Showing posts with label systems. Show all posts
Showing posts with label systems. 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 (, 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. 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.