Showing posts with label artificial intelligence. Show all posts
Showing posts with label artificial intelligence. Show all posts

Nanotechnology And Artificial Intelligence.


    Context - Is Nanotechnology Used In Artificial Intelligence?

    Biotechnology, information technology, and nanotechnology are three technologies that are increasingly reliant on current technical and scientific advancement. 

    The concept of combining bioscience, artificial intelligence (AI), and nanotechnology will usher in yet another revolution in science and technology, one that has been in the works for more than a decade. 

    Nonetheless, the anticipated interdisciplinary research integration is still in the works. 

    • Nanotechnology combines engineering and physical sciences knowledge; it is one of the most significant developing technological sectors, with applications in medical, engineering, and agricultural. 
    • AI is a method of incorporating human-like reasoning into any technological device. This is a study of how the human brain thinks, learns, chooses, and functions as it tries to solve issues.


    For the construction of common and most successful models, such as artificial neural networks (ANNs) and other similar algorithms, it is largely influenced by biological anatomy. 

    Improved machine functions linked to human intellect, such as reasoning, thinking, and problem solving, is an important AI aim. 

    • AI is being used in a growing number of disciplines, not just within AI itself, where machine learning, deep learning, and ANNs are increasingly effective approaches in their own right, but also in the number of domains and businesses where it now reigns supreme. 
    • AI, in conjunction with the Internet of Things (IoT) and other emerging sectors, has already changed many production and monitoring processes across a variety of industries, and the trend is continuing. 

    Nanotechnology is largely made up of sophisticated systems that aren't always compatible with certain parts of AI. 

    Nanotechnology, on the other hand, is thought to be a technique that AI will employ to converge to oneness. 

    Though such a picture may still seem futuristic, existing technology has begun to show signs of a similar harmonization. 

    • From fast-paced AI-assisted nanotechnology research to generating state-of-the-art materials to expanding the application area of AI utilizing nanotechnology-based computing devices, combining these two technologies may result in significant advances. 
    • A combined research may not only merge the two technologies, but it can also offer a boost to research in each area, perhaps leading to a slew of new techniques for gathering information and communication technologies. 
    • Biotechnology, cognitive studies, nanotechnology, robotics, AI, information and communication technology (ICT), and the sciences dealing with such issues are all exposed to larger political and societal debates. 
    • Meanwhile, AI has been used in nanoscience research for a variety of purposes, including analyzing exploratory procedures and assisting in the creation of novel nanodevices and nanomaterials. 

    There are various reasons why AI paradigms are used in nanoresearch. 

    • Nanotechnology is afflicted by the natural limits of size; the governing physical rules are vastly different from those that apply in other situations. 
    • As a result, one of the flaws that nanotechnology must solve is the right explanation of the consequences obtained from any such system (Ly et al. 2011). 
    • To make matters worse, the signal is highly influenced by numerous components in many systems. 
    • In these instances, developing theoretical approximations is difficult, hence simulation approaches have been used to achieve exact elucidations of the investigative outcomes. 

    Various AI machine learning paradigms may be used to generate research results as well as produce nanoapplications in the future. 

    • These strategies are particularly useful when dealing with a large number of connected factors at the same time, and they may effectively express and simplify complex/unknown data or functions (Mitchell 1997; Bishop 2006). 
    • Machine learning methodologies such as ANNs, a collection of weighted linked nodes, and link weights are used to investigate these types of functions, which will be quite valuable, utilizing the monitored or unmonitored algorithm. 

    Other AI approaches can tackle a variety of optimization and search challenges. 

    • There are various machine learning approaches that may be used in nanotechnology research for complex categorization, prediction, correlation, data mining, clustering, and other control issues. 
    • These techniques include decision trees, support vector machines, Bayesian networks, and others. 
    • A few studies have also been conducted on how AI techniques can take advantage of the computational power boost offered by future nanomaterials developed by nanoscience and used for fabricating nanodevices, and nanocomputing will provide powerful dedicated architectures for applying machine learning techniques. 


    The following section explores the bidirectional link between AI and nanotechnology via a variety of examples and applications. 


    In the nanoworld, scanning probe microscopy (SPM) is the most widely used imaging technology. 

    • This notion encompasses a variety of methods for obtaining pictures via the interaction of a pattern and a probe. 
    • The tunneling current between the pattern and the probe is used to characterize the pattern topography via their interaction. 
    • After the creation of the nanoscope, other strategies were created by altering the contacts between the tip and the sample. 

    SPM may also be used to manipulate atoms on a smaller scale. 

    Despite numerous attempts to improve the judgment and the ability to control atoms, there are still challenges with the interpretation of tiny information. 

    The probe–sample interactions are difficult to understand and are influenced by a variety of factors. 

    AI solutions might be a lifesaver in resolving such problems. 

    • In recent years, advances in multimodal SPM imaging for acquiring more complementary information (approximately the pattern) have created a massive quantity of data, making it even more difficult to understand individual sample attributes. 
    • To address this problem, a technique known as functional identification imaging (FR-SPM) has been developed, which seeks a direct identity of local behaviors detected from spectroscopic responses using neural networks trained on examples supplied by an expert. 
    • The cellular genetic algorithm (cGA), a Gas subclass, is based entirely on the evolutionary optimization method and is used to automate the imaging operation in SPM using software capable of enhancing the probe's exact state and related control parameters. 
    • As a result, superior atomic resolution images may be acquired with no human involvement other than the preparation of samples and tips (Huy et al. 2009; Woolley et al. 2011). 
    • ANNs are widely utilized for categorizing numerous behavioral, structural, and physical aspects of nanomaterials on the nanoscale, which are employed in a wide range of applications, including carbon nanotubes, quantum-dot semiconductor optics and devices, chemical technology, and manufacturing. 


    ANNs have recently been employed to investigate the nonlinear connection between input factors and output responses in the transparent conductive oxide deposition process. 

    • In optoelectronic devices such as solar cells, organic LEDs, and flat-panel displays, this kind of thin film is now utilized as an electrode (Bhosle et al. 2006). 
    • Better nanoantenna shapes were also developed through evolutionary optimization, outperforming the best existing radio-wave type of reference antennas. 
    • The fittest antenna shape among the linear dipole antennas, according to GA, combines the properties of the split-basic ring's magnetic resonance with the electrical one (Feichtner et al. 2012). 
    • By thoroughly researching the working principles of the generated geometries, this strategy will develop nanoantenna structures for unique uses and appropriately supply novel layout techniques. 
    • GAs have also been detected in the area of nano-optics. 
    • A careful design of nanoparticle mild concentrators will have a significant influence on a variety of nanooptics applications, including optical manipulators, solar cells, plasmon-enriched photodetectors, modulators, and nonlinear optical devices. 



    One of the primary challenges that scientists encounter while working at the nanoscale is the tool simulation that is being investigated, since genuine optical photographs at the nanoscale are not possible. 

    At this scale, images must be interpreted, and numerical simulations are sometimes the best method for obtaining an exact scheme of what is there in the picture. 

    • Nonetheless, they are difficult to use in many situations, and numerous factors must be considered in order to get an acceptable system representation. 
    • AI can help here by improving simulation performance and making data collection and interpretation easier. 
    • When functioning at the nanoscale, the use of ANNs in numerical simulations has been shown to be useful in a variety of ways. 
    • To begin, the software program may be manually modified to maintain the stability of numerical exactness and physical implications. 
    • Another use of ANNs in simulation software is to reduce the complexity of associated settings (Castellano-Hernández et al. 2012). 


    The combination of AI with existing and emerging nanocomputing technologies yields a wide range of applications (Service 2001; Bourianoff 2003). 

    • Since the inception of nanocomputers, AI paradigms have been utilized for different degrees of modeling, developing, and building prototypes of nanocomputing devices. 
    • Machine learning techniques applied to semiconductor-based hardware with the help of nano-hardware may also offer a basis for a new less expensive and portable age of computing that can include high overall performance computing, including programs, sensory data processing, and control activities (Uusitalo et al. 2011; Arlat et al. 2012). 

    Such challenges emerge in a variety of situations, but primarily with Big Data, which necessitates "computational intelligence" (Ladd et al. 2010; Maurer et al. 2012). 

    In this context, natural computing is usually done using several methodologies. 

    • Apart from various natural computing approaches, techniques such as DNA computing or quantum computing are now being thoroughly investigated (Darehmiraki 2010; Razzazi and Roayaei 2011; Ortlepp et al. 2012; Zha et al. 2013). 
    • Many variables are used in DNA computing. This is an example of how DNA computing AI methodologies may be used to purchase a final result from a little preliminary data collection, avoiding the need of all possible solutions. 

    Other approaches to examine are evolutionary and GAs. 

    Eventually, nanocomputing systems—of which only a handful are bioinspired—will include a broad range of new nanotechnologies. 

    These technologies will be able to leverage new data versions to apply machine learning paradigms in order to solve complicated issues in a broad range of applications when new physical working bases, reconfigurable architectural storage, and computational methods accumulate. 


    Food science is rapidly evolving in tandem with nanotechnology. 

    • Nanotechnology is the solution to the food market's desire for technology that is critical to maintaining market leadership within the food processing business in order to generate dependable, appropriate, and tasty fresh food items. 
    • Preservatives and wrapping are both done using nanoparticles ("nano inside," "nano outside"). 
    • Nanoscale food additives may be utilized to affect product flavor, nutritional content, shelf life, and texture; they may even be used to detect infections; and they may serve as quality indicators. 
    • Nanotechnology opens up a wide range of possibilities for new product development and food system applications. 
    • Research & development opportunities for food additives and packaging are aided by AI tactics. 


    It has been demonstrated that a large number of nanosystems are capable of interacting with living neurons. 

    • Because the detectors are threshold devices similar to spiking neurons, a few CNT features enable us to set down nanotube detectors that could assist implement the pulse-train neural network function (Lee et al. 2003). 
    • The identification of unstable chemical compounds using CNT-covered acoustic and visual sensors is a promising application of ANN algorithms. 
    • It is concluded that a first-rate categorization exchange may be realized by combining multiple modules of auditory and optical sensors, which is the state of affairs in which ANNs can fully realize their potential (Penza et al. 2005). 
    • On the domain of pharmacology and nanomedicine, ANNs have been deemed a well-known device for nanoparticle training analysis and modeling, with a high ability effect in chronic disease (Zarogoulidis et al. 2012). 

    Nanobots developed by researchers at the University of California, San Diego (UCSD) are capable of purifying the blood of toxins produced by bacteria. 

    These nanobots are roughly a quarter of the width of a human hair and can move 35 meters per second by "swimming" through the blood while being propelled by ultrasound. 

    • Nanobots developed by MIT researchers in 2018 are so small and light that they might float through the air. 
    • Linking 2D electronic additives to minute particles measuring between one billionth and one millionth of a meter may make this nanotechnology feasible. 

    The ultimate product is a robot that is about the size of an ovum or a grain of sand. 

    • The combination of photodiode semiconductors, which can detect radiation from an optical region and convert it to an electrical signal, allows for a constant supply of power to the environmental sensors installed in these robots. 
    • The modest electrical fee created is sufficient to allow this technology to function without the need of a battery. 

    When it comes to the value of these nanobots, the researchers want to send them on missions to faraway regions to expose things like pipelines and the human digestive system. 

    This minuscule emissary may be released into the opening, allowed to flow in the pipe's direction, and then retrieved at the pipe's exit. 

    The data acquired by its sensors, which include the spatiotemporal attention of positive chemical compounds such as hormones and enzymes, may then be downloaded and considered once harvested. 



    Many difficulties that arise in the research of nanotechnology might be solved using AI. 

    • The usage of ANNs and GAs has been investigated in a variety of scenarios, ranging from data interpretation in a microscope scanning probe to the characterization and classification of nanoscale fabric characteristics. 
    • It has also looked at numerous initiatives to build nano-machines and utilize them to implement cutting-edge synthetic intelligence paradigms. 
    • These ground-breaking initiatives call for a true confluence of nanotechnology and artificial intelligence in high-performance computer systems enabled by biomaterial-based nanocomputing devices. 

    Finally, the considerable capacity effect on the usage of AI techniques has been shown. 

    • Nanotechnology, on the other hand, has been applied in biomedical research, therapeutic applications, and food science. 
    • Nanotechnology focuses on bottom-up design, while AI research usually takes a top-down approach to solving problems. 
    • The merging of those areas will create approaches for a variety of complex problems that need several layers of explanation and relationships. 
    • As previously said, nanotechnology and artificial intelligence (AI) may assist in this attempt to revive.

    ~ Jai Krishna Ponnappan

    Find Jai on Twitter | LinkedIn | Instagram

    You may also want to read more about Artificial Intelligence here.

    You May Also Want To Read More About Nano Technology here.


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    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.