in Cryptocurrency Forecasting 


Let's look at an example of AI in action with real-time details. In this part, we demonstrate how artificial intelligence can be used in time-series forecasting, specifically using an artificial neural network (ANN).

The ANN is made up of a vast number of strongly integrated processing components, like how human brains function. The use of neural networks in natural language processing and computer data visioning is now considered one of the most advanced approaches for natural language processing and computer data visioning.

For example, the ANN algorithm outperforms several single or hybrid classical forecasting techniques such as ARIMA and GARCH in a study on bank and company bankruptcy prediction. In this short experiment, we forecast a sample using a mixture of well-known neural network algorithms including long short-term memory (LSTM), time-lagged neural network (TLNN), feed-forward neural network (FNN), and seasonal artificial neural network (SANN) (time-series). We measure the monthly average closing price in each year from the regular observations to make our study straightforward. We use a percentage of this data as research data and a percentage of this data as training data.

The four models listed above use this training approach to try to recognize regularities and trends in the input data, learn from historical data, and then provide us with generalized forecast values based on previously established knowledge. 

As a result, the system is self-adaptive and non-linear. As a result, it defies a priori statistical distribution assumptions. Our experiment shows that the LSTM model is a safer approach for forecasting bitcoin market movement based on the optimum parameters—such as root mean square errors (RMSE).

It shows that the price of cryptocurrencies has been declining since January of this year. However, as transaction costs and other financial or environmental exogenous shocks, such as economic lockout due to COVID-19, are factored in, the model becomes more complicated. 

Note that the aim of the above-mentioned experiment is to demonstrate the applicability of ANN rather than to draw policy conclusions from the findings.



The Difficulties in Using AI Since the COVID-19 Crisis Has Ended.


The AI ushers in a new era in the global economy. However, several reports, such as Roubini COVID-19 and Stiglitz COVID-19, pose significant concerns about the use of AI in the World Economic Forum (WEF). They state that a significant amount of money and R&D is needed to invest in AI-enabled robots that can perform complex tasks. www.cryptydatadownload.com provided the details.

In the rising economy, there is a limited potential to incorporate both small and large enterprises in the same model, which might not be viable. According to current research, a large work loss will stifle economic growth COVID-19. As businesses are willing to use alternate digital money such as cryptocurrencies, the economy's uncertainty may increase.




A lack of resources for small businesses can result in a wider performance gap between the public and private sectors, or between small and large businesses.


This could limit the reliability and precision of big data processing and the implementation of a universal business model. The ability of a small group of businesses to use AI to their advantage could stifle global economic growth. Furthermore, there is the possibility of a disastrous AI risk.

The problems associated with AI protection or alignment can be a major source of concern for businesses, particularly in the aftermath of the Coronavirus outbreak, where there could be a shortage of qualified personnel. Companies should rely on forward thinking taxonomy because it is difficult to be positive of potential uncertainty.

For example, a bio hacking company might use AI to decipher reported genomes, potentially causing a multi-pandemic COVID-19, and such a business model could build neural interfaces that negatively impact human brains. As a result, it's also unclear to what degree businesses will be able to use AI efficiently and successfully after the global economy has recovered from the COVID-19 pandemic.



We discuss a few challenges and major benefits that any company can take advantage of in the post-COVID-19 timeframe.


However, we recognize that we face enormous problems, and policymakers from all over the world should work together to address these concerns.

One of the key challenges facing policymakers is determining how to incorporate responsible commercial practices in order to safely transfer data so that it can be analyzed by AI-based technologies for the good of society. Local and foreign decision-makers must express their experience in order to inform the general public about technologies and reduce the chance of job loss.

Furthermore, by developing COVID-19 for "Artificial Intelligence Marketing," the world's economic growth can be restructured if regulators enable businesses to use AI to improve production-led profitability and mitigate risk through creative methods. 

We expect AI-led businesses to outperform all human tasks as soon as the global economy recovers from the COVID-19 pandemic, based on other studies' forecasts. 



In a nutshell, AI technologies in the post-COVID-19 period will allow individuals and businesses to collaborate for accelerated global growth by outweighing the negative aspects of technology use in society.


You may also like to read more analysis about applied technology during the COVID-19 pandemic here.






Social & Organizational Success With Data



The pace of digital change is creating new opportunities for customers and these opportunities require quick responses. The role of the digital executive/officer is one of the most demanding in business. They need to be strategic, creative, growth-minded and cost conscious of the world they live in.

Business success in the digital age will require organizations to take bold actions, including inventing new business models and changing the way they function. By 2017, 70% of successful digital business models will rely on deliberately unstable processes designed to shift with customers' needs.

Many organizations are either beginning, or in the midst of, digital business transformation initiatives. The prediction is that only 30% of these efforts will succeed. To be part of that 30%, business and IT leaders must be ready and willing to innovate rapidly from a business model, business process and technology perspective.


As a result of business model innovation, some business processes must become deliberately unstable, and deliberately unstable processes are designed for change and can dynamically adjust to customers' needs. They’re needed because they are agile, adaptable and "supermanoeuvrable" as customers' needs shift. They are also competitive differentiators, because they support customer interactions that are unpredictable and require ad hoc decision making to enable larger, more stable processes to continue.

It is imperative in 2016 to break away from linear business processes and deploy a spectrum of standardized and variable processes to reap the benefits of digital business. The need for this shift is intensified by the introduction of several new factors and many types of unmeasured KPIs/ Internet-connected 'things'  etc. into the business environment. Things like smart machines generate real-time information for other machines. Business processes must be designed for change to enable organizations to exploit this information.

There are many aspects to consider in harnessing big data and advanced analytics, and becoming an insights-driven organization. To help data professionals and IT leaders on their journey, here are a few Guiding Principles to not only drive value from big data and analytics, but to also put insights at the heart of your enterprise. Here are those principles:


Governing Principles

Principle 1: 

Embark on the journey to insights, within your business and technology context



The starting point must be your digital business objectives. Design your roadmap to harness new data sources based on how they will help achieve these objectives. Equally importantly, your journey must be dictated by where you start, not only in terms of data maturity but also technology.



 Principle 2: 

Enable your data landscape for the flood coming from connected people and things


There are many new technologies that enable the capture and management of the data flood. Your new data landscape should be a mix of these technologies, chosen to provide the right solution in terms of cost, flexibility and speed to suit each specific data set and meet the insight needs of the business.


Principle 3:

Master governance, security and privacy of your data assets







Insights from unreliable data are worse than no insights at all. Equally, programs fail and businesses leave themselves exposed if data is not handled securely and with consideration of relevant privacy issues. Maturing and industrializing an organization in its production of value from data, is a key lever to success.

Principle 4: 
Develop an enterprise data science culture

Data science unlocks insights. Appreciating and understanding how value is derived from data needs to become part of the culture of the organization. Only by embedding it throughout the enterprise, and systematically making all decisions better informed, can organizations achieve the transformation to becoming insights-driven.


Principle 5: 
Unleash data- and insights-as-a-service

The demand from business users for information and data-driven insights is ever increasing across all organizations. To harness this, business users must feel that they can rapidly access the insights they need where and when they need it. Setting up a powerful platform that delivers these insights ‘on-demand’, is the ultimate goal.


Principle 6: 
Make insight-driven value a crucial business KPI



Measure your measurement. Apply data science to your data science to see where you are adding value and where you are not. If data is becoming one of your most valuable assets, then treat it as such – include it in KPIs and business reviews.

Principle 7: 
Empower your people with Insights at the point of action



All functions in an organization are faced daily with a series of decision points and actions, both at the macro and micro level. Whether you are in Supply Chain, Finance, Procurement, Marketing or other parts of the business, empowering your business teams with real-time insights at the point of action makes the crucial difference.

From marketing to medicine, personalized treatment is taking hold. Customers across all industries expect more these days, and they will go elsewhere if they don't get what they want. The most advanced organizations are actively addressing this dynamic by blending traditional customer data with big data, then using analytics to fine-tune their products and services.

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Big Data Is Improving Lives of Americans: A White House Report








The White House has just issued a report looking at four of the top areas where big data has the potential to greatly improve the lives and safety of Americans. But there are just as many pitfalls as promises to be aware of.

Big Data’s Opportunities & Challenges for All Americans

The Obama Administration’s Big Data Working Group has just issued its comprehensive report looking at the opportunities and challenges around big data and four key areas of society:

Personal credit • Employment • Higher education • Law enforcement.

Entitled Big Data: A Report on Algorithmic Systems, Opportunity, and Civil Rights, the report notes that “big data and associated technologies have enormous potential for positive impact in the United States.” 

But big data also has the potential to create unintended discriminatory consequences if not used correctly. Here we look at the Problem the government is trying to solve; the Opportunity that big data presents; and the Challenge that will need to be overcome in order for a big data solution to work.

The Big Data Challenge: 
Expanding access to affordable credit while preserving consumer rights that protect against discrimination in credit eligibility decisions.

The right to be informed about and to dispute the accuracy of the underlying data used to create a credit score is particularly important because credit bureaus have significant data accuracy issues, which are likely to be exacerbated by the use of new, fast-changing data sources.

The Problem: Traditional hiring practices may unnecessarily filter out applicants whose skills match the job opening.


Even as recruiting and hiring managers look to make greater use of algorithmic systems and automation, the inclination remains for individuals to hire someone similar to themselves, an unconscious phenomenon often referred to as “like me” bias, which can impede diversity.44 Algorithmic systems can be designed to help prevent this bias and increase diversity in the hiring process.

The Big Data Opportunity: 
Big data can be used to uncover or possibly reduce employment discrimination.

Companies can use data-driven approaches to find potential employees who otherwise might have been overlooked based on traditional educational or workplace-experience requirements. Data-analytics systems allow companies to objectively consider experiences and skill sets that have a proven correlation with success.


The Big Data Challenge: 
Promoting fairness, ethics, and mechanisms for mitigating discrimination in employment opportunity.


Data-analytics companies are creating new kinds of “candidate scores” by using diverse and novel sources of information on job candidates. These sources, and the algorithms used to develop them, sometimes use factors that could closely align with race or other protected characteristics, or may be unreliable in predicting success of an individual at a job.

The Problem: Students often face challenges accessing higher education, finding information to help choose the right college, and staying enrolled.

Differences in the price of attendance across institutions affect financial returns, and may lead to differences in the amount that students have to borrow, which may also affect their career decisions and personal lives in meaningful ways. Despite the importance of this decision, there is a surprising lack of clear, easy to use, and accessible information available to guide the students making these choices. The opportunities to use big data in higher education can either produce or prevent discrimination—the same technology that can help identify and serve students who are more likely to be in need of extra help can also be used to deny admissions or other opportunities based on the very same characteristics.

The Big Data Opportunity: 
Using big data can increase educational opportunities for the students who most need them.

To address the lack of information about college quality and costs, the Obama Administration has created a new College Scorecard to provide reliable information about college performance.56 The College Scorecard is a large step toward helping students and their families evaluate college choices. Never-before-released national data about post-college outcomes—including the most comparable and reliable data on the earnings of colleges’ alumni and new data on student debt—and student-loan repayment provides students, families, and their advisers with a more accurate picture of college cost and value.

The Big Data Challenge: 
Administrators must be careful to address the possibility of discrimination in higher education admissions decisions.

In making admissions decisions, institutions of higher education may use big data techniques to try to predict the likelihood that an applicant will graduate before they ever set foot on campus.59 using these types of data practices, some students could face barriers to admission because they are statistically less likely to graduate. Institutions could also deny students from low-income families, or other students who face unique challenges in graduating, the financial support that they deserve or need to afford college.

The Problem: In a rapidly evolving world, law enforcement officials are looking for smart ways to use new technologies to increase community safety and trust.

Local, state, and federal law enforcement agencies are increasingly drawing on data analytics and algorithmic systems to further their mission of protecting America. Using information gathered from the field and through the use of new technologies, law enforcement officials are analyzing situations in order to determine the appropriate response.

The Big Data Opportunity:
 Data and algorithms can potentially help law enforcement become more transparent, effective, and efficient.

New technologies are replacing manual techniques, and many police departments now use sophisticated computer modeling systems to refine their understanding of crime hot spots, linking offense data to patterns in temperature, time of day, proximity to other structures and facilities, and other variables. Some of the newest analytical modeling techniques, often called “predictive policing,” might provide greater precision in predicting locations and times at which criminal activity is likely to occur. An analytical method known as “near-repeat modeling” attempts to predict crimes based on this insight.

The Big Data Challenge: 

The law enforcement community can use new technologies to enhance trust and public safety in the community, especially through measures that promote transparency and accountability and mitigate risks of disparities in treatment and outcomes based on individual characteristics.

Those leading efforts to use data analytics to create and implement predictive tools must work hard to ensure that such algorithms are not dependent on factors that disproportionately single out particular communities based on characteristics such as race, religion, income level, education, or other data inputs that may serve as proxies for characteristics with little or no bearing on an individual’s likelihood of association with criminal activity.

Looking to the Future


The use of big data can create great value for the American people, but as these technologies expand in reach throughout society, we must uphold our fundamental values so these systems are neither destructive nor opportunity limiting. Moving forward, it is essential that the public and private sectors continue to have collaborative conversations about how to achieve the most out of big data technologies while deliberately applying these tools to avoid—and when appropriate, address—discrimination.